{"id": "2603.01250", "title": "The MAMA-MIA Challenge: Advancing Generalizability and Fairness in Breast MRI Tumor Segmentation and Treatment Response Prediction", "abstract": "Breast cancer is the most frequently diagnosed malignancy among women worldwide and a leading cause of cancer-related mortality. Dynamic contrast-enhanced magnetic resonance imaging plays a central role in tumor characterization and treatment monitoring, particularly in patients receiving neoadjuvant chemotherapy. However, existing artificial intelligence models for breast magnetic resonance imaging are typically developed and evaluated using heterogeneous datasets, study populations, and assessment protocols, making direct comparison difficult and limiting understanding of model robustness across institutions and clinically relevant patient subgroups. The MAMA-MIA Challenge was designed to address these challenges by providing a standardized benchmark for the joint evaluation of primary tumor segmentation and prediction of pathologic complete response using pre-treatment magnetic resonance imaging only. The training cohort comprised 1,506 patients from multiple institutions in the United States, while evaluation was conducted on an external test set of 574 patients from three independent European centers to assess cross-continental and cross-institutional generalization. A unified scoring framework combined predictive performance with subgroup consistency across age, menopausal status, and breast density. Twenty-six international teams participated in the final evaluation phase. Results demonstrate substantial performance variability under a common external evaluation framework and reveal trade-offs between overall accuracy and subgroup fairness. The challenge provides standardized datasets, evaluation protocols, and public resources to promote the development of robust and equitable artificial intelligence systems for breast cancer imaging.", "date": "2026-03-01", "categories": []}
{"id": "2604.05435", "title": "CareTransition-Audit: A Benchmark to Audit Discharge Summaries for Efficient Care Transitions", "abstract": "Incomplete or inconsistent discharge documentation drives care fragmentation and avoidable readmissions. Despite its critical role in patient safety, auditing discharge summaries relies on manual review and does not scale. We propose an automated framework for auditing discharge summaries using large language models (LLMs). Our approach operationalizes the DISCHARGED framework into a checklist of 46 questions. Using 50 summaries from the MIMIC-IV database, with clinician ground-truth labels, we benchmark 11 LLMs. Model-assessed mean documentation completeness ranges from 54.9% to 74.2%, and the best-performing models achieve a Cohen's kappa values around 0.5 against clinician labels, indicating moderate agreement. All models struggle to identify ambiguous documentation (Unclear), highlighting a key gap in current automated auditing. This work provides a clinician-validated benchmark and zero-shot baselines for systematic quality improvement in clinical documentation.", "date": "2026-04-07", "categories": []}
{"id": "2605.00665", "title": "Prediction of Alzheimer's Disease Risk Factors from Retinal Images via Deep Learning: Development and Validation of Biologically Relevant Morphological Associations in the UK Biobank", "abstract": "The systemic, metabolic, lifestyle factors have established associations with Alzheimer's Disease (AD) through epidemiologic and AD-specific biomarker studies. Whether colored fundus photography (CFP) contains retinal structural signatures corresponding to these AD-related risk domains remains unclear. To determine whether deep learning (DL) models can predict 12 AD-related risk factors from CFP and to characterize the retinal structures underlying these predictions, thereby assessing whether CFP reflects pathways to AD vulnerability. Using 62,876 CFPs from 44,501 unique participants from the UK Biobank, DL models were trained to predict 12 factors linked to AD incidence: 6 categorical (sex, smoking, sleeplessness, economic status, alcohol use, depression) and 6 continuous (age, age at completing education, BMI, systolic, diastolic blood pressure, HbA1c). Model performance, model saliency, and saliency-derived scores (CAM-Score) were evaluated and compared to retinal morphometry. The scores were also compared between incident-AD cases (average 8.55 years before onset) and matched controls. Performance of DL ranged from AUROC= 0.5654-0.9480 for categorical and R2=-0.0291-0.7620 for continuous factors, outperforming most of the morphometry-machine learning models. Saliency-based score consistently highlighted biologically meaningful regions, particularly the optic nerve head and retinal vasculature. It also aligned with present morphometric variations. Several saliency-based scores differed significantly between incident AD and matched controls, suggesting potential overlap between retinal correlates of risk factors and preclinical AD-associated changes. CFP encodes retinal signatures linked to AD risk factors. Although not diagnostic, DL-derived retinal representations may uncover biologically meaningful risk-related structural changes mirroring the potential AD vulnerability.", "date": "2026-05-01", "categories": []}
{"id": "2605.29483", "title": "VitalAgent: A Tool-Augmented Agent for Reactive and Proactive Physiological Monitoring over Wearable Health Data", "abstract": "Wearable devices enable continuous monitoring of physiological signals such as ECG and PPG, but existing mHealth systems are largely limited to task-specific prediction pipelines or reactive question answering over static summaries. They lack the ability to support temporal reasoning, persistent physiological context, and proactive monitoring over long-term signal streams. We propose VitalAgent, a tool-augmented agentic framework for ECG/PPG-based mHealth that supports both reactive question answering and proactive monitoring. VitalAgent is built on a longitudinal physiological memory and a tool-augmented reasoning interface that enables dynamic computation over raw signals. We further introduce VitalBench, a longitudinal physiological monitoring benchmark dataset comprising 1,862 QA pairs for reactive question answering and 90.2 hours of continuous ECG/PPG recordings for proactive monitoring, covering cardiac, physical activity, and stress-related tasks. Experiments demonstrate that VitalAgent achieves over 25% improvement over prompt-based and ReAct baselines in reactive evaluation and supports proactive alert monitoring over long-term physiological signals, highlighting the importance of dynamic tool use and long-term physiological monitoring.", "date": "2026-05-28", "categories": []}
{"id": "2606.19371", "title": "ProMUSE: Progressive Multi-modal Uncertainty-guided Staged Evidential Alzheimer Disease Classification", "abstract": "Alzheimer's disease (AD) is a fatal disorder that destroys memory and cognitive skills in the elderly population. Most treatments for AD are effective in the early stage, leading to an increasing demand for early AD diagnosis. AD diagnosis increasingly relies on multimodal data such as clinical assessments, structural Magnetic Resonance Imaging (MRI), and Positron Emission Tomography (PET) imaging. However, MRI and PET acquisition remain costly and not universally accessible, making full-modality inference impractical in real-world clinical workflows. We propose ProMUSE, a Progressive Multi-modal Uncertainty Guided Staged Evidential Network that adaptively determines when additional modalities are necessary, helping reduce the overall cost of data acquisition while maintaining accuracy. ProMUSE first performs evidential classification using low-cost clinical data and quantifies uncertainty via a Dirichlet-based subjective logic model. When uncertainty exceeds a learned threshold, ProMUSE progressively incorporates MRI or PET features, fusing modality-wise belief and uncertainty through Dempster-Shafer theory to obtain a calibrated multimodal prediction. This staged acquisition strategy enables accurate diagnosis while minimizing reliance on expensive imaging. Experiments on ADNI, AIBL, and OASIS across CN-AD, CN-MCI, and MCI-AD tasks demonstrate that ProMUSE achieves competitive or superior accuracy compared to full-modality baselines while reducing MRI/PET usage by 50-90%, yielding substantial cost savings. These results highlight ProMUSE as a practical, uncertainty-aware, and resource-efficient solution for real-world AD screening.", "date": "2026-06-11", "categories": []}
{"id": "2606.19373", "title": "cAPM: Continual AI-Assisted Pace-Mapping with Active Learning", "abstract": "Ventricular tachycardia is a life-threatening rhythm disorder and a major cause of sudden cardiac death. Pace-mapping is a clinical procedure for identifying the intervention target during catheter ablation of VT. It requires clinicians to pace different sites in the ventricles and rapidly interpret the resulting electrocardiograms to determine where to pace next or whether a target site has been identified. Active learning AI models have been proposed to guide clinicians to the next pacing site, showing promise in reducing the number of pacing sites and improving the efficiency of pace-mapping. Existing methods require retraining each target without the ability to transfer knowledge across multiple VTs within the same patient or across patients. We introduce cAPM for continuous AI-assisted pace-mapping to capture and transfer knowledge accumulated from past pace-mapping data to reduce the number of pace-mapping data needed for future target VTs. This is made possible by a task-agnostic surrogate neural network that learns the mapping from pacing sites to 12-lead ECG morphology, an active-learning strategy that refines this surrogate model by selecting the most informative pacing site for each target, and a continual learning strategy to do so sequentially while retaining knowledge from prior targets. Evaluated on an in-silico testbed consisting of sequentially-presented localization tasks across different physiological conditions and ventricular geometries, cAPM with and without replay of past data samples achieved an 81% probability of localizing within clinical tolerance (5 mm accuracy) using 4.5 pace-mapping sites, compared to the state-of-the-art active-learning method achieving 38% probability using 13.7 pacing sites. These results provide a strong basis for preparing cAPM towards in-vivo preclinical and clinical studies where it can be used to guide pace-mapping.", "date": "2026-06-11", "categories": []}
{"id": "2606.19481", "title": "Insulin4RL: Real-Time Insulin Management in the Intensive Care Unit for Offline Reinforcement Learning", "abstract": "Offline reinforcement learning (ORL) offers the potential to improve the quality of clinical decision-making using historical electronic health record (EHR) data. Current training and evaluative practices in this field rely heavily on EHR datasets that have been temporally discretised into fixed, regular time intervals. Discretisation creates fictional representations of complex clinical scenarios and compromises the generalisability of retrospective model evaluations. In this paper, we introduce Insulin4RL, a healthcare ORL dataset featuring naturally irregular inputs and actions from real clinical trajectories. Derived from MIMIC-IV, Insulin4RL comprises over 375,000 labelled decisions across 12,209 patients requiring insulin infusion titration in the Intensive Care Unit. The dataset can thus be used for research into ORL model performance under realistic clinical sampling assumptions. We provide a description of the dataset's structure and characteristics, baseline performance metrics using model-free offline reinforcement learning, and a standardised evaluation protocol using fitted Q-evaluation. We conclude with suggested areas for future research that could be addressed using this resource.", "date": "2026-06-17", "categories": []}
{"id": "2606.20037", "title": "Alzheimer's Disease Diagnosis using a Multimodal Approach with 3D MRI and PET", "abstract": "Alzheimer's disease (AD) is an irreversible neurodegenerative disorder and a leading cause of death worldwide. Early diagnosis plays an important part especially at the Mild Cognitive Impairment stage, where timely intervention can help slow its progression before it advances to AD. Neuroimaging data, like Magnetic Resonance Imaging (MRI) and Positron Emission Tomography (PET) scans, can help detect brain changes early by providing structural and functional brain changes related to the disease. Yet, many multimodal models still fuse MRI and PET with static concatenation and apply identical computation to all subjects, which limits robustness to patient/site heterogeneity and can waste computation. To address these limitations, we present the first study of combining 3D convolutional feature extractors with three fusion strategies - concatenation, Gated Multimodal Unit (GMU), and gated self-attention - and a sparsely gated Mixture-of-Experts (MoE) classifier that performs input-adaptive routing, activating only the most informative experts per case. Finally, we utilize Grad-CAM to visualize disease-related regions, ensuring model interpretability. Experiments are performed across three binary classification tasks (NC vs. MCI, MCI vs. AD, and NC vs. AD). Results show that GMU achieves accuracies of 80.46 % (NC vs. MCI) and 95.47 % (NC vs. AD), while gated self-attention attains 82.08 % on MCI vs. AD. Ablations show that removing the MoE consistently degrades accuracy across all tasks. These findings underscore the value of input-adaptive, multimodal modeling for AD diagnosis by leveraging the complementary nature of MRI and PET.", "date": "2026-06-18", "categories": []}
{"id": "2606.20074", "title": "Evaluation of EEG Foundation Models for Event-Based Burst-Suppression Detection in ICU", "abstract": "Burst suppression (BS) is a clinically relevant electroencephalographic (EEG) pattern used to monitor sedation depth and brain activity in critically ill patients, particularly during induced coma in Intensive Care Units (ICUs). Automatic burst detection remains challenging because BS patterns vary substantially between patients and annotated datasets are scarce. Recently, EEG Foundation Models (FMs) have shown promise across several downstream EEG applications, but their usefulness for BS detection remains unexplored. We present the first study to evaluate EEG FMs for burst detection in reduced-montage ICU EEG without patient-specific calibration. We compare REVE-base, LUNA-large and LuMamba-Tiny with an adaptive thresholding baseline and a task-specific EEGNet baseline. Additionally, we complement conventional EEG window-based classification with event-based burst detection evaluation. This helps assessing clinically whether burst episodes are correctly detected, reducing the impact of expected annotation variability. The best model, REVE-base, achieved the highest event-based F1-score ($0.868 \\pm 0.167$) and reduced burst-per-minute error by 52.1% and 36.2% compared to EEGNet and adaptive thresholding respectively, supporting FMs for scalable EEG monitoring in ICU. Ablation experiments showed that full fine-tuning was the most effective adaptation strategy with respect to frozen-backbone training, two-step fine-tuning, and LoRA-based adaptation, improving event-based F1-score over frozen-backbone training by up to $+0.102$ for LUNA-large. With reduced labeled datasets, pretrained REVE-base outperformed random initialization by $+0.723$ event-based F1 points at 25% of the cohort, demonstrating the benefit of pretraining FM representations when adapted to burst detection with limited labeled data.", "date": "2026-06-18", "categories": []}
{"id": "2606.20172", "title": "Predicting gestational age at birth in the context of preterm birth from multi-modal fetal MRI", "abstract": "Preterm birth is associated with significant mortality and a risk for lifelong morbidity. The complex multifactorial aetiology hampers accurate prediction and thus optimal care. A pipeline consisting of bespoke machine learning methods for data imputation, feature selection, and regression models to predict gestational age (GA) at birth was developed and evaluated from comprehensive multi-modal morphological and functional fetal MRI data from 333 control cases and 93 preterm birth cases. The GA at birth predictions were classified into term and preterm categories and their accuracy, sensitivity, and specificity were reported. An ablation study was performed to further validate the design of the pipeline. Performance was evaluated using stratified 10-fold cross-validation. The pipeline achieves an R2 score of 0.13 and a mean absolute error of 2.74 weeks. It also achieves a 0.77 accuracy, 0.59 sensitivity, and 0.82 specificity across folds. The predominant features selected by the pipeline include cervical length and statistics derived from placental T2* values. The confluence of fast, motion-robust and multi-modal fetal MRI techniques and machine learning prediction allowed the prediction of the gestation at birth. This information is essential for any pregnancy. To the best of our knowledge, preterm birth had only been addressed as a classification problem in the literature. Therefore, this work provides a proof of concept. Future work will increase the cohort size to allow for finer stratification within the preterm birth cohort. Our code is available at https://github.com/dfajardorojas/ml-for-preterm-birth-.", "date": "2026-06-18", "categories": []}
{"id": "2206.05018", "title": "Going Beyond the Cookie Theft Picture Test: Detecting Cognitive Impairments using Acoustic Features", "abstract": "Standardized tests play a crucial role in the detection of cognitive impairment. Previous work demonstrated that automatic detection of cognitive impairment is possible using audio data from a standardized picture description task. The presented study goes beyond that, evaluating our methods on data taken from two standardized neuropsychological tests, namely the German SKT and a German version of the CERAD-NB, and a semi-structured clinical interview between a patient and a psychologist. For the tests, we focus on speech recordings of three sub-tests: reading numbers (SKT 3), interference (SKT 7), and verbal fluency (CERAD-NB 1). We show that acoustic features from standardized tests can be used to reliably discriminate cognitively impaired individuals from non-impaired ones. Furthermore, we provide evidence that even features extracted from random speech samples of the interview can be a discriminator of cognitive impairment. In our baseline experiments, we use OpenSMILE features and Support Vector Machine classifiers. In an improved setup, we show that using wav2vec 2.0 features instead, we can achieve an accuracy of up to 85%.", "date": "2022-06-10", "categories": []}
{"id": "2410.23503", "title": "Development and Comparative Analysis of Machine Learning Models for Hypoxemia Severity Triage in CBRNE Emergency Scenarios Using Physiological and Demographic Data from Medical-Grade Devices", "abstract": "This paper presents the development of machine learning (ML) models to predict hypoxemia severity during emergency triage, especially in Chemical, Biological, Radiological, Nuclear, and Explosive (CBRNE) events, using physiological data from medical-grade sensors. Gradient Boosting Models (XGBoost, LightGBM, CatBoost) and sequential models (LSTM, GRU) were trained on physiological and demographic data from the MIMIC-III and IV datasets. A robust preprocessing pipeline addressed missing data, class imbalances, and incorporated synthetic data flagged with masks. Gradient Boosting Models (GBMs) outperformed sequential models in terms of training speed, interpretability, and reliability, making them well-suited for real-time decision-making. While their performance was comparable to that of sequential models, the GBMs used score features from six physiological variables derived from the enhanced National Early Warning Score (NEWS) 2, which we termed NEWS2+. This approach significantly improved prediction accuracy. While sequential models handled temporal data well, their performance gains did not justify the higher computational cost. A 5-minute prediction window was chosen for timely intervention, with minute-level interpolations standardizing the data. Feature importance analysis highlighted the significant role of mask and score features in enhancing both transparency and performance. Temporal dependencies proved to be less critical, as Gradient Boosting Models were able to capture key patterns effectively without relying on them. This study highlights ML's potential to improve triage and reduce alarm fatigue. Future work will integrate data from multiple hospitals to enhance model generalizability across clinical settings.", "date": "2024-10-30", "categories": []}
{"id": "2511.05221", "title": "ActiTect: A Generalizable Machine Learning Pipeline for REM Sleep Behavior Disorder Screening through Standardized Actigraphy", "abstract": "Isolated rapid eye movement sleep behavior disorder (iRBD) is a major prodromal marker of $\\alpha$-synucleinopathies, often preceding the clinical onset of Parkinson's disease, dementia with Lewy bodies, or multiple system atrophy. While wrist-worn actimeters hold significant potential for detecting RBD in large-scale screening efforts by capturing abnormal nocturnal movements, they become inoperable without a reliable and efficient analysis pipeline. This study presents ActiTect, a fully automated, open-source machine learning tool to identify RBD from actigraphy recordings. To ensure generalizability across heterogeneous acquisition settings, our pipeline includes robust preprocessing and automated sleep-wake detection to harmonize multi-device data and extract physiologically interpretable motion features characterizing activity patterns. Model development was conducted on a cohort of 78 individuals, yielding strong discrimination under nested cross-validation (AUROC = 0.95). Generalization was confirmed on a blinded local test set (n = 31, AUROC = 0.86) and on two independent external cohorts (n = 113, AUROC = 0.84; n = 57, AUROC = 0.94). To assess real-world robustness, leave-one-dataset-out cross-validation across the internal and external cohorts demonstrated consistent performance (AUROC range = 0.84-0.89). A complementary stability analysis showed that key predictive features remained reproducible across datasets, supporting the final pooled multi-center model as a robust pre-trained resource for broader deployment. By being open-source and easy to use, our tool promotes widespread adoption and facilitates independent validation and collaborative improvements, thereby advancing the field toward a unified and generalizable RBD detection model using wearable devices.", "date": "2025-11-07", "categories": []}
{"id": "2605.10840", "title": "Clin-JEPA: A Multi-Phase Co-Training Framework for Joint-Embedding Predictive Pretraining on EHR Patient Trajectories", "abstract": "We present Clin-JEPA, a multi-phase co-training framework for joint-embedding predictive (JEPA) pretraining on EHR patient trajectories. JEPA architectures have enabled latent-space planning in robotics and high-quality representation learning in vision, but extending the paradigm to EHR data -- to obtain a single backbone that simultaneously forecasts patient trajectories and serves diverse downstream risk-prediction tasks without per-task fine-tuning -- remains an open challenge. Existing JEPA frameworks either discard the predictor after pretraining (I-JEPA, V-JEPA) or train it on a frozen pretrained encoder (V-JEPA 2-AC), leaving the encoder unaware of the rollout signal that the retained predictor must use at inference; co-training the encoder and predictor under a shared JEPA prediction objective would supply this grounding, but na\\\"ive co-training is unstable, with representation collapse and online/target drift causing autoregressive rollout to diverge. Clin-JEPA's five-phase pretraining curriculum -- predictor warmup, joint refinement, EMA target alignment, hard sync, and predictor finalization -- addresses each failure mode by phase, stably co-training a Qwen3-8B-based encoder and a 92M-parameter latent trajectory predictor. On MIMIC-IV ICU data, three independent evaluations support the framework: (1) latent $\\ell_1$ rollout drift uniquely converges ($-$15.7%) over 48-hour horizons while baselines and ablations diverge (+3% to +4951%); (2) the encoder learns a clinically discriminative latent geometry (deteriorating-patient cohorts displace 4.83$\\times$ further than stable patients in latent space, vs $\\leq$2.62$\\times$ for baseline encoders); (3) a single backbone outperforms strong tabular and sequence baselines on multi-task downstream evaluation. Clin-JEPA achieves mean AUROC 0.851 on ICareFM EEP and 0.883 on 8 binary risk tasks (+0.038 and +0.041 vs baseline average).", "date": "2026-05-11", "categories": []}
{"id": "2605.21528", "title": "A Reproducible Log-Driven AutoML Framework for Interpretable Pipeline Optimization in Healthcare Risk Prediction", "abstract": "Accurate disease risk prediction is challenged by heterogeneous features, limited data, and class imbalance. This study presents yvsoucom-iterkit, a deterministic AutoML framework that models pipeline optimization as a configuration-level system with full reproducibility and traceable execution logs, enabling systematic analysis of component attribution, interactions, similarity, and cross-seed robustness.\n  Experiments on the Pima Indians Diabetes and Stroke datasets across more than 18,000 pipeline configurations reveal a structured yet partially redundant search space, where performance is dominated by a small subset of interacting components.\n  Ensemble models achieve stable performance, reaching a Weighted-F1 of 0.89 on Pima and 0.94 on Stroke. Macro-F1 reaches approximately 0.88 on Pima but drops to 0.6560 on Stroke due to severe imbalance. Cross-seed experiments show that ensembles reduce variance compared to single models. Friedman testing ($p < 0.05$) confirms significant ranking differences across configurations.\n  Based on analysis of component attribution, interaction, and similarity, optimal configuration design reveals dataset-dependent behavior. For the Pima dataset, computational efficiency benefits from simplified search spaces where redundant components can be removed, with split ratio playing a key role. In contrast, the Stroke dataset requires enhanced imbalance-aware strategies, where RandomOverSampler improves Macro-F1 from 0.6560 to 0.6766.\n  These findings demonstrate that effective AutoML optimization is achieved through optimal configuration design, where carefully constraining the search space to high-impact components can improve performance, stability, and interpretability while reducing unnecessary search complexity.", "date": "2026-05-19", "categories": []}
{"id": "2606.10376", "title": "Belief-Space Control for Personalized Cancer Treatment via Active Inference", "abstract": "Cancer treatment is at the core a sequential decision-making problem with partial observability, latent patient heterogeneity, and explicit constraints on the budget for medical measurements. Unlike standard Reinforcement Learning (RL) approaches that control state trajectories, cancer treatments permanently modify patients' transition dynamics, changing how states evolve over time. We model cancer treatment as a belief-space planning problem using active inference, deriving an expected free-energy objective that unifies goal-directed control and information acquisition under measurement budgets without. We implement this framework using real clinical cancer data from the AACR Project GENIE Biopharma Collaborative dataset. Results on clinical data demonstrate a simultaneous patient categorization and high treatment efficacy, under real measurement and treatment constraints.", "date": "2026-06-09", "categories": []}
{"id": "2606.18506", "title": "Beyond AHI: An Interpretable Causal-Discovery-Guided Framework for Sleep Recovery in Connected Health", "abstract": "Objective sleep assessment relies on polysomnography (PSG), yet clinical impact is often better reflected in patient-reported outcomes (PROs) such as sleepiness and fatigue. Existing summary indices, including the Apnea-Hypopnea Index (AHI), provide limited insight into the multidomain physiology underlying functional recovery. We propose an interpretable, causal-discovery--guided framework for deriving a hierarchical Sleep Recovery Score (SRS) from multimodal PSG. Using two large population cohorts (MESA: n=1540; MrOS: n=825), we apply directed acyclic graph (DAG) learning to identify candidate physiological drivers spanning respiratory burden, hypoxic burden, sleep fragmentation, sleep architecture, and autonomic regulation. Although derived from clinical PSG, these domains map naturally to sensing streams increasingly available in connected health technologies, including wearable ECG, oximetry, and sleep-stage estimation devices. To preserve mechanistic plausibility, we introduce a two-stage screening process that combines physiology-based constraints with constrained LLM-assisted auditing to identify and remove structural confounders and construct-overlapping variables. Across cohorts, these five domains emerge as recurrent physiological domains associated with recovery, and the resulting SRS shows up to 2.5$\\times$ stronger alignment with perceived recovery than AHI. By linking multimodal sleep physiology to patient-centered outcomes through an interpretable, bias-aware, and domain structured framework, this work provides a practical foundation for recovery modeling across both clinical sleep studies and emerging smart and connected health settings.", "date": "2026-06-16", "categories": []}
{"id": "2606.18640", "title": "MetaboNet-Bench: A Multi-modal Benchmark for Glucose Forecasting in Type 1 Diabetes", "abstract": "Glucose forecasting algorithms are an important aspect of glycemic control management in type 1 diabetes. So far, the research community has developed numerous algorithms and models for forecasting. However, it is well-recognized that the lack of standardized model performance evaluation benchmarks makes fair comparison difficult and hinders further innovation, and thus benchmark standardization is in urgent need. Furthermore, many published glucose forecasting algorithms are limited to CGM data alone, ignoring other multimodal signals such as insulin dosing and carbohydrate intake. Here, we introduce MetaboNet-Bench, a benchmark for multimodal glucose forecasting for patients with type 1 diabetes that provides an extensible open-source evaluation framework for comparison of glucose forecasting algorithms that leverage glucose, insulin, and carbohydrate data. We then demonstrate its utility by benchmarking several recently published glucose forecasting models and a custom multimodal time-series model, representing different model architectures. The results show that the benefit of adding data modalities is conditioned on the complexity of the model and that incorporating more clinical metrics helps identify meaningful gaps to fill for future research.", "date": "2026-06-17", "categories": []}
{"id": "2606.19092", "title": "Context-Aware Optimization of Follow-Up Intervals for Type 2 Diabetes Care Using Markov Decision Processes", "abstract": "Chronic disease management relies on regular patient-provider interactions to follow-up on disease progression and control. For Type 2 Diabetes (T2D), current guidelines prescribe fixed time intervals between subsequent primary care visits for all patients, overlooking heterogeneity in clinical trajectories and patient characteristics. This study introduces a Contextual Markov Decision Process (CMDP) model to optimize subpopulation-specific follow-up interval decisions using Electronic Health Record (EHR) data from 22,154 T2D patients across 10 primary care clinics. Contexts are identified by: i) dimensionality reduction of variables representing the individual health trajectories utilizing Principal Component Analysis, and ii) assigning patients to contexts via principal components and additional patient-level features using clustering. Two distinct contexts emerged, representing a lower- and a higher-risk subpopulation. CMDP-derived policies recommend: (i) follow-up within 1 month if lab value at current visit is unmeasured; (ii) up to 3 months for elevated lab values or recent hospitalizations; and (iii) 6 to 12 months for sustained glycemic control, with shorter follow-up intervals for patients in high-risk context. The optimal policies achieved lower expected cumulative cost than benchmarks (e.g., in the higher-comorbidity context, the CMDP policy reduced cost by about 34.8%, and in the lower-comorbidity context by about 6.4%, relative to an American Diabetes Association-like fixed interval follow-up policy. These findings demonstrate how context-aware approaches can inform adaptive follow-up strategies, and have the potential to advance chronic care management in primary care by synthesizing machine learning and probabilistic decision models.", "date": "2026-06-17", "categories": []}
{"id": "2606.19140", "title": "ChronoSurv: A Clinical Pathway-Guided Graph Framework for Multimodal Survival Analysis", "abstract": "Accurate survival prediction is essential for personalized treatment planning in head and neck cancer, yet remains challenging due to the heterogeneous and high-dimensional nature of multimodal clinical data. While deep survival models have improved predictive performance over classical statistical approaches, existing methods typically rely on static fusion strategies or temporally agnostic modeling, limiting their ability to capture structured clinical workflows. In this work, we propose ChronoSurv, a heterogeneous hierarchical directed graph framework for multimodal survival analysis. ChronoSurv represents patient care as a progression-aware clinical trajectory using directed graphs aligned with key diagnostic steps. A hierarchical topology incorporates fine-grained, coarse, and global representations, further supporting flexible adaptation to missing modalities, while heterogeneous message passing models complex and asymmetric relationships across modalities and clinical steps. Experimental results on two public datasets demonstrate that ChronoSurv achieves state-of-the-art discriminative performance while maintaining statistically reliable calibration. Comprehensive ablation studies further confirm the contribution of each architectural component, highlighting the potential of trajectory-aware graph modeling for multimodal survival prediction.", "date": "2026-06-17", "categories": []}
{"id": "2606.19292", "title": "Risk Stratification for ICU Delirium using Pervasive Ambient Sensing Information", "abstract": "Delirium is a common and serious complication in the Intensive Care Unit (ICU), associated with increased morbidity, prolonged hospital stays, and higher healthcare costs. Despite its prevalence, early prediction and prevention remain challenging. Environmental factors such as ambient sound and light may influence the onset of delirium, yet they are often overlooked in risk assessments. In this study, we examined whether light intensity and sound pressure levels can independently predict delirium across multiple prediction horizons. We evaluated four efficient sequential neural network models on data collected from 9 ICUs across 309 patients to predict delirium for 10 prediction-window sizes. We reported feature importance and direction of influence using Shapley Additive Explanations analysis. The convolutional model achieved the strongest discrimination, with AUC = 0.80 on sound data and on combined data. Sound features were the dominant predictors overall. Integrating sound with light improved short-term ($<1$ week) prediction, with the combined model assigning the highest risk immediately after the sensing period. These findings suggest that passive ambient sensing, especially sound, can add a clinically meaningful, interpretable signal for delirium risk estimation and offer a practical pathway to enrich multimodal ICU prediction and prevention strategies.", "date": "2026-06-17", "categories": []}
{"id": "2604.16450", "title": "Evaluating Intersectional Fairness across Clinical Machine Learning Use Cases using Fairlogue and the All of Us Research Program", "abstract": "Intersectional biases in healthcare data can produce compound disparities in clinical machine learning models, yet most fairness evaluations assess demographic attributes independently. FairLogue, a toolkit for intersectional fairness auditing, was applied across multiple clinical prediction tasks to evaluate disparities across combined demographic groups. Using the All of Us dataset, two published models were selected for replication and evaluation: (A) prediction of selective serotonin reuptake inhibitor associated bleeding events and (B) two-year stroke risk in patients with atrial fibrillation. Observational fairness metrics were computed across race, gender, and intersectional subgroups, followed by counterfactual analysis to evaluate whether disparities were attributable to group membership. Intersectional evaluation revealed larger disparities than single-axis analyses; however, counterfactual diagnostics indicated that most observed disparities were comparable to those expected under randomized group membership. These results highlight the importance of intersectional fairness auditing and demonstrate how FairLogue provides deeper insight into bias in clinical machine learning systems.", "date": "2026-04-07", "categories": []}
{"id": "2606.00588", "title": "Response-Aware Multimodal Learning for Post-Treatment Visual Acuity Forecasting", "abstract": "Long-term visual acuity (VA) forecasting after anti-VEGF therapy is important for counseling and follow-up planning in diabetic macular edema (DME), yet remains challenging when only early post-treatment findings are available. While prior OCT-based methods mainly focus on short-term response or single-endpoint prediction, multi-horizon VA forecasting from early longitudinal data remains insufficiently under-explored. In this study, we assembled a real-world cohort of 188 anti-VEGF--treated DME patients with paired baseline and month-1 OCT scans, along with tabular OCT-derived biomarkers and non-imaging clinical variables. Using only these early data, we formulate a multi-horizon VA forecasting problem aimed at predicting visual outcomes at 3, 6, 12, 18, and 24 months, reflecting clinically meaningful follow-up intervals. We propose ReVA, a response-aware multimodal framework that combines baseline and month-1 OCT features with tabular variables to capture disease status and early treatment response. ReVA integrates spatial OCT attention, dependency-aware tabular encoding, and cross-modal fusion to predict patient-specific long-term VA trajectories. The proposed framework achieves MAE=0.1246, RMSE=0.1621, and R^2=0.6064 for 24-month VA prediction, with consistent performance across all forecast horizons. Our findings show that incorporating early treatment-response signals enables clinically meaningful long-term visual acuity forecasting, supporting data-driven decision support for routine anti-VEGF management. Code and pretrained models will be released on https://github.com/nguyenpbui/ReVA.", "date": "2026-05-30", "categories": []}
{"id": "2606.12623", "title": "Estimating Individualized Treatment Effects in Acute Ischemic Stroke with Causal Transformation Models (TRAM-DAG): A Multi-Centre Observational Study with External RCT Validation", "abstract": "Personalized medicine in acute ischemic stroke requires moving beyond average treatment effects (ATE) to individualized treatment effect (ITE) estimates to support treatment decisions. In acute ischemic stroke, mechanical thrombectomy has been shown to be more effective on average than lysis in randomized controlled trials (RCTs), such as the MR CLEAN study. We aim to identify which individual patients benefit most from mechanical thrombectomy compared to lysis. The outcome of interest is the modified Rankin Scale (mRS) at three months, an ordinal measure of functional disability (0: no symptoms, 6: death). We demonstrate that causal transformation models on directed acyclic graphs (TRAM-DAG) can be used for ITE estimation after being fitted on observational MAGIC multi-center stroke patient data. To ensure comparability with the MR CLEAN population, which we use for validation, we train the TRAM-DAG on a MAGIC sub-population with NIHSS at admission >= 6, corresponding to one inclusion criterion of MR CLEAN. The fitted model is then used to estimate ITEs for stroke patients in the MR CLEAN population. While these ITE estimates cannot be confirmed experimentally, we show that their average is consistent with the trial's reported ATE. Furthermore, the ITE estimates correctly rank trial patients by their observed frequency of a good outcome (mRS at three months <= 2). These findings support the use of causal models like TRAM-DAG for personalized decision-making in stroke care and highlight their ability to bridge the gap between observational evidence and clinical trials.", "date": "2026-06-10", "categories": []}
{"id": "2606.16337", "title": "Medical Heuristic Learning: An LLM-Driven Framework for Interpretable and Auditable Clinical Decision Rules", "abstract": "Predictive modeling for clinical tabular data is central to clinical decision support and therefore requires not only strong predictive performance but also transparent decision logic. Although deep learning and tree-based ensemble methods can achieve high accuracy, their black-box nature remains a major obstacle to clinical deployment. This challenge is further compounded by common characteristics of medical data, including limited sample sizes, severe class imbalance, and feature evolution arising from changes in diagnostic criteria and clinical documentation. To address these issues, we propose Medical Heuristic Learning (MHL), an instantiation of the learning-beyond-gradients paradigm for clinical tabular prediction. Instead of relying on neural network weight updates, MHL uses a large language model (LLM)-driven workflow that integrates statistical probes, medical knowledge probes, rule synthesis, and code-level iterative refinement to optimize a deterministic and executable decision system. The resulting model is expressed not as opaque parameters, but as versioned pure-Python decision rules that are explicitly interpretable, fully auditable, and clinically grounded. MHL also supports continual learning by starting from previously validated rules and iteratively revising them using updated feature information under data drift or feature evolution. Comprehensive experiments on medical datasets show that MHL achieves performance comparable to state-of-the-art methods while maintaining strong behavior in small-sample and highly imbalanced settings. The results further indicate that this explicit rule update mechanism can help alleviate catastrophic forgetting under feature evolution. Overall, these findings suggest that non-gradient-based heuristic systems offer a transparent and adaptable alternative for high-stakes clinical decision support.", "date": "2026-06-15", "categories": []}
{"id": "2606.17106", "title": "Informative Missingness to Generate Irregular Clinical Time Series", "abstract": "Laboratory tests in electronic health records are collected irregularly, and the absence of a test order can be as informative as the measurement itself. Such missingness reflects clinicians' decisions and patient physiology, making it important to model it directly rather than treat it as a preprocessing artifact. Here we present a diffusion-based approach for generating clinical time series that jointly models laboratory values and their observation patterns using the public Data Analytics Challenge on Missing Data Imputation (DACMI) benchmark derived from MIMIC-III. To preserve realistic sampling, we align chart times into 4-hour intervals and segment admissions into 7-day windows, producing trajectories that pair each lab value with a corresponding observation indicator. Standard transformations and normalization are applied to stabilize training. Our method extends the TimeDiff framework to learn continuous lab values and discrete missingness patterns through complementary diffusion objectives. Experiments show that the generated data closely match real patient trajectories across individual lab distributions and joint value-missingness embeddings, demonstrating that diffusion models can capture clinically meaningful dependencies between patient physiology and clinicians' testing behavior under MNAR-like (missing-not-at-random) missingness. These preliminary results indicate that our model can serve as an initial component toward developing clinical foundation models. By producing synthetic priors that preserve key physiology-missingness relationships, this work motivates the subsequent training of Prior-Data Fitted Networks capable of leveraging informative missingness, which we will investigate in the extended work.", "date": "2026-06-14", "categories": []}
{"id": "2606.17339", "title": "SpeechDx: A Multi-Task Benchmark for Clinical Speech AI", "abstract": "Speech offers a uniquely informative window into health by simultaneously engaging neurological, motor, respiratory, and vocal systems. Current clinical speech AI methods have largely progressed through isolated condition-specific studies, making results difficult to compare and generalization difficult to assess. We introduce SpeechDx, a large-scale benchmark for clinical speech AI spanning 12 datasets and 27 tasks across diverse health conditions. To enable evaluation across shared clinical mechanisms, SpeechDx structures tasks by the stage of speech production they disrupt: conceptualization, formulation, and articulation. The benchmark tests generalization by including tasks with limited labeled data and evaluating the same health condition across multiple datasets, distinguishing clinically meaningful patterns from dataset artefacts. We systematically evaluate 12 state-of-the-art audio encoders across all tasks and under zero-shot cross-condition transfer. Results show that large-scale speech models represent the strongest overall baselines, domain-specific models improve performance only on closely matched tasks, and no current representation generalizes reliably across the clinical speech landscape. SpeechDx establishes a shared evaluation framework for tracking progress toward general-purpose clinical speech representations", "date": "2026-06-15", "categories": []}
{"id": "2606.17405", "title": "Treatment Response Optimized Clinical Decision Support AI System via Digital Twin Simulation", "abstract": "Clinical decision support AI systems (CDSASs) must adapt to evolving patient conditions in real-time while adhering to strict safety constraints. We present an online adaptive framework that integrates Treatment Effect (TE) estimation to quantify clinical benefits, a patient Digital Twin (DT) to simulate treatment trajectories, and Reinforcement Learning (RL) for sequential decision-making. The AI system is initially trained on historical medical records and operates in a continuous learning loop. To ensure safety, a rule-based module monitors vital signs and blocks contraindicated treatments. Cases with strong internal model disagreement are flagged for clinician review, simulated in our experiments via a pre-trained outcome model. We validate our framework using both a synthetic clinical simulator and a real-world ovarian cancer dataset from The Cancer Genome Atlas (TCGA). In both simulated and clinical settings, our method demonstrated superior effectiveness and stability in recommending treatments compared to standard computational baselines. Furthermore, the AI system maintains low latency and requires expert consultation for only a minority of cases in our experimental validation, demonstrating its potential as a safe, clinician-supervised tool for personalized medicine that continuously improves through practical use.", "date": "2026-06-16", "categories": []}
{"id": "2606.17450", "title": "A Machine-Learned Comorbidity Index", "abstract": "Traditional comorbidity scores (e.g., Charlson and Elixhauser) are widely used for risk adjustment and patient stratification, but they have two key limitations: (i) they are largely mortality-centric and do not align well with other clinical outcomes, and (ii) their linear, rule-based structure cannot capture nonlinear, outcome-specific risk relationships. We propose a Machine-Learned Comorbidity Index (MLCI) that maps diagnosis codes to a single scalar by maximizing the normalized Hilbert-Schmidt Independence Criterion (nHSIC) between the learned score and multiple clinical outcomes. MLCI captures nonlinear risk-outcome dependence and is supported by a theory that characterizes when a unified, informative admission-level ordering can be achieved across outcomes. Empirical results on multiple benchmark electronic health record (EHR) datasets show that MLCI outperforms strong baselines across multiple evaluation metrics.", "date": "2026-06-16", "categories": []}
{"id": "2606.17973", "title": "Fine-tuning LLMs for Passive Depression Severity Estimation from AI Mental Health Dialogue", "abstract": "Depression is the leading cause of disability worldwide, and early detection of symptom change is essential for timely intervention. Validated instruments such as the Patient Health Questionnaire-9 (PHQ-9) support symptom monitoring at scale, but real-world completion rates are low, introducing response bias and systematic missingness. Passive approaches that infer severity from routinely generated data could close this gap. We address this by predicting PHQ-9 total scores directly from transcripts of conversations between users and an AI mental health application, requiring only conversation text and no additional clinical data. We fine-tune a Qwen3.5-27B backbone with a regression head, augment 3,111 ground-truth labels with pseudolabels generated by a reasoning model (Claude Opus) and iteratively trained intermediate models, for a combined dataset of 6,283 users. On a held-out test set of 842 users, our best model achieves MAE = 2.6, RMSE = 4.0, Pearson r = 0.80, and AUC = 0.91 at the PHQ-9 >= 10 clinical threshold. We also find AUC > 0.87 at every severity threshold from PHQ-9 >= 3 to PHQ-9 >= 24, demonstrating that the model captures depression severity across the full clinical spectrum. This work opens the door to passive, continuous symptom monitoring in AI mental health platforms, without requiring users to complete self-report measures.", "date": "2026-06-16", "categories": []}
{"id": "2401.06644", "title": "SeizNet: An AI-enabled Implantable Sensor Network System for Seizure Prediction", "abstract": "In this paper, we introduce SeizNet, a closed-loop system for predicting epileptic seizures through the use of Deep Learning (DL) method and implantable sensor networks. While pharmacological treatment is effective for some epilepsy patients (with ~65M people affected worldwide), one out of three suffer from drug-resistant epilepsy. To alleviate the impact of seizure, predictive systems have been developed that can notify such patients of an impending seizure, allowing them to take precautionary measures. SeizNet leverages DL techniques and combines data from multiple recordings, specifically intracranial electroencephalogram (iEEG) and electrocardiogram (ECG) sensors, that can significantly improve the specificity of seizure prediction while preserving very high levels of sensitivity. SeizNet DL algorithms are designed for efficient real-time execution at the edge, minimizing data privacy concerns, data transmission overhead, and power inefficiencies associated with cloud-based solutions. Our results indicate that SeizNet outperforms traditional single-modality and non-personalized prediction systems in all metrics, achieving up to 99% accuracy in predicting seizure, offering a promising new avenue in refractory epilepsy treatment.", "date": "2024-01-12", "categories": []}
{"id": "2403.19444", "title": "Leveraging Expert Input for Robust and Explainable AI-Assisted Lung Cancer Detection in Chest X-rays", "abstract": "Deep learning models show significant potential for advancing AI-assisted medical diagnostics, particularly in detecting lung cancer through medical image modalities such as chest X-rays. However, the black-box nature of these models poses challenges to their interpretability and trustworthiness, limiting their adoption in clinical practice. This study examines both the interpretability and robustness of a high-performing lung cancer detection model based on InceptionV3, utilizing a public dataset of chest X-rays and radiological reports. We evaluate the clinical utility of multiple explainable AI (XAI) techniques, including both post-hoc and ante-hoc approaches, and find that existing methods often fail to provide clinically relevant explanations, displaying inconsistencies and divergence from expert radiologist assessments. To address these limitations, we collaborated with a radiologist to define diagnosis-specific clinical concepts and developed ClinicXAI, an expert-driven approach leveraging the concept bottleneck methodology. ClinicXAI generated clinically meaningful explanations which closely aligned with the practical requirements of clinicians while maintaining high diagnostic accuracy. We also assess the robustness of ClinicXAI in comparison to the original InceptionV3 model by subjecting both to a series of widely utilized adversarial attacks. Our analysis demonstrates that ClinicXAI exhibits significantly greater resilience to adversarial perturbations. These findings underscore the importance of incorporating domain expertise into the design of interpretable and robust AI systems for medical diagnostics, paving the way for more trustworthy and effective AI solutions in healthcare.", "date": "2024-03-28", "categories": []}
{"id": "2411.13602", "title": "Translating Electrocardiograms to Cardiac Magnetic Resonance Imaging Useful for Cardiac Assessment and Disease Screening: A Multi-Center Study", "abstract": "Cardiovascular diseases (CVDs) are the leading cause of global mortality, necessitating accessible and accurate diagnostic tools. While cardiac magnetic resonance imaging (CMR) provides gold-standard insights into cardiac structure and function, its clinical utility is limited by high cost and complexity. In contrast, electrocardiography (ECG) is inexpensive and widely available but lacks the granularity of CMR. We propose CardioNets, a deep learning framework that translates 12-lead ECG signals into CMR-level functional parameters and synthetic images, enabling scalable cardiac assessment. CardioNets integrates cross-modal contrastive learning and generative pretraining, aligning ECG with CMR-derived cardiac phenotypes and synthesizing high-resolution CMR images via a masked autoregressive model. Trained on 159,819 samples from five cohorts, including the UK Biobank (n=42,483) and MIMIC-IV-ECG (n=164,550), and externally validated on independent clinical datasets (n=3,767), CardioNets achieved strong performance across disease screening and phenotype estimation tasks. In the UK Biobank, it improved cardiac phenotype regression R2 by 24.8% and cardiomyopathy AUC by up to 39.3% over baseline models. In MIMIC, it increased AUC for pulmonary hypertension detection by 5.6%. Generated CMR images showed 36.6% higher SSIM and 8.7% higher PSNR than prior approaches. In a reader study, ECG-only CardioNets achieved 13.9% higher accuracy than human physicians using both ECG and real CMR. These results suggest that CardioNets offers a promising, low-cost alternative to CMR for large-scale CVD screening, particularly in resource-limited settings. Future efforts will focus on clinical deployment and regulatory validation of ECG-based synthetic imaging.", "date": "2024-11-19", "categories": []}
{"id": "2505.09755", "title": "Explainability Through Human-Centric Design for XAI in Lung Cancer Detection", "abstract": "Deep learning models have shown promise in lung pathology detection from chest X-rays, but widespread clinical adoption remains limited due to opaque model decision-making. In prior work, we introduced ClinicXAI, a human-centric, expert-guided concept bottleneck model (CBM) designed for interpretable lung cancer diagnosis. We now extend that approach and present XpertXAI, a generalizable expert-driven model that preserves human-interpretable clinical concepts while scaling to detect multiple lung pathologies. Using a high-performing InceptionV3-based classifier and a public dataset of chest X-rays with radiology reports, we compare XpertXAI against leading post-hoc explainability methods and an unsupervised CBM, XCBs. We assess explanations through comparison with expert radiologist annotations and medical ground truth. Although XpertXAI is trained for multiple pathologies, our expert validation focuses on lung cancer. We find that existing techniques frequently fail to produce clinically meaningful explanations, omitting key diagnostic features and disagreeing with radiologist judgments. XpertXAI not only outperforms these baselines in predictive accuracy but also delivers concept-level explanations that better align with expert reasoning. While our focus remains on explainability in lung cancer detection, this work illustrates how human-centric model design can be effectively extended to broader diagnostic contexts - offering a scalable path toward clinically meaningful explainable AI in medical diagnostics.", "date": "2025-05-14", "categories": []}
{"id": "2510.19728", "title": "Enabling Granular Subgroup Level Model Evaluations by Generating Synthetic Medical Time Series", "abstract": "We present a novel framework for leveraging synthetic ICU time-series data not only to train but also to rigorously and trustworthily evaluate predictive models, both at the population level and within fine-grained demographic subgroups. Building on prior diffusion and VAE-based generators (TimeDiff, HealthGen, TimeAutoDiff), we introduce \\textit{Enhanced TimeAutoDiff}, which augments the latent diffusion objective with distribution-alignment penalties. We extensively benchmark all models on MIMIC-III and eICU, on 24-hour mortality and binary length-of-stay tasks. Our results show that Enhanced TimeAutoDiff reduces the gap between real-on-synthetic and real-on-real evaluation (``TRTS gap'') by over 70\\%, achieving $\\Delta_{TRTS} \\leq 0.014$ AUROC, while preserving training utility ($\\Delta_{TSTR} \\approx 0.01$). Crucially, for 32 intersectional subgroups, large synthetic cohorts cut subgroup-level AUROC estimation error by up to 50\\% relative to small real test sets, and outperform them in 72--84\\% of subgroups. This work provides a practical, privacy-preserving roadmap for trustworthy, granular model evaluation in critical care, enabling robust and reliable performance analysis across diverse patient populations without exposing sensitive EHR data, contributing to the overall trustworthiness of Medical AI.", "date": "2025-10-22", "categories": []}
{"id": "2602.03957", "title": "Temporal Validation Changes the Apparent Public-Health Utility of Under-Five Mortality Prediction in Bangladesh: A Four-Round DHS Machine-Learning Study", "abstract": "Background: Under-five mortality in Bangladesh remains uneven despite national progress. DHS-based prediction models may guide targeted follow-up, but only if validation reflects future use. We examined how validation design changes apparent prediction performance.\n  Methods: Four BDHS rounds (2011-2022; 33,962 children; 1,290 deaths) were analysed with a 26-feature pipeline and three model classes under four validation regimes, including cross-survey temporal validation (train 2011+2014, calibrate 2017, test 2022). A 32-unit ELU multilayer perceptron was selected via genetic-algorithm neural architecture search. AUROC used 2,000 bootstrap resamples; screening utility used sensitivity, PPV, and number needed to screen (NNS) at fixed capacity.\n  Results: Validation regime altered public-health interpretation more than model class. NAS MLP AUROC ranged from 0.669 (2022-only random) to 0.775 (pooled random), with temporal AUROC 0.730. At the top-10% temporal threshold, NAS identified 152/355 deaths in 2022 (sensitivity 42.8%, PPV 13.2%, NNS 7.6). NNS across designs ranged from 5.6 to 11.0.\n  Conclusions: Validation-regime choice changed screening workload and apparent policy value more than architecture. Temporal validation supports defensible estimates of follow-up and referral demand; DHS child-mortality studies should report sensitivity, PPV, and NNS before programmatic use.", "date": "2026-02-03", "categories": []}
{"id": "2602.06015", "title": "A Systematic Evaluation of Large Language Models for PTSD Severity Estimation: The Role of Contextual Knowledge and Modeling Strategies", "abstract": "Large language models (LLMs) are increasingly being used in a zero-shot (generative) fashion to assess mental health conditions, yet we have limited knowledge on what factors affect their accuracy. In this study, we use a clinical dataset of natural language narratives and self-reported PTSD severity scores from 1,437 individuals to comprehensively evaluate the performance of 11 state-of-the-art LLMs. To understand the factors affecting model's assessment accuracy, we systematically varied (i) contextual knowledge prompted to the models like subscale definitions, distribution summary, and interview questions, and (ii) modeling strategies including zero-shot vs few shot, amount of reasoning effort, model sizes, structured subscales vs direct scalar prediction, output rescaling and nine ensemble methods. Our findings indicate that (a) LLMs are most accurate when provided with detailed construct definitions and context of the narrative, even exceeding human raters agreement with self-reported scores; (b) increased reasoning effort leads to better estimation accuracy; (c) performance of open-weight models (Llama, DeepSeek) plateaus beyond 70B parameters while closed-weight (gpt-o3-mini, gpt-5) alternatives improve with newer generations; and (d) best performance is achieved when ensembling a supervised model with the zero-shot LLMs. Beyond agreement with self-reports, LLMs' estimates discriminated PTSD severity from depression, anxiety, and alcohol use, and prospectively predicted future mental healthcare expenditure. Together, these results suggest that contextual knowledge and modeling strategies meaningfully affect accuracy and clinical utility of LLM-based assessments of PTSD severity.", "date": "2026-02-05", "categories": []}
{"id": "2602.10385", "title": "Capture Timing-Attention of Events in Clinical Time Series", "abstract": "The contemporary paradigm of trajectory learning operates fundamentally at the level of group dynamics, systematically reducing individual-level complexity to fit group-level models, thus rendering effective patient subtyping difficult and individual-level modeling largely out of reach. We propose a data-driven paradigm that introduces a dedicated individual-level temporal variable to capture \\emph{Timing Attention} (i.e., the degree of concentration of an event's timing distribution across the patient cohort), thereby rendering timing a \\emph{computable dimension} that enables individualized temporal features in trajectory learning.\n  Instantiated as the Level-of-Individual Time Transformation (LITT) and applied to longitudinal EHR data from 3,276 breast cancer patients, the proposed paradigm demonstrates, for the first time to our knowledge: (1) automatic discovery of clinically significant patient trajectories, and (2) counterfactual timing deduction, that is, a \\emph{What-If Machine}. Both results are purely data-driven, requiring no prior domain knowledge. LITT further achieves strong performance on timing prediction and survival analysis tasks.", "date": "2026-02-11", "categories": []}
{"id": "2603.01131", "title": "MedCollab: IBIS-Guided Multi-Agent Collaboration with Hierarchical Disease Relation Chains for Clinical Diagnosis", "abstract": "Clinical diagnosis is a gradual process of evidence integration, in which physicians move from symptoms and medical history to examinations, competing hypotheses, disease relations, and treatment decisions. Large language models have advanced medical text understanding and generation. Yet their clinical use remains limited by weak evidence grounding, opaque reasoning, and inconsistent links among differential diagnosis, final diagnosis, diagnostic basis, and treatment planning. We introduce MedCollab, a multi-agent framework for full-cycle clinical diagnosis and report generation. MedCollab coordinates specialist and examination agents according to patient records. It structures agent deliberation with an Issue-Based Information System (IBIS) protocol, so that each diagnostic position is supported by patient-specific evidence and medical knowledge. It also builds Hierarchical Disease Relation Chains (HDRC) to connect accepted hypotheses through progression, complication, and comorbidity relations. During multi-round deliberation, a verifier-guided consensus module evaluates evidence support, medical plausibility, and logical conflicts. It then adjusts agent contributions and filters unsupported reasoning. Experiments on ClinicalBench and MIMIC-IV show that MedCollab outperforms leading LLMs and medical multi-agent baselines in diagnostic accuracy, evidence consistency, and clinical reasoning quality. These results indicate that structured and auditable collaboration can produce more faithful and clinically coherent diagnostic reports.", "date": "2026-03-01", "categories": []}
{"id": "2603.22530", "title": "Multimodal Training to Unimodal Deployment: Leveraging Unstructured Data During Training to Optimize Structured Data Only Deployment", "abstract": "Unstructured Electronic Health Record (EHR) data, such as clinical notes, contain clinical contextual observations that are not directly reflected in structured data fields. This additional information can substantially improve model learning. However, due to their unstructured nature, these data are often unavailable or impractical to use when deploying a model. We introduce a multimodal learning framework that leverages unstructured EHR data during training while producing a model that can be deployed using only structured EHR data. Using a cohort of 3,466 children evaluated for late talking, we generated note embeddings with BioClinicalBERT and encoded structured embeddings from demographics and medical codes. A note-based teacher model and a structured-only student model were jointly trained using contrastive learning and contrastive knowledge distillation loss, producing a strong classifier (AUROC = 0.985). Our proposed model reached an AUROC of 0.705, outperforming the structured-only baseline of 0.656. These results demonstrate that incorporating unstructured data during training enhances the model's capacity to identify task-relevant information within structured EHR data, enabling a deployable structured-only phenotype model.", "date": "2026-03-23", "categories": []}
{"id": "2605.01101", "title": "Virtual Speech Therapist: A Clinician-in-the-Loop AI Speech Therapy Agent for Personalized and Supervised Therapy", "abstract": "This paper develops Virtual Speech Therapist (VST), an intelligent agent-based platform that streamlines stuttering assessment and delivers customized therapy planning through automated and adaptive AI-driven workflows. VST integrates state-of-the-art deep learning-based stuttering classification, and multi-agent large language model (LLM) reasoning to support evidence-based clinical decision-making. The VST begins with the acquisition and feature extraction of patient speech samples, followed by robust classification of stuttering types. Building on these outputs, VST initiates an agentic reasoning process in which specialized LLM agents autonomously generate, critique, and iteratively refine individualized therapy plans. A dedicated critic agent evaluates all generated therapy plans to ensure clinical safety, methodological soundness, and alignment with peer-reviewed evidence and established professional guidelines. The resulting output is a comprehensive, patient-specific therapy draft intended for clinician review. Incorporating clinician feedback, the system then produces a finalized therapy plan suitable for patient delivery, thereby maintaining a clinician-in-the-loop paradigm. Experimental evaluation by expert speech therapists confirms that VST consistently generates high-quality, evidence-based therapy recommendations. These findings demonstrate the system's potential to augment clinical workflows, reduce clinician burden, and improve therapeutic outcomes for individuals with speech impairments. An interactive user interface for the proposed system is available online at: https://vocametrix.com/ai/stuttering-therapy-planning-agent , facilitating real-time stuttering assessment and personalized therapy planning.", "date": "2026-05-01", "categories": []}
{"id": "2606.15038", "title": "Fusion is not one-size-fits-all: Cross-Modal Representation Alignment for Time-to-Event Modeling", "abstract": "Accurate time-to-event (TTE) prediction from multimodal clinical data remains challenging due to modality imbalance and distribution shift. We introduce a foundation model-driven framework for cross-modal representation alignment between CT imaging and longitudinal EHR data, designed to generalize across tasks and institutions. CT and EHR modalities are encoded independently using domain-specific foundation models and aligned in a shared latent space through four principled fusion strategies: late fusion, contrastive alignment, cross-attention, and co-attention. We evaluate two clinically distinct TTE tasks: pulmonary embolism (PE) mortality and cardiovascular disease (CVD) outcomes, on large-scale multi-institutional cohorts (PE: N=3,099 train; 1,098 internal; 435 external; CVD: N=2,951 train; 837 internal; 682 external). Fusion consistently improves concordance index by 1.5-5.4% over unimodal baselines when modalities contribute comparably. Overall, contrastive multimodal fusion, particularly with CLMBR representations, provided the most consistent and statistically robust improvements, especially for PE mortality prediction. For MACE, cross-attention (one-hot) achieved the highest internal performance and image-guided co-attention achieved the best external performance. We therefore introduce a generalizable foundation model-based cross-modal alignment framework and provide the first systematic analysis of fusion behavior under modality imbalance in TTE prediction. Our results establish task-aware multimodal alignment as a necessary design principle for robust generalization and scalable clinical deployment.", "date": "2026-06-13", "categories": []}
{"id": "2606.15284", "title": "CAP: Towards PPG Universal Representation Learning with Patient-level Supervision", "abstract": "Photoplethysmography (PPG) plays a central role in wearable health monitoring and clinical decision support. Yet existing approaches to universal PPG representation learning largely focus on signal-level objectives and often overlook patient-level health context, which limits generalization to complex clinical tasks and heterogeneous cohorts. To address this gap, we construct a large-scale paired PPG-EHR multimodal dataset by distilling fragmented medical histories and clinical records into cohesive, patient-level electronic health records (EHR). Building on this resource, we propose Clinical Anchored Pretraining for PPG (CAP). During pretraining, CAP performs cross-modal contrastive alignment that anchors PPG representations to patient-level clinical semantics, guiding the encoder beyond waveform fitting toward modeling consistency in a patient's overall physiological state. During downstream adaptation, the pretrained PPG encoder provides clinically grounded representations that strengthen inductive bias and improve robustness and transferability. Experiments demonstrate that CAP consistently outperforms strong baselines on four diverse downstream tasks. CAP achieves a particularly large gain on respiratory rate prediction (up to +87.6% relative improvement over the state-of-the-art baseline) and delivers an average relative +26.7% across all tasks. We further enhance the interpretability of our approach through comprehensive analyses, including ablations and multiple complementary visualizations of the learned representations. The code for our experiments is available at: https://github.com/gody123gody/CAP .", "date": "2026-06-13", "categories": []}
{"id": "2606.15304", "title": "HemExp: Clinically-Guided Latent Diffusion for Modeling Hematoma Expansion", "abstract": "Hematoma expansion (HE) after spontaneous intracerebral hemorrhage (ICH) is a major determinant of acute triage and treatment decisions in neurosurgical care. However, most existing methods provide either a binary expansion risk or a single follow-up volume, limiting uncertainty-aware decisions. We introduce HemExp, a clinically-guided latent diffusion model that generates patient-specific follow-up non-contrast CT images, along with segmentations of intraparenchymal and intraventricular hemorrhage. Generation is conditioned on baseline imaging, clinical variables, and an explicit expansion indicator, enabling controllable simulation of realistic clinical scenarios. HemExp uses a hemorrhage-aware multi-head variational autoencoder and models progression as the difference between baseline and follow-up latent representations with a conditional diffusion model. The model is trained on paired scans from 450 patients across multiple centers and evaluated on 107 patients from a held-out institution. HemExp produces spatial HE probability maps by generating multiple synthetic follow-up images per patient to estimate distributions of plausible follow-up hematoma volumes. Perturbing clinical inputs such as symptom-onset-to-imaging time or anticoagulant status shifts the predicted follow-up volume distribution. HemExp extends binary predictors and demonstrates robust estimation of clinically relevant outcomes in the imaging space, such as hematoma volume, intraventricular involvement, and mass effects. Overall, our results support controllable latent diffusion as a promising direction for uncertainty-aware modeling of early ICH progression.", "date": "2026-06-13", "categories": []}
{"id": "2606.15447", "title": "Hierarchical Modeling of ICD Codes in EHR Foundation Models", "abstract": "Electronic health record foundation models typically treat ICD diagnosis codes as flat tokens, overlooking the clinically meaningful hierarchical structure that captures disease families, subcategories, and fine-grained diagnostic detail. As a result, existing EHR representation learning methods do not explicitly exploit the hierarchical structure already present in the coding system. In this work, we study ICD-10-CM hierarchy as a general inductive bias for clinical representation learning. We investigate two complementary mechanisms for incorporating hierarchy: first, by augmenting diagnosis sequences in a BERT-style transformer with tokens corresponding to different levels of the ICD hierarchy, and second, by injecting hierarchy into graph-based code representations through hierarchy-aware edges combined with diagnosis co-occurrence structure. Across these settings, we evaluate whether explicit hierarchy improves downstream prediction, which levels of the hierarchy are most useful, whether hierarchy encoding improves transfer across datasets, and how hierarchy reshapes embedding similarity structure. We conduct experiments on two large-scale real-world clinical datasets: MIMIC-IV, used for pretraining and in-domain evaluation, and eICU, used to assess cross-dataset transfer via frozen encoder probing. Our findings show that explicitly encoding ICD hierarchy improves over flat code representations in both in-domain and cross-dataset settings, while revealing that the most useful level of hierarchy depends on both the task and the modeling approach. More broadly, we focus on hierarchy-aware EHR representation learning and show that the benefits of encoding hierarchy are generalizable across modeling settings and hierarchy levels.", "date": "2026-06-13", "categories": []}
{"id": "2606.15927", "title": "An Exploratory Study of Blood Glucose Estimation from Photoplethysmography Signals using Machine Learning", "abstract": "Diabetes and extreme blood sugar levels are some of the major health problems faced by humans today across the world. While Continuous Glucose Monitoring (CGM) has emerged as an effective technology for management of diabetes as well as for monitoring blood sugar levels, this technology has traditionally been invasive (that is, requiring the piercing of the skin) and carries the risk of irritation, induration, etc. This highlights the need for accurate and non-invasive CGM methods that can be deployed at scale. With the emergence of various sensing technologies and their integration in wearables like the smart-watch, we now have the capability to continuously monitor body signals like the Photoplethysmogram (PPG) in a non-invasive manner. Having the ability to continuously monitor blood glucose through CGMs and continuously monitor PPG signals through a smart-watch offers an opportunity to get dense data on these two, opening the possibility of building machine learning and deep learning based models to estimate blood glucose level from PPG signals. In this work, we first present a paired dataset comprising continuous PPG signals from a smartwatch along with glucose values recorded using a CGM device. We also present the results of some preliminary experimental explorations performed on our dataset. These preliminary results suggest that some predictive signals may exist, though more exploration is needed with more data from a larger number of individuals. The dataset can be accessed at https://zenodo.org/records/20577959", "date": "2026-06-14", "categories": []}
{"id": "2606.16036", "title": "Trusting Right Predictions for Wrong Reasons: A LIME Based Analysis of Deep Learning Interpretability in Lung Cancer Diagnosis", "abstract": "Lung cancer is the leading cause of cancer-related mortality, with approximately 2.5 million new cases and 1.8 million deaths annually, making reliable diagnosis a clinical priority. Although deep learning models have achieved strong performance in lung cancer classification, evaluation has largely focused on predictive accuracy, leaving their decision-making processes insufficiently examined. This study compares three architecturally distinct models: a Convolutional Neural Network (CNN), a pretrained ResNet50, and a Vision Transformer (ViT), trained on the IQ-OTH/NCCD lung cancer CT dataset. Local Interpretable Model-Agnostic Explanations (LIME) were applied to investigate model reasoning. In addition to standard performance metrics, a dual-correlation framework was introduced to measure both prediction agreement and explanation agreement across model pairs. All three models achieved strong classification performance, with ResNet50 attaining 98.61% accuracy, CNN 97.91%, and ViT 93.75%, while all achieved ROC-AUC scores of 0.99. Prediction correlations exceeded 0.99 across all model pairs, indicating highly consistent outputs. However, LIME explanation correlations remained below 0.26, revealing substantial differences in the image regions used to reach those predictions. Analysis of misclassified samples further identified a consistent spatial pattern: incorrect predictions were associated with attention outside the lung parenchyma, whereas correct predictions focused primarily within lung regions. These findings demonstrate that prediction agreement is a poor proxy for reasoning consistency, and that interpretability evaluation must be treated as an independent validation criterion alongside predictive performance in clinical AI systems.", "date": "2026-06-14", "categories": []}
{"id": "2606.16056", "title": "Beyond the Blood Draw: Explainable Machine Learning for Non-Invasive Dysglycemia Risk Screening", "abstract": "Dysglycemia, encompassing both prediabetes and diabetes, affects huge numbers of adults worldwide, yet many of them remain undiagnosed. We developed and validated machine-learning (ML) models for non-invasive screening of dysglycemia risk that require no laboratory tests. Pooling data from the National Health and Nutrition Examination Survey (NHANES) 2017--2023 (n=14,352), we trained six ML models with stratified 5-fold cross-validation and compared them with two established clinical risk scores. LightGBM achieved the highest area under the receiver operating characteristic curve (AUC=0.820, 95% CI: 0.806--0.835), outperforming the Finnish Diabetes Risk Score (0.745) and American Diabetes Association Risk Test (0.783). SHAP analysis identified age, race/ethnicity, and waist-to-height ratio as the most influential predictors. Subgroup analyses confirmed consistent performance across demographic strata (AUC: 0.735--0.832). These results demonstrate the feasibility of explainable, laboratory-free dysglycemia screening for deployment in community settings and self-tracking health applications.", "date": "2026-06-14", "categories": []}
{"id": "2606.16149", "title": "LiteOdyssey: A Lightweight Reasoning AI Agent for Interpretable Rare-Disease Diagnosis", "abstract": "Most medical AI systems improve by scaling additional machinery: more fine-tuning data, more agents, and/or larger retrieval databases. In rare-disease diagnosis, however, such scaling can produce systems that are difficult to deploy, audit, and maintain. We asked whether state-of-the-art diagnostic performance could instead be achieved by extending the reasoning chain of a single AI agent: guiding it with a diagnostic policy, developed through human-AI collaboration and augmenting with freely available biomedical tools. We introduce LiteOdyssey, a lightweight rare-disease diagnostic framework that guides reasoning language model through a clinical genetics workflow. This framework was developed through Policy Iteration with Human Feedback (PIHF) and uses dynamic access to public biomedical tools. On two challenging benchmarks that provide only patient clinical features, LiteOdyssey achieved state-of-the-art performance, with an overall disease Recall@1 of 59.3% over the combined 1,243 cases of LIRICAL (n = 370) and the PhenoPacket Store (n = 873). Both benchmarks have a high proportion of ultra-rare disease (a prevalence below 1 in 1,000,000, with ultra-rare shares of approximately 45% and 52.8%, respectively). On the more difficult PhenoPacket subset, where causal diseases were not mapped to Orphanet in our rarity-mapping pipeline, LiteOdyssey achieved 60.7% Recall@1, compared with 10.7% for the same baseline model (GPT-5.4) without tools. This performance was achieved without fine-tuning, multi-agent ensembles, or a large case-retrieval database. Gains were also observed in the following: on cases never seen during development, on a private cohort of real-world rare disease patients, and on a smaller open-weights model. LiteOdyssey suggests a path toward rare-disease AI systems that are accurate, easier to deploy, and more transparent for physician review.", "date": "2026-06-15", "categories": []}
{"id": "2606.16583", "title": "Uncertainty Is Not a Safety Net for Clinical VQA, but Can It Anticipate Model Failure?", "abstract": "Safe deployment of clinical vision-language models (VLMs) requires reliable uncertainty estimation (UE): a signal indicating when predictions should be trusted or escalated to a clinician. We test whether current UE methods actually deliver this signal. Benchmarking 8 methods across 12 VLMs on clinical visual question-answering (VQA), we find that UE quality is not an intrinsic property of the UE method: it tracks model accuracy, degrading precisely where the model performance is weakest, and therefore where reliability is most needed. When we stress-test models by hiding the correct option among the multiple-choice answers (NOTA perturbations), accuracy collapses while uncertainty barely changes, leaving models systematically miscalibrated. Yet, we find that uncertainty on the unperturbed input reliably anticipates which predictions will collapse under NOTA, indicating that UE in current VLMs carries diagnostic information about model fragility. Our results position UE as a diagnostic tool for identifying fragile predictions and motivate perturbation-based evaluation as a path toward safe clinical deployment.", "date": "2026-06-15", "categories": []}
{"id": "2606.16890", "title": "Compositional Reasoning Depth Predicts Clinical AI Failure: Empirical Evidence Consistent with Transformer Compositionality Limits in Electronic Health Record Question Answering", "abstract": "Aggregate accuracy benchmarks conceal a systematic structure in how large language models fail at electronic health record (EHR) question answering: questions requiring more inferential steps produce disproportionately more errors. Motivated by theoretical results on transformer compositionality limits, we introduce a pre-specified hop-count taxonomy -- the number of distinct reasoning steps required to answer a clinical question from an EHR -- as a principled predictor of model failure. We annotate 313 clinician-generated MedAlign EHR question-answer pairs across four hop levels and evaluate 301 questions in a within-model ablation (claude-sonnet-4-6, zero-shot vs. extended thinking) and cross-architecture replications (gpt-4o and gpt-5.4-2026-03-05, zero-shot). All three models, spanning two providers and two OpenAI generations (GPT-4 and GPT-5), show monotone accuracy decline with hop count: Claude Sonnet zero-shot falls from 30.6% (hop=1) to 17.6% (hop=4) (Cochran-Armitage z=-2.30, p=0.011; OR per hop 0.72, 95% CI [0.56,0.92], p=0.008); GPT-4o replicates this (37.8% to 14.7%; OR 0.58 [0.45,0.75], p<0.001); and gpt-5.4-2026-03-05 confirms it (37.8% to 23.5%; OR 0.80 [0.66,0.98], p=0.027). A pre-specified context-sufficiency audit shows higher-hop questions are not differentially disadvantaged by EHR truncation (answerability 93-95% at hops 2-4 vs. 79% at hop=1), so the decline reflects compositional reasoning difficulty. Extended thinking did not significantly flatten the accuracy-depth curve across three reasoning conditions, and thinking-token usage scaled with hop count (r=0.31, p<0.0001), consistent with the predicted O(k) computational requirement. Hop count is thus a theory-motivated, cross-architecture predictor of large-language-model error on EHR question answering, with direct implications for deployment risk stratification of clinical AI.", "date": "2026-06-15", "categories": []}
{"id": "2410.15051", "title": "Automatic identification of diagnosis from hospital discharge letters via weakly supervised Natural Language Processing", "abstract": "Identifying patient diagnoses from hospital discharge letters is essential for large-scale cohort selection and epidemiological research, but traditional supervised approaches require extensive manual annotation, which is often impractical for large textual datasets. We present a weakly supervised Natural Language Processing (NLP) pipeline for classifying Italian discharge letters without document-level manual annotation. The method extracts diagnosis-related sentences, generates semantic embeddings using a transformer model further pre-trained on Italian medical documents, and applies a two-level clustering procedure to derive weak labels that are then used to train a document-level classifier.\n  The approach was evaluated in a case study on bronchiolitis using 33,176 discharge letters of children admitted to 44 emergency rooms or hospitals in the Veneto Region, Italy, between 2017 and 2020. The best weakly supervised model achieved an AUROC of 77.68% ($\\pm4.30\\%$), an AUPRC of 73.13% ($\\pm4.93\\%$), and an F1-score of 78.14% ($\\pm4.89\\%$) against manually annotated data. Performance surpassed unsupervised baselines and approached fully supervised models, while reducing the need for manual annotation by more than 1,500 hours for a dataset of this size. Similar model rankings were observed in a secondary validation on a smaller bronchitis dataset (3,188 discharge letters, 2020-2025), where the best weakly supervised model achieved an AUPRC of 76.72% ($\\pm 5.02\\%$).\n  These results suggest the potential of weakly supervised NLP methods for scalable disease identification from clinical discharge letters.", "date": "2024-10-19", "categories": []}
{"id": "2512.10966", "title": "Interpretable Alzheimer's Diagnosis via Multimodal Fusion of Regional Brain Experts", "abstract": "Accurate and early diagnosis of Alzheimer's disease (AD) is critical for effective intervention and requires integrating complementary information from multimodal neuroimaging data. However, conventional fusion approaches often rely on simple concatenation of features, which cannot adaptively balance the contributions of biomarkers such as amyloid PET and MRI across brain regions. In this work, we propose MREF-AD, a Multimodal Regional Expert Fusion model for AD diagnosis. It is a Mixture-of-Experts (MoE) framework that models mesoscopic brain regions within each modality as independent experts and employs a gating network to learn subject-specific fusion weights. Utilizing tabular neuroimaging and demographic information from the Alzheimer's Disease Neuroimaging Initiative (ADNI), MREF-AD achieves competitive performance over strong classic and deep baselines while providing interpretable, modality- and region-level insight into how structural and molecular imaging jointly contribute to AD diagnosis. The source code is available at https://github.com/PennShenLab/mref-ad.", "date": "2025-11-30", "categories": []}
{"id": "2606.13945", "title": "MedLatentDx: Latent Multi-Agent Communication for Cross-Hospital Rare-Disease Diagnosis", "abstract": "Rare diseases affect over $300$ million patients across more than $7{,}000$ conditions, yet no single hospital encounters enough cases of any one condition for reliable diagnosis. Cross-hospital collaboration could help by allowing a diagnosing institution to use distributed, case-specific diagnostic evidence, but privacy regulations restrict the transmission of identifiable clinical text across institutional boundaries. This setting raises two challenges: existing medical agent systems often rely on textual evidence exchange, while raw latent states such as hidden states and KV caches may still reveal prompt-derived clinical content. We introduce MedLatentDx, a latent multi-agent communication framework in which hospital agents keep private clinical records and retrieved cases local, and send compact latent KV blocks to a host agent for rare-disease diagnosis. MedLatentDx supports two deployment settings: same-backbone hospital agents use latent KV distillation, while hospitals with different LLM backbones use cross-family latent alignment. On CrossRare-Bench, a self-built large-scale rare-disease benchmark with hospital-level partitions, MedLatentDx improves cross-hospital diagnostic performance while reducing reconstructable clinical content relative to raw-latent communication baselines.", "date": "2026-06-11", "categories": []}
{"id": "2606.14608", "title": "Expert-Driven Survival Machines: Improving Stratification and Interpretability in Multiple Clinical Cohorts", "abstract": "Survival prediction plays a central role for healthcare providers and clinical researchers. Accurate risk stratification enables early intervention and improved patient management. Most existing deep survival models learn one common feature representation for all patients, which may hide important differences between patient subgroups. In contrast, a Mixture-of-Experts (MoE) framework allows different parts of the model to focus on different patient patterns, leading to more individualized representations. Therefore, in this work, we propose a mixture-of-experts enhanced adaptive deep clustering survival framework (AdaCSM) for modeling such heterogeneous survival patterns. We introduce a routing-based expert mechanism that enables conditional specialization within a parametric survival modeling framework. The proposed architecture allocates patients to specialized risk predictors dynamically while preserving the patient survival and subtype clustering objectives. We compare our method with state-of-the-art survival and deep clustering models on multiple real-world longitudinal clinical cohorts spanning diverse disease domains. The proposed method demonstrates improved predictive performance and leads to interpretable results in survival analysis.", "date": "2026-06-12", "categories": []}
{"id": "2606.12702", "title": "Deployment-Centered Evaluation: Predicting Query-Level Rejection Risk in a Clinical LLM System", "abstract": "Large language models (LLMs) are increasingly integrated into clinical systems, making it essential to evaluate the real-world utility of these systems. However, static benchmarks tend to measure correctness rather than user acceptance, aggregate performance across queries, and require densely annotated datasets -- leading to major blind spots for evaluating clinical systems. In this work, we perform a deployment-centered evaluation of an LLM system embedded within electronic health records at an academic medical center, where user feedback is sparse but closely reflects the deployment conditions. Specifically, we train a pre-response classifier that estimates the risk that a future interaction will result in the user rejecting the LLM response, based on query content and deployment-specific context available before generation. We conduct a prospective analysis of our model over 4.5 months of user feedback, finding that our prediction model achieves an AUROC of 0.719. Further, we estimate the benefit of such predictions in two downstream use cases (guardrail triggering and abstention). Our key conceptual insight is that making use of deployment-specific context (i.e., the provider type, department name, language model used for response), as opposed to only query content, improves the ability to predict whether the user will reject the system output. Altogether, our empirical case study demonstrates the feasibility of predicting user rejection using deployment-specific context, opening the door to targeted guardrails.", "date": "2026-06-10", "categories": []}
{"id": "2601.09072", "title": "Human-AI Co-design for Clinical Prediction Models", "abstract": "Developing safe, effective, and practically useful clinical prediction models (CPMs) traditionally requires iterative collaboration between clinical experts, data scientists, and informaticists. This process refines the often small but critical details of the model building process, such as which features/patients to include and how clinical categories should be defined. However, this traditional collaboration process is extremely time- and resource-intensive, resulting in only a small fraction of CPMs reaching clinical practice. This challenge intensifies when teams attempt to incorporate unstructured clinical notes, which can contain an enormous number of concepts. To address this challenge, we introduce HACHI, an iterative human-in-the-loop framework that uses AI agents to accelerate the development of fully interpretable CPMs by enabling the exploration of concepts in clinical notes. HACHI alternates between (i) an AI agent rapidly exploring and evaluating candidate concepts in clinical notes and (ii) clinical and domain experts providing feedback to improve the CPM learning process. HACHI defines concepts as simple yes-no questions that are used in linear models, allowing the clinical AI team to transparently review, refine, and validate the CPM learned in each round. In two real-world prediction tasks (acute kidney injury and traumatic brain injury), HACHI outperforms existing approaches, surfaces new clinically relevant concepts not included in commonly-used CPMs, and improves model generalizability across clinical sites and time periods. Furthermore, HACHI reveals the critical role of the clinical AI team, such as directing the AI agent to explore concepts that it had not previously considered, adjusting the granularity of concepts it considers, changing the objective function to better align with the clinical objectives, and identifying issues of data bias and leakage.", "date": "2026-01-14", "categories": []}
{"id": "2602.19502", "title": "Human-Guided Agentic AI for Multimodal Clinical Prediction: Lessons from the AgentDS Healthcare Benchmark", "abstract": "Agentic AI systems are increasingly capable of autonomous data science workflows, yet clinical prediction tasks demand domain expertise that purely automated approaches struggle to provide. We investigate how human guidance of agentic AI can improve multimodal clinical prediction, presenting our approach to all three AgentDS Healthcare benchmark challenges: 30-day hospital readmission prediction (Macro-F1 = 0.8986), emergency department cost forecasting (MAE = $465.13), and discharge readiness assessment (Macro-F1 = 0.7939). Across these tasks, human analysts directed the agentic workflow at key decision points, multimodal feature engineering from clinical notes, scanned PDF billing receipts, and time-series vital signs; task-appropriate model selection; and clinically informed validation strategies. Our approach ranked 5th overall in the healthcare domain, with a 3rd-place finish on the discharge readiness task. Ablation studies reveal that human-guided decisions compounded to a cumulative gain of +0.065 F1 over automated baselines, with multimodal feature extraction contributing the largest single improvement (+0.041 F1). We distill three generalizable lessons: (1) domain-informed feature engineering at each pipeline stage yields compounding gains that outperform extensive automated search; (2) multimodal data integration requires task-specific human judgment that no single extraction strategy generalizes across clinical text, PDFs, and time-series; and (3) deliberate ensemble diversity with clinically motivated model configurations outperforms random hyperparameter search. These findings offer practical guidance for teams deploying agentic AI in healthcare settings where interpretability, reproducibility, and clinical validity are essential.", "date": "2026-02-23", "categories": []}
{"id": "2606.10120", "title": "MetaPlate: Counterfactual-Guided RAG-LLM Tool for Personalized Food Recommendation and Hyperglycemia Prevention", "abstract": "Postprandial hyperglycemia is a key risk factor for metabolic disorders; however, existing dietary guidance is often static, impractical, and insufficiently personalized, providing recommendations that are difficult to follow or not impactful. While recent advances leverage continuous glucose monitoring (CGM) and machine learning to predict glycemic responses, these approaches are largely predictive and lack actionable guidance. Moreover, recommendation systems are often misaligned with user goals and require extensive input. We present MetaPlate, a counterfactual explanation (CF) guided, context-aware decision-support framework that generates personalized meal recommendations to mitigate postprandial glucose excursions in healthy adults. MetaPlate integrates multimodal data, including CGM readings, wearable-derived physiological signals, and user-provided meal inputs from $25$ individuals to model pre-meal context. A machine learning model predicts glucose response, while a CF optimization module adjusts meal composition modifying macronutrient amounts to maintain glucose levels within a target range ($\\leq 140$ mg/dL). An LLM-based retrieval-augmented generation (RAG) layer enhances interpretability by producing human-readable recommendations using constrained search of the USDA food database. We evaluate MetaPlate via a structured expert-in-the-loop assessment with registered dietitians (RDs), comparing performance before and after prompt refinement. Results show improvements in meal realism, portion suitability, and recommendation likelihood, with expert feedback indicating a shift from clinically implausible outputs to actionable, contextually appropriate recommendations. Our findings emphasize the importance of domain knowledge and structured constraints in LLM-driven systems and highlight the potential of MetaPlate as a real-time personalized dietary decision-support tool.", "date": "2026-06-08", "categories": []}
{"id": "2606.10725", "title": "Pre-AF 13: An Interpretable Atrial Fibrillation Risk Score Mined from Discharge Reports", "abstract": "Background. Atrial fibrillation (AF) is the most prevalent cardiac arrhythmia and a major determinant of prognosis. Established AF risk scores rely on factors (older age, hypertension) nearly ubiquitous among patients with cardiovascular disease (CVD), offering limited stratification in this high-risk group. Most target long-term (5-10 year) rather than medium-term prediction. We developed interpretable ML models predicting AF risk over a 24-month and entire follow-up horizon in CVD patients using routinely collected hospital data.\n  Methods. Single-center retrospective study of electronic health records from the National Research Cardiology Center (Russia) for patients aged >=18 with CVD but without pre-existing AF, hospitalized more than once between January 2012 and May 2019. A custom NLP pipeline transformed unstructured discharge reports into 73 structured features, combining a rule-based parser with transformer-based NER. Using LightAutoML we built a full model (73 features), a simple model (reduced subset), and a linear model for a bedside risk score. Performance was assessed by ROC AUC, compared with CHARGE-AF, C2HEST, MHS, and HAVOC, and interpreted via SHAP.\n  Results. Of 80,576 records from 45,000 patients, 17,562 met inclusion criteria; 1,438 (8.19%) developed AF. The full model reached ROC AUC 0.735 (24-month) and 0.696 (entire follow-up); the simple model was nearly identical (0.725, 0.696). All non-linear models outperformed the four clinical risk scores (ROC AUC 0.53-0.64). The simple model uses 13 features and is named Pre-AF 13. SHAP identified age and left atrial volume as dominant predictors. A linear risk score (Pre-AF 9) stratified observed 24-month AF incidence from ~7% to 36%.\n  Conclusion. Interpretable ML models built from routinely collected EHR data identify high-AF-risk CVD patients, outperforming established clinical risk scores.", "date": "2026-06-09", "categories": []}
{"id": "2606.11555", "title": "End-to-End Machine Learning for Depressive State Classification via EEG and fNIRS", "abstract": "The escalating demand for mental healthcare, driven by rising societal stress, highlights the limitations of traditional psychiatric diagnostics. Conventional methods - relying primarily on clinical interviews and patient self-reports - are inherently vulnerable to subjective bias and the varying empirical judgment of practitioners. To address the need for quantitative evaluation, biological signal-based detection, including electroencephalography (EEG) and functional near-infrared spectroscopy (fNIRS), has emerged as a promising objective alternative. Such technology is particularly vital for identifying latent depressive states that may be unrecognized by the subjects themselves. Furthermore, in aging populations, the high comorbidity between depression and dementia necessitates early differentiation to prevent mutual symptom exacerbation and maintain Quality of Life (QoL). This pilot study of eleven healthy students establishes a framework for biological signal-based depression detection, serving as a foundational step toward automated, objective diagnostic tools for clinical use.", "date": "2026-06-10", "categories": []}
{"id": "2606.12006", "title": "Tabular Foundation Models for Clinical Survival Analysis via Survival-Aware Adaptation", "abstract": "Predicting time-to-event outcomes such as mortality is a fundamental task in clinical decision-making, commonly addressed through survival analysis. While classical statistical and deep learning approaches have been widely studied, they typically require task-specific training and sufficient labeled data. Recent advances in tabular foundation models offer a new paradigm by learning general-purpose representations for structured data. However, their applicability to censored time-to-event prediction in clinical settings remains underexplored, as typical applications are restricted to discrete classification rather than survival analysis tasks.\n  In this work, we propose a lightweight adaptation approach for applying tabular foundation models to clinical survival analysis by directly training a survival-aware head on top of the pretrained representations. We study representative architectures, including TabPFN, TabDPT, and TabICL, and adapt them using a multi-task logistic regression (MTLR) head to model right-censored time-to-event outcomes. We evaluate this approach on a diverse set of public survival benchmarks and two large-scale ICU cohorts, MIMIC-IV and eICU.\n  Our results show that this transfer learning approach achieves competitive or superior performance compared to strong baselines. On MIMIC-IV, TabDPT-FT-MTLR reaches a C-index of 0.856, corresponding to a relative improvement of +1.4% over the best non-FM baseline (DeepSurv, 0.844) and +6.7% over the best zero-shot model (0.802). On eICU, TabICL-FT-MTLR achieves 0.797, yielding gains of +1.7% (DeepSurv, 0.784) and +6.4% (0.749), respectively. These findings highlight the importance of combining pretrained tabular representations with survival-aware objectives and suggest that tabular foundation models provide a practical and effective alternative for clinical survival prediction.", "date": "2026-06-10", "categories": []}
{"id": "2606.12140", "title": "Time-Conditioned and Multi-Time Survival Prediction from 2D PET/CT Projections in Lung Cancer", "abstract": "Accurate prediction of overall survival (OS) from positron emission tomography/computed tomography (PET/CT) can support personalized treatment and follow-up strategies in oncology. However, the impact of temporal modeling on imaging-based survival prediction remains insufficiently explored. We investigate how different temporal formulations influence survival prediction by developing two complementary approaches: Attention-guided Time-Conditioned Survival (ATCS) and Multi-Time Survival (MTS). We retrospectively analyzed pre-treatment PET/CT images from 848 patients with non-small cell lung cancer (NSCLC), including 556 for model development and 292 for held-out testing. A previously proposed Time-Conditioned Survival (TCS) model was used as a baseline. Models were trained using 5-fold cross-validation and evaluated on the test set using time-dependent area under the curve (AUC) at 6-month intervals from 0.5 to 5 years. Both ATCS and MTS outperformed the baseline TCS model, achieving mean AUCs of 0.794 and 0.793, respectively, compared to 0.767. ATCS performed better at earlier time points (0.5-3 years), whereas MTS performed better at later intervals (3.5-5 years). Combining tumor-specific and tissue-wise PET/CT features improved performance over either input alone. Finer temporal discretization improved short-term prediction, while coarser intervals provided more stable long-term estimates. These findings demonstrate that temporal modeling and input design influence PET/CT-based survival prediction. The proposed approaches enable time-specific survival estimation from pre-treatment imaging and may support improved risk stratification and clinical decision-making.", "date": "2026-06-10", "categories": []}
{"id": "2412.16758", "title": "Training Set Augmentation and Biology-Aware Harmonization Improve Radiomic Models for Lung Cancer Prediction in Indeterminate Nodules", "abstract": "CT radiomics-based machine learning has potential to predict lung cancer in pulmonary nodules (PNs) earlier than standard-of-care methods. Low malignancy rates in early-development PNs and variable image acquisition hinder development of radiomic models for diagnosing these PNs. To address these challenges, we augmented training using later-development PNs and harmonized for acquisition effects. We examine early-development benign and malignant PNs (n=106) below the sensitivity of standard-of-care diagnosis. Classifiers predicting malignancy performed near chance when trained on ComBat-harmonized radiomic features from only early-development PNs. We then augmented training with later-development benign and malignant PNs (n=225). We evaluated whether harmonization must incorporate biology that impacts acquisition effects in added training data. To correct variability from four acquisition protocols, we compared: 1) biology-unaware harmonization, 2) harmonizing with a covariate distinguishing early-development, later-development benign, later-development malignant datasets, 3) harmonizing each dataset separately. Models trained using augmentation, but biology-unaware harmonization, failed to improve consistently. Augmented training data harmonized with a covariate (ROC-AUC 0.74 [0.69-0.79]) or separately (ROC-AUC 0.71 [0.66-0.77]) yielded higher test ROC-AUC (Delong, p<=0.05) and PR-AUC (Wilcoxon, p<=0.05). In a proof-of-principle methodological study, we demonstrate with a small single-center dataset that combining radiomic features from later-development benign and malignant PNs requires biology-aware harmonization.", "date": "2024-12-21", "categories": []}
{"id": "2503.19158", "title": "Integrating Biological-Informed Recurrent Neural Networks for Glucose-Insulin Dynamics Modeling", "abstract": "Type 1 Diabetes (T1D) management is a complex task due to many variability factors. Artificial Pancreas (AP) systems have alleviated patient burden by automating insulin delivery through advanced control algorithms. However, the effectiveness of these systems depends on accurate modeling of glucose-insulin dynamics, which traditional mathematical models often fail to capture due to their inability to adapt to patient-specific variations. This study introduces a Biological-Informed Recurrent Neural Network (BIRNN) framework to address these limitations. The BIRNN leverages a Gated Recurrent Units (GRU) architecture augmented with physics-informed loss functions that embed physiological constraints, ensuring a balance between predictive accuracy and consistency with biological principles. The framework is validated using the commercial UVA/Padova simulator, outperforming traditional linear models in glucose prediction accuracy and reconstruction of unmeasured states, even under circadian variations in insulin sensitivity. The results demonstrate the potential of BIRNN for personalized glucose regulation and future adaptive control strategies in AP systems.", "date": "2025-03-24", "categories": []}
{"id": "2507.22017", "title": "Cyst-X: A Multi-Center MRI Benchmark and Federated Learning Framework for Malignancy-Risk Stratification of Pancreatic Cystic Neoplasm", "abstract": "Pancreatic cancer is projected to be the second-deadliest cancer by 2030, making early detection critical. Intraductal papillary mucinous neoplasms (IPMNs), key cancer precursors, present a clinical dilemma, as current guidelines struggle to stratify malignancy risk, leading to unnecessary surgeries or missed diagnoses. Here, we introduce Cyst-X, a multi-center MRI benchmark and a federated learning framework for IPMN malignancy-risk stratification. The dataset comprises 1,461 abdominal MRI scans from 764 patients at seven international centers, with three-tier malignancy labels anchored in histopathology or three-year imaging follow-up and expert pancreas segmentations. The pipeline couples the PanSegNet pancreas segmenter with a 3D DenseNet-121 classifier and a parallel radiomics predictor. On internal cross-validation, the deep learning classifier reached a mean area under the receiver operating characteristic curve (AUC) of 0.85 (95% confidence interval 0.84-0.86) on T2-weighted MRI for high-risk versus low- or no-risk discrimination, with the average precision rising from a prevalence baseline of 0.23 to 0.64. This performance was preserved (AUC 0.85, FedProx) when training was distributed across institutions without exchange of raw patient images. Benchmarked against three blinded radiologists on a 629-case reader subset evaluated under imaging-only conditions, the classifier matched or exceeded sensitivity at comparable specificity. To accelerate research in early pancreatic cancer detection, we publicly release the Cyst-X dataset, segmentation masks, and trained models as the first large-scale, multi-centre MRI resource for pancreatic cystic neoplasm analysis.", "date": "2025-07-29", "categories": []}
{"id": "2510.03844", "title": "On Using Large Language Models to Enhance Clinically-Driven Missing Data Recovery Algorithms in Electronic Health Records", "abstract": "Objective: Electronic health records (EHR) data are prone to missingness and errors. Previously, we devised an \"enriched\" chart review protocol where a \"roadmap\" of auxiliary diagnoses (anchors) was used to recover missing values in EHR data (e.g., a diagnosis of impaired glycemic control might imply that a missing hemoglobin A1c value would be considered unhealthy). Still, chart reviews are expensive and time-intensive, which limits the number of patients whose data can be reviewed. Now, we investigate the accuracy and scalability of a roadmap-driven algorithm, based on ICD-10 codes (International Classification of Diseases, 10th revision), to mimic expert chart reviews and recover missing values. Materials and Methods: In addition to the clinicians' original roadmap from our previous work, we consider new versions that were iteratively refined using large language models (LLM) in conjunction with clinical expertise to expand the list of auxiliary diagnoses. Using chart reviews for 100 patients from the EHR at an extensive learning health system, we examine algorithm performance with different roadmaps. Using the larger study of $1000$ patients, we applied the final algorithm, which used a roadmap with clinician-approved additions from the LLM. Results: The algorithm recovered as much, if not more, missing data as the expert chart reviewers, depending on the roadmap. Discussion: Clinically-driven algorithms (enhanced by LLM) can recover missing EHR data with similar accuracy to chart reviews and can feasibly be applied to large samples. Extending them to monitor other dimensions of data quality (e.g., plausability) is a promising future direction.", "date": "2025-10-04", "categories": []}
{"id": "2604.07085", "title": "Mining Electronic Health Records to Investigate Effectiveness of Ensemble Deep Clustering", "abstract": "In electronic health records (EHRs), clustering patients and distinguishing disease subtypes are key tasks to elucidate pathophysiology and aid clinical decision-making. However, clustering in healthcare informatics is still based on traditional methods, especially K-means, and has achieved limited success when applied to embedding representations learned by autoencoders as hybrid methods. This paper investigates the effectiveness of traditional, hybrid, and deep learning methods in heart failure patient cohorts using real EHR data from the All of Us Research Program. Traditional clustering methods perform robustly because deep learning approaches are specifically designed for image clustering, a task that differs substantially from the tabular EHR data setting. To address the shortcomings of deep clustering, we introduce an ensemble-based deep clustering approach that aggregates cluster assignments obtained from multiple embedding dimensions, rather than relying on a single fixed embedding space. When combined with traditional clustering in a novel ensemble framework, the proposed ensemble embedding for deep clustering delivers the best overall performance ranking across 14 diverse clustering methods and multiple patient cohorts. This paper underscores the importance of biological sex-specific clustering of EHR data and the advantages of combining traditional and deep clustering approaches over a single method.", "date": "2026-04-08", "categories": []}
{"id": "2606.08982", "title": "Baichuan-M4: A Clinical-Grade Medical Agent System for Continuous Care", "abstract": "Baichuan-M4 is Baichuan Intelligence's clinical-grade medical large model, designed for continuous care rather than single-turn medical question answering. It is built as a coordinated medical agent system around three pillars: Baichuan-Harness, a unified runtime that keeps reinforcement-learning training and real-world deployment consistent while enforcing action constraints, tool use, long-term patient memory, and multi-agent coordination; a core reasoning model trained with a continuous-care reinforcement-learning framework that integrates span-level reward modeling (SPAR++), reasoning-path compression, curriculum learning, and stabilized policy optimization; and a clinical tool layer for patient-memory management, authoritative evidence-based retrieval, and multimodal medical perception across documents, X-rays, and dermatology. On a cross-dimensional medical evaluation suite, Baichuan-M4 attains leading results in static medical knowledge and safety, dynamic OSCE-style consultation, long-context clinical memory, evidence-based retrieval, medical document OCR, and multimodal image understanding, while lowering the hallucination rate to 3.3%.", "date": "2026-06-08", "categories": []}
{"id": "2606.09860", "title": "Conformal Risk Prediction for Non-Alcoholic Fatty Liver Disease Using Gradient Boosting with Distribution-Free Coverages", "abstract": "Non-alcoholic fatty liver disease (NAFLD) affects roughly 25% of global adults, posing substantial hepatic and cardiovascular risks. Yet, population-level screening tools remain inadequate. We present Method, a machine-learning framework for NAFLD risk prediction coupling gradient-boosted decision trees with conformal prediction to yield calibrated, distribution-free coverage guarantees on individual risk estimates. It integrates a mutual-information-based stability selection procedure to identify a compact, clinically interpretable feature subset via bootstrap resampling, constructing prediction sets whose marginal coverage provably exceeds a user-specified confidence level. We evaluated Method on a multicenter cohort from Guangzhou, China (primary n=2,187; external validation n=412) using 78 candidate features across demographics, metabolic biomarkers, and lifestyle factors. Method achieves an AUROC of 0.912 internally and 0.891 externally, outperforming deep neural networks, TabNet, support vector machines, and logistic regression. Conformal prediction sets achieve 91.3% empirical coverage at the 90% nominal level. A three-tier risk stratification derived from these scores separates the population into distinct groups, with the high-risk subgroup showing a 12-month progression rate 4.7 times that of the low-risk tier. The selected features -- notably waist circumference, ALT, GGT, triglycerides, fasting glucose, and BMI -- align with established metabolic risk factors, providing biological plausibility.", "date": "2026-05-31", "categories": []}
{"id": "2606.09898", "title": "TRAPS: Therapeutic Response Analysis via Pathway-informed Stratification", "abstract": "Cancer treatment planning requires decisions across multiple clinical dimensions at once. Clinicians must determine whether a patient should receive targeted molecular therapy, radiation therapy, and whether they are likely to survive beyond six months. Existing pathway-informed deep learning models have been developed and tested in isolation, making fair comparison across architectures impossible. We present the first unified benchmark for pathway-guided therapy response modeling, evaluating three biologically informed architectures, BINN, GraphPath, and PATH, across five cancer cohorts drawn from The Cancer Genome Atlas, representing 2,622 patients encoded using Reactome pathway activity scores. Each model is trained jointly on all three clinical outcomes under identical data and evaluation conditions, the first study to treat pathway-structured deep learning as a combined therapy and survival prediction problem. Our results show that no single architecture wins across all tasks: PATH performs best for targeted molecular therapy prediction overall, BINN is most reliable for survival prediction, and no model produces useful predictions for radiation therapy, as the key drivers of that decision are clinical variables not captured in gene expression data. Most strikingly, GraphPath achieves an AUROC of 0.92 on prostate targeted molecular therapy prediction, the highest score in the entire benchmark, demonstrating that lateral co-regulation structure produces exceptional discriminative power when matched to a cohort with a narrow targetable driver programme, even under conditions of extreme class imbalance at only 11\\% positive prevalence.", "date": "2026-06-05", "categories": []}
{"id": "2606.09907", "title": "LongMoE: Longitudinal Multimodal Learning via Trajectory-Aware Mixture-of-Experts", "abstract": "Multimodal clinical learning is increasingly important for integrating diverse patient data, including imaging, text, and personalised health records. However, it faces two fundamental challenges: i) modality missingness, where arbitrary subsets of modalities are unavailable at a given patient visit, ii) longitudinal dynamics, where the diagnostic significance of an observation depends on the patient's evolving disease trajectory over time. Existing methods address these challenges in isolation: missing-modality frameworks treat each visit as an independent static snapshot and discard temporal context, while longitudinal models often assume complete modality availability and degrade under systematic modality incompleteness. We propose LongMoE (Longitudinal Mixture-of-Experts), the unified framework to jointly address both challenges. LongMoE combines a context-aware imputation module with an attentional tokenization module that captures frequency-domain temporal patterns across irregular visit sequences, a trajectory-aware encoder for modeling disease progression, and context-conditioned Sparse MoE routing for patient-specific expert selection. Experiments on ADNI, OASIS-3, and MIMIC-IV show that LongMoE improves robustness under missing or weak contemporaneous modalities and remains competitive in full-modality settings, establishing a strong foundation for longitudinally-aware multimodal clinical learning.", "date": "2026-06-06", "categories": []}
{"id": "2606.10279", "title": "Supervised Fine-tuning with Synthetic Rationale Data Hurts Real-World Disease Prediction", "abstract": "Supervised fine-tuning with synthetic rationale data is widely assumed to improve language model performance on clinical prediction tasks by teaching models not just what to predict but why. We test this assumption on five-year Alzheimer's disease and related dementias (ADRD) prediction from longitudinal health histories. Across a large-scale controlled experiment of 504 configurations, we find that rationale-based SFT consistently and substantially hurts prediction performance relative to label-only fine-tuning. The degradation persists across model families and data scales, and is not resolved by using a reasoning-oriented base model. Crucially, the failure is not explained by poor rationale quality: human expert annotation confirms that the generated rationales are medically accurate and faithfully grounded in patient-specific evidence, and few-shot experiments show that the same rationales improve performance when used as inference-time demonstrations rather than training targets. We identify the root cause as a structural conflict between narrative plausibility and discriminative optimization. We hope our work paves the path toward a more precise understanding of when and how rationale-based supervision helps and when it does not, guiding the responsible development of language models for high-stakes clinical prediction.", "date": "2026-06-09", "categories": []}
{"id": "2606.10796", "title": "Dep-LLM: Training-Free Depression Diagnosis via Evidence-Guided Structured Multi-factor with Reliable LLM Reasoning", "abstract": "Automatic Depression Detection (ADD) from clinical interviews is a pivotal task in computational mental health, yet it remains challenging due to two critical obstacles: 1) difficulty in modeling complex but sparsely distributed depression clues within lengthy, multi-topic clinical interviews, leading to superficial and unreliable reasoning; 2) scarcity of labeled data due to clinical privacy, together with high cost of training and fine-tuning, limiting the deployment of supervised ADD systems. To jointly address these challenges, we propose Dep-LLM, a training-free framework that mirrors the step-by-step reasoning of clinical psychiatrists and operates entirely on frozen off-the-shelf foundation LLMs. Dep-LLM comprises three stages. First, a Chain-of-Thought (CoT) Depression Multi-factor Analysis module structurally decomposes the long dialogue into five clinically aligned themes and produces evidence-grounded rationales, effectively handling long-context dependencies. Second, we introduce Confidence Analysis and Modulation module that quantifies the epistemic reliability from token-level entropy of each rationale and applies an intra-label and inter-theme modulation that amplifies trustworthy signals while suppressing uncertain ones without extra training. Third, a Collaborative Multi-factor Prediction module dynamically integrates multi-factor signals weighted by confidence into the final diagnosis. Extensive experiments on the DAIC-WOZ and E-DAIC datasets demonstrate the effectiveness and generalizability of Dep-LLM: it surpasses zero-shot baseline on nearly all 21 foundation LLMs across 9 metrics such as accuracy, macro F1 and weighted-average F1, and further outperforms state-of-the-art supervised domain-specific LLMs as well as the latest closed-source commercial LLMs, while requiring no extra training.", "date": "2026-06-09", "categories": []}
{"id": "2606.11144", "title": "OncoTraj: a public benchmark for longitudinal resistance prediction in EGFR-mutant non-small-cell lung cancer on osimertinib", "abstract": "Resistance to first-line osimertinib in EGFR-mutant non-small-cell lung cancer (NSCLC) is the canonical example of predictable clonal evolution under therapeutic pressure, yet no public benchmark exists for training or evaluating computational models on the corresponding longitudinal patient trajectories. We introduce OncoTraj, a public benchmark of 813 EGFR-mutant NSCLC patients receiving first-line osimertinib, harmonized from three real-world clinical-genomic sources: MSK-CHORD (672 patients), AACR Project GENIE BPC NSCLC (34 patients), and the FLAURA molecular-resistance supplement (107 patients). OncoTraj defines three locked tasks: (A) binary classification of progression by a fixed 12-month landmark, (B) regression of time-to-first-progression in days, and (C) six-class classification of the dominant resistance mechanism. We release the harmonized dataset, patient-level train/validation/test splits with an audited no-leakage guarantee, an open-source evaluation harness, and six reference baselines spanning a majority-class predictor, logistic regression, random forest, XGBoost, an LSTM, and a multi-task transformer. With v1's single-timepoint snapshot features, no task clears chance on clean within-source evaluation: the uniformity of this ceiling across every model class localizes the limit to the input modality (single-snapshot tissue NGS rather than serial ctDNA), not the algorithm. The benchmark does recover a reproducible literature-consistent association: TP53 co-mutation raises the 12-month progression rate from 29% to 59% cohort-wide. OncoTraj establishes a reproducible, leakage-audited baseline and converts the modality limit into concrete design requirements for a serial-ctDNA-enriched v2.", "date": "2026-06-09", "categories": []}
{"id": "2411.06469", "title": "ClinicalBench: Can LLMs Beat Traditional ML Models in Clinical Prediction?", "abstract": "Large Language Models (LLMs) hold great promise to revolutionize current clinical systems for their superior capacities on medical text processing tasks and medical licensing exams. Meanwhile, traditional ML models such as SVM and XGBoost have still been mainly adopted in clinical prediction tasks. An emerging question is: Can LLMs beat traditional ML models in clinical prediction? Thus, we build a new benchmark ClinicalBench to comprehensively study the clinical predictive modeling capacities of both general-purpose and medical LLMs, and compare them with traditional ML models. ClinicalBench embraces three common clinical prediction tasks, two databases, 14 general-purpose LLMs, 8 medical LLMs, and 11 traditional ML models. Through extensive empirical investigation, we discover that both general-purpose and medical LLMs, even with different model scales, diverse prompting or fine-tuning strategies, still cannot beat traditional ML models in clinical prediction yet, shedding light on their potential deficiency in clinical reasoning and decision-making. We call for caution when practitioners adopt LLMs in clinical applications. ClinicalBench can be utilized to bridge the gap between LLMs' development for healthcare and real-world clinical practice.", "date": "2024-11-10", "categories": []}
{"id": "2412.06147", "title": "Advancements in Machine Learning and Deep Learning for Early Detection and Management of Mental Health Disorder", "abstract": "For the early identification, diagnosis, and treatment of mental health illnesses, the integration of deep learning (DL) and machine learning (ML) have started playing a significant role. By evaluating complex data from imaging, genetics, and behavioral assessments, these technologies have the potential to improve clinical results significantly. However, they also present unique challenges relating to data integration and ethical issues. The development of ML and DL methods for the early diagnosis and treatment of mental health issues is reviewed in this survey. It examines a range of applications, with a particular emphasis on behavioral assessments, genetic and biomarker analysis, and medical imaging for the diagnosis of diseases like depression, bipolar disorder, and schizophrenia. Predictive modeling for illness development is further discussed in the review, focusing on the function of risk prediction models and longitudinal investigations. Important discoveries show how ML and DL might improve treatment outcomes and diagnostic accuracy while tackling methodological inconsistency, data integration, and ethical concerns. The study emphasizes the significance of building real-time monitoring systems for individualized treatment, improving data fusion techniques, and interdisciplinary collaboration. Upcoming studies should concentrate on surmounting these obstacles to maximize ML and DL's valuable and moral implementation in mental health services.", "date": "2024-12-09", "categories": []}
{"id": "2501.12421", "title": "Tackling Small Sample Survival Analysis via Transfer Learning: A Study of Colorectal Cancer Prognosis", "abstract": "Survival prognosis is crucial for medical informatics. Practitioners often confront small-sized clinical data, especially cancer patient cases, which can be insufficient to induce useful patterns for survival predictions. This study deals with small sample survival analysis by leveraging transfer learning, a useful machine learning technique that can enhance the target analysis with related knowledge pre-learned from other data. We propose and develop various transfer learning methods designed for common survival models. For parametric models such as DeepSurv, Cox-CC (Cox-based neural networks), and DeepHit (end-to-end deep learning model), we apply standard transfer learning techniques like pretraining and fine-tuning. For non-parametric models such as Random Survival Forest, we propose a new transfer survival forest (TSF) model that transfers tree structures from source tasks and fine-tunes them with target data. We evaluated the transfer learning methods on colorectal cancer (CRC) prognosis. The source data are 27,379 SEER CRC stage I patients, and the target data are 728 CRC stage I patients from the West China Hospital. When enhanced by transfer learning, Cox-CC's $C^{td}$ value was boosted from 0.7868 to 0.8111, DeepHit's from 0.8085 to 0.8135, DeepSurv's from 0.7722 to 0.8043, and RSF's from 0.7940 to 0.8297 (the highest performance). All models trained with data as small as 50 demonstrated even more significant improvement. Conclusions: Therefore, the current survival models used for cancer prognosis can be enhanced and improved by properly designed transfer learning techniques. The source code used in this study is available at https://github.com/YonghaoZhao722/TSF.", "date": "2025-01-21", "categories": []}
{"id": "2512.10745", "title": "PMB-NN: Physiology-Centred Hybrid AI for Personalized Hemodynamic Monitoring from Photoplethysmography", "abstract": "Continuous monitoring of blood pressure (BP) and hemodynamic parameters such as peripheral resistance (R) and arterial compliance (C) are critical for early vascular dysfunction detection. While photoplethysmography (PPG) wearables has gained popularity, existing data-driven methods for BP estimation lack interpretability. We advanced our previously proposed physiology-centered hybrid AI method-Physiological Model-Based Neural Network (PMB-NN)-in blood pressure estimation, that unifies deep learning with a 2-element Windkessel based model parameterized by R and C acting as physics constraints. The PMB-NN model was trained in a subject-specific manner using PPG-derived timing features, while demographic information was used to infer an intermediate variable: cardiac output. We validated the model on 10 healthy adults performing static and cycling activities across two days for model's day-to-day robustness, benchmarked against deep learning (DL) models (FCNN, CNN-LSTM, Transformer) and standalone Windkessel based physiological model (PM). Validation was conducted on three perspectives: accuracy, interpretability and plausibility. PMB-NN achieved systolic BP accuracy (MAE: 7.2 mmHg) comparable to DL benchmarks, diastolic performance (MAE: 3.9 mmHg) lower than DL models. However, PMB-NN exhibited higher physiological plausibility than both DL baselines and PM, suggesting that the hybrid architecture unifies and enhances the respective merits of physiological principles and data-driven techniques. Beyond BP, PMB-NN identified R (ME: 0.15 mmHg$\\cdot$s/ml) and C (ME: -0.35 ml/mmHg) during training with accuracy similar to PM, demonstrating that the embedded physiological constraints confer interpretability to the hybrid AI framework. These results position PMB-NN as a balanced, physiologically grounded alternative to purely data-driven approaches for daily hemodynamic monitoring.", "date": "2025-12-11", "categories": []}
{"id": "2606.02802", "title": "ChatHealthAI: Aligning Electronic Health Record Representations with Large Language Models for Grounded Clinical Reasoning", "abstract": "Large language models (LLMs) exhibit strong natural-language reasoning abilities for clinical decision support, but struggle to effectively model structured longitudinal electronic health records (EHRs). In contrast, EHR foundation models can learn predictive patient representations, yet lack interpretable language-based reasoning. To bridge this gap, we propose ChatHealthAI, a multimodal reasoning framework that aligns structured EHR representations from a pretrained EHR foundation model with the semantic space of a frozen LLM through a task-aware resampler. By integrating longitudinal patient representations with refined clinical event descriptions, ChatHealthAI enables clinically grounded natural-language reasoning while maintaining accurate patient prediction. We evaluated ChatHealthAI on three clinical predictive tasks from the EHRSHOT benchmark. Results show that ChatHealthAI improves reasoning quality and interpretability while preserving competitive predictive performance. These findings highlight the potential of integrating EHR foundation models with pretrained LLMs for interpretable clinical prediction.", "date": "2026-06-01", "categories": []}
{"id": "2606.07606", "title": "QDSP: An Interpretable Structured Learning Framework for Predicting Death or Cerebral Palsy in Very Low Birth Weight Infants", "abstract": "Very low birth weight infants (VLBWI) are at high risk of mortality and severe neurodevelopmental impairment, including cerebral palsy, yet reliable discharge-time prognostic stratification remains challenging in high-dimensional and data-limited clinical settings. To address this problem, we propose QDSP, an interpretable structured learning framework that integrates Quota-guided Subspace Sampling (QSS) and Differentiable-decision-guided Structure Perception (DSP). The QSS module constructs stability-aware and low-redundancy feature subspaces through bootstrap-based feature consistency estimation, whereas the DSP module employs differentiable soft oblique decision structures to model nonlinear clinical interactions while preserving traceable decision evidence. The proposed framework was evaluated on a real-world VLBWI cohort comprising 51 infants and further validated on three public medical tabular datasets. On the primary cohort, QDSP achieved an accuracy of 0.9200 and an AUC of 0.9714, outperforming representative machine learning and deep tabular learning baselines, including XGBoost, TabNet, and TabPFN. Across external datasets, QDSP maintained competitive discrimination and calibration under varying sample sizes and clinical distributions. In addition, SHAP-based analyses and differentiable decision-path tracing identified clinically relevant predictors, including cystic periventricular leukomalacia (cPVL) and birth weight, consistent with established neonatal pathophysiological evidence. These results suggest that QDSP provides an interpretable and robust framework for discharge-time risk stratification in VLBWI and may support early individualized clinical decision-making in neonatal intensive care settings.", "date": "2026-05-29", "categories": []}
{"id": "2606.07692", "title": "BCG-FM: A Foundation Model for Ambient Cardiac Health Sensing", "abstract": "Foundation models for wearable biosignals have matched or exceeded supervised specialists across a range of clinical tasks, yet all rely on modalities that require deliberate user action--wearing a device or visiting a sleep lab. We introduce BCG-FM, the first foundation model for ambient mechanical biosignals. A piezoelectric sensor embedded in the bed surface records ballistocardiography (BCG) each night without user effort; we pretrain BCG-FM with participant-level contrastive learning and using a total of 2.75 million hours of nightly recordings from 145,985 individuals, the largest raw-waveform biosignal pretraining corpus to date. Frozen BCG-FM embeddings achieve 3.26-year MAE on biological-age estimation (the lowest reported for any ambient, contactless modality) and yield clinically relevant discrimination across 15 self-reported health conditions and three independent external cohorts. Pretrained representations from only 500 labeled participants outperform a fully supervised baseline trained on 3,372, and representation quality scales log-linearly with contrastive batch size. These results establish ambient, longitudinal mechanical biosignals as a viable modality for health foundation models.", "date": "2026-06-05", "categories": []}
{"id": "2606.07798", "title": "Reconstructing and forecasting disease trajectories of patients with Alzheimer's disease using routine data in resource-constrained settings", "abstract": "Alzheimer's disease is a progressive neurodegenerative disorder, and its progression varies substantially across patients. Existing work aims to forecast patients' future cognitive state, with minimal focus on reconstructing the state from past visits. Furthermore, in current research, quantifying predictive uncertainty remains underexplored and relies on costly modalities such as MRI, PET, and CSF, limiting their deployment in resource-limited settings. In this research, our primary objectives are: First, bidirectional prediction of cognitive scores from irregular visits to present the complete disease trajectory. Second, to enable interpolation and extrapolation capabilities to assist clinicians in informed prognostic decision making, and third, to provide a well-calibrated uncertainty estimate for all predictions, and finally, to achieve the objectives using the modalities available during routine visits. We propose a unified framework, GNOVA: A GRU-Neural ODE Variational Autoencoder. The architecture combines a Gated Recurrent Unit encoder and a Neural ODE decoder within a variational autoencoder framework. In our work, we forecast the CDR-SB and MMSE Scores. The GRU encoder allows for any number of inputs at any time point. The Neural-ODE decoder performs continuous estimation, allowing interpolation and extrapolation at any desired time point. The Variational autoencoder allows for uncertainty estimation in predictions. We worked with 1,727 patients from the ADNI dataset over 10 years; the model achieved mean absolute errors of 1.35 and 2.28 for CDR-SB and MMSE scores, respectively, without requiring any neuroimaging or biomarker data. Feature-ablation studies revealed that age, BMI, and APOE4 status were strong predictors. The proposed framework enables the reconstruction of incomplete patient histories and the anticipation of future cognitive states.", "date": "2026-06-05", "categories": []}
{"id": "2606.08538", "title": "Routine laboratory trajectories encode the onset of organ-level complications in cancer", "abstract": "Routine laboratory panels drawn during cancer treatment constitute longitudinal physiological recordings of organ function, yet their temporal structure is discarded by single-timepoint prognostic tools. A transformer trained on 2,777,595 laboratory measurements from 3,905 patients with multiple myeloma or ovarian cancer predicted the two-year onset of 162 treatment-associated complications, including therapy-related myelodysplastic syndromes, spanning eight clinical categories, achieving 1.5- to 6.1-fold enrichment above prevalence at the group level. It matched or outperformed non-sequential baselines across grouped endpoints (AUROC gains up to +0.11), demonstrating that longitudinal laboratory trajectories capture evolving complication-specific physiology inaccessible from isolated measurements. Predictions generalised across both cancers, divergence concentrating in disease-specific complications, and biomarker masking recovered signatures consistent with established pathophysiology. External validation on MIMIC-IV and MMRF CoMMpass confirmed transferability across independent healthcare systems (AUROC up to 0.85). Routine oncological laboratory data encode organ deterioration weeks to months before clinical onset, enabling complication-specific surveillance without additional testing infrastructure.", "date": "2026-06-07", "categories": []}
{"id": "2606.08945", "title": "From Hazard Functions to Language Space: Cox-Supervised Distillation of Survival Risk into a Large Language Model", "abstract": "We investigate whether information about time-to-event risk estimated by a Cox proportional hazards model can be transferred into a generative large language model. We propose a text-based survival modelling pipeline in which structured clinical covariates are converted into text prompts and a Qwen-based large language model is fine-tuned to generate patient-specific survival risk using Cox model predictions as a training target. Across GBSG2, ACTG320, and WHAS500, the model achieves competitive held-out discrimination and calibration despite being trained as a text-generation task rather than with a conventional survival-analysis loss. We further analyse the geometry of the model's hidden states, where t-SNE visualisations reveal smooth risk gradients in latent space, suggesting that the model represents survival risk as a continuous structure rather than isolated risk categories. Together, these findings suggest that large language models can internalise survival-risk structure while supporting calibrated prediction, providing a route towards time-to-event reasoning in language models.", "date": "2026-06-08", "categories": []}
{"id": "2606.09249", "title": "MAGIS: Evidence-Based Multi-Agent Reasoning for Interpretable Strabismus Clinical Decision-Making", "abstract": "Strabismus is a common ocular disorder that requires fine-grained subtype diagnosis for individualized treatment planning. However, existing deep learning methods mainly provide diagnostic predictions without transparent reasoning, while recent large vision-language models (LVLMs), although promising for joint image understanding and report generation, remain highly prone to hallucination in this evidence-sensitive and rule-driven medical task. To address these challenges, we propose MAGIS, an evidence-based Multi-AGent reasoning for Interpretable Strabismus diagnosis framework. MAGIS transforms black-box end-to-end generation into a structured diagnostic process consisting of candidate hypothesis generation, dual-evidence constrained context, evidence-based corrective verification, and report generation. Specifically, we introduce a Dual-Evidence Constrained Context (DECC) mechanism that jointly organizes visual evidence from the photograph of the nine cardinal positions of gaze and evidence-based clinical diagnostic rules into a constrained context for reliable diagnostic reasoning. We further develop an Evidence-Based Corrective Verification (EBCV) mechanism that verifies whether the current diagnostic hypothesis is supported by visual evidence, heatmap-based visual cues, and evidence-based clinical diagnostic rules. Hypothesis refinement is triggered when inconsistency is detected. Experiments on a fine-grained strabismus benchmark demonstrate that MAGIS not only significantly outperforms other state-of-the-art diagnostic systems, improving the weighted F1 score from 72.0% to 91.3%, but also substantially improves the clinical reliability (consistency, alignment, and completeness) of generated diagnostic reports. These results demonstrate that MAGIS provides an effective solution for building accurate, evidence-based, and clinically interpretable strabismus diagnosis systems.", "date": "2026-06-08", "categories": []}
{"id": "2606.09671", "title": "Transition-Based Digital Twin Modelling for Alzheimer's Disease under Sparse Longitudinal Data", "abstract": "Alzheimer's disease (AD) progression is highly heterogeneous and is typically observed through sparse and irregular longitudinal data, posing challenges for prediction and personalised monitoring. Existing machine learning approaches have improved AD prediction using multimodal data, yet often focus on static classification or cohort-level risk estimation, providing limited support for subject-specific modelling and uncertainty-aware reasoning. To address these limitations, we present a personalised digital twin framework for AD prediction and scenario-based analysis using multimodal longitudinal data. The proposed approach integrates complementary modelling strategies to capture clinical transitions and temporal dependencies across visits. Using data from the Alzheimer's Disease Neuroimaging Initiative (ADNI), including cognitive assessments, clinical variables, and MRI-derived phenotypes, the framework predicts cognitive status and diagnostic categories while quantifying predictive uncertainty and enabling patient-specific what-if trajectory analysis. Evaluation on leak-free subject-level splits demonstrates strong performance in score forecasting and diagnosis classification. In this sparse and irregular ADNI setting, transition-based modelling of adjacent visits achieved higher predictive accuracy than the sequence-based branch, suggesting that local transition modelling may be more data-efficient. While sequence models remain valuable for uncertainty-aware trajectory forecasting, local transition modelling offers a more data-efficient and robust predictive strategy. These findings highlight the importance of aligning temporal modelling strategies with clinical data structure and suggest that transition-based digital twin formulations may provide a practical and interpretable approach for personalised disease forecasting in neurodegenerative disorders.", "date": "2026-06-08", "categories": []}
{"id": "2604.06684", "title": "GraphWalker: Patient Analogy Meets Information Gain for Clinical Reasoning with Large Language Models", "abstract": "Clinical reasoning over electronic health records (EHRs) is a fundamental yet challenging task in modern healthcare. While large language models (LLMs) offer a promising paradigm via in-context demonstrations that requires no task-specific parameter updates, existing methods for reasoning by patient analogy in EHR settings suffer from three core limitations: (1) Perspective Limitation, where data-driven similarity misaligns with LLM reasoning needs while model-driven signals are constrained by limited clinical competence; (2) Cohort Awareness, as demonstrations are selected independently without modeling population-level structure; and (3) Information Aggregation, where redundancy and interaction effects among demonstrations are ignored. We propose GraphWalker, a training-free framework that lets frozen LLMs reason by analogy over retrieved patient cases. GraphWalker (i) jointly leverages data-driven and model-driven perspectives, (ii) discovers patient cohorts to ground retrieval in population-level structure, and (iii) employs a lazy greedy search with frontier expansion to compose demonstrations with high marginal information gain. Extensive experiments on multiple real-world EHR benchmarks show that GraphWalker consistently outperforms state-of-the-art demonstration selection baselines, and remains substantially more robust under cross-dataset distribution shift, without task-specific parameter updates. GraphWalker further generalizes to black-box LLMs and composes naturally with agentic reasoning frameworks, positioning it as a pluggable patient-analogy skill in LLM-based clinical workflows. Our code is available at https://github.com/PuppyKnightUniversity/GraphWalker.", "date": "2026-04-08", "categories": []}
{"id": "2606.06516", "title": "Probabilistic learning to perform pre-onset individualised prediction of disease severity: application to Veno Occlusive Disease", "abstract": "We advance a new probabilistic supervised learning approach that permits reliable, automated, and early individualised prediction of the severity with which a disease will develop in a prospective patient. The prediction capacity is illustrated via the pre-transplant prediction of the score of severity of Veno Occlusive Disease (or VOD) in the digital twin (DT) of the considered prospective patient, where this score parametrises the severity with which VOD will develop in this patient, after they undergo their Bone Marrow Transplant. The learning of the relationship between the pre-transplant variables, and a severity score variable is undertaken by modelling this relationship as a (random) function that is treated as a sample function of an adequately-chosen stochastic process. The parameters of this underlying process are learnt using a training dataset that is generated using the real-time evolution of retrospective patients in a cohort, with this training dataset subsequently augmented in size by a probabilistic inverse learning of the score of prospective patients. The augmented training set, then permits the learning of the function that capacitates - at the pre-transplant stage - automated prediction of the score of the severity of VOD that characterises the DT of a physical patient in their unique pre-transplant state. This score is subsequently fed back to the real prospective patient as the severity with which VOD will develop in them, after this patient undergoes their transplant. Such a score then permits the treating Haematologist-Oncologists to decide on the treatment regimen, which in this illustration reduces to deciding on treating the patient with Defibrotide. An AI facility is developed to undertake such automated prediction, with the physician inputting the data on the pre-transplant state that characterises the DT of the prospective patient under consideration.", "date": "2026-06-02", "categories": []}
{"id": "2606.06881", "title": "GlucoFM-Bench: Benchmarking Time-Series Foundation Models for Blood Glucose Forecasting", "abstract": "Blood glucose forecasting models are foundational for modern diabetes management systems, as reliable short-term predictions can enable proactive interventions, support automated insulin delivery, and reduce the risk of hypo- and hyperglycemic events. From a modeling perspective, glucose forecasting poses unique challenges due to heterogeneous physiological dynamics across diabetes populations. Traditional machine learning and deep learning models have been extensively evaluated for glucose prediction, yet recent time-series foundation models (TSFMs) remain much less studied in this setting. To bridge this gap, we present GlucoFM-Bench, a comprehensive benchmark evaluating state-of-the-art TSFMs alongside supervised deep learning models for blood glucose forecasting. We assess eight representative architectures, including pre-trained TSFMs, time-series large language models, and task-specific deep learning models, across 15 publicly available diabetes-relevant datasets comprising 1,117 individuals with type 1 diabetes, type 2 diabetes, prediabetes, and no diabetes. Models are evaluated under zero-shot, few-shot, and full-shot protocols, with systematic variation in context length and prediction horizon. Across datasets, pre-trained TSFMs, especially Chronos-2 and TimesFM, show strong zero-shot and few-shot transfer, with the best zero-shot model performing within 5% of the best full-shot supervised model. Yet, when task-specific data are abundant, a lightweight LSTM remains strongest, outperforming TSFMs by 4--21% under full-shot training. Stratified analyses reveal persistent challenges in T1D cohorts and hypo-/hyperglycemic ranges, highlighting the need for evaluation beyond aggregate error metrics. Together, GlucoFM-Bench provides a standardized and reproducible foundation for evaluating, comparing, and improving foundation models for blood glucose forecasting.", "date": "2026-06-05", "categories": []}
{"id": "2308.12224", "title": "Enhancing cardiovascular risk prediction through AI-enabled calcium-omics", "abstract": "Background. Coronary artery calcium (CAC) is a powerful predictor of major adverse cardiovascular events (MACE). Traditional Agatston score simply sums the calcium, albeit in a non-linear way, leaving room for improved calcification assessments that will more fully capture the extent of disease.\n  Objective. To determine if AI methods using detailed calcification features (i.e., calcium-omics) can improve MACE prediction.\n  Methods. We investigated additional features of calcification including assessment of mass, volume, density, spatial distribution, territory, etc. We used a Cox model with elastic-net regularization on 2457 CT calcium score (CTCS) enriched for MACE events obtained from a large no-cost CLARIFY program (ClinicalTri-als.gov Identifier: NCT04075162). We employed sampling techniques to enhance model training. We also investigated Cox models with selected features to identify explainable high-risk characteristics.\n  Results. Our proposed calcium-omics model with modified synthetic down sampling and up sampling gave C-index (80.5%/71.6%) and two-year AUC (82.4%/74.8%) for (80:20, training/testing), respectively (sampling was applied to the training set only). Results compared favorably to Agatston which gave C-index (71.3%/70.3%) and AUC (71.8%/68.8%), respectively. Among calcium-omics features, numbers of calcifications, LAD mass, and diffusivity (a measure of spatial distribution) were important determinants of increased risk, with dense calcification (>1000HU) associated with lower risk. The calcium-omics model reclassified 63% of MACE patients to the high risk group in a held-out test. The categorical net-reclassification index was NRI=0.153.\n  Conclusions. AI analysis of coronary calcification can lead to improved results as compared to Agatston scoring. Our findings suggest the utility of calcium-omics in improved prediction of risk.", "date": "2023-08-23", "categories": []}
{"id": "2403.00965", "title": "Binary Gaussian Copula Synthesis: an LLM-powered data augmentation framework for early dialysis prediction in chronic kidney disease", "abstract": "Only a small fraction of patients with chronic kidney disease (CKD) progress to dialysis, creating severe class imbalance that limits the performance of machine learning models for early dialysis prediction. This challenge is compounded by the binary structure of electronic health record (EHR) data, for which most existing augmentation methods were not designed. We propose Binary Gaussian Copula Synthesis (BGCS), a two-stage data augmentation method tailored to binary clinical data. BGCS first generates synthetic minority-class samples using a Gaussian copula framework that explicitly models pairwise dependencies among binary features, then applies a fine-tuned GPT-2 classifier to filter out clinically implausible samples before training. We evaluated BGCS on a real-world EHR dataset of 15,169 patients with CKD from West Virginia collected between 2008 and 2022, benchmarking it against SMOTE, CTGAN, and standard Gaussian Copula across four machine learning classifiers over 25 independent runs. BGCS consistently outperformed all comparison methods, achieving the highest minority-class recall for 90-day dialysis prediction, with median values ranging from 0.78 to 0.87 across classifiers, and the strongest distributional fidelity to real data, with a mean p-value of 0.68 across features. The best-performing BGCS-augmented model was integrated into an interpretable decision tree-based clinical decision support system for dialysis risk stratification, with electrolyte imbalances, cardiovascular comorbidities, and renal monitoring indicators emerging as the most influential predictive features. These findings suggest that augmentation methods designed for the structural properties of binary EHR data can meaningfully improve early dialysis risk prediction and support the development of interpretable clinical decision-support tools for CKD care.", "date": "2024-03-01", "categories": []}
{"id": "2512.21335", "title": "Autonomous Uncertainty Quantification for Computational Point-of-care Sensors", "abstract": "Computational point-of-care (POC) sensors enable rapid, low-cost, and accessible diagnostics in emergency, remote and resource-limited areas that lack access to centralized medical facilities. These systems can utilize neural network-based algorithms to accurately infer a diagnosis from the signals generated by rapid diagnostic tests or sensors. However, neural network-based diagnostic models are subject to hallucinations and can produce erroneous predictions, posing a risk of misdiagnosis and inaccurate clinical decisions. To address this challenge, here we present an autonomous uncertainty quantification technique developed for POC diagnostics. As our testbed, we used a paper-based, computational vertical flow assay (xVFA) platform developed for rapid POC diagnosis of Lyme disease, the most prevalent tick-borne disease globally. The xVFA platform integrates a disposable paper-based assay, a handheld optical reader and a neural network-based inference algorithm, providing rapid and cost-effective Lyme disease diagnostics in under 20 min using only 20 uL of patient serum. By incorporating a Monte Carlo dropout (MCDO)-based uncertainty quantification approach into the diagnostics pipeline, we identified and excluded erroneous predictions with high uncertainty, significantly improving the sensitivity and reliability of the xVFA in an autonomous manner, without access to the ground truth diagnostic information of patients. Blinded testing using new patient samples demonstrated an increase in diagnostic sensitivity from 88.2% to 95.7%, indicating the effectiveness of MCDO-based uncertainty quantification in enhancing the robustness of neural network-driven computational POC sensing systems.", "date": "2025-12-24", "categories": []}
{"id": "2605.23453", "title": "Class-Dependent Hybrid Data Augmentation for Multiclass Migraine Classification under Severe Class Imbalance", "abstract": "We conducted a reproducibility-oriented re-evaluation of prior migraine classification studies, correcting for data leakage and metric bias. We then introduced (i) a clinically motivated aggregation of two hemiplegic subtypes following ICHD-3 {\\S}1.2.3, (ii) a class-dependent hybrid augmentation strategy that assigns generation methods based on per-class sample size, and (iii) the concept of fidelity asymmetry, motivating proportionally constrained growth as an alternative to full class balance. Experiments were performed on a dataset of 400 patients across seven migraine subtypes under a two-stage protocol, including the six-class configuration described above. Models were evaluated using stratified 5-fold cross-validation with macro-averaged F1 as the primary metric.\n  Correcting methodological flaws reduces previously inflated performance estimates, with the corrected macro-F1 baseline standing at 0.71. The proposed framework consistently outperformed individual augmenters in macro-F1 averaged across the eight evaluated classifiers (0.862 vs. 0.836 for Gaussian Copula, 0.815 for CTGAN, and 0.801 for the no-augmentation baseline), and achieved its peak result of 0.914 with FT-Transformer under proportional augmentation. The no-augmentation FT-Transformer baseline (0.896) shows that, at the per-classifier ceiling, clinically motivated class aggregation accounts for most of the absolute improvement; the framework's principal measurable contribution is the gain in average robustness across classifiers, highlighting the dominant role of problem formulation.", "date": "2026-05-22", "categories": []}
{"id": "2605.25956", "title": "RAPTOR+: A Visually Grounded Vision-Language Framework to Improve Clinical Trust and Auditability in Automated Cancer Referral Processing", "abstract": "Urgent suspected colorectal cancer (CRC) referrals create operational bottlenecks because semi-structured clinical documents often require manual review and transcription. The original RAPTOR system used Large Language Models for structured extraction but relied on a separate OCR stage, making it vulnerable to handwriting, layout variation, and loss of visual evidence linkage. We present RAPTOR+, a multimodal extension that uses Vision-Language Models (VLMs) for end-to-end referral understanding. We evaluate fine-tuned VLMs, commercial and open-source zero-shot VLMs, and the original OCR-based pipeline on 223 clinically curated CRC urgent referral forms. We also introduce a grounding-aware evaluation framework that measures both extraction accuracy and evidence localisation. Results show a clear grounding gap in zero-shot models. Gemini 2.5 Flash achieved 92.6% Reading Accuracy but only 1.2% Strict Safety. In contrast, fine-tuned Qwen3-VL-8B achieved 96.1% Reading Accuracy and 60.6% Strict Safety, substantially improving verifiable evidence grounding. These findings show that task-specific fine-tuning is essential for reliable, auditable clinical document understanding. RAPTOR+ enables extracted referral decisions to be linked to visual evidence, supporting safer and more efficient cancer referral triage.", "date": "2026-05-25", "categories": []}
{"id": "2606.05357", "title": "An interpretable and trustworthy AI framework for large-scale longitudinal structure-pain association studies using data from the Osteoarthritis Initiative (OAI)", "abstract": "Purpose: To develop an interpretable and trustworthy AI framework that combines deep learning based MRI Osteoarthritis Knee Score (MOAKS) prediction with interpretable statistical modeling to study structure-pain relationships at scale using data from the Osteoarthritis Initiative (OAI). Materials and Methods: We first developed a deep learning framework to predict MOAKS features directly from knee MRIs and incorporated conformal prediction to provide prediction uncertainty quantification. This uncertainty-aware strategy enables explicit filtering of model outputs, retaining only high-confidence MOAKS predictions at the knee level. Second, we applied a longitudinal latent class mixed model (LCMM) to examine associations between key structural abnormalities and four complementary knee pain measurements. Results: Among the three MRI-defined abnormalities (i.e., bone marrow lesions (BML), cartilage loss (CART), and meniscal extrusion (ME)), our framework substantially improved the Matthews correlation coefficient (MCC) and some other metrics. For example, MCC increased from 0.69 to 0.91 for BML, from 0.45 to 0.80 for CART, and from 0.59 to 0.89 for ME. Using these high-confidence predictions, we expanded the sample size to 2,175 knees for the LCMM analysis. Two distinct pain trajectories were identified (rapid and stable pain progression). The estimated odds ratios (95% CI) for the rapid progression group were 1.62 (1.12-2.35) for BML, 1.83 (1.24-2.70) for CART loss, and 2.50 (1.75-3.57) for ME. Conclusion: These results highlight the importance of these structural abnormalities as risk factors for pain and functional progression in osteoarthritis.", "date": "2026-06-03", "categories": []}
{"id": "2606.05994", "title": "HoT-SSM:Higher-order Temporal Knowledge Graph Reasoning with State Space Models for Health Care", "abstract": "Medical knowledge graphs (MKGs) infused with clinical knowledge have been increasingly used to model electronic health records (EHRs) to support interpretable predictions in healthcare domain. However, existing MKG-based approaches are limited in capturing pairwise relations between clinical concepts (e.g., conditions, procedures, and medications), and restricts their ability to model higher-order interactions among co-occurring or semantically related concepts. In addition, most representation learning methods that leverage MKGs either collapse temporal information across visits or lack an explicit mechanism for modeling long-range temporal dependencies, which is critical for clinical tasks such as mortality prediction. To mitigate these limitations, we propose HoT-SSM, a parameter efficient and higher-order temporal graph reasoning with state space models. For each visit, HoT-SSM constructs hypergraphs by grouping semantically related clinical concepts into hyperedges using domain knowledge, thereby preserving visit-level clinical context. Further, to model the temporal dynamics while learning the representations, we introduce a novel dynamic hypergraph-based state space model that explicitly captures patients latent state evolution over time while preserving long-range information. The learned representations are used for downstream clinical prediction and reasoning. Experiments on MIMIC-III and MIMIC-IV datasets shows significant performance improvement over the current state-of-the-art models, demonstrating the effectiveness of jointly modeling higher-order clinical interactions and long-range temporal dependencies.", "date": "2026-06-04", "categories": []}
{"id": "2606.06174", "title": "Learning to model pediatric asthma exacerbation from multiple risk factors: a case study in coastal Virginia", "abstract": "Childhood asthma is a common illness exacerbated by air pollution as well as meteorological and neighborhood-level socioeconomic factors. Modeling asthma exacerbation (AE) in large spatiotemporal datasets requires disentangling impacts from multiple contributors. In this case study, we compared three techniques that balance predictive power with interpretability to predict AE in Hampton Roads, a coastal Virginia region comprising 7 cities and over 1.5 million people. After collating ambient air pollution measurements, weather data, and measures of neighborhood opportunity, we modeled zip code-level acute AE visits to a regional children's hospital and affiliated providers from 2018-2023. Generalized linear models (GLM) provided a baseline while neural networks (NN) served as a maximally predictive target. To bridge between statistical models and deep learning, we developed a framework based on sparse dictionary learning to identify and interpret parsimonious nonlinear interacting equations. After comparing each model's predictive performance, we estimated relative risks for AE due to input exposure variables and found consensus across frameworks. Our work links statistical and interpretable machine learning models to highlight possible synergistic interactions influencing AE, and may enable future studies to guide public health interventions in coastal Virginia.", "date": "2026-06-04", "categories": []}
{"id": "2605.20468", "title": "CASCADE Conformal Prediction: Uncertainty-Adaptive Prediction Intervals for Two-Stage Clinical Decision Support", "abstract": "Effective medication management in Parkinson's Disease (PD) is challenging due to heterogeneous disease progression, variable patient response, and medication side effects. While AI models can forecast levodopa equivalent daily dose (LEDD) as a measure of medication needs, standard uncertainty quantification often fails to communicate the reliability of these predictions, treating high and low confidence clinical decisions identically. We introduce CASCADE (Calibrated Adaptive Scaling via Conformal And Distributional Estimation), a novel conformal prediction framework that propagates epistemic uncertainty from a screening classifier to adapt downstream predictions. Unlike standard conformal methods that rely on auxiliary residual regression, we leverage epistemic uncertainty from a primary classification task (identifying whether a medication change is needed) to dynamically scale the prediction intervals of a secondary regression task (predicting how much change). By mapping Venn-Abers multi-probabilistic uncertainty directly to non-conformity scores, our framework achieves continuous risk adaptation. We demonstrate that this ``cascade effect'' produces highly efficient intervals for confident patients (38.9% narrower than standard conformal baselines) while automatically expanding intervals to ensure robust coverage for uncertain cases, bridging the gap between discrete clinical decision-making and continuous dose forecasting in PD.", "date": "2026-05-19", "categories": []}
{"id": "2606.03995", "title": "Early Detection of Alzheimer's Disease Using Explainable Machine Learning on Clinical Biomarkers: A Multi-Class Classification Study Using the Alzheimer's Disease Neuroimaging Initiative (ADNI) Dataset", "abstract": "Background: Alzheimer's disease (AD) affects over 55 million people worldwide. Accurate, interpretable detection of normal cognition (NC), mild cognitive impairment (MCI), and AD from routine clinical assessments remains a critical unmet need. Methods: An XGBoost classifier was developed for three-class detection using eight clinical features from the Alzheimer's Disease Neuroimaging Initiative (ADNI): MMSE, CDR Global, CDR Sum of Boxes (CDR-SB), MoCA, FAQ, age, sex, and education. Hyperparameters were optimised using Optuna (50 trials); class imbalance was addressed with SMOTE. Performance was evaluated by macro AUC-ROC with 1,000-iteration bootstrap 95% confidence intervals, macro F1, balanced accuracy, and Cohen's kappa. SHAP values provided feature-level explainability. Results: The dataset comprised 1,641 baseline subjects (608 NC, 767 MCI, 266 AD). On five-fold cross-validation, mean macro AUC was 0.983 (SD 0.007), accuracy 0.944 (SD 0.006), and macro F1 0.929 (SD 0.008). On the held-out test set (n = 247), macro AUC was 0.982 (95% CI: 0.965--0.995), accuracy 0.943, balanced accuracy 0.932, macro F1 0.927, and Cohen's kappa 0.909. SHAP analysis identified CDR Global as the dominant predictor for NC and MCI, while CDR-SB and MMSE together drove AD classification. Conclusion: An explainable machine learning model trained on routine clinical assessments achieves near-perfect three-class Alzheimer's detection. SHAP analysis reveals clinically plausible, class-specific feature importance patterns supporting clinical validity. Future work will extend this framework with speech biomarkers for multimodal detection.", "date": "2026-04-14", "categories": []}
{"id": "2606.04338", "title": "Federated Learning for Multi-Center Sepsis Early Prediction with Privacy-Preserving", "abstract": "Privacy-sensitive and distributed characteristics of multi-center medical data bring severe obstacles to centralized modeling for accurate early prediction of sepsis. Federated learning (FL) has attracted growing attention as a promising framework for collaborative model development, as it allows multiple institutions to jointly train predictive models without directly sharing or centralizing raw data. Nevertheless, its practical performance, robustness, and privacy-preserving benefits remain insufficiently evaluated using real-world clinical datasets. To bridge this gap, this study systematically examines the application of federated learning to multi-center sepsis prediction. The experimental dataset consists of 648 clinically screened samples collected from three tertiary hospitals in China, with rigorous inclusion and exclusion criteria. We establish a centralized training paradigm as the performance baseline, and then implement a horizontal federated learning framework for distributed collaborative modeling. Extensive experimental results demonstrate that the federated learning-based model achieves highly comparable prediction accuracy to the centralized counterpart, while fundamentally avoiding privacy leakage. Further privacy security analysis verifies that malicious attackers cannot reconstruct the original patient data from the transmitted model parameters, indicating strong resistance against data reconstruction attacks. This work not only validates the practicality and security of federated learning in clinical sepsis prediction, but also provides a reliable and feasible solution for privacy-preserving multi-center medical collaboration.", "date": "2026-06-03", "categories": []}
{"id": "2606.04722", "title": "StrokeTimer: Robust Representation Learning for Ischemic Stroke Onset-Time Estimation from Non-contrast CT", "abstract": "Ischemic stroke is a major global disease. Treatment decisions are highly time-sensitive, as eligibility for reperfusion therapies relies on the interval between stroke onset and intervention. However, the true onset time is often uncertain in clinical practice, necessitating imaging-based assessment of tissue age as a surrogate marker. Early ischemic changes on routinely acquired non-contrast CT (NCCT) are often subtle, and real-world clinical datasets exhibit pronounced onset-time class imbalance and center-scanner-related heterogeneity. In this work, we propose StrokeTimer, a fully automated framework for onset-time estimation in acute ischemic stroke. StrokeTimer integrates self-supervised disentanglement learning with energy-guided contrastive learning to capture subtle ischemic patterns while addressing long-tailed data distributions under acquisition variability. Onset time is categorized into three clinically relevant windows: <4.5 h, 4.5-6 h, and >6 h. Experimental results on a large multi-center NCCT dataset from two national cohorts, MR CLEAN Registry and MR CLEAN LATE, show that StrokeTimer achieves a macro AUC of 0.69 and a macro F1-score of 0.57, improving the strongest baseline by nearly 50% (p < 0.005). In this realistic, challenging setting, representative baseline approaches exhibit near-chance macro performance. Model explanations further highlight subtle gray-white matter blurring and hypodense regions consistent with established radiological biomarkers. These findings demonstrate the potential of StrokeTimer to support treatment decision-making in acute ischemic stroke. Code is available at https://github.com/BrainVas/StrokeTimer.", "date": "2026-06-03", "categories": []}
{"id": "2606.04792", "title": "A Pathology Foundation Model for Gastric Cancer with Real-World Validation", "abstract": "Gastric cancer remains a major cause of cancer mortality, yet its histological and molecular heterogeneity complicates diagnosis and risk stratification. General-purpose pathology foundation models (PFMs) often plateau on fine-grained endpoints central to gastric cancer care, and few have undergone rigorous prospective validation or clinical reader studies. We present GRACE, a Gastric-specific foundation model for Real-world Assessment and Clinical dEcision support. GRACE was developed from multicenter gastric pathology datasets totaling 48,364 primarily HE-stained whole-slide images from 37,493 patients. When evaluated on 28 clinically relevant tasks, GRACE consistently outperformed representative pancancer PFMs, achieving a macro-AUC of 0.9188, with strong performance for precancerous lesion diagnosis (macro-AUC 0.9322), tumor histopathological assessment (macro-AUC 0.9119), molecular profiling (macro-AUC 0.8682), and prognostic prediction. Beyond benchmarking, GRACE's translational value was substantiated through a rigorous evidence chain. Under safety-gated criteria requiring 100% NPV for rule-out and 100% PPV for rule-in, GRACE streamlined review for up to 69.6% of malignancy-diagnosis cases and triaged 46.8% of MMR-IHC follow-up requests. This translational feasibility was further strengthened by a randomized crossover reader study of pathologist-AI collaboration. With GRACE assistance, diagnostic accuracy improved from 82.0% to 89.9%, yielding nearly twofold higher adjusted odds of a correct diagnosis (OR 1.987) alongside concurrent gains in sensitivity and specificity. AI assistance also reduced diagnostic time by 14.9%, elevated diagnostic confidence by 9.0%, and markedly improved inter-rater agreement. When calibrated to maintain non-inferior performance to senior pathologists, the AI-assisted workflow could triage 60.7% of atrophy and 82.7% of intestinal metaplasia cases.", "date": "2026-06-03", "categories": []}
{"id": "2606.02605", "title": "Cross-Modal Contrastive Learning of ECG and Angiography Representations for Severe Stenosis Classification", "abstract": "Coronary artery stenosis is a common cardiovascular disease, with severe, untreated cases posing significant risks of heart attack. Although coronary (X-ray) angiograms remain the standard for stenosis diagnosis, they are invasive, time- and resource-intensive, and therefore only performed on patients with a high probability of disease based on symptoms and prior clinical tests. However, a subset of patients, especially those without symptoms, may remain undiagnosed. Detecting indications of stenosis from ECGs, which are fast, cheap, non-invasive, and thus routinely acquired even in asymptomatic patients, would support early diagnosis. However, as no reliable stenosis-specific signal has been identified in ECGs, they can not currently be used for stenosis risk stratification. To address this, we introduce StenCE, a pretraining framework, allowing stratification of patients based on features derived directly from ECGs. Evaluations across varying stenosis severity thresholds and additional ECG disease classification tasks demonstrate consistent performance improvements across different ECG encoders, outperforming previous work. The obtained models successfully detect signals for stenosis diagnosis in ECGs and are the first to achieve high performance in severe stenosis classification. The source code is available at https://github.com/NikolaCenic/ecg-stenosis-cls.", "date": "2026-05-23", "categories": []}
{"id": "2606.02671", "title": "Aligning Data-Driven Predictors with Allocation: A Decision-Focused Approach to Survival Analysis", "abstract": "Machine learning predictors have become essential tools for guiding automated decision making. However, a major misalignment persists: predictive models are typically optimized in terms of standard statistical metrics in isolation from the algorithmic tasks they inform. We highlight this incongruity in the high-stakes domain of organ allocation by demonstrating that any algorithm relying on (even highly accurate) survival predictors optimized for standard metrics -- such as the Concordance index (C-index) -- can yield arbitrarily poor outcomes when used for allocation, failing to guarantee utility better than a uniform random selection. To bridge the gap between survival analysis and policy optimization, we introduce a decision-focused learning approach based on optimizing normalized discounted cumulative gain (NDCG), a mainstay metric in information retrieval. We establish the utility of NDCG in survival analysis by proving that it translates to guarantees on the performance of allocation. Empirically, we propose a bootstrapping approach to optimize the NDCG of existing survival models. Unlike prior work, we also address the challenge of right censorship when evaluating ranking. On historical heart transplant data from the US, our method dramatically boosts the NDCG of baseline models by 50-100%, which translates to tens of thousands of additional life years gained annually when deployed for transplant allocation. We anticipate that our framework will find broader applications in decision making with predictions.", "date": "2026-06-01", "categories": []}
{"id": "2606.02812", "title": "Traj-Evolve: A Self-Evolving Multi-Agent System for Patient Trajectory Modeling in Lung Cancer Early Detection", "abstract": "Modeling patient trajectories from longitudinal electronic health records (EHRs) requires reasoning over sparse, noisy, and long-context multimodal sequences. Existing LLM-based multi-agent systems address context length but process patients in isolation, failing to mirror how clinicians leverage accumulated experience from similar prior cases. We present Traj-Evolve, a self-evolving multi-agent system with two complementary evolving mechanisms. First, an Experience Pool (ExPool) acts as a non-parametric memory, indexing rejection-sampled reasoning traces to retrieve similar patients as few-shot contexts. Second, multi-agent reinforcement learning (MARL) via reward-ranked fine-tuning parametrically optimizes inter-agent and agent-memory collaboration. A leave-one-out cross-retrieval strategy unifies the two, aligning training- and inference-time behavior under retrieval augmentation. On a lung cancer prediction task utilizing up to five years of multimodal EHRs, Traj-Evolve outperforms 9 strong baselines on the overall population and a challenging never-smoker population. Analysis of the evolving dynamics highlights three key findings: (1) expanding the ExPool shifts optimal retrieval from diverse to specific samples; (2) under MARL, the manager agent's prediction loss converges quickly while the worker agents' temporal reasoning continues to benefit from more verified patients; and (3) the two mechanisms are complementary on the predicted risk, where ExPool improves specificity while MARL improves sensitivity.", "date": "2026-06-01", "categories": []}
{"id": "2606.02892", "title": "Multi-Modal Machine Learning for Breast Cancer Recurrence Prediction", "abstract": "Breast cancer recurrence, a leading cause of long-term mortality among survivors, requires timely and accurate risk assessment to guide follow-up care and treatment planning. Traditional predictive models, often limited to either structured or unstructured data alone, struggle to capture the full clinical context. This study examines the impact of integrating multi-modal clinical data, including treatment records, pathology reports, and clinician notes, on recurrence prediction. By integrating a rule-based regular expression extraction mechanism with a rigorous precedence-based conflict reconciliation strategy, our approach effectively recovers definitive tumor characteristics from free-text pathology narratives to augment structured records. We also benchmark performance against commonly used feature sets from prior breast cancer studies to assess the added value of multi-modal integration. Single-source and multi-modal inputs are evaluated across a range of machine learning models. Results show that multi-modal integration consistently improves predictive accuracy compared to single-modal methods.", "date": "2026-06-01", "categories": []}
{"id": "2411.15240", "title": "A Foundation Model for Wearable Movement Data in Mental Health Research", "abstract": "Wearable movement data is collected by nearly all commercially available smartwatches and is a valuable resource for mental health research, reflecting fine-grained temporal behavioral trends. Despite its promise, the development of foundation models for health wearable modeling remains limited when compared to clinical image and text analysis. We designed transformers with patch embeddings and used self-supervised masked autoencoder pretraining on minute-level week-long actigraphy (physical activity intensity measurement) sequences to develop and evaluate the Pretrained Actigraphy Transformer (PAT). PAT is an open-source foundation model for wearable movement time series that combines week-long temporal modeling, psychiatric outcome evaluation, and reproducibility on public data. Pretrained on data from 21,538 U.S. participants in a nationally representative cohort from the National Health and Nutrition Examination Survey (NHANES), PAT consistently outperformed non-foundation-model baselines across mental health prediction tasks-including benzodiazepine and SSRI use, depression, and sleep abnormalities. During the benzodiazepine medication usage prediction task, PAT demonstrated the largest improvement over non-foundational deep learning models commonly used for time-series modeling (i.e., 55.6% improvement over the LSTM, 21.4% improvement over the 1-D CNN, 14.8% improvement over the ConvLSTM). Beyond predictive accuracy, PAT provides interpretable attention maps highlighting specific periods of daily activity most important for clinical predictions, offering model transparency and potential clinical insights. The results suggest that PAT offers an easy-to-deploy, adaptable and scalable solution to advance clinical insight from wearable sensor data for researchers and clinicians.\n  GitHub: https://github.com/njacobsonlab/Pretrained-Actigraphy-Transformer/", "date": "2024-11-22", "categories": []}
{"id": "2501.10342", "title": "Hybrid Deep Learning Model for epileptic seizure classification by using 1D-CNN with multi-head attention mechanism", "abstract": "Epilepsy is a prevalent neurological disorder globally, impacting around 50 million people \\cite{WHO_epilepsy_50million}. Epileptic seizures result from sudden abnormal electrical activity in the brain, which can be read as sudden and significant changes in the EEG signal of the brain. The signal can vary in severity and frequency, which results in loss of consciousness and muscle contractions for a short period of time \\cite{epilepsyfoundation_myoclonic}. Individuals with epilepsy often face significant employment challenges due to safety concerns in certain work environments. Many jobs that involve working at heights, operating heavy machinery, or in other potentially hazardous settings may be restricted for people with seizure disorders. This certainly limits job options and economic opportunities for those living with epilepsy.", "date": "2025-01-17", "categories": []}
{"id": "2507.07339", "title": "Benchmarking Waitlist Mortality Prediction in Heart Transplantation Through Time-to-Event Modeling using New Longitudinal UNOS Dataset", "abstract": "Decisions about managing patients on the heart transplant waitlist are currently made by committees of doctors who consider multiple factors, but the process remains largely ad-hoc. With the growing volume of longitudinal patient, donor, and organ data collected by the United Network for Organ Sharing (UNOS) since 2018, there is increasing interest in analytical approaches to support clinical decision-making at the time of organ availability. In this study, we benchmark machine learning models that leverage longitudinal waitlist history data for time-dependent, time-to-event modeling of waitlist mortality. We train on 23,807 patient records with 77 variables and evaluate both survival prediction and discrimination at a 1-year horizon. Our best model achieves a C-Index of 0.94 and AUROC of 0.89, significantly outperforming previous models. Key predictors align with known risk factors while also revealing novel associations. Our findings can support urgency assessment and policy refinement in heart transplant decision making.", "date": "2025-07-09", "categories": []}
{"id": "2601.00175", "title": "Early Prediction of Liver Cirrhosis Up to Two Years in Advance: A Machine Learning Study Benchmarking Against the FIB-4 and APRI Scores", "abstract": "Objective: Develop and evaluate machine learning (ML) models for predicting incident liver cirrhosis (LC) one and two years prior to diagnosis using routinely collected electronic health record (EHR) data and benchmark their performance against the FIB-4 and APRI clinical scores. Methods: We conducted a retrospective cohort study using de-identified EHR data from a large academic health system. XGBoost models were developed for 1- and 2-year prediction horizons, with model-specific feature selection and Bayesian hyperparameter tuning applied to improve predictive performance. The model was then evaluated on held-out test sets, and its performance was compared with FIB-4 and APRI using accuracy, precision, recall, F1, area under the precision-recall curve (PR AUC), and area under the receiver operating characteristic curve (AUC). Results: Final modeling cohorts included 60,481 patients for the 1-year prediction and 47,322 for the 2-year prediction. Across both prediction windows, the tuned ML models consistently outperformed FIB-4 and APRI. The XGBoost models achieved AUCs of 0.872 and 0.839 for the 1- and 2-year predictions, respectively, compared with 0.756 and 0.723 for FIB-4 and 0.798 and 0.761 for APRI. Improvements were larger on the precision-recall metric, with PR AUCs of 0.657 and 0.562 for XGBoost compared with 0.456 and 0.373 for FIB-4 and 0.504 and 0.421 for APRI. Performance gains persisted with longer prediction horizons, indicating maintained early risk discrimination. Conclusions: Machine learning models leveraging routine EHR data substantially outperform the traditional FIB-4 and APRI scores for early prediction of liver cirrhosis. These models enable earlier and more accurate risk stratification and can be integrated into clinical workflows as automated decision-support tools to support proactive cirrhosis prevention and management.", "date": "2026-01-01", "categories": []}
{"id": "2602.11177", "title": "What Do LLMs Know About Alzheimer's Disease? Multi-loss Fine-Tuning and Probing for AD Detection", "abstract": "Reliable early detection of Alzheimer's disease (AD) is challenging, particularly due to the limited availability of labeled data. While large language models (LLMs) have shown strong transfer capabilities across do mains, adapting them to the AD domain through supervised fine-tuning remains largely unexplored. In this work, we empirically evaluate various model architectures across three heterogeneous transcript corpora (Pitt, CCC, ADRC) to investigate their effectiveness for text-based AD detection and analyze how task-relevant information is encoded within their internal representations. To the best of our knowledge, our fine-tuned BERT and T5 models establish a new state-of-the-art on the Pitt and CCC datasets, while achieving strong performance on ADRC. In parallel, the decoder-only Llama-1B achieves highly competitive results comparable to BERT and T5 across all three corpora, highlighting its effectiveness for AD detection. We further conduct a comprehensive evaluation of the Llama-1B backbone, analyzing cross-corpus transferability, optimal input chunk-size granularity, and the impact of clinical transcript markers. Also, we use linear probing to empirically show that fine-tuning shifts the representations of individual tokens, both linguistic markers and content words, in ways that reflect AD-related signal.", "date": "2026-01-20", "categories": []}
{"id": "2602.18195", "title": "LERD: Latent Event-Relational Dynamics for Neurodegenerative Classification", "abstract": "Alzheimer's disease (AD) alters brain electrophysiology and disrupts multichannel EEG dynamics, making accurate and clinically useful EEG-based diagnosis increasingly important for screening and disease monitoring. However, many existing approaches rely on black-box classifiers and do not explicitly model the latent event timing and cross-channel coordination behind their decisions. To address these limitations, we propose LERD, an end-to-end Bayesian latent event--relational dynamical system that infers latent neural events and their relational structure directly from multichannel EEG without event or interaction annotations. LERD combines a continuous-time event inference module with a stochastic event-generation process to capture flexible temporal patterns, while incorporating an electrophysiology-inspired dynamical prior to guide learning in a principled way. We further provide theoretical analysis that yields a tractable IVP-based KL regularizer and stability guarantees for the inferred relational dynamics. Extensive experiments on synthetic benchmarks and two real-world AD EEG cohorts demonstrate that LERD consistently outperforms strong baselines and yields physiology-aligned rate, timing, and graph summaries that help characterize group-level dynamical differences.", "date": "2026-02-20", "categories": []}
{"id": "2603.23582", "title": "AI Generalisation Gap In Comorbid Sleep Disorder Staging", "abstract": "Accurate sleep staging is essential for diagnosing OSA and hypopnea in stroke patients. Although PSG is reliable, it is costly, labor-intensive, and manually scored. While deep learning enables automated EEG-based sleep staging in healthy subjects, our analysis shows poor generalization to clinical populations with disrupted sleep. Using Grad-CAM interpretations, we systematically demonstrate this limitation. We introduce iSLEEPS, a newly clinically annotated ischemic stroke dataset (to be publicly released), and evaluate a SE-ResNet plus bidirectional LSTM model for single-channel EEG sleep staging. As expected, cross-domain performance between healthy and diseased subjects is poor. Attention visualizations, supported by clinical expert feedback, show the model focuses on physiologically uninformative EEG regions in patient data. Statistical and computational analyses further confirm significant sleep architecture differences between healthy and ischemic stroke cohorts, highlighting the need for subject-aware or disease-specific models with clinical validation before deployment. A summary of the paper and the code is available at https://himalayansaswatabose.github.io/iSLEEPS_Explainability.github.io/", "date": "2026-03-24", "categories": []}
{"id": "2604.01841", "title": "Retrieval-aligned Tabular Foundation Models Enable Robust Clinical Risk Prediction in Electronic Health Records Under Real-world Constraints", "abstract": "Clinical prediction from structured electronic health records (EHRs) is challenging due to high dimensionality, heterogeneity, class imbalance, and distribution shift. While tabular in-context learning (TICL) and retrieval-augmented methods perform well on generic benchmarks, their behavior in clinical settings remains unclear. We present a multi-cohort EHR benchmark comparing classical, deep tabular, and TICL models across varying data scale, feature dimensionality, outcome rarity, and cross-cohort generalization. PFN-based TICL models are sample-efficient in low-data regimes but degrade under naive distance-based retrieval as heterogeneity and imbalance increase. We propose AWARE, a task-aligned retrieval framework using supervised embedding learning and lightweight adapters. AWARE improves AUPRC by up to 12.2% under extreme imbalance, with gains increasing with data complexity. Our results identify retrieval quality and retrieval-inference alignment as key bottlenecks for deploying tabular in-context learning in clinical prediction.", "date": "2026-04-02", "categories": []}
{"id": "2605.22759", "title": "Towards a General Intelligence and Interface for Wearable Health Data", "abstract": "While ubiquitous wearable sensors capture a wealth of behavioral and physiological information, effectively transforming these signals into personalized health insights is challenging. Specifically, converting low-level sensor data into representations capable of characterizing higher-level states is difficult due to high phenotypic diversity and variation in individual baseline health, physiology, and lifestyle factors. Moreover, collecting wearable data paired with health outcome annotations is laborious and expensive, and retrospective annotation remains practically unfeasible, contributing to a scarcity of data with high-quality labels. To overcome these limitations, we propose a foundation model for wearable health that is pretrained on more than one trillion minutes of unlabeled sensor signals drawn from a large cohort of five million participants. We demonstrate that the joint scaling of model capacity and pretraining data volume leads to systematic improvements in performance, as evaluated on a diverse set of 35 health prediction tasks, spanning cardiovascular, metabolic, sleep, and mental health, as well as lifestyle choices and demographic factors. We find that this population scale representation unlocks label-efficient few-shot learning and generative capabilities for robust daily metric estimation. To further leverage this learned representation, we deploy a classroom of LLM agents to autonomously search the space of downstream predictive heads built on the model embeddings, showing broad performance improvements that increase with LLM model capacity. Finally, we show how integrating these downstream predictors into a Personal Health Agent can support model responses that are more relevant, contextually aware, and safe, and we validate this via 1,860 ratings from a cohort of clinicians.", "date": "2026-05-21", "categories": []}
{"id": "2606.00031", "title": "LLMs for Cardiovascular Risk Prediction from Structured Clinical Data", "abstract": "Coronary artery disease (CAD) remains one of the leading causes of death globally, highlighting the need for reliable predictive systems to support early diagnosis and risk assessment. While traditional machine learning models perform well on structured clinical data, large language models (LLMs) present new possibilities to interpret medical information expressed in natural language. In this work, we develop a hybrid framework that bridges structured clinical data and natural-language representations for CAD prediction. Using a publicly available dataset of 1,190 patient records with 11 clinical attributes, structured variables are converted into interpretable feature representations and synthetic clinical narratives using LLMs. A validation pipeline performs reverse extraction of clinical variables and computes a consistency score with the original records, achieving an average fidelity of 94.61%. We then evaluate four conventional machine learning models and compare their performance with LLM-based classification under zero-shot and few-shot prompting settings. We use two LLMs here, GPT and Gemini. Experimental results show that Random Forest achieves the highest accuracy. Despite this advantage, LLM-based classification remains beneficial in real-world clinical settings. This is because LLMs operate directly on natural language patient descriptions, meaning that sensitive numerical patient data such as exact lab values, blood pressure readings, and diagnostic codes are kept private. Findings suggest that combining structured clinical data with LLM-generated narratives can enable new directions for hybrid clinical prediction systems.", "date": "2026-04-21", "categories": []}
{"id": "2606.00074", "title": "CLSP-REQA: A Real-Time Quality-Aware Closed-Loop Seizure Prediction Framework with Mamba-BiLSTM and Confidence-Gated Intervention", "abstract": "Reliable seizure prediction is a prerequisite for closed-loop neurostimulation therapy, yet existing methods rarely account for the variability in EEG signal quality encountered in real-world deployment, and the overwhelming majority adopt non-strict evaluation protocols that overestimate generalisation performance. We propose CLSP-REQA (Closed-Loop Seizure Prediction with Real-time EEG Quality Assessment), a unified framework that embeds a lightweight signal quality estimator directly within the prediction pipeline. A Real-time EEG Quality Assessment (REQA) module runs in parallel with a Mamba-BiLSTM backbone, producing a scalar quality score q in [0,1] that modulates output confidence through a tiered non-linear fusion function (ECLO). Under strict cross-patient evaluation on the CHB-MIT Scalp EEG Database (n = 23 subjects, 198 seizures), CLSP-REQA achieves an AUC-ROC of 0.7426 +- 0.0199, outperforming the unadapted cross-patient baseline of 0.69 reported by Jemal et al., using only 16 EEG channels compared to 23 in prior work, and without requiring any target-patient data or domain adaptation. On the SIENA Scalp EEG Database (n = 14 subjects, 47 seizures), CLSP-REQA achieves AUC 0.7012 +- 0.0249, substantially surpassing the best domain-adapted cross-patient result of 0.61 on the same dataset, demonstrating strong cross-dataset generalisation. The framework outputs a structured four-tuple (p, q, c, Phi_SHAP) directly compatible with closed-loop neurostimulator interfaces.", "date": "2026-05-21", "categories": []}
{"id": "2606.00107", "title": "Motif-based morphology signatures for interpretable ECG screening and monitoring", "abstract": "Electrocardiography (ECG) remains central to cardiovascular screening, yet interpretation remains largely manual and episodic. Clinical practice relies on brief resting ECGs and, when required, long-duration ambulatory recordings, both generating data that require resource-intensive review. Consequently, subtle morphological changes or progressive drift preceding clinically apparent abnormalities may go unnoticed. We propose a motif-based framework that defines beat-aligned ECG motifs as interpretable cardiac signatures and quantifies morphological drift and deviation across short and long-term monitoring. Motifs are representative cardiac cycles capturing dominant morphology. We introduce three interpretable drift metrics: deviation from a normal sinus rhythm (NSR), deviation from a personalised baseline, and a motif instability index. Motifs are extracted by selecting beats that minimise Dynamic Time Warping (DTW) distance within fixed windows. We evaluate these metrics on short (PTB-XL) and long-duration (MIT-BIH Arrhythmia) ECG datasets. Interpretability is achieved through representative motif overlays and fiducial-based visualisations, enabling direct inspection of morphological changes. In MIT-BIH, the proposed metrics significantly separated predominantly normal from arrhythmic subjects (p<0.01). In PTB-XL, NSR deviation distinguished normal from abnormal ECGs across major diagnostic subtypes (p<1e-4, Cliff's delta up to 0.93). ECG motifs provide an interpretable representation of cardiac morphology, supporting scalable longitudinal monitoring and early detection of morphology-driven change.", "date": "2026-05-26", "categories": []}
{"id": "2606.00345", "title": "Longitudinal Multimodal Sensing of Physical Activity and Well-Being in Older Adults", "abstract": "Wearable and mobile sensing technologies enable continuous monitoring of human behavior and health in real-world settings. However, predictive modeling in longitudinal multimodal data remains challenging, particularly when targeting complex or clinically derived outcomes. In this work, we present a longitudinal multimodal study of 66 older adults conducted in real-world conditions and combining wearable sensing, behavioral monitoring, and clinical assessments. This setting provides a rare opportunity to study an underrepresented population in long-term, into-the-wild conditions. Building on this dataset, we investigate how the alignment between sensed signals and target variables affects predictive performance across health-related tasks. We design a unified evaluation framework spanning tasks with increasing levels of observability, including Activity Levels prediction, Sleep Duration estimation, and Sleep Apnea Severity classification. Our results reveal a clear gradient of predictability: highly observable behavioral targets achieve robust performance (macro-F1 65%), while more abstract outcomes remain challenging despite consistent improvements over baseline models. Moreover, through explainability analysis, we show that historical features consistently emerge as the most informative predictors, highlighting the central role of longitudinal information.", "date": "2026-05-29", "categories": []}
{"id": "2606.01028", "title": "MedGym:A Unified Continuous-Time Benchmark for Dynamic Medical Treatment Reinforcement Learning", "abstract": "Medical treatment recommendation poses several challenges to reinforcement learning (RL): patient physiology evolves in continuous time, measurements and interventions are performed at irregular intervals, and treatment effects vary substantially across individuals. Existing RL formulations and simulated environments, however, are based on discrete-time MDP or POMDP abstractions with fixed or pre-specified decision intervals. Thus, it remains difficult to evaluate whether RL methods can handle time-interval-dependent disease progression, personalized treatment response, and safety between consecutive measurement points. To address this gap, we introduce MedGym, a benchmark environment for dynamic treatment recommendation. MedGym models longitudinal patient evolution in a continuous-time framework and constructs a configurable medical RL benchmark from clinical data by using Physics-Informed Neural Networks. The resulting benchmark supports both offline and online RL, and enables direct comparison between discrete-time and continuous-time methods under irregular treatment timing and patient-specific dynamics. Besides, MedGym supports evaluation from clinically important perspectives, including personalization, trajectory-level safety, and the performance gap between model-based offline learning and online deployment. By providing a standardized and configurable benchmark for continuous-time dynamic treatment, MedGym aims to facilitate more realistic and informative evaluation of medical RL methods.", "date": "2026-05-31", "categories": []}
{"id": "2606.01678", "title": "Why Do Self-Harm Prediction Models Struggle to Generalise? Lexical and Semantic Variations in Emergency Department Triage Notes", "abstract": "Self-harm presentations to emergency departments (EDs) are strongly associated with higher suicide risk. NLP models have shown robust performance in detecting self-harm from triage notes within single hospitals, yet performance often declines across institutions. To examine potential causes, we compare ED triage notes from two hospitals by analyzing lexical characteristics, highly associated predictive features, and salient topics. Our results reveal variation in lexical expression and feature importance related to self-harm across hospitals, despite consistent core themes such as self-poisoning and self-injury. These documentation differences are associated with reduced cross-site performance. Our findings provide insight into how institutional variation affects the identification of self-harm in clinical text and highlight potential methods to improve model generalisability.", "date": "2026-06-01", "categories": []}
{"id": "2606.02156", "title": "Predicting the risk of colorectal anastomotic leak based on preoperative mapping of the blood supply of the bowel", "abstract": "Anastomotic leak remains one of the most serious complications following colorectal cancer surgery, substantially affecting patient outcomes, recovery trajectories, and healthcare costs. Despite advances in imaging technology, current preoperative assessment relies only on clinical assessment, a process that is subjective, error-prone, and highly dependent on individual expertise. To date, no validated CT-based method exists to predict anastomotic leak risk prior to surgery. This protocol paper outlines a comprehensive framework for developing and validating an AI-driven system for preoperative risk assessment using pre- and post-contrast CT imaging. The study describes the stages of data collection, ethical handling, and preprocessing of patient data in accordance with GDPR, image preprocessing, and the exploration of deep learning architectures designed to generate clinically interpretable outputs. Two integrated tools constitute the main deliverables of this workflow: 1) a risk assessment module, which quantifies the likelihood of leakage by analyzing vascular and tissue features in CT scans, and 2) a Content-Based Medical Image Retrieval (CBMIR) module, which identifies and displays similar historical cases to support evidence-based surgical decision making. The protocol paper requires close collaboration between hospitals and universities; this protocol demonstrates that such a system is technically feasible and clinically implementable within existing healthcare infrastructures. By following the proposed methodological stages and regulatory principles, other institutions can reproduce this workflow to develop analogous decision-support tools. Ultimately, this interdisciplinary framework aims to enhance surgical planning, reduce leak incidence, and contribute to a broader paradigm shift toward explainable, data-driven precision surgery.", "date": "2026-06-01", "categories": []}
{"id": "2606.02228", "title": "Bayesian meta-learning for modeling Alzheimer's disease progression", "abstract": "Predicting whether an individual with Alzheimer's disease will experience mild or severe disease progression is essential for personalized treatment. Typically, practitioners seek to predict the distribution of a discrete disease score, conditional on an individual's current MRI volume and their historical disease trajectory. Classical statistical regression models and single-task neural networks are not well-suited for this purpose because fitting separate models is infeasible (since each individual typically has few observations), while ignoring individual-level correlation leads to poor generalization. Meta-learning, in contrast, provides a natural avenue to dynamically predict distributions without retraining and model nonlinear relationships between the outcome and covariates. Motivated by this, we propose a Bayesian meta-learner that is trained on multiple individuals but tailors the predictive disease score distribution to each individual's historical data. Our model predicts on unseen individuals without retraining, scales linearly with the number of historical observations, and is guaranteed to be less overconfident when predicting long-term disease scores compared to its deterministic counterpart. On real-world data from the Alzheimer's Disease Neuroimaging Initiative (ADNI) database, our model achieves performance competitive with both single-task models and deterministic meta-learners, while substantially improving performance when predicting long-term disease progression.", "date": "2026-06-01", "categories": []}
{"id": "2606.02509", "title": "When Rating Scales Fall Short: LLM-Assisted Discovery of ADHD Signals in Turkish Teacher Narratives", "abstract": "Attention Deficit Hyperactivity Disorder (ADHD) is one of the most common neurodevelopmental disorders in childhood, and its diagnosis relies on assessments combining clinician judgment with standardized rating scales and reports from parents and teachers. While structured instruments such as the Conners' Teacher Rating Scale-Revised Short Form (CTRS-R:S) quantify ADHD-related behaviors, teachers also provide open-ended narratives that may contain complementary signals not captured by structured assessments. However, it remains unclear to what extent teacher narratives encode signals overlooked by rating scales. In this study, we analyze de-identified Turkish teacher evaluation forms collected during clinical ADHD assessments, including both CTRS-R:S scores and open-ended teacher narratives. We compare predictive signals from structured scores and narrative text and identify cases where structured assessments fail to clearly distinguish ADHD from non-ADHD students while narrative-based models capture distinct behavioral patterns. Notably, these cases show minimal overlap with those missed by the narrative model, suggesting that structured and narrative information encode complementary signals. To interpret these differences, we apply a large language model (LLM)-assisted theme discovery pipeline that reveals distinct attention, behavioral, and family-related patterns, highlighting the potential of natural language processing (NLP) to uncover clinically relevant signals from teacher narratives and to complement traditional ADHD screening tools.", "date": "2026-06-01", "categories": []}
{"id": "2606.02545", "title": "Transferable Self-Harm Surveillance from Emergency Department Triage Notes Using an Evidence-Augmented Machine Learning Approach", "abstract": "Self-harm is a major public health concern, but current surveillance relying on hospital presentations is inadequate due to the low sensitivity of diagnostic codes. Emergency Department (ED) triage notes, recorded at the initial point of contact, provide a succinct summary of presentations and an opportunity to identify self-harm. We developed a three-stage approach, augmenting traditional machine learning with large language model-based screening and evidence extraction to detect self-harm in ED triage notes. We assessed model transferability across three Australian hospitals. Our approach showed AUPRCs of 0.887 +/- 0.016 and 0.884 +/- 0.012 during internal and external validation. Prospectively, it achieved AUPRC of 0.881 +/- 0.008 at the development site, and 0.879 +/- 0.012 and 0.816 +/- 0.015 at two external sites without site-specific retraining. A key advantage of the approach is that it enables identification of the primary self-harm method with an accuracy of 95%, supporting more granular surveillance beyond binary classification.", "date": "2026-06-01", "categories": []}
{"id": "2602.17531", "title": "Position: Evaluation of ECG Representations Must Be Fixed", "abstract": "This position paper argues that current benchmarking practice in 12-lead ECG representation learning must be fixed to ensure progress is reliable and aligned with clinically meaningful objectives. The field has largely converged on three public multi-label benchmarks (PTB-XL, CPSC2018, CSN) dominated by arrhythmia and waveform-morphology labels, even though the ECG is known to encode substantially broader clinical information. We argue that downstream evaluation should expand to include an assessment of structural heart disease and patient-level forecasting, in addition to other evolving ECG-related endpoints, as relevant clinical targets. Next, we outline evaluation best practices for multi-label, imbalanced settings, and show that when they are applied, the literature's current conclusion about which representations perform best is altered. Furthermore, we demonstrate the surprising result that a randomly initialized encoder with linear evaluation matches state-of-the-art pre-training on many tasks. This motivates the use of a random encoder as a reasonable baseline model. We substantiate our observations with an empirical evaluation of five representative ECG pre-training approaches across six evaluation settings: the three standard benchmarks, a structural disease dataset, hemodynamic inference, and patient forecasting.", "date": "2026-02-19", "categories": []}
{"id": "2605.01134", "title": "To Use AI as Dice of Possibilities with Timing Computation", "abstract": "The dominant noun-based modeling paradigm has fundamentally constrained AI development, precluding any adequate representation of the future as an open temporal dimension. This paper introduces a verb-based paradigm, together with precise definitions of \\emph{timing computation} and \\emph{possibility}, that enables AI to function as an effective instrument for realizing the grammar of our thought.\n  Applied to longitudinal EHR data from 3,276 breast cancer patients, the framework empirically demonstrates: (1) automatic discovery of clinically significant patient trajectories, and (2) counterfactual timing deduction. Both results are purely data-driven, require no prior domain knowledge, and, to our knowledge, represent the first such demonstrations in the machine learning literature.", "date": "2026-05-01", "categories": []}
{"id": "2605.30590", "title": "Counterfactual Evaluation Reveals Hidden Capability Profiles in Clinical LLMs and Agents", "abstract": "Two clinical AI systems can score nearly identically on coverage-based rubrics yet behave radically differently when their patient inputs change: one updates its recommendations to match the new clinical signal, while the other produces the same output regardless. We introduce the Causal Sensitivity Score (CSS), a pre-registered interventional metric that mutates oncology tumor-board cases along five clinically meaningful dimensions - biomarker flips, prior-treatment failures, biomarker removals, surgery-status changes, and stage perturbations - and scores whether each model updates its recommendations in the pre-registered correct direction using a {0, 0.5, 1.0} scale. Benchmarked against the Consensus Match Score (CMS), a coverage-based weighted recall metric, six frontier models from three labs evaluated in single-shot inference across 224 cases rank in nearly opposite orders: all six models change rank, the CMS-worst model becomes CSS-best, and one upper-mid CMS model ranks last on CSS. We further surface a universal safety blind spot: every frontier model fails on surgery-status interventions (at most 17.2% CSS on Family D), a finding CMS does not expose. The metric also transfers to tool-using agents: in a ReAct-style experiment, tool use improves CSS for five of six models (+2.5 to +20.3 percentage points), yet the lowest-CSS model retrieves the same chart sections and still fails to update its recommendations - revealing a structural responsiveness deficit visible only under counterfactual evaluation. Cross-judge replication and three-rater medical-professional validation confirm the aggregate findings. Interventional pre-registered metrics like CSS complement coverage-based evaluation for clinical AI agents: they capture responsiveness that coverage metrics miss and offer a candidate dense reward signal for future agentic RL systems.", "date": "2026-05-28", "categories": []}
{"id": "2605.30637", "title": "EHRBench: An Automated and Reliable EHR-based Benchmark for Clinical Decision Making with LLMs", "abstract": "Clinical decision-making (CDM) is central to real-world clinical workflows, where clinicians infer diagnoses, select treatments, or anticipate future health outcomes under incomplete evidence. LLMs are increasingly used to support these decisions due to strong language capabilities, broad biomedical knowledge, and efficiency, yet the reliability of LLMs on real-world clinical decision tasks remains insufficiently understood. To evaluate CDM models, especially LLM-based models, an ideal and practical medical decision benchmark should be constructed via an automated yet reliable pipeline to ensure both scale and quality. Moreover, the grounding of a CDM benchmark in real patient EHRs can better support evaluation on practical CDM tasks that require substantive biomedical knowledge and clinical inference. To fill the gaps, we introduce EHRBench, an automated and reliable EHR-grounded benchmark for evaluating LLM-based clinical decision-making at scale. To ensure scalability and reliability, EHRBench is constructed through an EHR-LLM-KB(knowledge-base) interaction pipeline. For efficiency, we use a specialized LLM to automatically convert encounter-level EHR trajectories into structured templates and deterministically instantiate the templates into QA items. In parallel, we apply systematic KB-based verification and enrichment to filter hallucinated or ambiguous relations and to improve reliability. Using this pipeline, we construct nearly 1M (960,067) QA items spanning three core inference-required clinical decision tasks: diagnosis, treatment, and prognosis. We benchmark more than 30 representative LLMs on EHRBench and provide detailed analyses of performance and robustness. The results show consistent capability trends across settings, further validating the reliability of EHRBench and highlighting actionable gaps toward clinically reliable LLM systems.", "date": "2026-05-28", "categories": []}
{"id": "2605.30734", "title": "Beyond Accuracy: Evaluating Efficiency, Robustness and Explainability in Deep Learning for Malaria Diagnosis", "abstract": "Malaria remains a leading cause of mortality in sub-Saharan Africa, where scarce diagnostic infrastructure makes timely, accurate diagnosis particularly challenging. While deep learning offers a compelling path toward automated malaria screening, clinical adoption is hindered by computational cost and opacity in decision-making. This work benchmarks four deep learning models spanning a wide range of designed design architectures and model capacities on the NLM-Malaria dataset, jointly evaluating predictive performance, robustness, and post-hoc explainability. We find that lightweight, efficient-by-design models match their heavier counterparts in predictive performance, and the Friedman test confirms no statistically significant performance differences. CAM-based XAI methods consistently localize diagnostically relevant regions, while fine-grained attribution methods produce less targeted explanations, particularly with heavier backbones. Robustness evaluation under three types of image corruption further reveals that model confidence degrades faster than accuracy, providing a practical signal for human review. However, no XAI method is robust to corruption, with explanation reliability degrading at noise levels plausible in clinical practice, even when predictions remain accurate. These findings support the deployment of lightweight architectures for malaria diagnosis in resource-constrained settings, while highlighting the vulnerability of post-hoc explanations as an important consideration for responsible clinical deployment.", "date": "2026-05-29", "categories": []}
{"id": "2605.30865", "title": "GlucoFM: A Dual-Stream Foundation Model for Continuous Glucose Monitoring", "abstract": "Continuous glucose monitoring (CGM) provides a dense view of daily metabolic physiology, yet existing generic time-series and CGM-specific foundation models often encode glucose traces as entangled single-stream sequences, leaving the distinct temporal structure of glycemic dynamics only implicitly modeled. We present GlucoFM, a lightweight CGM foundation model that aligns irregular recordings to a 24-hour chronological grid, preserves observation masks, and decomposes glucose dynamics into slow physiological state and transient event streams, capturing low-frequency glycemic baselines and short-term deviations that may reflect acute physiological responses or sensor artifacts. GlucoFM is pretrained on 109,066 hours of unlabeled CGM recordings from 477 subjects with two complementary objectives: masked contextual latent prediction over fused daily representations and temporal dynamics prediction over state and event streams. Across four diverse cohorts and seven clinical prediction tasks, GlucoFM achieves the strongest subject-disjoint linear-probing performance among evaluated baselines, improving average PR-AUC by 4.1 points over the best CGM-specific foundation model. Its gains are most pronounced on core metabolic outcomes, leading PR-AUC on all diabetes-risk and $\\beta$-cell dysfunction tasks and on 3 of 4 insulin-resistance tasks. GlucoFM also achieves the best overall cross-dataset transfer performance and strong few-shot adaptation among evaluated methods, and consistent gains when aggregating multiple days for subject-level prediction, highlighting physiology-aware decomposition as an effective inductive bias for transferable CGM representation learning.", "date": "2026-05-29", "categories": []}
{"id": "2605.31512", "title": "Reliable Multilingual Orthopedic Decision Support from Clinical Narratives: Language-Aware Adaptation and Verification-Guided Deferral", "abstract": "Multilingual orthopedic decision support remains challenging in low-resource healthcare settings, where clinical narratives contain specialized terminology, mixed scripts, incomplete evidence, label imbalance and language-dependent documentation patterns. This article presents a reliability-oriented framework for classifying free-text orthopedic notes in English, Hindi and Punjabi. We compare task-aligned multilingual transformer encoders, a task-fine-tuned DistilBERT baseline, zero-shot instruction-tuned large language models (LLMs) and a domain-adaptive encoder, IndicBERT-HPA. IndicBERT-HPA augments IndicBERT with language-aware orthopedic adapter heads to support clinically relevant multilingual representation learning. Evaluation extends beyond aggregate accuracy to per-class performance, ROC-AUC, AUPRC, expected calibration error, cross-language stability and robustness under controlled balanced and natural-prevalence distributions. The evaluated zero-shot LLMs remain substantially less effective than task-adapted encoders for closed-set classification, with language-dependent instability. Under natural clinical prevalence, IndicBERT-HPA achieves the strongest overall performance, reaching an averaged Macro-F1 of 0.8792, Macro-AUROC of 0.894 and AUPRC of 0.902. We further implement a deterministic selective-verification layer combining confidence gating, evidence-consistency checking and language-risk screening. On a randomly selected held-out 5,000-record subset, it achieves 84.4% selective accuracy and 0.76 selective Macro-F1 at 72.3% coverage, compared with 71.5% accuracy and 0.65 Macro-F1 for accept-all prediction. These results support reliability-oriented multilingual clinical decision support with explicit deferral.", "date": "2026-05-29", "categories": []}
{"id": "2605.31539", "title": "Automated Prediction of Postoperative Pancreatic Fistula Using Preoperative Computed Tomography", "abstract": "Postoperative pancreatic fistula (POPF) is a serious complication after pancreatic resection, increasing morbidity, hospital stay, and healthcare costs. We present an automatic, end-to-end deep learning pipeline-from pancreatic segmentation to classification-for preoperative POPF risk estimation and stratification using preoperative CT scans. A data set with auto-segmented pancreas volumes and surgical outcomes was used to evaluate multiple architectures, including a custom lightweight 3D CNN baseline (CNN3D), R(2+1)D ResNet-18, and ResNet-MC3-18 models. Evaluation across multiple 3D architectures demonstrated promising predictive performance. This approach offers a clinically valuable tool and a methodological benchmark for pancreas-specific CT classification, supporting improved preoperative decision-making in pancreatic surgery.", "date": "2026-05-29", "categories": []}
{"id": "2502.16548", "title": "A Composable Multimodal Framework for cine CMR-Text-Driven Prediction of Heart Failure Outcomes", "abstract": "Objective. Heart failure is one of the leading causes of death worldwide, with millions of deaths each year, according to data from the World Health Organization (WHO) and other public health agencies. While significant progress has been made in the field of heart failure, leading to improved survival rates and improvement of ejection fraction, there remains substantial unmet needs, due to the complexity and multifactorial characteristics. This study aims to propose and evaluate a composable strategy framework for assessment and treatment optimization in heart failure, designed to provide more holistic patient evaluation and management. Approach. The framework leverages multi-modal algorithms to analyze a comprehensive range of patient data, explicitly integrating cine cardiac magnetic resonance (cine CMR) sequences, structured clinical metrics (e.g., lab results, demographics), and unstructured textual records (e.g., medical history, prescriptions). By integrating these various data sources, our framework offers a more holistic evaluation and optimized treatment plan for patients. Main results. The multi-modal framework demonstrates superior accuracy in HF prognosis prediction compared to single-modal AI algorithms. Additionally, it enables a detailed evaluation of the impact of various pathological indicators on HF outcomes. Significance. By integrating heterogeneous clinical data in a systematic manner, this approach supports more comprehensive prognosis assessment and facilitates optimized, personalized treatment planning for heart failure patients.", "date": "2025-02-23", "categories": []}
{"id": "2605.26255", "title": "Prospective evaluation of multimodal respiratory failure prediction: Do chest X-rays improve performance beyond EHR signals?", "abstract": "Early prediction of respiratory failure is critical for timely clinical intervention in intensive care units. Existing electronic health record (EHR)-based models can continuously monitor physiologic deterioration, but they may not fully capture pulmonary pathophysiology reflected in chest radiographs (CXRs). In this study, we ask whether CXR information improves prospective prediction of invasive mechanical ventilation beyond EHR signals alone. We develop a gated multimodal framework that integrates structured EHR time-series data with CXR foundation-model representations. The gating module adaptively controls the contribution of imaging features based on patient-specific clinical context, allowing the model to selectively rely on imaging information when it is informative. We prospectively evaluate the framework for predicting invasive mechanical ventilation within 24 hours in ICU patients and compare it with an established EHR-only model (Ventio), physician predictions obtained at matched clinical time points, and alternative multimodal variants. The gated multimodal models achieved higher discrimination than the EHR-only baseline, with AUROC values of 0.860 and 0.858 using REMEDIS and MedInsight CXR representations, respectively, compared with 0.752 for Ventio. Relative to physician predictions, the multimodal framework substantially improved sensitivity while maintaining favorable specificity. Compared with the EHR-only model, multimodal integration increased specificity and positive predictive value, suggesting that CXR information can refine risk estimation in selected patients. These findings support adaptive multimodal fusion as a practical strategy for incorporating imaging into prospective respiratory failure prediction.", "date": "2026-05-25", "categories": []}
{"id": "2605.29236", "title": "SigmaMedStat: Temporal Signal Modeling for ICU False Alarm Reduction", "abstract": "Alarm fatigue in intensive care units (ICUs) is a well documented patient safety crisis. Clinical monitors generate 350 or more alarms per patient per day, out of which 72-99% are clinically irrelevant. Staff desensitization to non-actionable alarms increases the risk of missed true emergencies. This paper presents SigmaMedStat, a machine learning system that evaluates the trustworthiness of physiological alarm signals before clinical action is taken. Four approaches were evaluated on the PhysioNet/Computing in Cardiology Challenge 2015 dataset of 498 four-channel ICU alarm recordings. Primary contribution is a temporal modeling framework that splits each 60 second recording into six consecutive 10-second chunks, and this in turn generates Continuous Wavelet Transform (CWT) scalograms per chunk, encodes each chunk with a shared EfficientNet-B0 encoder, and passes the resulting feature sequence to a two-layer Long Short-Term Memory (LSTM) network. Five-fold stratified cross-validation yields a mean AUC of 0.822 +/- 0.016 (95% CI: [0.790,0.853]), compared to 0.641 for a static EfficientNet baseline trained on the full 60-second window. Ablation studies confirm that temporal chunking and multi-channel signal fusion both contribute independently to classification performance. Per-alarm type analysis reveals that Ventricular Flutter is the most accurately classified alarm type (AUC 0.820) while Asystole remains the hardest (AUC 0.722). Error analysis identifies 65 false negatives and 85 high-confidence misclassifications as the primary failure modes. All code and results are publicly available at https://github.com/Arun-K-Ram/sigmamedstat.", "date": "2026-05-28", "categories": []}
{"id": "2605.30275", "title": "Digitally enriching a screening population for pancreatic cancer using routine blood-based measures and clinical histories", "abstract": "Earlier detection of pancreatic cancer is key to enabling wider access to curative treatment and reducing cancer deaths; however, screening is presently not viable. Latent indicators of pathology are evident in an individual's disease and blood test trajectories and may predict the development of pancreatic cancer. Longitudinal sequences of coded diagnoses and blood test values accrued by patients throughout their clinical interactions were used to train a custom Transformer-based neural network with a multi-head attention mechanism to predict risk of pancreatic cancer with a multi-year lead time and risk-stratify populations for targeted screening. The cohort comprised 6,017 adults with pancreatic cancer and 177,081 controls (overall median age 75, 45% female) with median 12 years (interquartile range 6.9-16.2) of medical history prior to pancreatic cancer diagnosis. External validation via leave-one-site-out, out-of-sample testing predicting pancreatic cancer 1-, 2-, and 3-years prior to diagnosis demonstrated mean area under the receiver operating characteristic of 0.837 (95% confidence interval 0.827-0.848), 0.797 (95% confidence interval 0.782-0.813), and 0.760 (95% confidence interval 0.745-0.776), respectively. Estimated pancreatic cancer risks were well-calibrated (calibration plot slope 1.08, intercept of -0.077; Brier score 0.025), and a Bayesian population pancreatic cancer prevalence update allows estimated cancer risk outputs to be transportable across settings. At testing, a screening threshold of >3.3% risk of pancreatic cancer in 1-year offered a diagnostic odds ratio of 18.2. Our work therefore lays the foundation for a first population-level digital enrichment tool to widen access to curative-intent management of pancreatic cancer.", "date": "2026-05-28", "categories": []}
{"id": "2605.24678", "title": "Exploration of Perceptual Speech Features for Clinical Decision-Support in Mental Health Care", "abstract": "Speech and language technologies offer valuable opportunities for supporting mental health assessment through objective and interpretable cues. We present a systematic feature-based analysis framework leveraging perceptually grounded acoustic and linguistic characteristics, including prosody, vocal quality, semantic coherence, syntactic structure, and sarcasm. Using statistical analysis and interpretable machine learning (XGBoost with SHAP and LIME), we examine associations between speech features and validated symptom measures of depression, anxiety, and ADHD. Evaluated on both controlled benchmark datasets (StressID, DAIC-WOZ, Androids, EATD) and a real-world clinical dataset, the framework reveals stable and consistent relationships between symptom severity and vocal irregularities (e.g., shimmer, jitter), lexical-syntactic patterns, and affective tone. An ablation study conducted across all datasets further identifies the most informative feature groups. This work explores a transparent and clinically interpretable approach to speech-based mental health analysis.", "date": "2026-05-23", "categories": []}
{"id": "2605.27583", "title": "Information-theoretic Multimodal Representation Learning for Electrocardiogram Signals", "abstract": "Electrocardiograms (ECGs) are widely used non-invasive measurements of cardiac activity and play a central role in clinical diagnosis. Recent multimodal approaches align ECG signals with clinical reports to incorporate diagnostic semantics, but clinical reports often fail to preserve the rich physiological structure of ECG waveforms, particularly across multiple levels of abstraction ranging from coarse diagnostic categories to fine-grained morphology. To address this limitation, we formulate ECG representation learning from an information-theoretic perspective and derive a tractable objective that jointly preserves signal structure and integrates clinical semantics. Based on this principle, we propose \\textbf{MERIT} (Multimodal ECG Representation via Information Theory), a dual-branch pretraining framework combining masked ECG modeling with ECG--text contrastive alignment. Extensive experiments on PTB-XL and additional benchmarks demonstrate consistent improvements over prior methods, including gains exceeding $3%$ F1 on PTB-XL All and $5%$ F1 on SubClass classification. In zero-shot evaluation, MERIT further improves performance by up to $ +2.66\\%$ AUC and $ +2.11\\%$ F1 on PTB-XL SubClass, while also demonstrating robustness under multiple distribution-shift settings. Moreover, leveraging the learned ECG representations for ECG-conditioned clinical text generation with large language models improves text quality across several metrics, including ROUGE and METEOR. Together, these results demonstrate that MERIT learns more informative and clinically meaningful ECG representations, particularly for fine-grained clinical applications.", "date": "2026-05-26", "categories": []}
{"id": "2605.27799", "title": "GraD-IBD: Graph Representation Learning from Diagnosis Trajectories for Early Detection of Inflammatory Bowel Disease", "abstract": "International Classification of Diseases (ICD) is a globally recognized coding system that records diagnostic events during each patient encounter, providing a standardized data foundation for various clinical tasks. However, the irregular and hierarchical nature of ICD code sequences poses challenges for N-D lattice-based sequential modeling methods, leading to overly complex model designs. In this paper, we propose GraD-IBD, a graph diagnosis model that reformulates longitudinal ICD trajectories as visit-bucketized, temporally directed graphs to detect the risk of inflammatory bowel disease (IBD). A novel context-aware, time-decay message passing mechanism was developed to capture temporal dependencies while reducing model complexity. The experimental results using a real-world clinical dataset demonstrated consistent and robust improvements in IBD detection over state-of-the-art methods, with significant reductions in computational complexity compared to sequential models. These findings highlight the potential of graph representation learning to enable efficient, scalable, and accurate disease risk prediction from longitudinal ICD diagnosis codes.", "date": "2026-05-27", "categories": []}
{"id": "2605.28060", "title": "Challenges in Explaining Pretrained Clinical Text Classifiers", "abstract": "Explaining the predictions of neural models in clinical NLP remains a significant challenge, especially for complex tasks involving long, unstructured medical texts. While post-hoc methods like LIME and SHAP are widely used, they often fall short when applied to clinical narratives. In this paper, we identify core limitations of token-level and perturbation-based explanation techniques through targeted demonstra- tions on a hospital length-of-stay prediction task. Our findings reveal issues such as overemphasis on non-informative tokens, instability in at- tributions, and high-confidence predictions for incoherent input variants. These results underscore the need for explanation strategies that are clin- ically meaningful, semantically grounded, and robust to linguistic noise.", "date": "2026-05-27", "categories": []}
{"id": "2605.28129", "title": "Do Clinical Models Change Treatment Decisions?", "abstract": "Clinical foundation models are evaluated with factual or exam-style medical QA, but treatment decisions must change when patient context changes. We introduce ClinPivot, an auditable treatment-decision benchmark built from biomedical relations and pivoted patient contexts. ClinPivot asks whether models change treatment choices when new clinical constraints shift the action space. We find that strong medical QA performance does not reliably predict decision-making performance: frontier models and task-adapted Qwen variants often fail to change decisions correctly, and model rankings shift across evaluation regimes. Decision-structured supervision improves pivot-sensitive decision-making and medical QA under matched knowledge budgets, while lightweight replay reduces losses in general assistant ability.", "date": "2026-05-27", "categories": []}
{"id": "2512.21602", "title": "An Empirical Study of Machine Learning Robustness and Scalability for Imbalanced Tabular Clinical Data in Emergency and Critical Care", "abstract": "Every year, millions of patients pass through emergency departments and intensive care units, where clinicians must make high-stakes decisions under time pressure and uncertainty. Machine learning could support prediction of deterioration, triage, and rare critical outcomes, but clinical data are often severely imbalanced, biasing models toward majority classes and reducing predictive performance. Developing robust and efficient models for imbalanced clinical tabular data therefore remains an important challenge.\n  We evaluated six model families on imbalanced tabular data from the MIMIC-IV-ED and eICU databases: Decision Tree, Random Forest, XGBoost, TabNet, TabICL, and TabPFN v2.6. Trainable models were optimized using Bayesian hyperparameter tuning, while foundation models were evaluated in their pretrained inference regime without task-specific reweighting. Models were assessed using Macro F1-score, robustness to increasing imbalance, and computational scalability across seven clinical prediction tasks.\n  Results differed across datasets. On MIMIC-IV-ED, TabPFN v2.6 and TabICL achieved the strongest average Macro F1 ranks, with XGBoost remaining competitive. On eICU, XGBoost consistently performed best, followed by other tree-based methods, while foundation models achieved intermediate performance. Across both datasets, TabNet showed the largest degradation under increasing imbalance and the highest computational cost. Training-time analysis showed that tree-based methods scaled most favorably with dataset size, while foundation models offered low per-task adaptation cost.\n  These findings suggest that no single model family dominates across all clinical settings. However, tabular foundation models are narrowing the performance gap with strong classical baselines while offering a distinct efficiency-performance trade-off that may benefit resource-constrained clinical environments.", "date": "2025-12-25", "categories": []}
{"id": "2602.12833", "title": "Vital Trace: Protocol-Constrained Patient-State Reasoning for Longitudinal Clinical Trajectories", "abstract": "Longitudinal clinical reasoning over electronic health records requires tracking evolving physiological measurements, laboratory results, and interventions across extended patient trajectories. Existing LLM-based clinical reasoning systems often rely on repeatedly serializing patient histories or exchanging unconstrained textual agent messages, leading to context drift, unstable reasoning, and growing inference cost over long horizons. We present Vital Trace, a protocol-constrained multi-agent framework for future clinical risk prediction over evolving ICU trajectories. Instead of maintaining unbounded textual histories, Vital Trace uses a compact persistent patient-state memory together with staged reasoning performed by four coordinated agents: a Router, Reasoner, Auditor, and Steward. To support temporally coherent reasoning, we introduce a manually curated Global Protocol containing physiological state-transition rules and a dynamic patient-state representation that tracks hemodynamic, respiratory, renal, metabolic, and inflammatory instability over time. We evaluate Vital Trace on MIMIC-IV and eICU using future vasopressor-support, respiratory-support, renal-support, and deterioration prediction tasks. Results show that structured protocol-constrained reasoning improves temporal consistency, communication stability, calibration, and interpretability compared with free-form multi-agent baselines while achieving strong predictive performance across long ICU trajectories.", "date": "2026-02-13", "categories": []}
{"id": "2605.26190", "title": "HRVConformer: Neonatal Hypoxic-Ischemic Encephalopathy Classification from the Heart Rate signals", "abstract": "This paper presents the HRVConformer, a novel deep learning architecture for the classification of hypoxic-ischemic encephalopathy (HIE) using the instantaneous heart rate (HR) signal. Unlike conventional approaches that rely on handcrafted features, HRVConformer directly processes raw HR signals in an end-to-end manner, capturing both local and long-range dependencies through a hybrid Convolution-Transformer framework. By integrating convolutional layers for local feature extraction and Transformer-based attention mechanisms for global context modelling, the architecture effectively enhances signal representation and classification performance. The model was trained using supervised learning on a large HR dataset consisting of 1,573 one-hour epochs, including 259 one-hour expert-annotated epochs and a substantial set of weakly labelled data. A 314-hour validation set provided a robust performance estimation, while an independent 215-hour dataset with expert annotations was reserved for final testing. HR signals were extracted from electrocardiogram (ECG) recordings using an improved Pan-Tompkins algorithm, which significantly enhanced both signal quality and data availability. Experimental results demonstrate that the HRVConformer achieves an AUC of 83.23\\% and accuracy of 74.56\\% on the test set. These results surpass the performance of the Transformer, ResNet50 and fully convolutional networks baselines, highlighting the advantages of integrating convolutional and Transformer-based components for HR-based HIE classification. The proposed method provides a promising step toward a more accurate and automated assessment of HIE using HR signals. The code is available at: https://github.com/syu-kylin/HRVConformer.", "date": "2026-05-25", "categories": []}
{"id": "2605.26376", "title": "BioFact-MoE: Biologically Factorized Mixture of Experts for Vision-Language Prognostic Modeling in Hepatocellular Carcinoma", "abstract": "Hepatocellular carcinoma (HCC) is biologically heterogeneous, shaped by the interplay between hepatic functional reserve and tumor-related oncologic factors; thus, similar survival outcomes may reflect fundamentally different underlying biological processes. Prognostic modeling in HCC is informed by rich multimodal information from multiparametric MRI and radiology reports from routine clinical practice. Existing prognostic vision-language models (VLMs) learn a single entangled latent representation that blends hepatic and tumor-related factors, limiting both accuracy and biological interpretability. We present BioFact-MoE, a biologically factorized Mixture of Experts (MoE) framework that explicitly decomposes liver and tumor factors via biologically supervised experts within a residual MoE survival architecture. On a HCC cohort of N=588 patients (pretrained on 4,582 3D MRI image-report pairs), BioFact-MoE consistently improves survival prediction over all baselines across time horizons, achieving 12-, 18-, and 24-month AUCs of 75.33%, 75.85%, and 73.96%. Beyond scalar risk prediction, gated expert weights enable phenotype-aware risk stratification. Pathway-informed gating uncovers clinically meaningful treatment-associated survival heterogeneity. In held-out validation, hepatic and tumor embeddings show selective associations with liver function and tumor burden markers, respectively (p<0.05), without supervision. The code is available at https://github.com/jy-639/BioFact-MoE.", "date": "2026-05-25", "categories": []}
{"id": "2605.26589", "title": "Few-shot Cross-country Generalization of Tabular Machine Learning and Foundation Models for Childhood Anemia Prediction under Distribution Shift", "abstract": "Childhood anemia affects around 40% of children aged 6-59 months globally and arises from heterogeneous factors, limiting model generalizability. We evaluate a transformer-based tabular foundation model against classical supervised methods under cross-country and data-scarce settings.\n  We used DHS data from 16 countries across Africa, Asia, Latin America, the Caucasus, and the Middle East (n=68,856). We compared Logistic Regression, XGBoost, LightGBM, and TabPFN v2.6. Performance was assessed using AUC-ROC, Brier score, and ECE. Generalization was evaluated using leave-one-country-out (LOCO), reverse-LOCO, and few-shot settings. Subgroup analyses included sex, age, residence, maternal education, and wealth. Feature importance was estimated using SHAP.\n  TabPFN outperformed classical models in low-data regimes (<200 samples), showing higher discrimination and better calibration. Across countries, it achieved the lowest Brier score (0.042) and ECE (0.203). Under full-data settings, AUC-ROC ranged from 0.59-0.76 with small between-model differences ($\\leq 0.05$). LOCO performance was stable (0.58-0.69), driven by country context. Reverse-LOCO showed asymmetric transferability. Subgroup performance was consistent with no systematic demographic bias. SHAP identified child age, altitude, and height-for-age z-score as dominant predictors, followed by wealth and maternal education.\n  Performance in childhood anemia prediction is driven more by population variation than model choice. TabPFN provides advantages in low-resource settings through improved discrimination and calibration, highlighting foundation models as promising tools for data-scarce global health prediction.", "date": "2026-05-26", "categories": []}
{"id": "2605.27189", "title": "Beyond Binary: Speech Representations Across the Cognitive Score Hierarchy", "abstract": "This study examines the relationship between speech representations and the hierarchical structure of cognitive assessment in mild cognitive impairment. Utilizing 5,754 German neuropsychological assessment recordings, we evaluate six cognitive tasks across three score levels: task, domain, and global levels. We compare hand-crafted acoustic features with self-supervised learning (SSL) embeddings. Results show that although SSL representations generally outperform hand-crafted features at lower levels, this trend reverses for MCI classification. Furthermore, task-specific constraints influence performance: tasks with greater response freedom exhibit performance dilution as hierarchical levels increase, suggesting ``specialist'' representations, whereas the performance of highly structured tasks increases toward higher levels, suggesting ``generalist'' representations. These findings show links between task constraints and assessment hierarchy in automated clinical speech analysis.", "date": "2026-05-26", "categories": []}
{"id": "2505.05371", "title": "From Sleep Staging to Spindle Detection: A Case Study on End-to-End Automated Sleep Analysis", "abstract": "Automation of sleep analysis, including both macrostructural (sleep stages) and microstructural (e.g., sleep spindles) elements, promises to enable large-scale sleep studies and to reduce variance due to inter-rater incongruencies. While individual steps, such as sleep staging and spindle detection, have been studied separately, the feasibility of automating multi-step sleep analysis remains unclear. In this case study, we evaluate whether a fully automated analysis using validated machine learning models for sleep staging (RobustSleepNet) and subsequent spindle detection (SUMOv2) can replicate findings from an expert-based study of bipolar disorder. The automated analysis qualitatively reproduced key findings from the expert-based study, including significant differences in fast spindle densities between bipolar patients and healthy controls, accomplishing in minutes what previously took months to complete manually. While the results of the automated analysis differed quantitatively from the expert-based study, possibly due to biases between expert raters or between raters and the models, the models individually performed at or above inter-rater agreement for both sleep staging and spindle detection. Our results demonstrate that fully automated approaches have the potential to facilitate large-scale sleep research. We are providing public access to the tools used in our automated analysis by sharing our code and introducing SomnoBot, a privacy-preserving sleep analysis platform.", "date": "2025-05-08", "categories": []}
{"id": "2509.12194", "title": "Teaching large language models to reason like expert diagnosticians", "abstract": "Differential diagnosis is an iterative process that integrates patient information with broader medical knowledge. Clinical case series such as the NEJM Clinicopathologic Conferences (CPCs), published continuously since 1923, feature expert physicians who demonstrate diagnostic reasoning to peers, and have been used for decades to evaluate AI. However, prior AI evaluations have largely focused on final diagnostic accuracy rather than nuanced clinical reasoning. Here, we introduce Dr. CaBot, an agentic AI system that emulates an expert diagnostician by generating written and narrated slide-based presentations from an initial case description alone. CaBot recently generated the first AI diagnosis published in the 100+ year history of the NEJM CPCs. In blinded evaluations, physicians misclassified the source of the differential (CaBot vs. physician-written) in 46/62 (74%) of trials and rated them favorably across quality dimensions. When tasked with solving cases for 72 patients with undiagnosed disease from the NIH Undiagnosed Diseases Network, CaBot identified the working diagnosis in 50/72 (69%) of cases from referral notes alone. To promote transparency and research, we also developed CPC-Bench, a physician-validated benchmark based on 7,102 CPCs and 47,648 questions across 10 tasks. We show that CaBot outperforms frontier models on CPC-Bench, and release both CaBot and CPC-Bench publicly to foster progress in clinical AI.", "date": "2025-09-15", "categories": []}
{"id": "2510.08350", "title": "DeepEN: A Deep Reinforcement Learning Framework for Personalized Enteral Nutrition in Critical Care", "abstract": "Objective: Enteral nutrition (EN) delivery in the ICU remains suboptimal due to limited personalization and uncertainty regarding appropriate calorie, protein, and fluid targets under dynamic metabolic demands. We introduce DeepEN, a reinforcement learning (RL) framework for personalized EN optimization using electronic health record data.\n  Methods: DeepEN was trained on over 11,000 ICU patients from MIMIC-IV to generate 4-hourly, patient-specific caloric, protein, and fluid targets. The state representation incorporated demographics, comorbidities, vital signs, laboratory values, and recent interventions. A physiologically aligned reward framework balanced biomarker stability with long-term survival. Policy learning employed a dueling double deep Q-network with Conservative Q-Learning regularization to enable safe offline training.\n  Results: DeepEN achieved the highest estimated policy value ($V^\\pi = 9.48$) and the lowest calibrated mortality (18.8 +/- 1.0%), representing a 4.0 percentage-point absolute reduction compared with clinician practice (22.8%). The policy also demonstrated superior metabolic stability, achieving the highest proportion of glucose, phosphate, and sodium values within target range. Furthermore, deviation from the DeepEN policy was independently associated with increased mortality and biomarker instability, whereas deviation from a random policy showed no such association. Interpretability analyses further indicated that recommendations were conditioned on physiologically relevant markers of organ function and metabolic status rather than static dosing heuristics.\n  Conclusion: DeepEN demonstrates the feasibility of conservative offline RL for safe, individualized EN optimization, highlighting the potential of data-driven personalization to complement guideline-based approaches in critical care.", "date": "2025-10-09", "categories": []}
{"id": "2603.08072", "title": "Hybrid Quantum Neural Network for Multivariate Clinical Time Series Forecasting", "abstract": "Forecasting physiological signals can support proactive monitoring and timely clinical intervention by anticipating critical changes in patient status. In this work, we address multivariate multi-horizon forecasting of physiological time series by jointly predicting heart rate, oxygen saturation, pulse rate, and respiratory rate at forecasting horizons of 15, 30, and 60 seconds. We propose a hybrid quantum-classical architecture that integrates a Variational Quantum Circuit (VQC) within a recurrent neural backbone. A GRU encoder summarizes the historical observation window into a latent representation, which is then projected into quantum angles used to parameterize the VQC. The quantum layer acts as a learnable non-linear feature mixer, modeling cross-variable interactions before the final prediction stage. We evaluate the proposed approach on the BIDMC PPG and Respiration dataset under a Leave-One-Patient-Out protocol. The results show competitive accuracy compared with classical and deep learning baselines, together with greater robustness to noise and missing inputs. These findings suggest that hybrid quantum layers can provide useful inductive biases for physiological time series forecasting in small-cohort clinical settings. The code is available at https://github.com/arco-group/quantum-ml.", "date": "2026-03-09", "categories": []}
{"id": "2604.10783", "title": "Learning Preference-Based Objectives from Clinical Narratives for Dynamic Sepsis Treatment", "abstract": "Designing reward functions for reinforcement learning (RL) in healthcare remains challenging because clinically meaningful outcomes are sparse, delayed, and difficult to explicitly specify. Although structured clinical data capture physiologic states, they often fail to reflect broader aspects of patient trajectories such as treatment response, recovery dynamics, and intervention burden. Clinical narratives, by contrast, encode longitudinal clinician assessments of disease progression, treatment effectiveness, and recovery, providing a potential source of trajectory-level supervision beyond predefined outcome metrics. We propose Clinical Narrative-informed Preference Rewards (CN-PR), a framework that learns reward functions directly from discharge summaries by treating clinical narratives as scalable supervision for trajectory-level preferences. Using a large language model, we derive trajectory quality scores and construct pairwise preferences between patient trajectories to learn rewards through preference-based optimization. To account for variability in narrative informativeness, we incorporate a task relevance signal that weights supervision according to its relevance to the downstream decision-making task. We evaluate CN-PR in dynamic sepsis treatment using offline RL. The learned reward demonstrated strong monotonic alignment with trajectory quality scores and produced policies associated with improved recovery-related outcomes, including increased organ support-free days and faster shock resolution, while maintaining mortality performance comparable to outcome-based reward baselines. These findings were preserved under external validation. Our results suggest that clinical narratives provide a scalable and expressive source of supervision for reward learning in dynamic treatment regimes.", "date": "2026-04-12", "categories": []}
{"id": "2605.23148", "title": "When Symptoms Are Not Enough: Evidence-Weighting Patterns in Large Language Model Psychiatric Screening", "abstract": "As demand for mental health care outpaces clinician-delivered assessment, scalable screening tools are increasingly needed. Large language models (LLMs) may identify psychiatric risk from patient narratives, but their reliability across diagnoses, demographic subgroups, and evidence-use patterns remains uncertain. We introduce a SCID-anchored benchmark of 555 semi-structured experiential interviews paired with diagnostic reference labels for anxiety disorder, major depressive disorder, post-traumatic stress disorder, and any current mental health disorder. Using zero-shot task-specific prompting, we evaluated five state-of-the-art LLMs and examined whether false-negative errors reflected missed psychiatric evidence or differential weighting of symptom, functional-impairment, and protective-context cues. Performance varied across tasks and models, with accuracy ranging from 0.49 to 0.86 and Matthews correlation coefficients from 0.16 to 0.38. GPT-4.1 Mini and GPT-5 Mini showed the most consistent disorder-specific accuracy. Subgroup analyses found higher depression-classification accuracy among male than female participants, no consistent age-related pattern, and modest non-uniform variation across race strata. Evidence-integration analyses showed that false-negative anxiety and PTSD classifications often contained explicit symptom evidence but were accompanied by preserved functioning, coping ability, or social support. Functional-impairment evidence shifted model outputs toward positive classifications, whereas protective-context evidence shifted outputs away. These findings suggest that LLMs may support scalable psychiatric screening, but their tendency to discount symptom evidence in the presence of preserved functioning or protective context requires careful validation before clinical deployment.", "date": "2026-05-22", "categories": []}
{"id": "2605.23977", "title": "A Multi-Probe Audit of Clinical-Interview Depression Detection Benchmarks", "abstract": "This paper audits benchmark evaluation in clinical-interview depression detection through four complementary probes across DAIC/E-DAIC, CMDC, ANDROIDS, MODMA, and PDCH. First, we re-evaluate E-DAIC under strict subject-disjoint leave-one-subject-out cross-validation. A lightweight hybrid text-plus-LLM-score model reaches macro-F1 = 0.723 - the highest reported under this protocol, to our knowledge - providing a conservative out-of-fold reference point that does not depend on the privileged official holdout. Second, we test whether the E-DAIC official split supports fine-grained leaderboard rankings by sweeping 96 model configurations across modality bundles, pooling strategies, and learners. Development-side cross-validation and official-test rankings align only moderately: the best cross-validation configuration ranks twentieth on the official test, the official-test winner ranks forty-first by cross-validation, top-3 overlap is zero, and the apparent winner is rank-1 in only 32.3% of subject bootstraps. Third, we externally validate strong public CMDC and ANDROIDS baselines that achieve near-ceiling in-domain performance. Zero-shot transfer to external corpora is substantially weaker. Finally, we stress-test E-DAIC text and audio models using paired symptom-dense versus symptom-light interview slices defined by an SRDS-based annotator. Text scores rise sharply on symptom-dense slices, whereas audio scores remain nearly flat; the text-minus-audio gap is positive across all five seeds.", "date": "2026-05-13", "categories": []}
{"id": "2605.24012", "title": "Deep Learning-Based Automated Quantification of TIMI Myocardial Perfusion Frame Count (DL-TMPFC) from Coronary Angiography: A Novel Framework for Rapid Assessment of Microvascular Dysfunction", "abstract": "Aims: Coronary microvascular dysfunction (CMVD) affects approximately 40%-60% of patients with ischemia and non-obstructive coronary arteries, yet diagnosis remains challenging due to reliance on invasive functional testing or subjective Thrombolysis In Myocardial Infarction (TIMI) flow grade. The TIMI Myocardial Perfusion Frame Count (TMPFC) offers an objective, angiography-based quantitative measure of CMVD, but its clinical translation is hindered by cumbersome manual calculation and insufficient validation. This study aims to develop and validate a deep learning-powered TMPFC calculation (DL-TMPFC), enabling integration into clinical workflows.\n  Methods and results: DL-TMPFC framework comprised two components. A stenosis detection network first excluded obstructive coronary artery disease (CAD). A territory-aware segmentation network then identified perfusion territories and TMPFC calculation module automatically determined the first and last frames from angiographic sequences. The framework was validated in a cohort of 655 patients (445 of obstructive CAD, 100 of confirmed CMVD, 110 of control group) from three independent institutions. DL-TMPFC showed excellent agreement with expert manual measurements (bias: -0.93 frames; 95% LoA: -5.33 to +3.47; r =0.98). DL-TMPFC markedly enhanced clinical feasibility by fully automating TMPFC and removing observer dependence. Clinically, DL-TMPFC accurately identified CMVD across a full spectrum of coronary pathologies and captured the continuous severity of CMVD beyond binary classification, enabling quantitative risk stratification.\n  Conclusion: DL-TMPFC enabled automatic, standardized, and accurate quantification of CMVD directly from routine angiography. By providing an automatic and objective measure, this tool provided immediate diagnostic information for timely recognition and management of CMVD in clinical practice.", "date": "2026-05-20", "categories": []}
{"id": "2605.24172", "title": "EPPC-OASIS: Ontology-Aware Adaptation and Structured Inference Refinement for Electronic Patient-Provider Communication Mining in Secure Messages", "abstract": "Secure patient-provider messages contain clinically important communication behaviors that are difficult to characterize manually at scale. The Electronic Patient-Provider Communication (EPPC) framework provides an ontology for coding these behaviors, but automated extraction remains challenging because predictions must preserve fine-grained code/sub-code structure while grounding annotations in message text. We developed EPPC-OASIS, an ontology-aware adaptation approach for structured EPPC extraction, and combined it with deployable inference-refinement procedures designed to improve the coherence of final annotations. EPPC-OASIS augments supervised fine-tuning with a Wasserstein alignment objective that encourages alignment between model representation neighborhoods and EPPC ontology-derived neighborhoods, while inference refinement uses verification, self-consistency, hybrid correction, and selection or ensembling to address residual prediction errors. We evaluated the framework on a de-identified corpus of secure patient-provider messages against prompting, supervised fine-tuning, preference-based, and robustness-oriented baselines across multiple open-weight language models. Across model families, the best deployable pipeline achieved 77.13% Code+Sub-code F1 and 63.83% Triplet F1, corresponding to modest but consistent absolute gains of +1.39 and +2.12 F1 points over the strongest supervised fine-tuning baseline. These results suggest that ontology-aware adaptation with structured inference refinement can support scalable retrospective EPPC mining, although external validation is needed before operational use.", "date": "2026-05-22", "categories": []}
{"id": "2605.24576", "title": "Associations between echocardiographic traits and AI-ECG predictions of heart failure", "abstract": "Artificial intelligence-enabled electrocardiography (AI-ECG) can detect heart failure (HF), including disease not captured by left ventricular ejection fraction (LVEF), but the cardiac phenotypes underlying model predictions remain unclear. We therefore investigated whether AI-ECG-predicted HF risk aligns with established echocardiographic measures of myocardial dysfunction, remodelling, and filling pressures.\n  We retrospectively analysed ECG and echocardiography data from 8147 patients who underwent both examinations within three days at Akershus University Hospital between 1 January 2023 and 1 June 2025. A previously validated AI-ECG model for HF detection was applied to all ECGs. Spearman's rank correlation $\\rho$ quantified associations between echocardiographic parameters and AI-ECG risk. Subgroup analyses were performed by sex and left ventricular ejection fraction (LVEF). External validation included 36,286 ECG-echocardiography pairs from Columbia University Irving Medical Center.\n  Global longitudinal strain (GLS) showed the strongest correlation ($\\rho$=0.57), followed by mitral annular plane systolic excursion (MAPSE) ($\\rho$=-0.49) and LVEF ($\\rho$=-0.45). In patients with LVEF>50%, correlations remained substantial for GLS, MAPSE, and diastolic-related parameters. Volumetric left ventricular indices correlated less strongly in women, whereas diastolic indices showed stronger correlations in women than in men.\n  Physiological validation showed that AI-ECG HF risk predictions align primarily with measures of systolic function, particularly global longitudinal strain, while also capturing diastolic-related abnormalities in patients with preserved LVEF. This approach may improve clinical interpretability and identify opportunities for model refinement.", "date": "2026-05-23", "categories": []}
{"id": "2605.24755", "title": "Automated Detection and Classification of Delusion-related Content in Naturalistic Audio Diaries Using Multi-Agent Language Models", "abstract": "Speech monologues recorded in naturalistic settings provide opportunities to characterize mental illness phenomenology and detect symptom exacerbation. Large language models (LLMs) offer new possibilities for automating this process, as they require annotated data primarily for evaluation rather than training. In this paper, we present a novel automated, multi-agent LLM pipeline for the fine-grained, multi-label extraction of language suggestive of delusional beliefs, associated affective responses, and behavioral responses from transcripts of naturalistic audio diaries collected from people with moderate persecutory ideation. Evaluating an ensemble of three foundation models, we demonstrate that detailed diagnostic prompt instructions successfully reduce false positives for delusional theme classification, but also constrain the interpretation of affective or behavioral responses. Furthermore, comparing multi-agent adjudication frameworks shows that complex conversational debate between agents diminishes accuracy on clinically ambiguous text by inducing premature consensus. Instead, majority voting establishes robust performance (Micro F1 of 0.872 and 0.779 for delusion detection and classification respectively). This work provides a validated and scalable pipeline for the automated detection and characterization of content suggesting delusional beliefs in naturalistic speech.", "date": "2026-05-23", "categories": []}
{"id": "2605.24912", "title": "Explainable Retinal Imaging for Prediction of Multi-Organ Dysfunction in Type 2 Diabetes", "abstract": "Background: Type 2 diabetes mellitus (T2DM) is increasingly recognised as a systemic disease characterised by coordinated dysfunction across metabolic, renal, lipid, and inflammatory pathways. Existing clinical assessments often fail to capture this multi-dimensional burden.\n  Methods: We conducted a retrospective study of 1,195 patients using routinely collected laboratory biomarkers. System-level abnormality indices were constructed to quantify organ-specific dysfunction, and multi-system involvement was defined as abnormalities in two or more systems. Supervised machine learning models, including logistic regression, random forest, and gradient boosting, were trained to predict multi-system dysregulation. Model interpretability was achieved using SHapley Additive exPlanations (SHAP).\n  Results: The gradient boosting model demonstrated near-perfect discrimination (AUC = 1.000), significantly outperforming logistic regression (AUC = 0.925). Feature attribution analysis revealed that hyperglycaemia, renal impairment, dyslipidaemia, and inflammation were the dominant drivers of multi-system risk. Dose-response relationships observed in partial dependence analyses further supported the biological plausibility of model predictions.\n  Conclusion: This study presents an interpretable, data-driven framework for quantifying systemic disease burden in T2DM. By linking routine biomarkers to multi-organ dysfunction, our approach provides both predictive accuracy and mechanistic insight, offering potential for improved risk stratification and precision medicine in diabetes care.\n  The data and code used in this study are openly available on GitHub at: https://github.com/MiniHanWang/Type-2-Diabetes-1.git", "date": "2026-05-24", "categories": []}
{"id": "2605.24961", "title": "MedMamba: Multi-View State Space Models with Adaptive Graph Learning for Medical Time Series Classification", "abstract": "Medical time series are central to healthcare, enabling continuous monitoring and supporting timely clinical decisions. Despite recent progress, existing methods struggle to jointly model local-global dynamics and handle nonstationarities like baseline drift, while often failing to capture latent channel interactions. To address these challenges, we propose MedMamba, an end-to-end architecture that integrates state space models with domain-specific inductive biases. Specifically, MedMamba first employs multi-scale convolutional embeddings to capture discriminative local morphology. Second, to mitigate nonstationarity, we introduce a tri-branch differential state space encoder that processes raw, temporal-difference, and frequency-domain views, fusing them to emphasize informative patterns while suppressing drift. Furthermore, to uncover latent channel correlations, we design a spatial graph Mamba module that learns a directed dependency structure regularized toward sparsity and acyclicity, which obviates the need for predefined graphs. Extensive experiments on five real-world datasets demonstrate that MedMamba achieves state-of-the-art performance while maintaining linear computational complexity, and ablation studies validate each component's contribution.Code is available at https://github.com/zhangda1018/MedMamba.", "date": "2026-05-24", "categories": []}
{"id": "2605.25050", "title": "Multimodality Stacking with Blockwise missing values and application to the PIONeeR biomarkers study for prediction of resistance to immunotherapy", "abstract": "Integrating multimodal datasets in clinical oncology is frequently hindered by high dimensionality and blockwise missingness, where entire data sources are unavailable for specific patient subsets. Standard survival models often struggle with these gaps, leading to biased results or patient exclusion.\n  We introduce Multimodality Stacking with Blockwise missing values (MSB), a late-fusion framework for survival analysis that independently models modality-specific features before aggregating predictions via a cross-validated stacking meta-learner. MSB was validated on the PIONeeR study (n=443 patients, 378 biomarkers across eight heterogeneous sources) to predict progression-free survival in advanced non-small cell lung cancer patients receiving immunotherapy.\n  MSB yielded higher predictive performance (C-index) than baseline algorithms. Improvements varied by baseline strength: linear models showed a 15.9% increase (p<0.001 for the Wilcoxon signed-rank test), random survival forests gained 5.4% (p=0.002), and gradient boosting methods improved by 2.1% (p=0.030). Beyond discrimination, MSB reduced the generalization gap (train-test difference in 5 folds cross-validation repeated 3 times: 0.055 vs 0.380 for linear models). Permutation importance analysis identified routine laboratory markers, clinical features, and PD-L1 expression as primary predictive drivers. Missing block indicators showed negligible importance, suggesting the model learned from biomarker values rather than data availability patterns.\n  MSB provides a statistically validated framework for multimodal survival prediction with blockwise missingness. By enabling systematic biomarker evaluation without requiring complete data, MSB offers a practical tool for predictive modeling in biomedical research, pending external validation. Implementation is available at https://github.com/MohamedBoussena/MSB under Inria license.", "date": "2026-05-24", "categories": []}
{"id": "2605.25446", "title": "A Signal-Language Foundation Model for Broad-Spectrum Cardiovascular Assessment from Routine Electrocardiography", "abstract": "Electrocardiography (ECG) is central to cardiovascular care, but conventional AI models are often restricted to common arrhythmias and may generalize poorly across populations or clinically subtle diseases. We developed ECG Contrastive Language-Image Pre-training (ECGCLIP), a signal-language contrastive learning framework that aligns ECG waveforms with expert diagnostic reports. ECGCLIP was pre-trained on 2,837,962 ECG studies from 1,324,856 patients and evaluated on a held-out internal test set plus nine independent external cohorts comprising about 1.5 million ECGs. Evaluation covered 89 downstream tasks, including 45 ECG diagnoses, 39 echocardiographic targets, and 5 rare cardiac diseases, using PRAUC as the primary metric. ECGCLIP consistently improved performance over random initialization and Merl-R18 baselines. On the internal test set, ECGCLIP-R34 achieved strong performance for atrial fibrillation (PRAUC 0.900) and ST-segment elevation myocardial infarction (PRAUC 0.383), with robust generalization across all external cohorts. It also improved low-prevalence and diagnostically elusive diseases, including Ebstein anomaly, constrictive pericarditis, dextrocardia, and cardiac amyloidosis, with internal PRAUC values of 0.253, 0.175, 0.121, and 0.201, respectively. ECGCLIP was data efficient, matching or exceeding full-dataset baseline performance with only 10% of training data. Feature visualization and saliency analysis suggested clinically meaningful representations aligned with established electrocardiographic criteria. These findings indicate that large-scale ECG-report contrastive pre-training can expand routine ECG interpretation beyond common arrhythmias toward broad cardiovascular assessment and opportunistic screening of echocardiographic and rare conditions.", "date": "2026-05-25", "categories": []}
{"id": "2605.25595", "title": "How Far Has AI Come in Liver Fibrosis Staging? A Large-Scale Real-World Dataset and Benchmark", "abstract": "Despite years of methodological progress, how far AI has come in liver fibrosis staging has never been systematically evaluated under the heterogeneous, multi-center conditions that define clinical practice. To address this gap, we introduce LiFS, a large-scale dataset and benchmark derived from the MICCAI 2025 CARE-Liver challenge, comprising 610 patients across multiple centers and scanners with multi-sequence MRI. To the best of our knowledge, LiFS is the first benchmark providing complete gadoxetic acid-enhanced sequences with histopathology-confirmed annotations from diverse real-world scanners. Through systematic evaluation of 9 independently developed methods selected from 96 registered teams against in-cohort radiologist reference results, our findings address how far current AI has progressed toward clinical-level liver fibrosis staging from three complementary perspectives. First, against radiologists, the best AI methods were broadly comparable to the senior radiologist and significantly exceeded the junior radiologist in selected settings, while median AI performance generally approached junior-radiologist levels. Second, from a data perspective, cross-center heterogeneity, label imbalance, and contrast-enhanced sequence variability emerge as the dominant challenges for AI methods. Third, from a technical perspective, methodological design choices, including spatial registration, input dimensionality, multi-modal fusion strategy, and backbone architecture, appear to modulate cross-center robustness, although no single choice alone closes the gap. Overall, LiFS provides a rigorous real-world benchmark for positioning the current state of AI in liver fibrosis staging and for enabling future research on the key challenges that limit clinically reliable deployment.", "date": "2026-05-25", "categories": []}
{"id": "2605.25878", "title": "A Clinically Validated Foundation Model for Comprehensive Lung Pathology Interpretation", "abstract": "Pathological assessment guides lung cancer diagnosis, treatment selection, and prognostic evaluation, yet current CPath approaches rely on task-specific models for isolated objectives. Although pan-cancer foundation models offer versatility, they lack subspecialty-level depth and have not been evaluated across clinical workflows or prospectively validated in real-world settings. We introduce PulmoFoundation, a multi-center, prospectively validated, randomized controlled trial (RCT)-evaluated foundation model for comprehensive lung pathology assessment across pre-operative, intra-operative, and post-operative care. Built upon Virchow2 via subspecialty-specific pretraining using ~40,000 diagnostic H&E-stained whole-slide images (WSIs), PulmoFoundation was systematically evaluated on ~26,000 WSIs across 32 clinically relevant tasks. In addition to accurately predicting molecular markers and patient survival, our model achieves clinical-grade performance in core diagnostic tasks across biopsy, frozen section, and surgical resection slides. In a registered prospective study of 1,357 patients across 11 diagnostic tasks, our model achieved an average AUC of 92.3%. Using pre-specified triage thresholds, PulmoFoundation could reduce additional second-review burden for 68.8% of biopsies and 83.0% of frozen sections, and defer 44.5% of IHC stain orders, with PPVs of 1.0, 0.991, and 0.966. Beyond prospective validation, we conducted a crossover RCT with eight pathologists, in which AI assistance improved diagnostic accuracy across 4,928 case-reader pairs (91.7% w/ AI vs. 83.8% w/o AI). AI assistance also reduced median diagnostic time by 19.6%, increased diagnostic confidence by 8.7%, and improved inter-rater agreement from moderate (kappa = 0.56) to substantial (kappa = 0.76). Together, these evaluations support PulmoFoundation as a clinically validated decision-support system for lung pathology.", "date": "2026-05-25", "categories": []}
{"id": "2605.26007", "title": "Forgotten Words: Benchmarking NeoBERT for Dementia Detection in Low-Resource Conversational Filipino and English Speech", "abstract": "Dementia detection from spontaneous speech offers a scalable approach to cognitive screening, yet NLP systems remain predominantly English-centric. This limitation is especially acute in the Philippines, where Filipino-English code-switching is pervasive and no prior work has addressed NLP-based dementia detection. We present the first systematic evaluation of transformer-based dementia detection in Filipino speech and the first assessment of NeoBERT in a clinical NLP setting. To separate language from domain effects, we construct a parallel bilingual dataset of 4,000 DementiaBank-derived transcripts, with Filipino translations produced manually to preserve discourse-level markers of cognitive decline. We evaluate five model families, TF-IDF + LogReg, BERT, NeoBERT, XLM-R, and RoBERTa-Tagalog, under monolingual, zero-shot cross-lingual, and bilingual fine-tuning settings. We find that in-domain performance does not transfer across languages, with English-trained BERT dropping to Macro-F1 = 0.455 on Filipino, and that architectural modernization alone does not improve robustness. Bilingual fine-tuning, however, eliminates cross-lingual degradation across all transformer models, converging to Macro-F1 = 0.969-0.973. These results suggest that multilingual clinical NLP performance is driven primarily by linguistic coverage during training rather than model scale or architecture.", "date": "2026-05-25", "categories": []}
{"id": "2505.03784", "title": "Insulin Resistance Prediction From Wearables and Routine Blood Biomarkers", "abstract": "Insulin resistance, a precursor to type 2 diabetes, is characterized by impaired insulin action in tissues. Current methods for measuring insulin resistance, while effective, are expensive, inaccessible, not widely available and hinder opportunities for early intervention. In this study, we remotely recruited the largest dataset to date across the US to study insulin resistance (N=1,165 participants, with median BMI=28 kg/m2, age=45 years, HbA1c=5.4%), incorporating wearable device time series data and blood biomarkers, including the ground-truth measure of insulin resistance, homeostatic model assessment for insulin resistance (HOMA-IR). We developed deep neural network models to predict insulin resistance based on readily available digital and blood biomarkers. Our results show that our models can predict insulin resistance by combining both wearable data and readily available blood biomarkers better than either of the two data sources separately (R2=0.5, auROC=0.80, Sensitivity=76%, and specificity 84%). The model showed 93% sensitivity and 95% adjusted specificity in obese and sedentary participants, a subpopulation most vulnerable to developing type 2 diabetes and who could benefit most from early intervention. Rigorous evaluation of model performance, including interpretability, and robustness, facilitates generalizability across larger cohorts, which is demonstrated by reproducing the prediction performance on an independent validation cohort (N=72 participants). Additionally, we demonstrated how the predicted insulin resistance can be integrated into a large language model agent to help understand and contextualize HOMA-IR values, facilitating interpretation and safe personalized recommendations. This work offers the potential for early detection of people at risk of type 2 diabetes and thereby facilitate earlier implementation of preventative strategies.", "date": "2025-04-30", "categories": []}
{"id": "2512.20298", "title": "Patterns vs. Patients: Evaluating LLMs against Mental Health Professionals on Personality Disorder Diagnosis through First-Person Narratives", "abstract": "Growing reliance on LLMs for psychiatric self-assessment raises questions about their ability to interpret qualitative patient narratives. This depth over breadth case study directly compares state-of-the-art LLMs and mental health professionals in assessing Borderline (BPD) and Narcissistic (NPD) Personality Disorders based on Polish-language first-person autobiographical accounts. Within our sample, the overall diagnostic scores of the top-performing Gemini Pro models (65.48%) were 21.91 percentage points higher than the average scores of the human professionals (43.57%). While both models and human experts excelled at identifying BPD (F1 = 83.4 & F1 = 80.0, respectively), models severely underdiagnosed NPD (F1 = 6.7 vs. 50.0), showing a potential reluctance toward the value-laden term \"narcissism.\" Qualitatively, models provided confident, elaborate justifications focused on patterns and formal categories, while human experts remained concise and cautious, emphasizing the patients' sense of self and temporal experience. Our findings demonstrate that while LLMs might be competent at interpreting complex first-person clinical data, their outputs still carry critical reliability and bias issues.", "date": "2025-12-23", "categories": []}
{"id": "2603.02719", "title": "An Empirical Analysis of Calibration and Selective Prediction in Multimodal Clinical Condition Classification", "abstract": "As artificial intelligence systems move toward clinical deployment, ensuring reliable prediction behavior is fundamental for safety-critical decision-making tasks. One proposed safeguard is selective prediction, where models can defer uncertain predictions to human experts for review. In this work, we empirically evaluate the reliability of uncertainty-based selective prediction in multilabel clinical condition classification using multimodal ICU data. Across a range of state-of-the-art unimodal and multimodal models, we find that selective prediction can substantially degrade performance despite strong standard evaluation metrics. This failure is driven by severe class-dependent miscalibration, whereby models assign high uncertainty to correct predictions and low uncertainty to incorrect ones, particularly for underrepresented clinical conditions. Our results show that commonly used aggregate metrics can obscure these effects, limiting their ability to assess selective prediction behavior in this setting. Taken together, our findings characterize a task-specific failure mode of selective prediction in multimodal clinical condition classification and highlight the need for calibration-aware evaluation to provide strong guarantees of safety and robustness in clinical AI.", "date": "2026-03-03", "categories": []}
{"id": "2604.06885", "title": "Time-driven Survival Analysis from FDG-PET/CT in Non-Small Cell Lung Cancer", "abstract": "Purpose: Automated medical image-based prediction of clinical outcomes, such as overall survival (OS), has great potential in improving patient prognostics and personalized treatment planning. We developed a deep regression framework using tissue-wise FDG-PET/CT projections as input, along with a temporal input representing a scalar time horizon (in days) to predict OS in patients with Non-Small Cell Lung Cancer (NSCLC).\n  Methods: The proposed framework employed a ResNet-50 backbone to process input images and generate corresponding image embeddings. The embeddings were then combined with temporal data to produce OS probabilities as a function of time, effectively parameterizing the predictions based on time. The overall framework was developed using the U-CAN cohort (n = 556) and evaluated by comparing with a baseline method on the test set (n = 292). The baseline utilized the ResNet-50 architecture, processing only the images as input and providing OS predictions at pre-specified intervals, such as 2- or 5-year.\n  Results: The incorporation of temporal data with image embeddings demonstrated an advantage in predicting OS, outperforming the baseline method with an improvement in AUC of 4.3%. The proposed model using clinical + IDP features achieved strong performance, and an ensemble of imaging and clinical + IDP models achieved the best overall performance (0.788), highlighting the complementary value of multimodal inputs. The proposed method also enabled risk stratification of patients into distinct categories (high vs low risk). Heat maps from the saliency analysis highlighted tumor regions as key structures for the prediction.\n  Conclusion: Our method provided an automated framework for predicting OS as a function of time and demonstrates the potential of combining imaging and tabular data for improved survival prediction.", "date": "2026-04-08", "categories": []}
{"id": "2604.11679", "title": "Towards Brain MRI Foundation Models for the Clinic: Findings from the FOMO25 Challenge", "abstract": "Clinical deployment of automated brain MRI analysis faces a fundamental challenge: clinical data is heterogeneous and noisy, and high-quality labels are prohibitively costly to obtain. Self-supervised learning (SSL) can address this by leveraging the vast amounts of unlabeled data produced in clinical workflows to train robust \\textit{foundation models} that adapt out-of-domain with minimal supervision. However, the development of foundation models for brain MRI has been limited by small pretraining datasets and in-domain benchmarking focused on high-quality, research-grade data. To address this gap, we organized the FOMO25 challenge as a satellite event at MICCAI 2025. FOMO25 provided participants with a large pretraining dataset, FOMO60K, and evaluated models on data sourced directly from clinical workflows in few-shot and out-of-domain settings. Tasks covered infarct classification, meningioma segmentation, and brain age regression, and considered both models trained on FOMO60K (method track) and any data (open track). Nineteen foundation models from sixteen teams were evaluated using a standardized containerized pipeline. Results show that (a) self-supervised pretraining improves generalization on clinical data under domain shift, with the strongest models trained \\textit{out-of-domain} surpassing supervised baselines trained \\textit{in-domain}. (b) No single pretraining objective benefits all tasks: MAE favors segmentation, hybrid reconstruction-contrastive objectives favor classification, and (c) strong performance was achieved by small pretrained models, and improvements from scaling model size and training duration did not yield reliable benefits.", "date": "2026-04-13", "categories": []}
{"id": "2605.22833", "title": "RAG4Outcome: A Retrieval-Augmented Multimodal Framework for Prognostic Prediction in Chronic Osteomyelitis", "abstract": "Chronic osteomyelitis presents substantial prognostic challenges due to its high recurrence risk and complex postoperative recovery trajectories. Traditional assessment often relies on manual scoring systems, which limit scalability, efficiency, and consistency in clinical practice. Furthermore, the heterogeneous nature of clinical data poses challenges for current multimodal learning approaches that require aligned inputs and large annotated datasets. In this work, we propose RAG4Outcome, a retrieval-augmented generation (RAG) framework for prognostic prediction in chronic osteomyelitis. Our method integrates multimodal clinical data, including PET-CT imaging reports, structured surgical and diagnostic records, and unstructured follow-up notes, into a unified prediction pipeline. By combining a domain-specific retrieval corpus with expert-guided prompting, the framework enables more interpretable, evidence-grounded, and clinically reliable prognosis. Preliminary results on real-world cases demonstrate promising effectiveness and clinical alignment, highlighting the potential of RAG4Outcome for AI-assisted infection management and postoperative decision support.", "date": "2026-04-24", "categories": []}
{"id": "2605.22858", "title": "Classification of IED-free EEG Responses for Assisted Epilepsy Diagnosis", "abstract": "Diagnosing epilepsy is challenging when routine EEGs lack interictal epileptiform discharges (IEDs). Intermittent photic stimulation (IPS) and hyperventilation (HV) can increase diagnostic yield, but their interpretation is subjective. We propose a reproducible pipeline that classifies EEG recordings acquired during stimulation procedures, using machine-learning features spanning temporal, spectral, wavelet, and connectivity domains, and a stacked ensemble to combine complementary feature sets. Performance is evaluated with leave-one-subject-out (LOSO) cross-validation on the TUH Epilepsy Corpus and a clinical Erasmus MC (EMC) cohort, including IED-free analyses on TUH. On TUH, ensembles achieve up to 97.8\\% AUC / 93.1\\% BAC on IED-free resting-state EEG and 94.1\\% AUC / 86.8\\% BAC on IED-free IPS. On EMC, IPS provides the strongest discrimination (79.4\\% AUC / 73.9\\% BAC), while HV performance benefits from stratifying subjects by responsiveness. These results indicate that stimulation-evoked activity, particularly IPS, contains meaningful discriminative information for IED-free epilepsy classification and that multi-domain ensembling improves robustness.", "date": "2026-05-19", "categories": []}
{"id": "2605.22968", "title": "Uncertainty-aware classification and triage of structural heart disease using electrocardiography and echocardiography metrics", "abstract": "Machine learning methods provide a methodological innovation that can help screen for cardiovascular disease through noninvasive and readily available measurement modalities. Recent investments in using electrocardiogram (ECG) data to screen for structural heart disease (SHD) are one example, where ECGs provide a low-cost, available modality for screening. This has led to the EchoNext dataset, a paired ECG-echocardiogram data repository for testing new methods of SHD detection. However, relatively few studies have investigated how more probabilistic classification through Bayesian inference may improve uncertainty quantification in this setting. Moreover, few studies have considered how triage systems can be developed to alleviate healthcare bottlenecks, such as the review of data from underserved, rural clinics by expert sonographers for SHD assessment. In this study, we leverage existing ECG-echocardiogram data to compare frequentist and Bayesian neural network classifiers. We show that the Bayesian approach is comparable or better than frequentist methods in SHD classification, and that they have a more robust uncertainty quantification attached to them. We provide an example of how this uncertainty-aware classification scheme can be used for screening SHD, providing a proof-of-concept for how machine learning can help with triage in getting individuals expert sonographer input when SHD is highly likely or measurements are highly uncertain.", "date": "2026-05-21", "categories": []}
{"id": "2512.16739", "title": "AI-Driven Prediction of Cancer Pain Episodes: A Hybrid Decision Support Approach", "abstract": "Lung cancer patients frequently experience breakthrough pain episodes, with up to 91% requiring timely intervention. To enable proactive pain management, we propose a hybrid machine learning and large language model pipeline that predicts pain episodes within 48 and 72 hours of hospitalization using both structured and unstructured electronic health record data. A retrospective cohort of 266 inpatients was analyzed, with features including demographics, tumor stage, vital signs, and WHO-tiered analgesic use. The machine learning module captured temporal medication trends, while the large language model interpreted ambiguous dosing records and free-text clinical notes. Integrating these modalities improved sensitivity and interpretability. Our framework achieved an accuracy of 0.876 (48h) and 0.917 (72h), with improvements in sensitivity of 10.6% and 10.7%, respectively, attributable to large language model augmentation. This hybrid approach offers a clinically interpretable and scalable tool for early pain episode forecasting, with potential to enhance treatment precision and optimize resource allocation in oncology care.", "date": "2025-12-18", "categories": []}
{"id": "2602.05286", "title": "HealthMamba: An Uncertainty-aware Spatiotemporal Graph State Space Model for Effective and Reliable Healthcare Facility Visit Prediction", "abstract": "Healthcare facility visit prediction is essential for optimizing healthcare resource allocation and informing public health policy. Despite advanced machine learning methods being employed for better prediction performance, existing works usually formulate this task as a time-series forecasting problem without considering the intrinsic spatial dependencies of different types of healthcare facilities, and they also fail to provide reliable predictions under abnormal situations such as public emergencies. To advance existing research, we propose HealthMamba, an uncertainty-aware spatiotemporal framework for accurate and reliable healthcare facility visit prediction. HealthMamba comprises three key components: (i) a Unified Spatiotemporal Context Encoder that fuses heterogeneous static and dynamic information, (ii) a novel Graph State Space Model called GraphMamba for hierarchical spatiotemporal modeling, and (iii) a comprehensive uncertainty quantification module integrating three uncertainty quantification mechanisms for reliable prediction. We evaluate HealthMamba on four large-scale real-world datasets from California, New York, Texas, and Florida. Results show HealthMamba achieves around 6.0% improvement in prediction accuracy and 3.5% improvement in uncertainty quantification over state-of-the-art baselines.", "date": "2026-02-05", "categories": []}
{"id": "2605.21563", "title": "Embedding-Based Federated Learning with Runtime Governance for Iron Deficiency Prediction", "abstract": "Recent reviews find that the vast majority of published healthcare federated learning (FL) studies never reach real-world deployment. We developed an embedding-based FL pipeline for iron deficiency prediction from routine full blood count (FBC) data and deployed it across real institutional environments at Amsterdam University Medical Centre (AUMC) and NHS Blood and Transplant (NHSBT), two clinical environments that differ markedly in iron deficiency prevalence, ferritin distribution, and subject populations. A frozen domain-specific haematology foundation model, DeepCBC, performs site-local representation extraction, restricting federated training to a compact downstream classifier and substantially reducing recurrent communication relative to full-encoder federation. The two clinical datasets are structurally not independent and identically distributed (non-IID), with heterogeneity arising from distinct population differences rather than sampling artefacts. Runtime governance is enforced by FLA$^3$, a healthcare-oriented FL platform providing study-scoped execution, policy-based authorisation, and signed audit logging. Standard sample-size-weighted aggregation (FedAvg) reduced the area under the receiver operating characteristic curve (ROC-AUC) at both sites relative to local-only training, as the global update was biased towards the larger AUMC distribution. FedMAP, a personalised aggregation method, raised ROC-AUC from 0.9470 to 0.9594 at AUMC and from 0.8558 to 0.8671 at NHSBT relative to local-only training, achieving the highest macro ROC-AUC of 0.9133 and the best macro balanced accuracy overall. These results support personalised aggregation in clinical federations where client sample size and task relevance diverge substantially.", "date": "2026-05-20", "categories": []}
{"id": "2605.21566", "title": "Calibration, Uncertainty Communication, and Deployment Readiness in CKD Risk Prediction: A Framework Evaluation Study", "abstract": "Machine learning models for chronic kidney disease (CKD) risk prediction often post strong discrimination scores on internal test sets. Calibration and uncertainty quantification get far less attention, leaving clinicians without reliable information about whether the probability outputs are accurate.\n  We trained five classifiers on the UCI CKD dataset (400 patients, 62.5% CKD prevalence): logistic regression, random forest, XGBoost, SVM with Platt scaling, and Gaussian naive Bayes. We evaluated each across calibration quality, conformal prediction coverage, and an eight-criterion deployment readiness framework. A distributional stress-test applied the best-calibrated variant of each model to the open-access MIMIC-IV demo cohort (97 patients, 23.7% CKD) to assess behaviour under prevalence shift and feature missingness. We measured calibration before and after Platt scaling and isotonic regression using Expected Calibration Error and Brier Score, and quantified uncertainty through split conformal prediction targeting 90% marginal coverage.\n  All five models reached AUROC 1.00 on the UCI test set. Isotonic recalibration reduced internal ECE to 0.000-0.022. On MIMIC-IV, AUROC fell to 0.48-0.58, ECE rose to 0.68-0.76, and conformal coverage dropped from 0.80-0.98 to 0.21-0.25 against a 90% target. No model scored above 4 out of 16 on the deployment readiness checklist.\n  Near-perfect internal performance did not transfer. Calibration stability and conformal coverage should be evaluated on external data before any clinical prediction model moves toward deployment.", "date": "2026-05-20", "categories": []}
{"id": "2605.21745", "title": "Quantitative coronary calcification analysis for prediction of myocardial ischemia using non-contrast CT calcium scoring", "abstract": "Non-contrast computed tomography calcium scoring (CTCS) is widely recognized as an effective tool for cardiovascular risk stratification. This study aimed to develop a novel machine learning framework for predicting myocardial ischemia from routine non-contrast CTCS scans using quantitative coronary calcium assessment. This study analyzed 1,375 patients who underwent both non-contrast CTCS and regadenoson stress cardiac positron emission tomography myocardial perfusion imaging within one year at University Hospitals Cleveland Medical Center. A total of 74 variables, including clinical variables, Agatston score, and calcium-omics features, were evaluated. Relevant features were identified using XGBoost with Shapley Additive exPlanations (SHAP). Predictive models were trained and evaluated using 5-fold cross-validation. Among 987 patients, 89 (9%) were positive for myocardial ischemia. The final model incorporated the Agatston score, eight calcium-omics features, and age. The proposed model achieved a precision of 98.9+/-3.0%, sensitivity of 79.2+/-8.4, and F1 score of 87.7+/-5.3%. The addition of calcium-omics features significantly improved predictive performance compared with models using clinical variables alone or clinical variables with the Agatston score (p<0.05). Interestingly, the number of calcified arteries, despite being the lowest-ranked feature based on SHAP analysis, showed the strongest association with myocardial ischemia in logistic regression analysis (odds ratio: 3.63, 95% confidence interval: 2.80-4.77, p<0.00001). We developed a machine learning approach for predicting myocardial ischemia using routinely acquired non-contrast CTCS scans. Calcium-omics features provided incremental predictive value beyond conventional risk factors and Agatston scoring and may support more accessible cardiovascular risk stratification.", "date": "2026-05-20", "categories": []}
{"id": "2605.21762", "title": "Machine learning prediction of obstructive coronary artery disease using opportunistic coronary calcium and epicardial fat assessments from CT calcium scoring scans", "abstract": "Non-contrast computed tomography calcium scoring (CTCS) is a cost-effective imaging modality widely used to detect coronary artery calcifications. This study aimed to develop an advanced machine learning framework that utilizes quantitative analyses of coronary calcium and epicardial fat from CTCS images to predict obstructive coronary artery disease (CAD). The study population consisted of 1,324 patients from the SCOT-HEART clinical trial who underwent both CTCS and coronary CT angiography. We extracted and analyzed a broad range of features, including 24 clinical variables, 189 calcium-omics, and 211 epicardial fat-omics features from the CTCS images. Feature selection was conducted using the CatBoost algorithm combined with SHapley Additive exPlanation (SHAP) values. Predictive modeling utilized the CatBoost gradient boosting method, focusing on the most informative features. From an initial set of 424 candidate features, 14 were identified as most predictive through the CatBoost-SHAP method. The top two predictive features originated from fat-omics, with the remaining 12 features derived from calcium-omics. The optimized model achieved robust predictive capabilities, demonstrating a sensitivity of 83.1+/-4.6%, specificity of 93.8+/-1.7%, accuracy of 85.3+/-2.0%, and an F1 score of 73.9+/-3.3%. Inclusion of calcium-omics and fat-omics data significantly improved predictive performance. Notably, the model also showed reliable predictive accuracy in patients with diverse coronary calcium scores, including cases with obstructive CAD despite a zero-calcium score. This innovative approach holds promise for improving clinical decision-making and potentially reducing dependence on contrast-enhanced or invasive diagnostic procedures, particularly within low-to intermediate-risk patient groups.", "date": "2026-05-20", "categories": []}
{"id": "2605.22075", "title": "Can Breath Biomarkers Causally Influence Blood Glucose? Investigating VOC-Mediated Modulation in Diabetes", "abstract": "Diabetes is a global health burden, and early detection is critical for timely intervention. This study explores a non-invasive, data-driven framework to identify individuals at risk of diabetes using Volatile Organic Compounds (VOCs) and lifestyle variables. We use causal inference techniques to estimate the impact of VOCs such as acetone, isopropanol, isoprene, and ethanol on blood glucose levels. Additionally, we designed a classifier to distinguish diabetics from non-diabetics using non-invasive markers. We created a risk-based ranking system for individuals in the \"gray zone,\" and identified natural clusters in the population using Gaussian Mixture Model. Our results suggest that specific VOCs exhibit a strong causal influence on glucose levels and that machine learning models can reliably classify and stratify individuals at high risk. This integrated causal-explainable analysis can support the development of tool for non-invasive early screening of diabetes.", "date": "2026-05-21", "categories": []}
{"id": "2605.22286", "title": "EmoTrack: Robust Depression Tracking from Counseling Transcripts across Session Regimes", "abstract": "Text-based counseling is an important interface for AI mental-health support, where transcripts may be used to monitor depression severity and flag sessions requiring timely human review. However, robust PHQ-8 prediction across session regimes remains challenging: fine-tuning-based methods can exploit richer supervision but may generalize poorly under data scarcity, while prompt-based LLM methods are data-efficient but usually treat each transcript holistically and provide limited support for longitudinal context. We study robust depression tracking from counseling transcripts across single-session and multi-session regimes. We introduce LongCounsel, a multi-session counseling dataset with session-level PHQ-8 supervision for evaluating repeated-session tracking under partial symptom disclosure and cross-session continuity. We further propose EmoTrack, a PHQ-8 prediction framework that combines LLM-extracted clinical signals with frozen turn-level semantic embeddings and trains symptom-specific predictors over the resulting transcript representation. When prior sessions are available, EmoTrack can further incorporate them through compact cross-session memory. Experiments on LongCounsel and DAIC-WOZ show that EmoTrack achieves a clear gain on the real single-session benchmark, including a 13.5% relative MAE reduction over the strongest DAIC-WOZ baseline, and remains competitive with the strongest longitudinal baseline on LongCounsel.", "date": "2026-05-21", "categories": []}
{"id": "2605.22611", "title": "Benchmarking Machine Learning Architectures for Antimicrobial Stewardship in Pediatric ICUs", "abstract": "Antimicrobial stewardship (AMS) is critical in pediatric intensive care units (PICUs), where diagnostic uncertainty often drives broad-spectrum antibiotic use, increasing antimicrobial resistance and potential long-term harms. Machine learning offers a promising approach for identifying patient-level opportunities for stewardship interventions from electronic health record data, yet prior work has focused largely on adult populations and static tabular representations. We present a systematic benchmarking study of AMS intervention prediction in the PICU across a public dataset and a private institutional cohort. We define four clinically relevant proxy targets for reducing antibiotic exposure: intravenous-to-oral switching, de-escalation, discontinuation, and short-course therapy. Under a unified evaluation framework, we compare tabular, sequence-based, and graph-based temporal models at multiple temporal resolutions. We find that predictive performance is driven primarily by target prevalence and dataset characteristics rather than model complexity. Sequence models improve the precision-recall trade-off over tabular approaches at coarse (24-hour) resolution, while finer temporal modeling provides limited additional benefit. However, these gains come at the cost of poorer calibration, with simpler tabular models yielding more reliable probability estimates. Multi-task learning produces only marginal improvements, suggesting limited shared structure across stewardship targets. Our findings highlight the importance of target design, temporal representation, and calibration in clinical machine learning, and provide practical guidance for developing reliable decision support systems for pediatric AMS.", "date": "2026-05-21", "categories": []}
{"id": "2503.08292", "title": "Do LLMs Triage Like Clinicians? A Dynamic Study of Outpatient Referral", "abstract": "Outpatient referral (OR) is a core clinical workflow that assigns patients to hospital departments under incomplete and evolving information, yet it is commonly simplified as a static classification problem despite being inherently interactive in practice. In this work, we study outpatient referral as a dynamic process driven by information acquisition and uncertainty reduction. We analyze both static scenarios based on fixed patient information and dynamic scenarios involving multi-turn dialogue, to test whether large language models (LLMs) improve referral outcomes through better prediction or more effective questioning. Our findings show that LLMs offer limited advantages over traditional classifiers in static referral accuracy, but consistently outperform them in dynamic settings by asking discriminative follow-up questions that reduce uncertainty over candidate departments. These results suggest that the primary value of LLMs in outpatient referral lies not in static prediction, but in supporting interactive, uncertainty-aware clinical decision-making.", "date": "2025-03-11", "categories": []}
{"id": "2604.27195", "title": "Evaluating TabPFN for Mild Cognitive Impairment to Alzheimer's Disease Conversion in Data Limited Settings", "abstract": "Accurate prediction of conversion from Mild Cognitive Impairment (MCI) to Alzheimers Diseases (AD) is essential for early intervention, however, developing reliable conversion predictive models is difficult to develop due to limited longitudinal data availability We evaluate TabPFN (Tabular Pre-Trained Foundation Network) against traditional machine learning methods for predicting 3 year MCI to AD conversion using the TADPOLE dataset derived from ADNI. Using multimodal biomarker features extracted from demographics, APOE4, MRI volumes, CSF markers, and PET imaging, we conducted an experimental comparison across varying training set sizes (N=50 to 1000) and models including XGBoost, Random Forest, LightGBM, and Logistic Regression. TabPFN achieved one the highest performance (AUC=0.892), outperforming LightGBM (AUC=0.860) and demonstrating advantages in low data settings. At N=50 training samples, TabPFN maintained strong AUC while the traditional machine learning models struggles at small training samples. These findings demonstrate that foundation models are promising for disease prediction in data limited scenarios, such as Alzheimers diseases.", "date": "2026-04-29", "categories": []}
{"id": "2605.20292", "title": "TreeText-CTS: Compact, Source-Traceable Tree-Path Evidence for Irregular Clinical Time-Series Prediction", "abstract": "Numerical time-series models can effectively process irregular electronic health record (EHR) trajectories, but they do not naturally expose the measurements and temporal patterns supporting each risk estimate as readable evidence. Existing text-based interfaces improve readability, but typically rely on either raw serialization, which is lengthy and redundant, or patient-level free-form summaries, which are difficult to trace to source measurements and time windows. To bridge this gap, we introduce TreeText-CTS (Clinical Time-Series), which converts irregular EHR trajectories into human-readable, compact, source-traceable tree-path evidence units without patient-level summarization or inference-time autoregressive decoding. TreeText-CTS routes multi-scale window summaries through frozen XGBoost models and verbalizes activated tree paths as deterministic, source-traceable evidence units composed of threshold conditions. An evidence selector assembles an informative subset of these units, which a language-model encoder then integrates for prediction. Across PhysioNet 2012 mortality, MIMIC-III mortality, and PhysioNet 2019 sepsis-onset forecasting, TreeText-CTS achieves the best AUROC and AUPRC among evaluated text-based EHR time-series interfaces, improving AUPRC by 6.0 to 9.7 absolute percentage points over the strongest prior text-based interface while remaining competitive with numerical time-series models. Ablations show that tree-path evidence construction, evidence selection, and language-model composition each contribute to performance. Because every span passed to the language-model encoder is constructed from activated tree-path threshold conditions, TreeText-CTS makes the evidence supplied to the final predictor inspectable and source-traceable.", "date": "2026-05-19", "categories": []}
{"id": "2605.20469", "title": "HalluCXR: Benchmarking and Mitigating Hallucinations in Medical Vision-Language Models for Chest Radiograph Interpretation", "abstract": "Vision-language models (VLMs) are increasingly used for medical image interpretation, yet they frequently hallucinate, generating clinically plausible but factually incorrect findings that pose direct patient safety risks. We introduce HalluCXR, a benchmark evaluating six architecturally diverse VLMs across 856 stratified MIMIC-CXR chest radiographs and three query types, yielding 15,408 model evaluations. An eight-category hallucination taxonomy with clinical severity ratings and a two-layer detection pipeline are validated against 250 human annotations (auto-detection F1=0.959; LLM judge F1=0.907). We find that 61.9--82.3% of outputs contain hallucinations, with clinically dangerous errors in up to 80.2%. Three key patterns emerge: normal radiographs paradoxically attract the most severe hallucinations, common findings are systematically over-fabricated while rare findings go under-detected, and response length alone predicts hallucination risk (AUC up to 0.908). A six-model ensemble reduces fabrication by up to 84.8% at the cost of increased omission; a three-model subset retains comparable performance at half the cost. These results establish that hallucination auditing, verbosity-based risk monitoring, and ensemble-based safety layers are prerequisites for clinical deployment.", "date": "2026-05-19", "categories": []}
{"id": "2605.20523", "title": "Machine-Learning-Enhanced Non-Invasive Testing for MASLD Fibrosis: Shallow-Deep Neural Networks Versus FIB-4, Tabular Foundation Models, and Large Language Models", "abstract": "Advanced fibrosis is a major determinant of liver-related morbidity in metabolic dysfunction-associated steatotic liver disease (MASLD). FIB-4 is widely used as a first-line non-invasive test, but its fixed formula may underuse diagnostic information contained in age, aspartate aminotransferase, alanine aminotransferase, and platelet count. We evaluated whether machine-learning-enhanced non-invasive testing (MLE-NIT) can improve advanced fibrosis detection while preserving this FIB-4 variable space.\n  We used three biopsy-confirmed MASLD cohorts from China, Malaysia, and India (n=784). The Chinese cohort was split into 486 training and 54 internal validation/tuning patients; final performance was reported only on the Malaysian and Indian external cohorts. Models used five variables: age, FIB-4, aspartate aminotransferase, platelet count, and alanine aminotransferase. We compared FIB-4 with a shallow-deep neural network (s-DNN), TabPFN, and gpt-4o-2024-08-06.\n  FIB-4 achieved external ROC-AUCs of 0.75 and 0.60 in Malaysia and India, respectively. TabPFN achieved 0.69 and 0.66, fine-tuned GPT-4o achieved 0.75 and 0.63, and the s-DNN achieved 0.77 and 0.67, respectively. The s-DNN contained only 354 trainable parameters, compared with 7,244,554 for TabPFN, yet provided a more balanced external operating profile. Calibration showed s-DNN Brier scores of 0.18 and 0.22, and permutation importance identified AST and FIB-4 as dominant variables. Compact non-linear MLE-NITs may enhance FIB-4-based fibrosis assessment without increasing clinical data requirements.", "date": "2026-05-19", "categories": []}
{"id": "2605.21256", "title": "Reliable Automated Triage in Spanish Clinical Notes: A Hybrid Framework for Risk-Aware HIV Suspicion Identification", "abstract": "Standard clinical Natural Language Processing (NLP) benchmarks often yield inflated metrics by forcing deterministic classification on ambiguous instances, thereby obscuring the clinical risks of overconfident predictions. To bridge this gap, we propose a risk-aware hybrid selective classification framework, evaluated on early Human Immunodeficiency Virus suspicion identification in Spanish clinical notes. Our dual-verification approach explicitly decouples aleatoric uncertainty through Mondrian conformal prediction and epistemic uncertainty using a Multi-Centroid Mahalanobis Distance veto. Empirical evaluations reveal that standard uncertainty metrics and baseline classifiers are structurally insufficient for safe medical triage, suffering severe coverage collapse when forced to operate under strict reliability constraints. In contrast, by demanding that clinical narratives pass both probabilistic and geometric safeguards, the proposed framework successfully isolates a highly trustworthy operational domain.", "date": "2026-05-20", "categories": []}
{"id": "2504.08381", "title": "An Empirical Investigation of Reconstruction-Based Models for Seizure Prediction from ECG Signals", "abstract": "Epileptic seizures are transient neurological events characterized by abnormal and excessive neuron activity in the brain, which are often associated with measurable disturbances in the cardiovascular system. Traditionally, electroencephalogram (EEG) signals have served as the primary modality for seizure prediction due to their direct measurement of brain activity and high diagnostic precision. However, their cost, sensitivity to noise, and practical deployment constraints limit their applicability outside controlled clinical environments. To overcome these challenges, recent studies have increasingly investigated electrocardiogram (ECG) signals as a practical and non-invasive alternative for seizure prediction in real-world settings. Evidence suggests that ECG-derived cardiac signatures may precede clinical seizure onset, offering a viable window for early detection. In this paper, we propose a reconstruction-based anomaly detection framework that integrates time-frequency representations with advanced deep learning models to capture deviations in heart rate dynamics associated with seizure onset. Afterward, reconstruction error is smoothed, and an adaptive thresholding strategy is applied to reduce false alarms. The method was evaluated on the Siena database, achieving a specificity of 99.16%, accuracy of 76.05%, and a false positive rate (FPR) of 0.01/h, with an average prediction horizon of 45 minutes prior to seizure onset. These results demonstrate that ECG-based prediction can provide clinically actionable early warnings while improving patient accessibility and comfort. Nevertheless, this performance reflects a trade-off favoring high specificity over sensitivity, resulting in reduced FPR and aligning with clinical requirements for reliable deployment.", "date": "2025-04-11", "categories": []}
{"id": "2505.18191", "title": "Quantifying the Generalization Gap in Seizure Detection: A Large-Scale Empirical Benchmark via the SzCORE Challenge", "abstract": "Reliable automatic seizure detection from long-term electroencephalography (EEG) remains an unsolved challenge, as current models often fail to generalize across patients or clinical settings. Manual EEG review still is the standard of care, highlighting the need for robust models and standardized evaluation. The current literature often reports high efficacy, yet these models frequently fail when deployed to unseen patient populations. To rigorously assess this generalization gap, we conducted a large-scale empirical study evaluating 28 state-of-the-art algorithmic architectures, ranging from classical feature engineering to modern Deep Learning. These algorithms were collected by organizing a competition. A strictly held-out private dataset of continuous EEG recordings from 65 subjects, totaling 4,360 hours of data, was utilized to evaluate algorithm performance. Expert neurophysiologists annotated these recordings, establishing the ground truth for seizure events. Algorithms were evaluated using event-based metrics from the SzCORE framework, including sensitivity, precision, F1-score, and false positive rate per day. Results revealed significant performance variability among state-of-the-art approaches, with the top F1 score of 32% (sensitivity 37%, precision 29%), highlighting the persistent difficulty of this task. Analysis uncovered a discordance between peak performance and population-level stability. The algorithms achieving the highest aggregate F1-scores did not achieve the most consistent ranking across subjects. This independent evaluation exposed a notable gap between self-reported efficacies and hold-out performance, underscoring the critical need for standardized, rigorous benchmarking. The evaluation infrastructure transitions into a continuously open benchmarking platform, fostering reproducible research and accelerating robust seizure detection algorithm development.", "date": "2025-05-19", "categories": []}
{"id": "2605.18878", "title": "Prognostic Value of Lung Ultrasound Biomarkers for Readmission Risk in Congestive Heart Failure: A Pilot Data-Driven Analysis", "abstract": "Hospital readmission within 30 days of discharge is a leading driver of morbidity, mortality, and avoidable healthcare expenditure in congestive heart failure (CHF). Current clinical risk stratification tools rely primarily on non-imaging data and exhibit limited predictive performance. Point-of-care lung ultrasound (LUS) offers a sensitive, noninvasive window into the pulmonary congestion that characterizes CHF decompensation, yet its prognostic utility for readmission prediction remains largely unexplored.\n  We present a pilot feasibility study, the first systematic machine learning study using B-mode LUS acquired during hospitalization to predict 30-day CHF readmission. Quantitative spatiotemporal embeddings are extracted from a pretrained Temporal Shift Module (TSM) ResNet-18 encoder, and interpretable biomarker features are separately evaluated. Through structured ablations over lung view, temporal representation, multi-view fusion, and cross-lung augmentation, we identify the key imaging factors driving readmission risk.\n  Our findings reveal that (1) dependent lower-lung regions (Left-3, Right-3) carry the strongest prognostic signal, consistent with their greater susceptibility to hydrostatic congestion; (2) temporal difference features between sequential examinations substantially outperform single-timepoint representations, highlighting the importance of capturing disease trajectory; and (3) multi-view feature concatenation yields the best overall performance, with our top MLP model achieving an F1 score of 0.80 (95% CI: 0.62-0.96). Biomarker analysis further reveals that pleural-line abnormalities, including breaks and indentations, are as informative as the canonical A-line and B-line markers. These results support POCUS-derived biomarkers as practical, interpretable tools for noninvasive CHF risk stratification.", "date": "2026-05-16", "categories": []}
{"id": "2605.18880", "title": "A Multi-Dimensional Clustering Approach for Identifying Inborn Errors of Immunity", "abstract": "Rare diseases such as inborn errors of immunity (IEI) require early diagnosis to prevent end organ damage and improve quality of life. Hurdles in accessing and curating large scale electronic health record (EHR) data limit routine data driven analyses to remain on the forefront of IEI and other rare disease trends. Development of machine learning (ML) algorithms in IEI for pattern recognition as well as published methodology examining how to systematically process and integrate complex medical data is limited. Our proposed pipeline, including data curation and ML clustering algorithms, is designed to recognize novel rare disease patterns and extract IEI- associated features from a national data registry. Our methodology for EHR data formatting and processing presents the pipeline that transforms raw immunologic lab data into vectors. This is further combined with hyperparameter tuning for diseases pattern recognition via clustering. This study refines IEI feature awareness, develops data tool kits for rare disease populations analysis, and expands on transforming complex medical records in data structures interpretable by unsupervised ML.", "date": "2026-05-16", "categories": []}
{"id": "2605.18900", "title": "A Logistic Regression Model to Predict Malaria Severity in Children", "abstract": "One of the main causes of death around the globe is malaria. Researchers have sought to develop predictive models for malaria outbreaks based on meteorological data, climate data and the breeding cycle of Plasmodium, the causative agent of malaria. This study predicts the severity of malaria based on environmental and biological factors. A logistic regression model was developed in this study to predict the severity of malaria based on such factors as sickle cell disease, stagnant water, garbage dump, wet lawns, and the use of treated mosquito nets, with an 83.3% accuracy rate. The study was carried out in the Bosomtwe District of Ghana with 417 respondents. It was deduced that although children in the District are highly prone to malaria infection, the severity is very low. The study recommends that not just having a good sample size alone is important during machine learning model development, but also having a good sample representation of the various class labels is equally important.", "date": "2026-05-17", "categories": []}
{"id": "2605.19132", "title": "CLIC: Contextual Language-Informed Cardiac Pathology Classification", "abstract": "The electrocardiogram (ECG) is the gold standard for non-invasive diagnosis of cardiac pathologies and is a fundamental pillar of cardiovascular medicine. Recent progress in deep learning has led to the development of robust automated classifiers that achieve high performance by processing raw physiological signals. However, in clinical practice, diagnosis is rarely based solely on the signal. Cardiologists commonly support their interpretation with the patient's characteristics and the specific data-acquisition context. Despite this, most current algorithms remain restricted to signal-only analysis, failing to integrate technical metadata and demographic variables. This paper proposes Contextual Language-Informed Cardiac pathology classification (CLIC), a multimodal framework that significantly enhances diagnostic precision by encoding these variables through natural language. We demonstrate that translating patient-level contextual data into descriptive text provides an informative anchor that helps the model disambiguate complex physiological patterns. We further investigate the use of Large Language Models to synthesize richer clinical descriptions and observe that, while these generated texts remain competitive, controlled template-based contextual clinical text leads to consistent improvements in downstream classification performance.", "date": "2026-05-18", "categories": []}
{"id": "2512.05136", "title": "Fine-tuning an ECG Foundation Model to Predict Coronary CT Angiography Outcomes", "abstract": "CAD remains a major global public health burden, yet scalable screening tools are limited. Although CCTA is a first-line non-invasive diagnostic modality, its use is constrained by resource requirements and radiation exposure. AI-ECG may offer a complementary approach for CAD risk stratification. In this multicenter study, we developed and validated an AI-ECG model using CCTA as the anatomical reference standard to predict vessel-specific coronary stenosis. In internal validation, the model achieved AUC values of 0.683-0.744 across vessels and showed consistent external performance. Discrimination was maintained in clinically normal ECGs and remained broadly stable across subgroups. Model-predicted probabilities increased monotonically with CCTA-defined stenosis severity. Model probabilities were converted into vessel-specific low-, intermediate-, and high-risk strata using predefined sensitivity- and specificity-based thresholds. Calibration analysis showed agreement between predicted and observed risk, while DCA indicated net clinical benefit over treat-all and treat-none strategies. Integrating AI-derived risk strata with guideline-based PTP categories improved rule-out performance, reduced the gray-zone proportion, and achieved positive NRI compared with PTP alone. In a longitudinal follow-up cohort, Kaplan-Meier analysis showed clear separation of major adverse cardiovascular event risk across model-defined risk groups. Waveform- and attribution-based analyses further identified structured ECG morphology differences and physiologically meaningful signal regions associated with high-risk predictions. These findings support AI-ECG as a feasible tool for complementary CAD screening, anatomical risk estimation, and clinical triage, while prospective studies are needed to confirm its clinical impact.", "date": "2025-11-29", "categories": []}
{"id": "2601.16414", "title": "PyHealth 2.0: A Comprehensive Open-Source Toolkit for Accessible and Reproducible Clinical Deep Learning", "abstract": "Difficulty replicating baselines, high computational costs, and required domain expertise create persistent barriers to clinical AI research. To address these challenges, we introduce PyHealth 2.0, an enhanced clinical deep learning toolkit that enables predictive modeling in as few as 7 lines of code. PyHealth 2.0 offers three key contributions: (1) a comprehensive toolkit addressing reproducibility and compatibility challenges by unifying 15+ datasets, 20+ clinical tasks, 25+ models, 5+ interpretability methods, and uncertainty quantification including conformal prediction within a single framework that supports diverse clinical data modalities - signals, imaging, and electronic health records - with translation of 5+ medical coding standards; (2) accessibility-focused design accommodating multimodal data and diverse computational resources with up to 39x faster processing and 20x lower memory usage, enabling work from 16GB laptops to production systems; and (3) an active open-source community of 400+ members lowering domain expertise barriers through extensive documentation, reproducible research contributions, and collaborations with academic health systems and industry partners, including multi-language support via RHealth. PyHealth 2.0 establishes an open-source foundation and community advancing accessible, reproducible healthcare AI. Available at pip install pyhealth.", "date": "2026-01-23", "categories": []}
{"id": "2601.23154", "title": "On Safer Reinforcement Learning for Sedation and Analgesia in Intensive Care", "abstract": "Pain management in intensive care usually involves complex trade-offs, since both inadequate and excessive treatment can compromise patient safety. Prior work on reinforcement learning for sedation and analgesia has explored how to optimize these interventions, but has not considered patient survival or partial observability. To investigate the risks of these design choices, we developed an offline deep reinforcement learning framework that suggests hourly medication doses based on recurrent state representations. Using retrospective data from 47,144 ICU stays in the MIMIC-IV database, we trained and evaluated behavior-regularized actor-critic models that prescribe continuous doses of opioids, propofol, benzodiazepines, and dexmedetomidine according to two goals: reduce pain or jointly reduce pain and 30-day post-discharge mortality. Although the two resulting policies were associated with lower pain, clinician agreement with the pain-only policy was positively correlated with mortality ($\\rho$=0.119, p<0.0001), while agreement with the joint policy was negatively correlated ($\\rho$=-0.316, p<0.0001). We found that such divergence arose from a different response to high levels of comorbidity. This suggests that valuing post-discharge outcomes could be critical for learning safer treatment policies, even if a short-term goal remains the primary objective.", "date": "2026-01-30", "categories": []}
{"id": "2603.07900", "title": "EveryQuery: Zero-Shot Clinical Prediction via Task-Conditioned Pretraining over Electronic Health Records", "abstract": "Foundation models pretrained on electronic health records (EHR) have demonstrated zero-shot clinical prediction capabilities by generating synthetic patient futures and aggregating statistics over sampled trajectories. However, this autoregressive inference procedure is computationally expensive, statistically noisy, and not natively promptable because users cannot directly condition predictions on specific clinical questions. In this preliminary work, we introduce EveryQuery, an EHR foundation model that achieves zero-shot inference through task-conditioned pre-training. Rather than generating future events, EveryQuery takes as input a patient's history and a structured query specifying a clinical task, and directly estimates the likelihood of the outcome occurring in the future window via a single forward pass. EveryQuery realizes this capability by pre-training over randomly sampled combinations of query tasks and patient contexts, directly training the model to produce correct answers to arbitrary input prompts. This enables zero-shot prediction for any task in the query space without finetuning, linear probing, or trajectory generation. On MIMIC-IV, EveryQuery outperforms an autoregressive baseline on 82% of 39 randomly sampled prediction tasks, with a mean AUC improvement of +0.16 (95% CI: [0.10,0.22]). This advantage remains consistent on tasks that were explicitly held out from the pre-training distribution. Further, EveryQuery's performance gains are most pronounced for rare clinical events, affirming and demonstrating a solution to the fundamental limitation of autoregressive inference for low-prevalence outcomes. However, at present, EveryQuery underperforms on tasks requiring disjunctive reasoning over multiple codes, such as 30-day readmission, exposing a concrete expressiveness limitation of the current query language.", "date": "2026-03-09", "categories": []}
{"id": "2604.28010", "title": "Learning from Disagreement: Clinician Overrides as Implicit Preference Signals for Clinical AI in Value-Based Care", "abstract": "We reframe clinician overrides of clinical AI recommendations as implicit preference data - the same signal structure exploited by reinforcement learning from human feedback (RLHF), but richer: the annotator is a domain expert, the alternatives carry real consequences, and downstream outcomes are observable. We present a formal framework extending standard preference learning with three contributions: a five-category override taxonomy mapping override types to distinct model update targets; a preference formulation conditioned on patient state s, organizational context c, and clinician capability kappa, where kappa decomposes into execution capability kappa-exec and alignment capability kappa-align; and a dual learning architecture that jointly trains a reward model and a capability model via alternating optimization, preventing a failure mode we term suppression bias-the systematic suppression of correct-but-difficult recommendations when clinician capability falls below the execution threshold. We argue that chronic disease management under outcome-based payment contracts produces override data with uniquely favorable properties-longitudinal density, concentrated decision space, outcome labels, and natural capability variation-and that training environments combining longitudinal outcome measurement with aligned financial incentives are a necessary condition for learning a reward model aligned with patient trajectory rather than with encounter economics. This framework emerged from operational work to improve clinician capability in a live value-based care deployment.", "date": "2026-04-30", "categories": []}
{"id": "2605.12987", "title": "Leveraging Multimodal Self-Consistency Reasoning in Coding Motivational Interviewing for Alcohol Use Reduction", "abstract": "BACKGROUND: Coding Motivational Interviewing (MI) sessions is essential for understanding client behaviors and predicting outcomes, but it requires substantial time and labor from trained MI professionals. Recent advances in audio-language models (ALMs) offer new opportunities to automate MI coding by capturing multimodal behavioral signals. OBJECTIVE: This study aims to develop an automatic MI coding approach based on ALMs that analyzes raw audio input and integrates predictions from multiple reasoning trajectories using self-consistency to improve coding robustness. METHODS: We experimented with five recorded sessions from de-identified MI audio tapes. We deployed ALMs with four complementary analytic prompts to support utterance-level reasoning: analytic prompting for verbal cues, prosody-aware prompting for acoustic cues, evidence-scoring prompting for quantitative hypothesis testing, and comparative prompting for contrastive reasoning. Three stochastic samples were drawn for each prompt, generating 12 independent reasoning trajectories per utterance. Final predictions were determined by majority voting across all trajectories. RESULTS: Performance was evaluated using accuracy, precision, recall, and macro-F1 scores. The proposed multimodal self-consistency approach achieved 52.56% accuracy, 54.03% precision, 47.45% recall, and a macro-F1 score of 46.40%, exceeding baseline methods. Systematic ablation experiments that removed individual modules consistently degraded performance on the primary metrics. CONCLUSIONS: Multimodal self-consistency outperforms single-pass baseline prompting approaches for MI coding. These findings suggest that incorporating both what clients say and how they say it can support more reliable automatic MI coding.", "date": "2026-05-13", "categories": []}
{"id": "2605.16319", "title": "Forecasting Medium-Horizon Alzheimer's Disease Progression: Residual Gap-Aware Transformers for 24-Month CDR-SB Change from ADNI Clinical and Biomarker Histories", "abstract": "Medium-horizon Alzheimer's disease progression prediction is difficult because future clinical scores can remain tied to baseline severity, while biomarker histories are irregular and incompletely observed. We develop an anchor-based analysis of 24-month Clinical Dementia Rating Sum of Boxes (CDR-SB) change using harmonized Alzheimer's Disease Neuroimaging Initiative (ADNI) tables. Each labeled sample is anchored at a mild cognitive impairment visit, uses only clinical and biomarker history observed at or before that anchor, and defines the response as CDR-SB at the future visit closest to 24 months within an 18--30 month window minus anchor CDR-SB. The analytic cohort contains 2,600 labeled anchors from 858 participants and 7,276 longitudinal rows. We propose a residual gap-aware transformer that combines a mixed-effects statistical reference with transformer-based residual learning from pre-anchor clinical and biomarker histories. The model uses participant-level random intercepts in the mixed-effects reference, observation-level triplet tokenization for irregular histories, and a learned nonnegative time-gap penalty inside self-attention. We compare the proposed model with a Bayesian-information-criterion-selected linear mixed-effects baseline, GRU-D, and STraTS under repeated participant-level train--test splits. Across five participant-level random seeds, the proposed model achieves the best mean test performance across all reported metrics, reducing MSE by 13.1% and increasing prediction--observation correlation by 26.4% relative to the mixed-effects baseline. It also improves over both GRU-D and STraTS in mean error and correlation. These results show that statistical anchoring and gap-aware residual learning provide a useful structure for medium-horizon Alzheimer's disease progression prediction.", "date": "2026-05-04", "categories": []}
{"id": "2605.16326", "title": "A Machine Learning Framework for EEG-Based Prediction of Treatment Efficacy in Chronic Neck Pain", "abstract": "Chronic neck pain is a leading cause of disability worldwide, and current treatment selection remains largely trial and error. We present a machine learning framework that uses electroencephalography to predict treatment efficacy in patients with chronic neck pain, with the goal of supporting individualized therapy and reducing the burden on healthcare systems. The framework centers on a rigorous data preprocessing stage tailored to the characteristics of each EEG recording type. For resting-state EEG, the preprocessing pipeline comprises baseline signal removal, bad channel identification and exclusion, re-referencing, bandpass and notch filtering, Independent Component Analysis, and power spectral density analysis. For motor execution and motor imagery recordings, the same initial steps are applied, after which signals are aligned to trigger events so that event-related desynchronization (ERD) and event-related synchronization (ERS) can be quantified. Synchronously recorded electromyography data are bandpass filtered and smoothed with a moving average, then correlated with the corresponding EEG channels to characterize the EEG EMG relationship during attempted movement. In parallel, we performed an extensive literature review of machine learning models applied to clinical EEG (763 records initially screened, 16 patient and 47 healthy-control studies retained), to inform the post-processing strategy. Through this combined preprocessing and review effort, we aim to develop a robust predictive model that can support personalized healthcare strategies in chronic pain management.", "date": "2026-05-05", "categories": []}
{"id": "2605.16365", "title": "Machine Learning-Based Pre-Test Risk Stratification for PCR-Confirmed Chlamydia Using Patient-Reported Data and Urine Biomarkers", "abstract": "Early identification of individuals at elevated risk of Chlamydia trachomatis infection may enable optimal use of molecular testing in resource-aware screening. We evaluate the feasibility of pre-test risk stratification (PTRS) using machine-learning models trained on routinely available, non-invasive clinical data.\n  A curated dataset of 93 urine samples with PCR reference labels was analyzed using three feature groups: patient-reported history and symptoms, urine biomarkers from standard urinalysis, and their combination. Five supervised classifiers were evaluated using stratified 5-fold cross-validation with out-of-fold probability estimates. Performance was assessed using area under the receiver operating characteristic curve (AUC) and threshold-dependent metrics, with uncertainty quantified via bootstrap confidence intervals.\n  Models using only patient-reported data showed moderate discrimination (AUC up to 0.72). Urine biomarker-based models demonstrated slightly lower peak discrimination but more consistent performance, with ensemble methods yielding the strongest results. Combining feature groups marginally increased the peak AUC and reduced performance variability across models, indicating improved robustness.\n  Findings indicate that urine biomarkers provide a reliable predictive signal for PTRS that is complementary to patient-reported information, while feature integration enhances robustness. This work supports the integration of non-invasive, routinely available information for PTRS into screening workflows, including decentralized or home-based PCR contexts, to optimize testing prioritization.", "date": "2026-05-09", "categories": []}
{"id": "2605.16380", "title": "ReTAMamba: Reliability-Aware Temporal Aggregation with Mamba for Irregular Clinical Time Series Prediction", "abstract": "Clinical time-series data are difficult to model with methods designed for regular sequences because they exhibit irregular sampling, frequent missing values, and heterogeneous observation patterns across variables. Existing approaches commonly use observation masks and time-gap information, but they do not continuously capture the decaying reliability of past observations or consistently organize multi-resolution information within a coherent temporal context during aggregation. To address these limitations, we propose Reliability-aware Temporal Aggregation with Mamba (ReTAMamba), which reconstructs clinical time series as time-variable token sequences, estimates observation reliability from missingness and elapsed time, and augments interval summaries with statistical descriptors. Chronological Weaving is used to integrate short- and long-term temporal information within a coherent temporal context, and a budgeted token router is applied to constrain sequence length while preserving informative summaries. Experiments on MIMIC-IV, eICU, and PhysioNet 2012 show that ReTAMamba consistently improves AUPRC over strong baselines, with average relative gains of 7.51%, 7.80%, and 10.15%, respectively. Cohort-level and patient-level analyses on eICU further showed that the learned mean decay for more dynamic signals, such as heart rate and blood pressure, was 24.3% larger than that for relatively static signals, such as laboratory test variables. These findings suggest that effective prediction in irregular clinical time series requires modeling not only what was measured, but also when and how it was observed, including information freshness and observation timeliness.", "date": "2026-05-11", "categories": []}
{"id": "2605.16639", "title": "MedMIX: Modality-Internal Expert Fusion for Multimodal Medical Diagnosis", "abstract": "Multimodal clinical prediction faces three challenges: multiple foundation models (FMs) with complementary strengths per modality, pervasive missing modalities at training and test time, and sample-specific variation in modality contributions. We introduce MedMIX, a multimodal framework that combines intra-modality expert fusion, learned inter-modality fusion, and training-only large--small model collaboration for robust medical prediction under incomplete modalities. Within each modality, MedMIX aggregates complementary embeddings from multiple small expert models; across modalities, it performs learned fusion over available modalities; and during training, it leverages large teacher models to improve deployed representations without additional inference cost. Across three heterogeneous benchmarks (OpenI, MIMIC-IV-MM, and MMIST-ccRCC), MedMIX achieves consistently strong performance while remaining robust under controlled missing-modality perturbations, and further demonstrates sustained robustness under cross-cohort shift on MIMIC-III. These results highlight MedMIX as a practical framework that unifies within-modality expert collaboration, sample-specific cross-modality fusion, and efficient large--small model collaboration while remaining robust to incomplete modalities.", "date": "2026-05-15", "categories": []}
{"id": "2605.16860", "title": "PhysioSeq2Seq: A Hybrid Physiological Digital Twin and Sequence-to-Sequence LSTM for Long-Horizon Glucose Forecasting in Type 1 Diabetes", "abstract": "Accurate long-horizon glucose forecasting is critical for automated insulin delivery systems, which help people with type 1 diabetes (T1D) manage their glucose and avoid dangerous hypoglycemia. However, standard recursive long short-term memory (LSTM) networks suffer from systematic negative bias at longer horizons due to error compounding, while purely mechanistic ordinary differential equation (ODE) models fail to generalize across individuals when parameterized at the population level. We propose PhysioSeq2Seq, a hybrid architecture that combines patient-specific physiological modeling with a sequence-to-sequence (Seq2Seq) LSTM. For each glucose segment, twin matching searches a population of 300 parameterized digital twins to identify the best-fitting physiological match from a 3-hour continuous glucose monitoring (CGM) history. The 10 internal ODE state variables of the matched twin are injected as exogenous covariates into both the encoder and decoder of the Seq2Seq LSTM. This simultaneous 48-step prediction strategy eliminates recursive error compounding, while the ODE features provide a physics-grounded constraint that bounds long-horizon drift within physiologically plausible ranges. PhysioSeq2Seq was trained on CGM and insulin data from 348 participants in the Type 1 Diabetes Exercise Initiative (T1DEXI) dataset and evaluated on 74 held-out participants. At the 240-minute horizon, PhysioSeq2Seq achieves a mean absolute error of 39.28 mg/dL and a mean error of -10.62 mg/dL, reducing bias by 13.89 mg/dL over the recursive LSTM and reducing mean absolute error by 28.62 mg/dL over the ODE-based digital twin. These results show that eliminating architectural feedback and injecting patient-matched physiological states is an effective and clinically meaningful strategy for long-horizon glucose forecasting in T1D.", "date": "2026-05-16", "categories": []}
{"id": "2605.16927", "title": "From Static Risk to Dynamic Trajectories: Toward World-Model-Inspired Clinical Prediction", "abstract": "Clinical decision-making is a feedback system where risk estimates influence treatment, which in turn changes disease trajectories, and both shape clinicians' measurement practices. Static prediction often fails clinically: models trained on observational care logs conflate disease biology with clinician behavior, particularly under treatment confounder feedback and irregular or informative observation. This Review focuses on intervention-aware disease trajectory modeling in clinical AI--methods estimating patient-specific longitudinal disease evolution and assessing trajectory changes under alternative treatments. We organize the field around six linked components: three decision tasks (factual forecasting, counterfactual estimation, policy evaluation) and three data-generating mechanisms (disease evolution, treatment assignment, observation process) that determine identifiability. We present the first unified framework bridging forecasting, counterfactual trajectories, and policy evaluation across discrete/continuous time, explicitly addressing treatment assignment, time-varying confounding, and observation bias. We synthesize key method families (multistate/joint models, temporal point-process, deep sequence architectures, longitudinal causal inference), map them to relevant components, and align evaluation with claim strength via overlap diagnostics, uncertainty quantification, off-policy robustness, and target-trial validation. This synthesis advances benchmark prediction to decision-grade clinical evidence, enabling treatment-sensitive individualized futures, pre-deployment policy stress-testing, and safer closed-loop learning health systems that adapt/abstain when evidence is insufficient.", "date": "2026-05-16", "categories": []}
{"id": "2605.16969", "title": "Brain Vascular Age Prediction Using Cerebral Blood Flow Velocity and Machine Learning Algorithms", "abstract": "Defining vascular age in terms of physiological function has become one focal point of the extensive studies to categorize and track chronological age. Transcranial Doppler (TCD) is a method by which cerebral blood flow velocity is measured along the major arteries feeding the human brain. This study aims to use features extracted from TCD to estimate chronological age and assess accelerated aging in subjects with various brain diseases. We predict subjects with various brain diseases to present with accelerated cerebrovascular aging when tested on various regression models trained by healthy subjects. 168 healthy subjects and 277 diseased subjects with bilateral TCD recordings of the middle cerebral artery were analyzed using the Morphological Analysis and Clustering of Intracranial Pressure (MOCAIP) algorithm. MOCAIP-generated features and heart rate variability features were used as input features for regression models to predict the brain vascular age. 66 subjects with acute stroke, 27 subjects with post stroke, 26 subjects with Alzheimer's disease, 23 subjects with mild cognitive impairment, and 135 established subjects were tested against the machine learning model to assess for accelerated cerebrovascular age. The trained model, on average, predicted healthy subjects' cerebrovascular age to be 3.69 years above their chronological age. Subjects with different disease conditions exhibited varying levels of age acceleration. The differences in healthy and diseased subjects' performances suggest that features generated using TCD may be relevant when evaluating accelerated cerebrovascular aging. Moreover, imbalanced datasets have been observed to affect the performance of machine-learning-based brain age prediction models.", "date": "2026-05-16", "categories": []}
{"id": "2605.16993", "title": "Adversarial Fragility and Language Vulnerability in Clinical AI: A Systematic Audit of Diagnostic Collapse Under Imperceptible Perturbations and Cross-Lingual Drift in Low-Resource Healthcare Settings", "abstract": "Current clinical artificial intelligence (AI) systems are evaluated almost exclusively on clean, standardised, English-language inputs, conditions that do not reflect the realities of healthcare delivery in low-resource settings. This study presents the first systematic dual audit of two orthogonal safety vulnerabilities in clinical AI: adversarial image fragility and cross-lingual diagnostic drift. Using DenseNet121, the architecture underlying CheXNet, fine-tuned on the COVID-QU-Ex chest X-ray dataset (85,318 images; COVID-19, Non-COVID Pneumonia, Normal), we demonstrate that diagnostic accuracy collapses from 89.3% to 62.0% under a Fast Gradient Method (FGM) perturbation of epsilon=0.021, a magnitude imperceptible to the human eye. Standard defensive strategies including Gaussian smoothing and ensemble voting failed to restore clinical safety. In a parallel language fragility experiment, we tested Llama3.1:8b and NatLAS (N-ATLAS) on 20 COVID-19 clinical cases presented in Standard English, Nigerian Pidgin (Naija), and Yoruba-inflected English. Both models exhibited significant accuracy degradation: Llama3.1:8b dropped from 80.0% to 65.0% on Pidgin; NatLAS, an African-context model, collapsed from 85.0% to 55.0%, with diagnosis consistency falling to 50%. These findings establish a quantitative failure envelope for clinical AI under conditions representative of Primary Health Centre (PHC) deployment in Nigeria, and motivate urgent calls for adversarially hardened, linguistically inclusive clinical AI architectures.", "date": "2026-05-16", "categories": []}
{"id": "2605.17308", "title": "Reasoning Before Diagnosis: Physician-Inspired Structured Thinking for ECG Classification", "abstract": "Electrocardiogram (ECG) diagnosis in clinical practice relies on structured reasoning over multiple hierarchical aspects, including cardiac rhythm, conduction properties, waveform morphology, and overall diagnostic impression. However, most existing approaches predict labels directly from ECG signals without explicit clinical reasoning, resulting in opaque decisions that lack clinical alignment. To bridge this gap, we propose CardioThink, a physician-inspired multimodal large language model (MLLM) framework that explicitly models the diagnostic reasoning process through human-interpretable intermediate stages (rhythm, conduction, morphology, and impression) to derive final classification results. Furthermore, we introduce Structured Set Policy Optimization (SSPO) to jointly optimize adherence to this structured reasoning format and the accuracy of variable-size diagnostic sets, without requiring manually annotated reasoning traces. Extensive experiments on diverse ECG benchmarks demonstrate the significant superiority of our approach in diagnostic accuracy, while simultaneously providing interpretable clinical reasoning. Notably, reasoning quality evaluations confirm that SSPO substantially enhances the clinical validity of the generated rationales. These findings reveal that moving beyond direct label prediction toward structured reasoning offers a more clinically aligned direction for future ECG modeling.", "date": "2026-05-17", "categories": []}
{"id": "2605.17436", "title": "Medical Context Distorts Decisions in Clinical Vision Language Models", "abstract": "Vision-language models (VLMs) are increasingly proposed for clinical decision support, yet their reliability in real-world scenarios that require integrating both visual and textual context from medical records remains poorly characterized. This paper identifies three failure modes: (1) modality over-reliance on text over images, (2) spurious reliance on irrelevant clinical history, and (3) prompt sensitivity across semantically equivalent inputs. We evaluate a diverse set of general-domain and medically-tuned open and closed VLMs on chest x-ray tasks using MIMIC-CXR. By systematically manipulating image-text alignment, clinical history, and prompt formulations, we found that VLM decisions are dominated by the text modality, even when visual evidence is available. Moreover, we observed that VLMs are heavily influenced by irrelevant reports, while minor prompt changes can reverse correct image-based predictions. Our findings underscore the need for explicit safeguards and stress-testing before considering the use of these models in clinical practice.", "date": "2026-05-17", "categories": []}
{"id": "2605.17580", "title": "ECG-WM: A Physiology-Informed ECG World Model for Clinical Intervention Simulation", "abstract": "Electrocardiogram (ECG)-based models have achieved strong performance in diagnostic tasks, yet they remain limited in modeling how cardiac dynamics evolve under external interventions. In particular, existing approaches focus primarily on static prediction and lack mechanisms to capture ECG variations under different pharmacological conditions. In this work, we propose an ECG World Model for action-conditioned predictive simulation of cardiac electrophysiology. Moving beyond disjoint pipelines, our framework features a principled integration of physiological ordinary differential equation (ODE) priors into latent diffusion dynamics via energy regularization. This structural constraint enables the synthesis of physiologically plausible post-intervention ECG trajectories while effectively mitigating generative hallucinations. Building on this simulation process, we introduce an uncertainty-aware evaluation strategy that leverages the stochasticity of diffusion sampling to characterize both the expected clinical risk and its variability, allowing a more reliable comparative assessment of candidate interventions. We evaluate our method across diverse settings, including controlled drug-response scenarios and real-world clinical records. Beyond standard waveform metrics, experimental results demonstrate improved risk calibration and strong alignment with expert-informed treatment preferences. These results establish our approach as a robust foundation for safe and intervention-aware clinical decision support.", "date": "2026-05-17", "categories": []}
{"id": "2605.17755", "title": "Bridging the Version Gap: Multi-version Training Improves ICD Code Prediction, Especially for Rare Codes", "abstract": "Clinical coding maps clinical documentation to standardized medical codes, an essential yet time-consuming administrative task that could benefit from automation. Current models on ICD coding are typically optimized for codes from a specific ICD version. However, in reality, ICD systems evolve continuously, and different versions are adopted across time periods and regions. Moreover, ICD coding suffers from the long-tail problem, and rare code performance can be a bottleneck for developing implementable models. We examine whether it is viable to train version-independent models by combining data annotated in different ICD versions, which may help address these challenges. We add ICD-9 data to the training of a modified label-wise attention model for ICD-10 prediction, and find that despite the version mismatch, adding ICD-9 yields a 27% increase in micro F1 for 18K rare ICD codes compared to training on ICD-10 alone. On 8K frequent ICD-10 codes, the multi-version training also substantially improves macro metrics, with far fewer model parameters.", "date": "2026-05-18", "categories": []}
{"id": "2605.17875", "title": "HexagonalWarriorMamba: Superior Threshold-Dependent Multi-label Classification of 12-Lead ECG Cardiac Abnormalities", "abstract": "The accurate automated diagnosis of cardiac abnormalities from 12-lead electrocardiograms (ECGs) is critical for managing cardiovascular disease. However, detecting concurrent conditions remains a challenge for traditional deep learning models, which often have limited ability to model the long-range dependencies inherent in ECG signals. This manuscript proposes HexagonalWarriorMamba (HWMamba), a framework built on the Mamba architecture that processes 12-lead ECGs as single-channel 2D images rather than conventional 1D time series. By integrating a hierarchical architecture with a 2D Selective Scan mechanism, HWMamba is designed to model global context and complex spatial relationships within the data. The model is evaluated on the PhysioNet/Computing in Cardiology Challenge 2021 dataset, which includes 26 diagnostic labels and comprises recordings collected from seven institutions across four countries and three continents. Results demonstrate that HWMamba outperforms current state-of-the-art (SOTA) methods across five key threshold-dependent metrics, including Challenge Score and Subset Accuracy. These improvements provide a balance between strong discriminative capability and effective threshold selection derived from the training data, while maintaining near-SOTA performance in Macro AUROC. This Hexagonal Warrior performance, reflecting consistent performance across multiple evaluation dimensions, positions HWMamba as a robust and versatile approach for multi-label ECG classification.", "date": "2026-05-18", "categories": []}
{"id": "2605.18701", "title": "Learning Normal Representations for Blood Biomarkers", "abstract": "Blood-based biomarkers underpin clinical diagnosis and management, yet their interpretation relies largely on fixed population reference intervals that ignore stable, intra-patient variability. As such, population-based interpretation can mask meaningful deviation from an individual's baseline, risking delayed disease detection. To remedy this, there have been increasing efforts to personalize blood biomarker interpretation using individual testing histories. However, these methods may overfit to sparse data, inflating false-positive rates and unnecessary follow-up, and can also unwittingly include unrecognized or subclinical disease. Here, we leverage nearly 2 billion longitudinal laboratory measurements from over 1.6 million individuals across North America, the Middle East, and East Asia, to show that while laboratory values are highly individual, purely personalized intervals routinely overfit, classifying up to 68% of measurements as abnormal, without corresponding associations with adverse clinical outcomes. We then introduce NORMA, a conditional transformer-based framework that generates reference intervals by conditioning on both a patient's history and population-level data about \"normal\" variation. NORMA-derived intervals achieve higher precision for predicting outcomes, including mortality, acute kidney injury, and chronic disease. These findings caution against over-personalization in laboratory medicine and demonstrate that anchoring individual trajectories to population-level priors outperforms either approach alone. To promote transparency, we publicly release the model, code, and an interactive user interface for accessible, individualized laboratory interpretation.", "date": "2026-05-18", "categories": []}
{"id": "2510.10454", "title": "Traj-CoA: Patient Trajectory Modeling via Chain-of-Agents for Lung Cancer Risk Prediction", "abstract": "Large language models (LLMs) offer a generalizable approach for modeling patient trajectories, but suffer from the long and noisy nature of electronic health records (EHR) data in temporal reasoning. To address these challenges, we introduce Traj-CoA, a multi-agent system involving chain-of-agents for patient trajectory modeling. Traj-CoA employs a chain of worker agents to process EHR data in manageable chunks sequentially, distilling critical events into a shared long-term memory module, EHRMem, to reduce noise and preserve a comprehensive timeline. A final manager agent synthesizes the worker agents' summary and the extracted timeline in EHRMem to make predictions. In a zero-shot one-year lung cancer risk prediction task based on five-year EHR data, Traj-CoA outperforms baselines of four categories. Analysis reveals that Traj-CoA exhibits clinically aligned temporal reasoning, establishing it as a promisingly robust and generalizable approach for modeling complex patient trajectories. Implementation of Traj-CoA is available on https://github.com/zengsihang/Traj-CoA.", "date": "2025-10-12", "categories": []}
{"id": "2605.15235", "title": "MuteBench: Modality Unavailability Tolerance Evaluation for Incomplete Multimodal Fusion", "abstract": "Multimodal physiological data powers clinical AI systems from intensive care units to wearable devices, but sensors routinely fail in practice. Two failure modes are common: modality missing, where an entire channel is absent, and within-modality missing, where a contiguous time segment is lost. No existing benchmark evaluates multiple fusion architectures under both failure modes at controlled severity levels across diverse clinical datasets. We present MuteBench, a benchmark covering 9 datasets from 7 clinical domains, 6 fusion architectures, and 2 missing-data modes over 125,000 samples. Through this benchmark, we find that architecture family is the strongest predictor of robustness, outweighing parameter count. Channel-independent models tolerate modality missing well but can be sensitive to within-modality missing, especially on short sequences. Curriculum modality dropout protects reliably only up to the maximum dropout rate used in training. We also find that channel count, sequence length, and modality alignment jointly determine which failure mode poses the greater threat. Finally, a PTB-XL case study suggests that diffusion-based imputation can improve downstream classification under within-modality missing, with the largest gains for models whose expert routing is most sensitive to corrupted inputs, though broader validation across datasets remains an open direction. MuteBench provides practitioners with concrete guidance for both selecting existing architectures and informing the design of future robust multimodal fusion methods.", "date": "2026-05-13", "categories": []}
{"id": "2605.15465", "title": "Toward World Modeling of Physiological Signals with Chaos-Theoretic Balancing and Latent Dynamics", "abstract": "Physiological time series signals reflect complex, multi-scale dynamical processes of the human body. Existing modeling studies focus on static tasks such as classification, event forecasting, or short-horizon next step prediction, while long-horizon signal-level forecasting and predictive nature of physiological signals remain underexplored. We introduce NormWear-2, a world model that encodes both multivariate physiological signals and clinical intervention variables into a shared latent space and models their joint temporal evolution as a dynamical system. Our approach combines inference from prior pre-trained knowledge (intuition) with instant non-parametric latent state transition adaptation (insight), enabling coherent forecasting across multiple temporal scales, conditioned on heterogeneous clinical interventions. During the pretraining phase, we find that chaos-theoretic balancing of dynamical regime diversity yields more robust representations, with a smaller balanced corpus outperforming one twice its size and capturing bifurcation regimes. We evaluate the world model performance across diverse real-world physiological datasets spanning heterogeneous temporal resolutions and intervention regimes, covering daily life, point-of-care, and clinical settings, including fitness planning, hemodialysis, diabetes management, and surgical monitoring. These evaluation datasets comprise records from 8,026 subjects, spanning study durations from 3.2 hours for high-resolution signal data to 2.3 years for longitudinal clinical biomarker tracking. NormWear-2 achieves the best overall forecasting performance across time, frequency, and latent representation domains, with significant improvements over state-of-the-art time series foundation models, while maintaining competitive downstream representation quality, providing a step toward general-purpose world models for physiological signals.", "date": "2026-05-14", "categories": []}
{"id": "2605.16077", "title": "Can Large Language Models Imitate Human Speech for Clinical Assessment? LLM-Driven Data Augmentation for Cognitive Score Prediction", "abstract": "Accurate assessment of cognitive decline from spontaneous speech remains challenging due to limited dataset size and class imbalance. In this work, we propose a large language model (LLM)-driven data augmentation framework to improve the prediction of cognitive scores from speech. Experiments are conducted on a Japanese corpus in which each participant provides both a spontaneous oral narrative and a written response to the same clinical prompt. The written responses serve as semantic anchors to generate multiple oral-like monologues in different styles using GPT-5. We then predict Hasegawa Dementia Scale scores, a widely used cognitive screening tool in Japan, using a Partial Least Squares regression model trained on Sentence-BERT speech embeddings. We investigate two augmentation strategies: random class-balanced selection, which yields moderate but unstable improvements, and similarity-guided class-balanced selection. The latter prioritizes semantically close synthetic samples, leading to more consistent improvements and substantially reducing prediction error for minority low-score participants while maintaining performance for the majority group. Overall, our findings demonstrate the potential of semantically guided LLM-driven augmentation as a principled approach for addressing class imbalance and improving data efficiency in clinical speech analysis.", "date": "2026-05-15", "categories": []}
{"id": "2506.11067", "title": "A Large Language Model Based Pipeline for Review of Systems Entity Recognition from Clinical Notes", "abstract": "Objective: Develop a cost-effective, large language model (LLM)-based pipeline for automatically extracting Review of Systems (ROS) entities from clinical notes. Materials and Methods: The pipeline extracts ROS section from the clinical note using SecTag header terminology, followed by few-shot LLMs to identify ROS entities such as diseases or symptoms, their positive/negative status and associated body systems. We implemented the pipeline using 4 open-source LLM models: llama3.1:8b, gemma3:27b, mistral3.1:24b and gpt-oss:20b. Additionally, we introduced a novel attribution algorithm that aligns LLM-identified ROS entities with their source text, addressing non-exact and synonymous matches. The evaluation was conducted on 24 general medicine notes containing 340 annotated ROS entities. Results: Open-source LLMs enable a local, cost-efficient pipeline while delivering promising performance. Larger models like Gemma, Mistral, and Gpt-oss demonstrate robust performance across three entity recognition tasks of the pipeline: ROS entity extraction, negation detection and body system classification (highest F1 score = 0.952). With the attribution algorithm, all models show improvements across key performance metrics, including higher F1 score and accuracy, along with lower error rate. Notably, the smaller Llama model also achieved promising results despite using only one-third the VRAM of larger models. Discussion and Conclusion: From an application perspective, our pipeline provides a scalable, locally deployable solution to easing the ROS documentation burden. Open-source LLMs offer a practical AI option for resource-limited healthcare settings. Methodologically, our newly developed algorithm facilitates accuracy improvements for zero- and few-shot LLMs in named entity recognition.", "date": "2025-05-31", "categories": []}
{"id": "2601.22197", "title": "Neural Signals Generate Clinical Notes in the Wild", "abstract": "Generating clinical reports that summarize abnormal patterns, diagnostic findings, and clinical interpretations from long-term EEG recordings remains labor-intensive. We present CELM, the first clinical EEG-to-Language foundation model capable of summarizing long-duration, variable-length EEG recordings and performing end-to-end clinical report generation at multiple scales. CELM integrates pretrained EEG foundation models with language models to enable scalable multimodal learning. We curate a large-scale clinical EEG dataset containing 9,922 reports paired with approximately 11,000 hours of EEG recordings from 9,048 patients to train CELM, and release the benchmark with an automated report-structuring pipeline to facilitate future research. Experimental results show that CELM consistently outperforms existing methods across all evaluation settings. Importantly, we further conduct human evaluation with clinical experts, demonstrating that CELM generates reports that are more clinically coherent, diagnostically reliable, and better aligned with expert interpretation. We release our model and benchmark construction pipeline at https://github.com/Jathurshan0330/CELM.", "date": "2026-01-29", "categories": []}
{"id": "2605.13897", "title": "Attention-Based Multimodal Survival Prediction with Cross-Modal Bilinear Fusion", "abstract": "We propose a novel multimodal deep learning framework for patient-level survival prediction, which integrates whole-slide histology features, RNA-seq expression profiles, and clinical variables. Our architecture combines an ABMIL module~\\cite{ilse2018attention} for slide-level representation with feedforward encoders for RNA and clinical data. These embeddings are then integrated through low-rank bilinear cross-modal fusion~\\cite{liu2018efficient} to model conditional interactions across modalities while controlling parameter growth. The model outputs continuous risk scores that are subsequently mapped to survival times using a nonparametric calibration procedure based on the Kaplan--Meier estimator~\\cite{kaplan1958nonparametric}. By decomposing multimodal reasoning into independent pairwise interactions, the proposed fusion design promotes structural interpretability and parameter efficiency compared with full tensor and hierarchical fusion strategies. Experiments on the CHIMERA challenge dataset demonstrate improved predictive performance over concatenation-based baselines and competitive generalization on hidden evaluation cohorts. These results indicate that the proposed framework is a promising approach for multimodal survival prediction in HR-NMIBC. The implementation is publicly available at https://github.com/hassancpu/ChimeraChallenge2025_Task_3.", "date": "2026-05-12", "categories": []}
{"id": "2605.14147", "title": "A Systematic Evaluation of Imbalance Handling Methods in Biomedical Binary Classification", "abstract": "Objective: The primary goal of this study was to systematically examine the impact of commonly used imbalance handling methods (IHMs) on predictive performance in biomedical binary classification, considering the interplay between model complexity and diverse data modalities.\n  Material and Methods: We evaluated five representative IHMs: random undersampling (RUS), random oversampling (ROS), SMOTE, re-weighting (RW), and direct F1-score optimization (DMO), against a raw training (RAW) baseline. The evaluation encompassed three public biomedical datasets: MIMIC-III (tabular), ADE-Corpus-V2 (text), and MURA (image), spanning three common biomedical data modalities. To assess varying model complexity, we employed a range of architectures, from classical logistic regression and random forest to deep neural networks, including multilayer perceptron (MLP), BiLSTM, BERT, DenseNet, and DINOv2.\n  Results: For simpler models such as logistic regression on tabular data, IHMs yielded no significant advantage over the RAW baseline, aligning with prior findings. However, clear benefits were observed for more complex models and unstructured data: (a) ROS and RW consistently enhanced the performance of powerful models; (b) direct F1-score optimization demonstrated utility primarily for unstructured text and image data; and (c) RUS and SMOTE consistently degraded performance and are therefore not recommended.\n  Conclusion: The effectiveness of IHMs depends on both model complexity and data modality. Performance gains are most pronounced when leveraging appropriate IHMs, such as ROS, RW, and DMO, on high-complexity models.", "date": "2026-05-13", "categories": []}
{"id": "2605.14227", "title": "DT-Transformer: A Foundation Model for Disease Trajectory Prediction on a Real-world Health System", "abstract": "Accurate disease trajectory prediction is critical for early intervention, resource allocation, and improving long-term outcomes. While electronic health records (EHRs) provide a rich longitudinal view of patient health in clinical environments, models trained on curated research cohorts may not reflect routine deployment settings, and those trained on single-hospital datasets capture only fragments of each patient's trajectory. This highlights the importance of leveraging large, multi-hospital health systems for training and validation to better reflect real-world clinical complexity. In this work, we develop DT-Transformer, a foundation model trained on 57.1M structured EHR entries over 1.7M patients from Mass General Brigham (MGB), spanning 11 hospitals and a broad network of outpatient clinics. DT-Transformer achieves strong discrimination in both held-out and prospective validation settings. Next-event prediction achieves a median age- and sex-stratified AUC of 0.871 across 896 disease categories, with all categories exceeding AUC 0.5. These results support health system-scale training as a path toward foundation models suited to real-world clinical forecasting.", "date": "2026-05-14", "categories": []}
{"id": "2605.14242", "title": "Artificial Intelligence-Assistant Cardiotocography: Unified Model for Signal Reconstruction, Fetal Heart Rate Analysis, and Variability Assessment", "abstract": "The monitoring of fetal heart rate (FHR) and the assessment of its variability are crucial for preventing fetal compromise and adverse outcomes. However, traditional methods encounter limitations arising from equipment performance, data transmission, and subjective assessments by doctors. We have developed a tailored AI-based FHrCTG model specifically for FHR monitoring, which effectively mitigates noise interference and precisely reconstructs signals. Our model was pre-trained on a massive dataset consisting of 558,412 unlabeled data points and further refined using 7,266 expert-reviewed entries. To validate FHR, we introduced the Intersection Overlapping Labels (IOL) approach, which transforms rate analysis into categorical judgments. Testing revealed that our model demonstrates high sensitivity and specificity in detecting critical FHR decelerations (89.13% and 87.78%, respectively) and accelerations (62.5% and 92.04%, respectively). Furthermore, based on Fischer's criteria for clinical application, our model achieved impressive AUC scores of 0.7214 and 0.9643 for verifying FHR periodicity and amplitude variation, respectively.", "date": "2026-05-14", "categories": []}
{"id": "2605.14698", "title": "NeuroAtlas: Benchmarking Foundation Models for Clinical EEG and Brain-Computer Interfaces", "abstract": "Foundation models (FMs) promise to extract unified representations that generalize across downstream tasks. They have emerged across fields, including electroencephalography (EEG), but it is less clear how effective they are in this particular field. Published evaluations differ in datasets, in the EEG-specific preprocessing that might influence reported results, and in the reported metrics, frequently obscuring the clinical relevance in EEG. We introduce NeuroAtlas, the largest EEG benchmark to date: 42 datasets and 260k hours covering clinical EEG (epilepsy, sleep medicine, brain age estimation) and brain-computer interfaces, and include multiple datasets per task along with bespoke clinical evaluation metrics. Besides evaluating EEG-FMs with respect to supervised baselines, we present results from generic time-series FMs. We report three findings. First, EEG-specific FMs do not consistently outperform time-series FMs, which have neither EEG-focused architectures nor been pretrained on EEG. Second, standard machine learning metrics are insufficient to assess clinical utility: thus, we thoroughly evaluate more appropriate measures such as the quality of event-level decision-making, hypnogram-derived features, and the brain-age gap in the domains of epilepsy, sleep, and brain age, respectively. Third, model rankings and performance can vary substantially within domains. We conclude that pretrained models perform largely on par, with only narrow advantages for a few, and that current models do not yet deliver on the promise of an out-of-the-box unified EEG model. NeuroAtlas exposes this gap and provides the datasets and metrics for the next generation of unified EEG FMs.", "date": "2026-05-14", "categories": []}
{"id": "2605.14949", "title": "A CUBS-Compatible Ultrasound Morphology and Uncertainty-Aware Baseline for Carotid Intima-Media Segmentation and Preliminary Risk Prediction", "abstract": "Carotid atherosclerosis is a major contributor to ischemic stroke and transient ischemic attack. Conventional ultrasound assessment is commonly based on intima-media thickness, plaque appearance, stenosis degree, and peak systolic velocity, but these morphology- and velocity-based indicators may not fully capture patient-specific vascular risk. This study presents AtheroFlow-XNet, a CUBS-compatible ultrasound morphology and uncertainty-aware learning baseline for carotid intima-media segmentation and preliminary risk prediction. Using the Carotid Ultrasound Boundary Study dataset, manual lumen-intima and media-adventitia boundary annotations were converted into dense intima-media masks for supervised segmentation. Clinical variables were incorporated into an auxiliary risk-prediction branch, and Monte Carlo dropout was used for uncertainty-aware inference. The model was evaluated using a patient-level train-validation-test split with 1,522 training images, 326 validation images, and 328 testing images. The proposed model achieved a Dice coefficient of 0.7930 for LI-MA mask segmentation, a segmentation loss of 0.2359, and an area under the receiver operating characteristic curve of 0.6910 for preliminary risk prediction. Qualitative results showed that predicted masks were generally aligned with manual annotations, while uncertainty maps highlighted ambiguous wall-boundary regions. These results suggest that ultrasound-derived carotid morphology can support automated wall analysis and uncertainty-aware interpretation. Since CUBS does not provide Doppler waveforms or CFD-derived hemodynamic biomarkers, this work should be interpreted as a reproducible morphology-driven baseline. Future work will incorporate Doppler-derived flow profiles, patient-specific vascular reconstruction, and CFD-based wall shear biomarkers.", "date": "2026-05-14", "categories": []}
{"id": "2605.15009", "title": "DeepTokenEEG Enhancing Mild Cognitive Impairment and Alzheimers Classification via Tokenized EEG Features", "abstract": "The detection of Alzheimers disease (AD) is considered crucial, as timely intervention can improve patient outcomes. Electroencephalogram (EEG)-based diagnosis has been recognized as a non-invasive, accessible, and cost-effective approach for AD detection; however, it faces challenges related to data availability, accuracy of modern deep learning methods, and the time-consuming nature of expert-based interpretation. In this study, a novel lightweight and high-performance model, DeepTokenEEG, was designed for the diagnosis of AD and the classification of EEG signals from AD patients, individuals with other neurological conditions, and healthy subjects. Unlike traditional heavy-weight models, DeepTokenEEG ultilizes spatial and temporal tokenizer that effectively captures AD-related biomarkers in both temporal and frequency domain with only 0.29 million paramaters. Trained in a combined dataset of 274 subjects, including 180 AD cases, and 94 healthy controls, the proposed method achieves a maximum recorded accuracy of 100% on specific frequency bands, representing an improvement of 1.41-15.35% over state-of-the-art methods on the same dataset. These results indicate the potential of DeepTokenEEG for early detection and screening of AD, with promising applicability for deployment due to its compact size.", "date": "2026-05-14", "categories": []}
{"id": "2605.15016", "title": "COTCAgent: Preventive Consultation via Probabilistic Chain-of-Thought Completion", "abstract": "As large language models empower healthcare, intelligent clinical decision support has developed rapidly. Longitudinal electronic health records (EHR) provide essential temporal evidence for accurate clinical diagnosis and analysis. However, current large language models have critical flaws in longitudinal EHR reasoning. First, lacking fine-grained statistical reasoning, they often hallucinate clinical trends and metrics when quantitative evidence is textually implied, biasing diagnostic inference. Second, non-uniform time series and scarce labels in longitudinal EHR hinder models from capturing long-range temporal dependencies, limiting reliable clinical reasoning. To address the above limitations, this work presents the Probabilistic Chain-of-Thought Completion Agent (COTCAgent), a hierarchical reasoning framework for longitudinal electronic health records. It consists of three core modules. The Temporal-Statistics Adapter (TSA) converts analytical plans into executable code for standardized trend output. The Chain-of-Thought Completion (COTC) layer leverages a symptom-trend-disease knowledge base with weighted scoring to evaluate disease risk, while the bounded completion module acquires structured evidence through standardized inquiries and iterative scoring constraints to ensure rigorous reasoning. By decoupling statistical computation, feature matching, and language generation, the framework eliminates reliance on complex multi-modal inputs and enables efficient longitudinal record analysis with lower computational overhead. Experimental results show that COTCAgent powered by Baichuan-M2 achieves 90.47% Top-1 accuracy on the self-built dataset and 70.41% on HealthBench, outperforming existing medical agents and mainstream large language models. The code is available at https://github.com/FrankDengAI/COTCAgent/.", "date": "2026-05-14", "categories": []}
{"id": "2605.15168", "title": "Text Knows What, Tables Know When: Clinical Timeline Reconstruction via Retrieval-Augmented Multimodal Alignment", "abstract": "Reconstructing precise clinical timelines is essential for modeling patient trajectories and forecasting risk in complex, heterogeneous conditions like sepsis. While unstructured clinical narratives offer semantically rich and contextually complete descriptions of a patient's course, they often lack temporal precision and contain ambiguous event timing. Conversely, structured electronic health record (EHR) data provides precise temporal anchors but misses a substantial portion of clinically meaningful events. We introduce a retrieval-augmented multimodal alignment framework that bridges this gap to improve the temporal precision of absolute clinical timelines extracted from text. Our approach formulates timeline reconstruction as a graph-based multistep process: it first extracts central anchor events from narratives to build an initial temporal scaffold, places non-central events relative to this backbone, and then calibrates the timeline using retrieved structured EHR rows as external temporal evidence. Evaluated using instruction-tuned large language models on the i2m4 benchmark spanning MIMIC-III and MIMIC-IV, our multimodal pipeline consistently improves absolute timestamp accuracy (AULTC) and improves temporal concordance across nearly all evaluated models over unimodal text-only reconstruction, without compromising event match rates. Furthermore, our empirical gap analysis reveals that 34.8% of text-derived events are entirely absent from tabular records, demonstrating that aligning these modalities can produce a more temporally faithful and clinically informative reconstruction of patient trajectories than either source alone.", "date": "2026-05-14", "categories": []}
{"id": "2505.04152", "title": "SocialLM: Social Signal Processing of Patient-Provider Communication using LLMs and Contextual Aggregation", "abstract": "Effective patient-provider communication is difficult to assess at scale. We examine whether large language models (LLMs) can track 20 social behaviors from clinical transcripts without fine-tuning. Across three model families and multiple prompting strategies, LLMs reliably detect social signals, though performance varies by patient race and visit segment. To address this variability under query-only API constraints, we introduce an agreement-weighted ensemble using group-level agreement patterns. This approach improves both accuracy and stability over the best individual model, demonstrating a practical pathway for scalable social signal tracking in clinical conversations.", "date": "2025-05-07", "categories": []}
{"id": "2602.03730", "title": "Efficient Generative Prediction for EHR Foundation Models: The SCOPE and REACH Estimators", "abstract": "Generative foundation models trained on tokenized electronic health record (EHR) timelines show promise for clinical outcome prediction via Monte Carlo sampling of simulated future trajectories. However, this approach suffers from three coupled limitations: sparse estimate distributions that poorly differentiate patient risk levels, extreme computational cost, and high sampling variance. We propose two new estimators that leverage next-token probability distributions underutilized by standard Monte Carlo: the Sum of Conditional Outcome Probability Estimator (SCOPE) and Risk Estimation from Anticipated Conditional Hazards (REACH). We prove both are unbiased, that REACH guarantees variance reduction over Monte Carlo for any model and outcome, and that REACH is a Rao-Blackwellization of any naive importance sampling scheme that preserves the non-outcome token distribution. Empirically, across $11$ clinically important outcomes in MIMIC-IV and the UChicago health system, SCOPE and REACH match $100$-sample Monte Carlo accuracy with median token reductions of $2.5\\times$ to $3.4\\times$ and reductions exceeding $80\\times$ for the rarest outcomes, with calibration preserved throughout. Because SCOPE reuses a single sampled pool across an arbitrary number of outcomes at no marginal generation cost while REACH provides a per-task variance guarantee, the two estimators are complementary in deployment and together meaningfully reduce the inference budget required for generative EHR foundation models, particularly for rare, high-impact outcomes in healthcare.", "date": "2026-02-03", "categories": []}
{"id": "2603.29475", "title": "Survival In-Context: Amortized Bayesian Survival Analysis via Prior-Fitted Networks", "abstract": "Survival analysis is crucial for many medical applications, but remains challenging for modern machine learning due to limited data, censoring, and the heterogeneity of tabular covariates. While the prior-fitted paradigm, which relies on pretraining models on large collections of synthetic datasets, has recently facilitated tabular foundation models for classification and regression, its suitability for time-to-event modeling remains unclear. We propose a flexible survival data generation framework that defines a rich survival prior with explicit control over covariates and time-event distributions. Building on this prior, we introduce Survival In-Context (SIC), a prior-fitted in-context learning model for survival analysis that is pretrained exclusively on synthetic data. SIC is trained to approximate Bayesian posterior predictive inference under the synthetic survival prior, enabling individualized survival prediction in a single forward pass, requiring no task-specific training or hyperparameter tuning. Across a broad evaluation on real-world survival datasets, SIC achieves competitive or superior performance compared to classical and deep survival models, particularly in small and medium-sized data regimes, highlighting the promise of a prior-fitted paradigm for survival analysis. The code and pretrained models will be made available upon publication.", "date": "2026-03-31", "categories": []}
{"id": "2605.11533", "title": "Checkup2Action: A Multimodal Clinical Check-up Report Dataset for Patient-Oriented Action Card Generation", "abstract": "Clinical check-up reports are multimodal documents that combine page layouts, tables, numerical biomarkers, abnormality flags, imaging findings, and domain-specific terminology. Such heterogeneous evidence is difficult for laypersons to interpret and translate into concrete follow-up actions. Although large language models show promise in medical summarisation and triage support, their ability to generate safe, prioritised, and patient-oriented actions from multimodal check-up reports remains under-benchmarked. We present \\textbf{Checkup2Action}, a multimodal clinical check-up report dataset and benchmark for structured \\textit{Action Card} generation. Each card describes one clinically relevant issue and specifies its priority, recommended department, follow-up time window, patient-facing explanation, and questions for clinicians, while avoiding diagnostic or treatment-prescriptive claims. The dataset contains 2,000 de-identified real-world check-up reports covering demographic information, physical examinations, laboratory tests, cardiovascular assessments, and imaging-related evidence. We formulate checkup-to-action generation as a constrained structured generation task and introduce an evaluation protocol covering issue coverage and precision, priority consistency, department and time recommendation accuracy, action complexity, usefulness, readability, and safety compliance. Experiments with general-purpose and medical large language models reveal clear trade-offs between issue coverage, action correctness, conciseness, and safety alignment. Checkup2Action provides a new multimodal benchmark for evaluating patient-oriented reasoning over clinical check-up reports.", "date": "2026-05-12", "categories": []}
{"id": "2605.12817", "title": "Training Large Language Models to Predict Clinical Events", "abstract": "Longitudinal clinical notes contain rich evidence of how patients evolve over time, but converting this signal into training supervision for clinical prediction remains challenging. We extend Foresight Learning to clinical prediction by converting time-ordered MIMIC-III notes into examples consisting of past patient context, a natural-language question about a possible future event, and a label resolved from later documentation. This process yields 6,900 prediction examples from 702 admissions across medications, procedures, organ support, microbiology, and mortality. A small LoRA adapter trained on these examples improves over the prompted base model, reducing expected calibration error from 0.1269 to 0.0398 and Brier score from 0.199 to 0.145, while slightly outperforming GPT-5 point estimates on held-out questions. The approach enables reusable clinical prediction supervision from longitudinal notes without hand-engineered structured features or endpoint-specific classifiers.", "date": "2026-05-12", "categories": []}
{"id": "2605.13464", "title": "A Unified Three-Stage Machine Learning Framework for Diabetes Detection, Subtype Discrimination, and Cognitive-Metabolic Hypothesis Testing", "abstract": "Diabetes mellitus affects over 537 million adults worldwide and remains a major challenge in preventive healthcare. Existing machine-learning studies primarily formulate diabetes prediction as a binary classification problem, while subtype-oriented analysis and glycaemic-cognitive associations remain comparatively underexplored. We present a reproducible three-stage machine learning framework for diabetes detection, subtype-oriented clustering, and metabolic-cognitive association analysis. In Stage 1, five supervised classifiers together with a stacking ensemble are benchmarked on the NCSU Diabetes Dataset using stratified five-fold cross-validation and evaluation metrics including ROC-AUC, balanced accuracy, recall, and F1-score. SVM-RBF and Logistic Regression achieve the highest ROC-AUC ($0.825 \\pm 0.026$), while Random Forest achieves the highest accuracy ($0.762 \\pm 0.030$). SHAP explainability identifies Glucose, BMI, and Age as the dominant predictive biomarkers. In Stage 2, silhouette-validated K-Means clustering ($k=2$, silhouette $\\approx 0.116$) is applied to confirmed diabetic cases using Glucose, Insulin, and Age, recovering clinically plausible subtype-oriented partitions without requiring ground-truth subtype labels. In Stage 3, statistical analysis of the Ohio Longitudinal Cognitive Dataset ($n=373$) reveals a significant positive association between glycaemic control and cognitive function ($\\rho_s = 0.208$, $p = 5.29 \\times 10^{-5}$), which survives Holm correction. The findings support the utility of statistically grounded and interpretable ML pipelines for reproducible diabetes analytics and subtype-aware exploratory analysis.", "date": "2026-05-13", "categories": []}
{"id": "2605.13568", "title": "Dynamical Predictive Modelling of Cardiovascular Disease Progression Post-Myocardial Infarction via ECG-Trained Artificial Intelligence Model", "abstract": "Myocardial infarction (MI) is a leading cause of death, and its adverse outcomes are urgent to predict. Yet ECG-based prognostic models underperform because deep learning requires large, labelled datasets, which are scarce in medicine. Foundation models can learn from unlabelled ECGs via selfsupervision, but medically relevant training strategies remain underexplored. We propose a pretrained artificial intelligence model that combines patient-specific temporal information using contrastive learning with supervised multitask heads, then fine-tunes on post-MI outcome prediction. The proposed model outperformed a model trained from scratch (0.794 vs 0.608 AUC) showing that clinically structured ECG modelling improves classification in limited data regimes.", "date": "2026-05-13", "categories": []}
{"id": "2605.13623", "title": "Multimodal Graph-based Classification of Esophageal Motility Disorders", "abstract": "Diagnosing esophageal motility disorders pose significant challenges due to the complexity of high-resolution impedance manometry (HRIM) data and variability in clinical interpretation. This work explores the feasibility of a multimodal Machine Learning (ML)-based classification approach that combines HRIM recordings with patient-specific information and incorporates a graph-based modeling of esophageal physiology. We analyze HRIM recordings with corresponding patient information from 104 patients with esophageal motility disorders. Patient data includes demographic, clinical, and symptom information extracted from structured questionnaires and free-text notes using keyword detection and large language model-based processing. HRIM data is represented as spatio-temporal graphs, where nodes correspond to pressure values along the esophagus and edges encode spatial adjacency and impedance dynamics. A graph neural network (GNN) is applied to learn physiologically meaningful representations, which are fused with patient embeddings for multi-category, multi-class classification of swallow events. The impact of patient features and graph-based modeling is evaluated by ablation studies and comparison to vision-based classifier baselines. The proposed multimodal approach indicates improvements over models that rely solely on HRIM-derived features across all classification categories. Additionally, the graph-based modeling provides gains compared to vision-based baselines. Our experiments systematically assess the complementary contribution of multiple modalities, as well as demonstrate the feasibility of our proposed graph-based approach. Our initial findings demonstrate that integrating patient-level data with graph-based representations of HRIM signals appears to be a promising direction for more accurate classification of esophageal motility disorders.", "date": "2026-05-13", "categories": []}
{"id": "2605.13786", "title": "Interpretable Machine Learning for Antepartum Prediction of Pregnancy-Associated Thrombotic Microangiopathy Using Routine Longitudinal Laboratory Data", "abstract": "Background: Pregnancy-associated thrombotic microangiopathy (P-TMA) is rare but life-threatening. Early risk prediction before overt clinical presentation remains challenging, as the associated laboratory abnormalities are subtle, multidimensional, and frequently masked by common physiological changes such as gestational thrombocytopenia and pregnancy-related proteinuria, thus overlapping heavily with benign obstetric and renal conditions. This complexity is poorly captured by univariate or rule-based approaches; however, it is addressable by machine learning, which can extract latent, time-dependent risk signatures from longitudinal clinical tests. Methods: This retrospective study included 300 pregnancies comprising 142 P-TMA cases and 158 controls. After exclusion of identifiers and non-informative variables, 146 longitudinal laboratory predictors were retained. Participants were divided into a training cohort (80%) and a held-out test cohort (20%) using stratified sampling. Five algorithms were evaluated: logistic regression, support vector machine with radial basis function kernel, random forest, extra trees, and gradient boosting. The final model was selected by mean cross-validated AUROC, refitted on the full training cohort, and evaluated once in the held-out test cohort. Interpretability analyses examined global feature importance and distributional patterns of leading predictors. Results: Gradient boosting was prespecified by cross-validation in the training cohort. The model achieved an AUROC of 0.872 (95% CI: 0.769-0.952) and an AUPRC of 0.883 (95% CI: 0.780-0.959) in a held-out test cohort, with sensitivity of 0.750 and specificity of 0.812. Conclusions: Longitudinal clinical laboratory tests obtained during routine care contained informative and clinically plausible signals for P-TMA risk. Notably, cystatin C at week 6 showed promise as an early monitoring indicator.", "date": "2026-05-13", "categories": []}
{"id": "2506.17501", "title": "DSA-NRP: No-Reflow Prediction from Angiographic Perfusion Dynamics in Stroke EVT", "abstract": "Following successful large-vessel recanalization via endovascular thrombectomy (EVT) for acute ischemic stroke (AIS), some patients experience a complication known as no-reflow, defined by persistent microvascular hypoperfusion that undermines tissue recovery and worsens clinical outcomes. Although prompt identification is crucial, standard clinical practice relies on perfusion magnetic resonance imaging (MRI) within 24 hours post-procedure, delaying intervention. In this work, we introduce the first-ever machine learning (ML) framework to predict no-reflow immediately after EVT by leveraging previously unexplored intra-procedural digital subtraction angiography (DSA) sequences and clinical variables. Our retrospective analysis included AIS patients treated at UCLA Medical Center (2011-2024) who achieved favorable mTICI scores (2b-3) and underwent pre- and post-procedure MRI. No-reflow was defined as persistent hypoperfusion (Tmax > 6 s) on post-procedural imaging. From DSA sequences (AP and lateral views), we extracted statistical and temporal perfusion features from the target downstream territory to train ML classifiers for predicting no-reflow. Our novel method significantly outperformed a clinical-features baseline(AUC: 0.7703 $\\pm$ 0.12 vs. 0.5728 $\\pm$ 0.12; accuracy: 0.8125 $\\pm$ 0.10 vs. 0.6331 $\\pm$ 0.09), demonstrating that real-time DSA perfusion dynamics encode critical insights into microvascular integrity. This approach establishes a foundation for immediate, accurate no-reflow prediction, enabling clinicians to proactively manage high-risk patients without reliance on delayed imaging.", "date": "2025-06-20", "categories": []}
{"id": "2511.11935", "title": "SurvBench: A Standardised Preprocessing Pipeline for Multi-Modal Electronic Health Record Survival Analysis", "abstract": "Deep-learning survival models for electronic health record (EHR) data are hard to compare across papers because the upstream preprocessing step, which includes cohort definition, time discretisation, missingness handling, and censoring rules, is typically undocumented and inconsistent. A reported difference in concordance between two mortality models can therefore reflect any of these choices rather than a modelling contribution.\n  We present SurvBench, an open-source preprocessing pipeline that converts raw PhysioNet exports into model-ready tensors for survival analysis. SurvBench covers four critical-care databases (MIMIC-IV, eICU, MC-MED, HiRID) and four input modalities: time-series vitals and laboratory values, static demographics, International Classification of Diseases (ICD) codes, and radiology report embeddings. Every preprocessing decision is controlled through YAML configuration. Imputation, scaling, and feature filtering are fit on the training fold only. Missingness is recorded as a binary mask alongside each feature tensor. The pipeline handles single-risk endpoints (in-hospital and in-ICU mortality) and competing-risks endpoints (a three-way emergency-department admission pathway, with home discharge treated as administrative censoring). We also provide support for harmonised cross-dataset external validation between eICU and MIMIC-IV.\n  SurvBench is publicly available at https://github.com/munibmesinovic/SurvBench, providing a robust platform that future deep-learning EHR survival work, especially nascent multi-modal approaches, can be measured against under matched preprocessing.", "date": "2025-11-14", "categories": []}
{"id": "2605.03895", "title": "From Data Lifting to Continuous Risk Estimation: A Process-Aware Pipeline for Predictive Monitoring of Clinical Pathways", "abstract": "This paper presents a reproducible and process-aware pipeline for predictive monitoring of clinical pathways. The approach integrates data lifting, temporal reconstruction, event log construction, prefix-based representations, and predictive modeling to support continuous reasoning on partially observed patient trajectories, overcoming the limitations of traditional retrospective process mining. The framework is evaluated on COVID-19 clinical pathways using ICU admission as the prediction target, considering 4,479 patient cases and 46,804 prefixes. Predictive models are trained and evaluated using a case-level split, with 896 patients in the test set. Logistic Regression achieves the best performance (AUC 0.906, F1-score 0.835). A detailed prefix-based analysis shows that predictive performance improves progressively as new clinical events become available, with AUC increasing from 0.642 at early stages to 0.942 at later stages of the pathway. The results highlight two key findings: predictive signals emerge progressively along clinical pathways, and process-aware representations enable effective early risk estimation from evolving patient trajectories. Overall, the findings suggest that predictive monitoring in healthcare is best conceived as a continuous, dynamically aware process, in which risk estimates are progressively refined as the patient journey evolves.", "date": "2026-05-05", "categories": []}
{"id": "2605.11091", "title": "ASD-Bench: A Four-Axis Comprehensive Benchmark of AI Models for Autism Spectrum Disorder", "abstract": "Automated ASD screening tools remain limited by single-architecture evaluations, axis-restricted assessment, and near-exclusive focus on adult cohorts, obscuring age-specific diagnostic patterns critical for early intervention. We introduce ASD-Bench, a systematic tabular benchmark evaluating ML, deep learning, and foundation model configurations across three age cohorts (children 1-11 yr, adolescents 12-16 yr, adults 17-64 yr) on four axes: predictive performance, calibration, interpretability, and adversarial robustness. Applied to a curated v3 dataset of 4,068 AQ-10 records, our benchmark spans classical models (XGBoost, AdaBoost, Random Forest, Logistic Regression), neural networks (MLP), deep tabular transformers (TabNet, TabTransformer, FT-Transformer), and TabPFN v2. We introduce the Heuristic Aggregate Penalty (HAP): a cost-sensitive metric penalising false negatives more heavily and incorporating cross-validation variance for deployment stability. Adult classification yields high performance (10/17 models achieve perfect F1 and AUC), while adolescents present a harder task (F1 ceiling 0.837 vs. 0.915 for children). Feature hierarchies shift across cohorts: A9 (social motivation) dominates for children, A5 (pattern recognition) leads for adolescents, and adults exhibit a flatter importance profile consistent with developmental social masking. Accuracy and calibration are dissociated: AdaBoost achieves F1=1.000 on adults with ECE=0.302, confirming single-metric evaluation is insufficient for clinical AI. Cohort-specific deployment recommendations are provided. All findings should be interpreted as proof-of-concept evidence on questionnaire-derived labels rather than clinically validated diagnostic performance.", "date": "2026-05-11", "categories": []}
{"id": "2605.11143", "title": "ClinicalBench: Stress-Testing Assertion-Aware Retrieval for Cross-Admission Clinical QA on MIMIC-IV", "abstract": "Reasoning benchmarks measure clinical performance on clean inputs. We evaluate the step before reasoning: retrieval over real EHR notes, where negation, temporality, and family-versus-patient attribution can flip a correct answer to a wrong one. EpiKG carries an assertion label and a temporality tag with every fact in a patient knowledge graph, then routes retrieval by question intent. ClinicalBench is a 400-question test over 43 MIMIC-IV patients across 9 assertion-sensitive categories. A 7-condition ablation tests each piece of EpiKG across six LLMs (Claude Opus 4.6, GPT-OSS 20B, MedGemma 27B, Gemma 4 31B, MedGemma 1.5 4B, Qwen 3.5 35B). Three physicians blindly adjudicated 100 paired items. The author-blind primary endpoint, leave-author-out paired exact McNemar on 50 unanimous-strict items rated by two external physicians, yields +22.0 percentage points (95 percent Newcombe CI [+5.1, +31.5], p=0.0192). The architectural novelty, intent-aware KG-RAG over a Contriever dense-RAG baseline (C2b to C4g_kw on the change-excluded n=362 endpoint), is +8.84 percentage points (paired McNemar p=1.79e-3); +12.43 percentage points under oracle intent. Sensitivities agree directionally: three-rater physician majority +24.0 percentage points (subject to single-author circularity); deterministic keyword reproducibility proxy +39.5 percentage points. Across the six models, the gain shrinks as the LLM-alone baseline rises (beta=-1.123, r=-0.921, p=0.009). With n=6 this looks more like regression to the mean than encoding substituting for model size. Physician adjudication identified 56 percent of auto-generated reference answers as defective, a methodological finding indicating that NLP-pipeline clinical-QA benchmarks require physician adjudication to be usable. ClinicalBench, the frozen evaluator, three-rater adjudication data, and the EpiKG output stack are publicly released.", "date": "2026-05-11", "categories": []}
{"id": "2605.11284", "title": "Rethinking external validation for the target population: Capturing patient-level similarity with a generative model", "abstract": "Background: External validation is essential for assessing the transportability of predictive models. However, its interpretation is often confounded by differences between external and development populations. This study introduces a framework to distinguish model deficiencies from case-mix effects.\n  Method: We propose a framework that quantifies each external patient's similarity to the development data and measures performance in subgroups with varying levels of alignment to the development distribution. We use generative models, specifically autoencoders, to estimate similarity, offering a more flexible alternative to traditional linear approaches and enabling validation without sharing the original development data. The utility of autoencoder-based similarity measure is demonstrated using synthetic data, and the framework's application is illustrated using data from the Netherlands Heart Registration (NHR) to predict mortality after transcatheter aortic valve implantation.\n  Results: Our framework revealed substantial variation in model performance across similarity-defined subgroups, differences that remain hidden under conventional external validation yet can meaningfully alter conclusions. In several settings, conventional external validation suggested poor overall performance. However, after accounting for differences in patient characteristics, for some sub-groups, the model performance was consistent with internal validation results. Conversely, apparently acceptable overall performance could mask clinically relevant performance deficits in specific subgroups.\n  Conclusion: The proposed framework enhances the interpretability of external validation by linking model performance to population alignment with the development data. This provides a more principled basis for deciding whether a model is transportable and to which patients it can be safely applied.", "date": "2026-05-11", "categories": []}
{"id": "2605.11774", "title": "From Token to Token Pair: Efficient Prompt Compression for Large Language Models in Clinical Prediction", "abstract": "By processing electronic health records (EHRs) as natural language sequences, large language models (LLMs) have shown potential in clinical prediction tasks such as mortality prediction and phenotyping. However, longitudinal or highly frequent EHRs often yield excessively long token sequences that result in high computational costs and even reduced performance. Existing solutions either add modules for compression or remove less important tokens, which introduce additional inference latency or risk losing clinical information. To achieve lossless compression of token sequences without additional cost or loss of performance, we propose Medical Token-Pair Encoding (MedTPE), a layered method that extends standard tokenisation for EHR sequences. MedTPE merges frequently co-occurring medical token pairs into composite tokens, providing lossless compression while preserving the computational complexity through a dependency-aware replacement strategy. Only the embeddings of the newly introduced tokens of merely 0.5-1.0% of the LLM's parameters are fine-tuned via self-supervised learning. Experiments on real-world datasets for two clinical scenarios demonstrate that MedTPE reduces input token length by up to 31% and inference latency by 34-63%, while maintaining or even improving both predictive performance and output format compliance across multiple LLMs and four clinical prediction tasks. Furthermore, MedTPE demonstrates robustness across different input context lengths and generalisability to scientific and financial domains and different languages.", "date": "2026-05-12", "categories": []}
{"id": "2605.12241", "title": "Pretraining Strategies and Scaling for ECG Foundation Models: A Systematic Study", "abstract": "Specialized foundation models are beginning to emerge in various medical subdomains, but pretraining methodologies and parametric scaling with the size of the pretraining dataset are rarely assessed systematically and in a like-for-like manner. This work focuses on foundation models for electrocardiography (ECG) data, one of the most widely captured physiological time series world-wide. We present a comprehensive assessment of pretraining methodologies, covering five different contrastive and non-contrastive self-supervised learning objectives for ECG foundation models, and investigate their scaling behavior with pretraining dataset sizes up to 11M input samples, exclusively from publicly available sources. Pretraining strategy has a meaningful and consistent impact on downstream performance, with contrastive predictive coding (slightly ahead of JEPA) yielding the most transferable representations across diverse clinical tasks. Scaling pretraining data continues to yield meaningful improvements up to 11M samples for most objectives. We also compare model architectures across all pretraining methodologies and find evidence for a clear superiority of structured state space models compared to transformers and CNN models. We hypothesize that the strong inductive biases of structured state space models, rather than pretraining scale alone, are the primary driver of effective ECG representation learning, with important implications for future foundation model development in this and potentially other physiological signal domains.", "date": "2026-05-12", "categories": []}
{"id": "2605.12335", "title": "EHR-RAGp: Retrieval-Augmented Prototype-Guided Foundation Model for Electronic Health Records", "abstract": "Electronic Health Records (EHR) contain rich longitudinal patient information and are widely used in predictive modeling applications. However, effectively leveraging historical data remains challenging due to long trajectories, heterogeneous events, temporal irregularity, and the varying relevance of past clinical context. Existing approaches often rely on fixed windows or uniform aggregation, which can obscure clinically important signals. In this work, we introduce EHR-RAGp, a retrieval-augmented foundation model that dynamically integrates the most relevant patient history across diverse clinical event types. We propose a prototype-guided retrieval module that acts as an alignment mechanism and estimates the relevance of retrieved historical chunks with respect to a given prediction task, guiding the model towards the most informative context. Across multiple clinical prediction tasks, EHR-RAGp consistently outperforms state-of-the-art EHR foundation models and transformer-based baselines. Furthermore, integrating EHR-RAGp with existing clinical foundation models yields substantial performance gains. Overall, EHR-RAGp provides a scalable and efficient framework for leveraging long-range clinical context to improve downstream performance.", "date": "2026-05-12", "categories": []}
{"id": "2505.18184", "title": "AI- Enhanced Stethoscope in Remote Diagnostics for Cardiopulmonary Diseases", "abstract": "The increase in cardiac and pulmonary diseases presents an alarming and pervasive health challenge on a global scale responsible for unexpected and premature mortalities. In spite of how serious these conditions are, existing methods of detection and treatment encounter challenges, particularly in achieving timely diagnosis for effective medical intervention. Manual screening processes commonly used for primary detection of cardiac and respiratory problems face inherent limitations, increased by a scarcity of skilled medical practitioners in remote or under-resourced areas. To address this, our study introduces an innovative yet efficient model which integrates AI for diagnosing lung and heart conditions concurrently using the auscultation sounds. Unlike the already high-priced digital stethoscope, our proposed model has been particularly designed to deploy on low-cost embedded devices and thus ensure applicability in under-developed regions that actually face an issue of accessing medical care. Our proposed model incorporates MFCC feature extraction and engineering techniques to ensure that the signal is well analyzed for accurate diagnostics through the hybrid model combining Gated Recurrent Unit with CNN in processing audio signals recorded from the low-cost stethoscope. Beyond its diagnostic capabilities, the model generates digital audio records that facilitate in classifying six pulmonary and five cardiovascular diseases. Hence, the integration of a cost effective stethoscope with an efficient AI empowered model deployed on a web app providing real-time analysis, represents a transformative step towards standardized healthcare", "date": "2025-05-18", "categories": []}
{"id": "2506.12944", "title": "Unsupervised risk factor identification across cancer types and data modalities via explainable artificial intelligence", "abstract": "Risk stratification is a key tool in clinical decision-making, yet current approaches often fail to translate sophisticated survival analysis into actionable clinical criteria. We present a novel method for unsupervised machine learning that directly optimizes for survival heterogeneity across patient clusters through a differentiable adaptation of the multivariate logrank statistic. Unlike most existing methods that rely on proxy metrics, our approach represents novel methodology for training any neural network architecture on any data modality to identify prognostically distinct patient groups. We thoroughly evaluate the method in simulation experiments and demonstrate its utility in practice by applying it to two distinct cancer types: analyzing laboratory parameters from multiple myeloma patients and computed tomography images from non-small cell lung cancer patients, identifying prognostically distinct patient subgroups with significantly different survival outcomes in both cases. Post-hoc explainability analyses uncover clinically meaningful features determining the group assignments which align well with established risk factors and thus lend strong weight to the methods utility. This pan-cancer, model-agnostic approach represents a valuable advancement in clinical risk stratification, enabling the discovery of novel prognostic signatures across diverse data types while providing interpretable results that promise to complement treatment personalization and clinical decision-making in oncology and beyond.", "date": "2025-06-15", "categories": []}
{"id": "2507.11185", "title": "Explainable Machine Learning Framework for Cardiovascular Disease Diagnosis and Prognosis", "abstract": "Heart disease continues to pose a critical worldwide health issue, more specifically in areas with insufficient access to healthcare infrastructure and diagnostic systems. Conventional diagnostic approaches often fall short in accurately detecting and managing heart disease risks, resulting in unfavorable outcomes. Machine learning presents a powerful means to boost the precision and reliability of cardiovascular disease prognosis and diagnosis. In this research, we introduced a unified approach incorporating classification techniques for detecting heart disease and regression techniques for forecasting associated risks. The analysis utilized the dataset, named Heart Disease, containing 1,035 instances. To mitigate the problem of data disproportion, the SMOTE was implemented, producing 100,000 additional synthetic samples. Evaluation metrics such as F1-score, recall, precision, accuracy, MAE, RMSE, MSE, and R2 were adopted to evaluate the performance of the models. Among the classification algorithms, Random Forest delivered the most notable results, attaining an accuracy of 0.972 on actual data and 0.976 on artificially generated data. For prediction modeling, for both synthetic and real samples, linear regression produced the best R2 values of 0.992 and 0.984, respectively, along with the least amount of measurement errors. Furthermore, Explainable AI methods were utilized to improve the comprehensibility of the model outcomes. This paper emphasizes the transformative capabilities of machine learning for diagnosing cardiovascular disease and estimating risk levels, thereby supporting timely interventions and enhancing clinical settings.", "date": "2025-07-15", "categories": []}
{"id": "2604.23876", "title": "Cardiac Stability Theory: An Axiomatically Grounded Framework for Continuous Cardiac Health Monitoring via Smartphone Photoplethysmography", "abstract": "We present Cardiac Stability Theory (CST), an axiomatically grounded framework formally defining cardiovascular health as a stability margin around a cardiac dynamical attractor. From four axioms we derive the Cardiac Stability Index (CSI), a composite scalar in [0,1] integrating the largest Lyapunov exponent, recurrence determinism, and signal entropy via time-delay embedding. The ECG-based model (CSISurrogateV2, CNN-Transformer) achieves $R^2=0.8788$, MAE$=0.0234$ on PTB-XL (21,799 recordings). We extend CSI to smartphone PPG via Complementary Domain Transfer (CDT): CSISurrogateV2 generates pseudo-labels for the BUT PPG dataset (48 recordings, 12 subjects), training TinyCSINet (122,849 parameters), achieving MAE$=0.0557$, $\\rho=0.660$ on the held-out test set ($n=1065$ windows) at ${<}30$ ms mobile latency. CDT is validated on BIDMC, Welltory, and RWS-PPG. Paired validation on 5,035 BIDMC windows yields $r=0.454$ ($\\rho=0.485$, $p<10^{-295}$), confirming correlated cardiac stability across modalities. CSI is negatively correlated with age (slope $= -0.000225$ CSI/year, PTB-XL), discriminates atrial fibrillation from normal sinus rhythm (AUROC$=0.89$), and is robust under Perturbation Invariance Training (max AUC drop 1.65\\%). We derive HeartSpan, a longitudinal stability metric relative to population age norms, enabling continuous non-invasive cardiac monitoring from commodity smartphones for longevity tracking and cardiac risk stratification.", "date": "2026-04-26", "categories": []}
{"id": "2605.06226", "title": "A Versatile AI Agent for Rare Disease Diagnosis and Risk Gene Prioritization", "abstract": "Accurate and timely diagnosis is essential for effective treatment, particularly in the context of rare diseases. However, current diagnostic workflows often lead to prolonged assessment times and low accuracy. To address these limitations, we introduce Hygieia, a multi-modal AI agent system designed to support precision disease diagnosis by integrating diverse data sources, including phenotypic features, genetic profiles, and clinical records. Hygieia features a router-based and knowledge-enhanced framework that mitigates hallucination and tailors diagnostic strategies to different disease categories. Notably, it prioritizes risk-related genomic factors for rare diseases and provides confidence scores to assist clinical decision-making. We conducted a comprehensive evaluation demonstrating that Hygieia achieves state-of-the-art performance across multiple diagnostic benchmarks. In collaboration with clinical experts from Yale School of Medicine and Duke-NUS Medical School, we further validated its practical utility by showing (1) Hygieia's superior diagnostic performance compared to physicians with an improvement from 12%-60% and (2) its effectiveness in assisting clinicians with medical records for handling real-world cases. Our findings indicate that Hygieia not only enhances diagnostic accuracy and interpretability but also significantly reduces clinician workload, highlighting its potential as a valuable tool in clinical decision support systems.", "date": "2026-05-07", "categories": []}
{"id": "2605.08198", "title": "FairHealth: An Open-Source Python Library for Trustworthy Healthcare AI in Low-Resource Settings", "abstract": "We present FairHealth, an open-source Python library that provides a unified, modular framework for trustworthy machine learning in healthcare applications, with particular focus on low-resource and low-income country (LMIC) settings such as Bangladesh. FairHealth addresses four critical gaps in existing healthcare AI toolkits: (1) the absence of integrated fairness auditing for biosignals and clinical tabular data; (2) the lack of privacy-preserving federated learning tools compatible with standard ML workflows; (3) missing explainability tools tailored for low-bandwidth clinical decision support; and (4) no existing toolkit covering Global South healthcare datasets. Built from five peer-reviewed research contributions, FairHealth provides six modules covering federated learning with homomorphic encryption (fairhealth.federated), intersectional fairness metrics (fairhealth.fairness), hybrid fuzzy-SHAP explainability (fairhealth.explain), multilingual dengue triage (fairhealth.lowresource), equitable disaster aid allocation (fairhealth.equity), and public dataset loaders (fairhealth.datasets). All datasets used are publicly available without institutional data use agreements. FairHealth is installable via pip install fairhealth(PyPI: pypi.org/project/fairhealth/) and available at https://github.com/Farjana-Yesmin/fairhealth.", "date": "2026-05-05", "categories": []}
{"id": "2605.08199", "title": "Domain-Adaptive Arrhythmia Classification Using a Hybrid Transformer on Wearable Heart Signals", "abstract": "Cardiovascular disease remains the leading cause of death globally, underscoring the need for effective, accessible monitoring solutions, particularly through wearable devices that enable continuous, real-time tracking of heart rhythms in home settings. However, deploying deep learning models trained on clinical electrocardiogram (ECG) datasets to wearable devices remains challenging, as differences in recording equipment, signal quality, and patient populations introduce domain shifts that degrade model performance. We propose a hybrid transformer model that processes continuous ECG signals alongside seven heart rate variability (HRV) features, where the raw signal path captures beat-level morphological patterns and the HRV path encodes rhythm regularity statistics, allowing the model to jointly leverage complementary information from both representations. To enhance the model's ability to generalize across domains, we employ representation learning techniques, including Maximum Mean Discrepancy (MMD), a non-parametric kernel-based metric that quantifies the distance between feature distributions of different domains, to align feature distributions between source and target domains, addressing the challenge of domain shifts between public datasets and wearable device data. By leveraging five public ECG datasets for training, the model learns robust, generalized representations that mitigate domain-specific biases. When tested on wearable device data with an unseen domain, the model achieved an F1-macro 95% and balanced accuracy of 96.15%. These results demonstrate minimal performance degradation, with only a 2% drop in F1-macro compared to seen-domain evaluation, highlighting the model's generalization capabilities and its potential for reliable, real-time heart monitoring applications in home and ambulatory settings.", "date": "2026-05-05", "categories": []}
{"id": "2605.08207", "title": "A Breast Vision Pathology Foundation Model for Real-world Clinical Utility", "abstract": "Pathology foundation models have shown strong retrospective performance, but whether such systems can support clinically relevant use remains unclear. This challenge is particularly important in breast cancer, where pathological assessment serves as the gold standard for diagnosis and guides treatment planning, surgical decision-making and risk stratification across pre-, intra- and post-operative stages. Here we present \\textbf{BRAVE}, a breast-adaptive pathology foundation model developed and evaluated using a total resource of 101,638 breast whole-slide images from 32 sources across Asia, Europe and North America. We assessed BRAVE across 34 tasks in 82 cohorts spanning pre-operative biopsy, intra-operative frozen section and post-operative resection, using an evidence chain comprising retrospective benchmarking, clinically challenging scenarios, workflow-oriented clinical impact simulations, prospective observational validation with the thresholds locked in the retrospective cohorts and crossover pathologist-AI interaction studies. Across these settings, BRAVE supported practical roles in the clinical workflow, including safe exclusion of low-risk cases from routine review, AI-assisted second-review rescue of initially missed positives and prioritization of cases for further assessment. In prospective validation across three centres, BRAVE excluded 76.9% of negative biopsy cases (NPV 0.953) and 70.1% of negative frozen-section cases (NPV 0.973), and triaged 78.8% of post-operative subtyping cases as high-confidence clear-cut cases (NPV 1.000). In reader studies, AI assistance improved balanced accuracy from 88.5% to 95.1% (OR 3.14, P<0.001), with better efficiency, confidence and inter-rater agreement. BRAVE-derived scores also independently predicted disease-free survival (adjusted HR 4.79, P<0.001) and overall survival (adjusted HR 8.14, P<0.001).", "date": "2026-05-06", "categories": []}
{"id": "2605.08230", "title": "Social Determinants of Health and Fentanyl Overdose Mortality Across US Counties: An XGBoost and SHAP Analysis Identifying Silent Risk Counties and Treatment Deserts", "abstract": "Background: Fentanyl overdose deaths are still increasing across the U.S. We do not fully understand which county-level social and structural conditions lead to higher overdose death rates. Social determinants of health, including disability, treatment access, and behavioral health issues, may help identify vulnerable counties before deaths become severe. No earlier study has used explainable machine learning with SHAP attribution on 2022 CDC WONDER data to study treatment access gaps and silent risk counties.\n  Methods: We combined data from four government sources for 975 U.S. counties, including CDC WONDER (2022) overdose mortality data, CDC Social Vulnerability Index (SVI), CDC PLACES health behavior data, and Area Health Resources Files. An XGBoost model was used to predict overdose mortality risk using Standardized Mortality Ratio (SMR). Five-fold cross-validation was used to test model accuracy, and SHAP values were used to show which factors increase or decrease risk.\n  Results: XGBoost outperformed all tested models (Spearman rho=0.67, R2=0.457, MAE=0.409, high-risk recall=71.1%). Top predictors were disability rate, hypertension, smoking, and lack of vehicle access. Treatment desert counties had 52.6% higher overdose mortality (SMR 1.786 vs 1.170; p<0.0001). K-means identified 143 silent risk counties. Overdose deaths were spatially clustered (Moran's I=0.505, p=0.001) with 75 hotspots and 136 coldspots. Suppressed counties were 58.2% of WONDER counties, mostly rural (72%) and treatment deserts (65%).\n  Conclusions: County-level SDOH factors predict overdose deaths, especially disability, treatment access, and behavioral health burden. MOUD expansion should prioritize treatment desert counties, and silent risk counties need early intervention before mortality worsens.", "date": "2026-05-06", "categories": []}
{"id": "2605.08302", "title": "SGC-RML: A reliable and interpretable longitudinal assessment for PD in real-world DNS", "abstract": "Real-world digital Parkinson's disease assessment faces challenges such as heterogeneous modalities, cross-device bias, and incomplete labeling. Existing methods often focus on average predictive performance, lacking the reliability mechanisms needed for retrospective reliability-aware assessment - namely, determining when the model is reliable, when to reject an assessment, when to retest, and from which symptom dimensions the predictions are based. This paper proposes SGC-RML, which maps speech, gait, wearable motion, mobility tasks, and clinical variables to a shared 8-dimensional symptom node space (7 clinical symptom nodes and 1 reliability_state auxiliary node), unifying motor and non-motor representations through a symptom atlas. By jointly introducing uncertainty estimation, conformal calibration, and selective decision routing, the model can not only predict symptoms and severity but also reject assessments or suggest retests when evidence is insufficient. We validate this framework on five real-world PD datasets, covering classification, regression, event detection, and longitudinal severity prediction. Experiments show that SGC-RML achieves an MAE of 4.579 / R^2 of 0.772 on PPMI, an AUC of 0.953 on mPower, and an AUC of 0.825 on PADS. Under leak-free temporal anchoring, as few as 5 subject-specific anchors transform UCI from an essentially non-predictive subject-independent setting (motor MAE 8.38, CCC 0.02) into a calibrated longitudinal assessment (motor MAE 3.24, CCC 0.756) with split-conformal coverage held at the 0.80 target. Under the Daphnet LOSO protocol, it achieves an F1 of 0.803 / AUC of 0.872. These results demonstrate that SGC-RML provides a unified paradigm for accurate, calibrated, auditable, and symptom-interpretable retrospective longitudinal assessment of PD under incomplete multimodal conditions.", "date": "2026-05-08", "categories": []}
{"id": "2605.08445", "title": "Measuring What Matters: Benchmarking Generative, Multimodal, and Agentic AI in Healthcare", "abstract": "AI models are increasingly deployed in live clinical environments where they must perform reliably across complex, high-stakes workflows that standard training and validation datasets were never designed to capture. Evaluating these systems requires benchmarks: structured combinations of tasks, datasets, and metrics that enable reproducible, comparable measurement of what a model can do. The central challenge in healthcare AI is not performance alone, but the absence of systematic methods to measure reliability, safety, and clinical relevance under real-world conditions. Most existing benchmarks test what a model knows; too few test whether it can perform reliably and without failing across the full complexity of real clinical tasks. Current benchmarks have accumulated through ad hoc dataset construction optimized for narrow task performance: frontier models achieve near-perfect scores on medical licensing examinations, but when evaluated across real clinical tasks, performance degrades sharply, scoring 0.74--0.85 on documentation, 0.61--0.76 on clinical decision support, and only 0.53--0.63 on administrative and workflow tasks \\cite{medhelm}. High benchmark scores give a false sense of deployment readiness, and the gap between performance and utility widens precisely as AI systems take on more consequential clinical roles. Without a principled framework for benchmark design, the field cannot determine whether poor clinical performance reflects model limitations or failures in how performance is being measured.", "date": "2026-05-08", "categories": []}
{"id": "2605.08533", "title": "Human-LLM Dialogue Improves Diagnostic Accuracy in Emergency Care", "abstract": "Clinical decision-making in emergency medicine demands rapid, accurate diagnoses under uncertainty. Despite benchmark progress, evidence for LLMs as interactive aids in live physician workflows remains sparse. MedSyn lets physicians iteratively query an LLM provided with the full clinical record while initially viewing only the chief complaint. Seven physicians (three seniors, four residents) completed baseline and AI-assisted sessions across 52 MIMIC-IV cases stratified by difficulty. Blinded evaluation showed residents' Hard-case correctness rose from 0.589 to 0.734; difficulty-standardised completely-correct rates confirmed a medium effect ({\\Delta} = 0.092; p = 0.071; d = 0.47). Automated metrics corroborated these gains: standardised any-match accuracy improved by 0.156 (p < 0.0001), and residents showed the largest F1 gain ({\\Delta} = 0.138; p < 0.0001). Dialogue analysis revealed expertise-dependent strategies (seniors asked targeted, hypothesis-driven questions; residents relied on broader queries) and cross-expertise concordance increased ({\\Delta} = 0.145; p < 0.0001). Interactive LLM support meaningfully enhances diagnostic reasoning.", "date": "2026-05-08", "categories": []}
{"id": "2605.08897", "title": "Shapley Regression for Rare Disease Diagnosis Support: a case study on APDS", "abstract": "Activated PI3K8 Syndrome (APDS) is a rare genetic immune disorder caused by variants in PIK3CD or PIK3R1, with highly heterogeneous symptoms that often delay diagnosis. Early recognition is hampered by overlapping clinical presentations and limited clinician awareness, motivating systematic, data-driven approaches to detect APDS-associated phenotypic patterns in routine electronic health records. Traditional linear scoring systems cannot capture complex symptom interactions, while deep learning models, though expressive, often lack interpretability. To bridge this gap, we propose Shapley regression, a novel game-theoretic model replacing the linear predictor with a k-additive cooperative game, explicitly modeling co-occurrence of symptoms while maintaining the transparency and convexity of logistic regression. We carry out an empirical study of our lightweight method on eight public biomedical datasets, showing that a 2-additive model with $l_{2}$ regularization achieves an optimal trade-off between predictive power and noise robustness. We also apply it to a real-world cohort of 222 patients, on which Shapley regression accurately distinguished APDS cases from matched controls, confirming and validating phenotypes known to be associated with APDS, and facilitating the exploration of pairwise interactions between symptoms, validated by clinical experts.", "date": "2026-05-09", "categories": []}
{"id": "2605.08955", "title": "Outlier detection for patient monitoring and alerting", "abstract": "We develop and evaluate a data-driven approach for detecting unusual (anomalous) patient-management decisions using past patient cases stored in electronic health records (EHRs). Our hypothesis is that a patient-management decision that is unusual with respect to past patient care may be due to an error and that it is worthwhile to generate an alert if such a decision is encountered. We evaluate this hypothesis using data obtained from EHRs of 4486 post-cardiac surgical patients and a subset of 222 alerts generated from the data. We base the evaluation on the opinions of a panel of experts. The results of the study support our hypothesis that the outlier-based alerting can lead to promising true alert rates. We observed true alert rates that ranged from 25\\% to 66\\% for a variety of patient-management actions, with 66\\% corresponding to the strongest outliers.", "date": "2026-05-09", "categories": []}
{"id": "2605.09634", "title": "Can We Trust LLMs for Mental Health Screening? Consistency, ASR Robustness, and Evidence Faithfulness", "abstract": "LLMs can estimate Hospital Anxiety and Depression Scale (HADS) scores from speech in a zero-shot manner, but clinical deployment requires reliability across three dimensions: intra-model consistency, ASR robustness, and evidence faithfulness. We evaluate three LLMs (Phi-4, Gemma-2-9B, and Llama-3.1-8B) on 111 English-speaking participants using ground-truth transcripts and three Whisper ASR variants (Large, Medium, Small), with three independent runs per model-condition pair. We find that (i) Phi-4 and Gemma-2-9B achieve excellent intra-model consistency (ICC > 0.89) with minimal degradation under ASR; (ii) Llama-3.1-8B shows ASR-fragile consistency, with ICC dropping from 0.82 to 0.36 at 10% WER; (iii) predictive validity is largely preserved under ASR for robust models; and (iv) keyword groundedness exceeds 93% for Phi-4 and Gemma-2-9B but falls to 77-81% for Llama-3.1-8B. Inter-model keyword agreement is far lower than score-level agreement, revealing a score-evidence dissociation with implications for clinical interpretability.", "date": "2026-05-10", "categories": []}
{"id": "2605.09765", "title": "WISTERIA: Learning Clinical Representations from Noisy Supervision via Multi-View Consistency in Electronic Health Records", "abstract": "Representation learning in electronic health records (EHR) has largely followed paradigms inherited from natural language processing, relying on sequence modeling and reconstruction based objectives that treat clinical labels as ground truth. However, real world clinical supervision is inherently weak, arising from heterogeneous, noisy, and institution specific labeling processes such as billing codes, heuristic phenotypes, and incomplete annotations. In this work, we propose WISTERIA, a weakly supervised representation learning framework that models labels as stochastic observations of an underlying latent clinical state. Instead of optimizing against a single supervision signal, WISTERIA constructs multiple weak supervision operators and learns representations by enforcing consistency across their induced label distributions. This multi view formulation induces an implicit denoising mechanism, allowing the model to recover clinically meaningful structure by reconciling disagreement between noisy labelers. We further incorporate ontology aware regularization in the label space to impose semantic structure over supervision signals. Empirically, WISTERIA improves predictive performance across standard EHR benchmarks, demonstrates strong robustness to label noise, and exhibits superior cross institutional generalization compared to sequence based pretraining objectives. These results suggest that explicitly modeling the supervision process rather than treating labels as fixed targets provides a more appropriate inductive bias for learning robust and clinically meaningful representations from EHR data.", "date": "2026-05-10", "categories": []}
{"id": "2605.09771", "title": "Marrying Generative Model of Healthcare Events with Digital Twin of Social Determinants of Health for Disease Reasoning", "abstract": "Despite the central role of sensor-derived measurements such as imaging traits and plasma biomarkers in biomedical research and clinical practice, existing generative models for disease prediction largely depend on event-level representations from hospital and registry data. Given the multi-factorial nature of human disease, the absence of explicit modeling of social determinants of health (SDoH), even in the limited form of ICD-coded proxies (chapters Z and V--Y in ICD-10), limits the capacity for personalized disease modeling and clinical decision support. To address this limitation, we propose a generative model with ICD-coded proxies of SDoH for \\textit{in silico} modeling of disease reasoning, a conditioned latent diffusion framework that establishes the connection between multi-organ sensor data with tokenized healthcare events. Specifically, we introduce a novel geometric diffusion model to characterize the temporal evolution of complex data representation such as brain networks (region-to-region connectivity encoded in a graph), in parallel with diffusion models for tabular data from other organ systems. Together, we integrate the generative model with digitalized SDoH proxies (coined \\modelname{}) for simulated intervention and reasoning of future disease trajectories. We conduct extensive experiments on the UK Biobank (UKB) dataset, which contains organ-specific imaging traits, including brain (44,834), heart (23,987), liver (28,722), and kidney (32,155), along with nearly 500k medical history sequences (age range: 25$\\sim$89 years). Our \\modelname{} achieves significant improvements over state-of-the-art human disease autoregressive models and imaging trait generative baselines.", "date": "2026-05-10", "categories": []}
{"id": "2605.09838", "title": "The Association of Transformer-based Sentiment Analysis with Symptom Distress and Deterioration in Routine Psychotherapy Care", "abstract": "Sentiment analysis has been of long-standing interest in psychotherapy research. Recently, the Transformer deep learning architecture has produced text-based sentiment analysis models that are highly accurate and context-aware. These models have been explored as proxies for emotion measurement instruments in psychotherapy, but not investigated as stand-alone psychometric tools. Using proposed utterance-level and session-level sentiment features derived from a fine-grained sentiment model on a large corpus of psychotherapy sessions (N = 751), we investigate the distribution of session aggregated sentiment scores. Further, we characterize the relationship of these features to individual components and the overall score of the OQ-45 instrument and find that this sentiment feature is most strongly correlated to components related to emotional valence in directionally intuitive ways. Finally, we report that there are statistically significant differences between the sentiment distributions for patients flagged as at risk of deterioration or dropping out of care via either the OQ Rational or Empirical outcome models. These correlations to a fully-validated psychometric instrument demonstrate that these proposed sentiment features are, at least, adjunctive measures of client distress and deterioration.", "date": "2026-05-11", "categories": []}
{"id": "2605.09951", "title": "Generating synthetic electronic health record data using agent-based models to evaluate machine learning robustness under mass casualty incidents", "abstract": "ML models in healthcare are typically evaluated using curated real-world EHR data. A key limitation of such evaluations is that they may fail to assess the robustness of ML models to changes in the data at deployment, which is a common issue because EHR data used for ML model development cannot capture all such changes. Mass casualty incidents (MCIs) caused by disasters are critical instances where this will be an issue, as they induce rare, uncertain, and novel changes to routine system conditions. Because real-world EHR data from MCIs are often limited or unavailable, assessing ML robustness under such conditions before deployment remains challenging. Here, we propose an agent-based modelling approach for generating synthetic EHR data to evaluate the robustness of ML models under MCI scenarios. We use real-world EHR data to develop and calibrate an agent-based model (ABM) of an emergency department (ED) that explicitly models patient arrivals, resource capacity, and clinical workflow. By changing these system conditions to reflect plausible MCI scenarios, the ED model generates synthetic versions of the real-world EHR data that exhibit shifts in system behaviour. Using these synthetic data, we test ML models for predicting length of stay. We observed consistent declines in recall under MCI conditions relative to baseline system conditions, resulting in an increase in the number of patients with prolonged length of stay that were missed by the ML models. These results highlight the impact of changes in system conditions on patient outcomes, EHR data, and ML model performance. Our work establishes ABM-based synthetic EHR data generation as a proactive and systematic approach for evaluating the robustness of ML models under MCI or other system conditions not captured in real-world EHR data, supporting the safer and more effective deployment of ML models in healthcare systems.", "date": "2026-05-11", "categories": []}
{"id": "2311.08433", "title": "Clinical Characteristics and Laboratory Biomarkers in ICU-admitted Septic Patients with and without Bacteremia", "abstract": "Few studies have investigated the diagnostic utilities of biomarkers for predicting bacteremia among septic patients admitted to intensive care units (ICU). Therefore, this study evaluated the prediction power of laboratory biomarkers to utilize those markers with high performance to optimize the predictive model for bacteremia. This retrospective cross-sectional study was conducted at the ICU department of Gyeongsang National University Changwon Hospital in 2019. Adult patients qualifying SEPSIS-3 (increase in sequential organ failure score greater than or equal to 2) criteria with at least two sets of blood culture were selected. Collected data was initially analyzed independently to identify the significant predictors, which was then used to build the multivariable logistic regression (MLR) model. A total of 218 patients with 48 cases of true bacteremia were analyzed in this research. Both CRP and PCT showed a substantial area under the curve (AUC) value for discriminating bacteremia among septic patients (0.757 and 0.845, respectively). To further enhance the predictive accuracy, we combined PCT, bilirubin, neutrophil lymphocyte ratio (NLR), platelets, lactic acid, erythrocyte sedimentation rate (ESR), and Glasgow Coma Scale (GCS) score to build the predictive model with an AUC of 0.907 (95% CI, 0.843 to 0.956). In addition, a high association between bacteremia and mortality rate was discovered through the survival analysis (0.004). While PCT is certainly a useful index for distinguishing patients with and without bacteremia by itself, our MLR model indicates that the accuracy of bacteremia prediction substantially improves by the combined use of PCT, bilirubin, NLR, platelets, lactic acid, ESR, and GCS score.", "date": "2023-11-14", "categories": []}
{"id": "2509.02826", "title": "Ensemble Learning for Healthcare: A Comparative Analysis of Hybrid Voting and Ensemble Stacking in Obesity Risk Prediction", "abstract": "Obesity is a critical global health issue driven by dietary, physiological, and environmental factors, and is strongly associated with chronic diseases such as diabetes, cardiovascular disorders, and cancer. Machine learning has emerged as a promising approach for early obesity risk prediction, yet a comparative evaluation of ensemble techniques -- particularly hybrid majority voting and ensemble stacking -- remains limited. This study aims to compare hybrid majority voting and ensemble stacking methods for obesity risk prediction, identifying which approach delivers higher accuracy and efficiency. The analysis seeks to highlight the complementary strengths of these ensemble techniques in guiding better predictive model selection for healthcare applications. Two datasets were utilized to evaluate three ensemble models: Majority Hard Voting, Weighted Hard Voting, and Stacking (with a Multi-Layer Perceptron as meta-classifier). A pool of nine Machine Learning (ML) algorithms, evaluated across a total of 50 hyperparameter configurations, was analyzed to identify the top three models to serve as base learners for the ensemble methods. Preprocessing steps involved dataset balancing, and outlier detection, and model performance was evaluated using Accuracy and F1-Score. On Dataset-1, weighted hard voting and stacking achieved nearly identical performance (Accuracy: 0.920304, F1: 0.920070), outperforming majority hard voting. On Dataset-2, stacking demonstrated superior results (Accuracy: 0.989837, F1: 0.989825) compared to majority hard voting (Accuracy: 0.981707, F1: 0.981675) and weighted hard voting, which showed the lowest performance. The findings confirm that ensemble stacking provides stronger predictive capability, particularly for complex data distributions, while hybrid majority voting remains a robust alternative.", "date": "2025-09-02", "categories": []}
{"id": "2605.07561", "title": "Multimodal Stepwise Clinically-Guided Attention Learning for Pathological Complete Response Prediction in Breast Cancer", "abstract": "Pathological complete response (pCR) is a key prognostic factor in breast cancer patients undergoing neoadjuvant therapy, strongly associated with long-term survival and treatment personalization. However, accurate pre-treatment pCR prediction remains challenging due to severe class imbalance and limited generalizability across diverse clinical settings. In this work, we propose a multimodal stepwise clinically-guided attention learning framework for pCR prediction from breast magnetic resonance imaging (MRI), designed to address these limitations through medically grounded spatial guidance and multimodal integration. The approach follows a stepwise training strategy inspired by physician reasoning: the model first learns global discriminative imaging patterns, then attention mechanisms are introduced to constrain the network toward tumor regions, and finally clinical variables are integrated to refine decision-making. This guidance strategy encourages prioritization of task-relevant features, improving identification of responders despite their limited representation in the dataset. Moreover, grounding attention in anatomically consistent tumor regions reduces reliance on dataset-specific patterns, thereby enhancing cross-institutional generalization. The framework is evaluated through external validation across heterogeneous MRI cohorts. Compared to non-guided single-stage baselines, the proposed approach improves sensitivity while maintaining competitive specificity, and produces anatomically coherent attention maps that support interpretation of the model's predictions. These findings highlight the potential of clinically-guided multimodal attention learning for robust and generalizable pCR prediction in breast cancer.", "date": "2026-05-08", "categories": []}
{"id": "2511.22038", "title": "Early Risk Prediction with Temporally and Contextually Grounded Clinical Language Processing", "abstract": "Clinical notes in Electronic Health Records (EHRs) capture rich temporal information on events, clinician reasoning, and lifestyle factors often missing from structured data. Leveraging them for predictive modeling can be impactful for timely identification of chronic diseases. However, they present core natural language processing (NLP) challenges: long text, irregular event distribution, complex temporal dependencies, privacy constraints, and resource limitations. We present two complementary methods for temporally and contextually grounded risk prediction from longitudinal notes. First, we introduce HiTGNN, a hierarchical temporal graph neural network that integrates intra-note temporal event structures, inter-visit dynamics, and medical knowledge to model patient trajectories with fine-grained temporal granularity. Second, we propose ReVeAL, a lightweight test-time framework that distills LLMs' reasoning into smaller verifier models. Applied to opportunistic screening for Type 2 Diabetes (T2D) using temporally realistic cohorts curated from private and public hospital corpora, HiTGNN achieves the highest predictive accuracy, especially for near-term risk, while preserving privacy and limiting reliance on large proprietary models. ReVeAL enhances sensitivity to true T2D cases and retains explanatory reasoning. Our ablations confirm the value of temporal structure and knowledge augmentation, and fairness analysis shows HiTGNN performs more equitably across subgroups.", "date": "2025-11-27", "categories": []}
{"id": "2602.12828", "title": "Risk Horizons: Structured Hypothesis Spaces for Longitudinal Clinical Prediction", "abstract": "Predicting future clinical events from longitudinal electronic health records (EHRs) requires selecting plausible outcomes from a large and structured event space under sparse observations. While clinical coding systems provide hierarchical organization of events, cross-modal and temporal relationships are not explicitly specified and must instead be inferred from data, making prediction difficult for weakly observed longitudinal transitions. We introduce Risk Horizons, a geometry-aware framework for constructing patient-specific candidate spaces for multi-modal next-visit prediction. Risk Horizons combines deterministic coding hierarchies with data-driven lagged cross-modal associations, embeds the resulting clinical graph in hyperbolic space, and retrieves candidate futures using directional risk cones. This reframes longitudinal prediction as ranking within a compact, clinically coherent hypothesis space rather than scoring an unconstrained vocabulary. Experiments on MIMIC-IV and eICU demonstrate competitive next-visit prediction performance, with consistently improved hierarchy consistency across diagnoses, procedures, and medications. Further analysis suggests that hyperbolic structured candidate retrieval is the primary driver of performance, while LLMs are effective as constrained inference-time rerankers operating over clinically grounded candidate sets.", "date": "2026-02-13", "categories": []}
{"id": "2604.18753", "title": "Handling and Interpreting Missing Modalities in Patient Clinical Trajectories via Autoregressive Sequence Modeling", "abstract": "An active challenge in developing multimodal machine learning (ML) models for healthcare is handling missing modalities during training and deployment. As clinical datasets are inherently temporal and sparse in terms of modality presence, capturing the underlying predictive signal via diagnostic multimodal ML models while retaining model explainability remains an ongoing challenge. In this work, we address this by re-framing clinical diagnosis as an autoregressive sequence modeling task, utilizing causal decoders from large language models (LLMs) to model a patient's multimodal trajectory. We first introduce a missingness-aware contrastive pre-training objective that integrates multiple modalities in datasets with missingness in a shared latent space. We then show that autoregressive sequence modeling with transformer-based architectures outperforms baselines on the MIMIC-IV and eICU fine-tuning benchmarks. Finally, we use interpretability techniques to move beyond performance boosts and find that across various patient stays, removing modalities leads to divergent behavior that our contrastive pre-training mitigates. By abstracting clinical diagnosis as sequence modeling and interpreting patient stay trajectories, we develop a framework to profile and handle missing modalities while addressing the canonical desideratum of safe, transparent clinical AI.", "date": "2026-04-20", "categories": []}
{"id": "2605.05213", "title": "Nationwide EHR-Based Chronic Rhinosinusitis Prediction Using Demographic-Stratified Models", "abstract": "Chronic rhinosinusitis (CRS) is a common heterogeneous inflammatory disorder that causes substantial morbidity and healthcare costs. CRS is difficult to identify early from routine encounters, as symptom presentations overlap with common conditions such as allergic rhinitis, and heterogeneous phenotypes further obscure risk patterns. Prior predictive studies often rely on single-institutional cohorts , which reduce population-level generalizability. To overcome this, we leveraged nationwide longitudinal EHR data from the \\textit{All of Us} Research Program to predict CRS diagnosis using two years of pre-diagnostic history. To address extreme feature sparsity and dimensionality in coded EHR data, we implemented a hybrid feature-selection pipeline that combines prevalence-based statistical screening with model-based importance ranking, compressing approximately 110,000 candidate codes into 100 interpretable features. To capture demographic heterogeneity, we trained demographic stratified models across six adult sex and life-stage subgroups with subgroup-specific hyperparameter tuning. Our framework achieved an overall AUC of 0.8461, improving discrimination by 0.0168 over the best baseline. These results demonstrate that routinely collected EHR data may support population-representative CRS risk stratification and inform earlier triage and referral prioritization in primary care.", "date": "2026-04-16", "categories": []}
{"id": "2605.05214", "title": "MedMamba: Recasting Mamba for Medical Time Series Classification", "abstract": "Medical time series, such as electrocardiograms (ECG) and electroencephalograms (EEG), exhibit complex temporal dynamics and structured cross-channel dependencies, posing fundamental challenges for automated analysis. Conventional convolutional and recurrent models struggle to capture long-range dependencies, while Transformer-based approaches incur quadratic complexity and often introduce redundant interactions that are misaligned with the intrinsic structure of physiological signals. To address these limitations, we propose MedMamba, a principle-driven multi-scale bidirectional state space architecture tailored for medical time series classification. Our design is guided by three key inductive biases of physiological signals: spatial centralization, multi-timescale temporal composition, and non-causal contextual dependency. These principles are instantiated through a lightweight channel-mixing module for cross-channel reparameterization, multi-scale convolutional tokenization for temporal decomposition, and bidirectional Mamba blocks for efficient global context modeling with linear complexity. Extensive experiments on six benchmark datasets spanning EEG, ECG, and human activity signals demonstrate that MedMamba consistently outperforms state-of-the-art methods across diverse modalities. Notably, it achieves 85.97% accuracy on PTB and establishes new state-of-the-art performance on the challenging ADFTD dataset (54.72% accuracy and 52.01% F1-score). Strong results on long-sequence benchmarks, such as SleepEDF, further validate its capability in modeling long-range dependencies. Moreover, MedMamba achieves a speedup of 4.6x in inference, highlighting its practicality for real-time clinical deployment. These results suggest that principle-guided state space modeling offers an effective and scalable alternative to Transformer-based approaches for medical time series analysis.", "date": "2026-04-17", "categories": []}
{"id": "2605.06562", "title": "Feature Dimensionality Outweighs Model Complexity in Breast Cancer Subtype Classification Using TCGA-BRCA Gene Expression Data", "abstract": "Accurate classification of breast cancer subtypes from gene expression data is critical for diagnosis and treatment selection. However, such datasets are characterized by high dimensionality and limited sample size, posing challenges for machine learning models.\n  In this study, we evaluate the impact of model complexity and feature selection on subtype classification performance using TCGA-BRCA gene expression data. Logistic regression, random forest, and support vector machine (SVM) models were trained using varying numbers of highly variable genes (50 to 20,518). Performance was evaluated using stratified 5-fold cross-validation and assessed with accuracy and macro F1 score. While all models achieved high accuracy, macro F1 analysis revealed substantial differences in subtype-level performance. Logistic regression demonstrated the most stable and balanced performance across subtypes, including improved detection of rare classes. Random forest underperformed on minority subtypes despite strong overall accuracy, while SVM showed sensitivity to feature dimensionality. These findings highlight the importance of model simplicity, evaluation metrics, and feature selection in high-dimensional biological classification tasks.", "date": "2026-05-07", "categories": []}
{"id": "2511.20179", "title": "Human-computer interactions predict mental health", "abstract": "Scalable assessments of mental illness remain a critical roadblock toward accessible and equitable care. Here, we show that everyday human-computer interactions encode mental health with biomarker accuracy. We introduce MAILA, a MAchine-learning framework for Inferring Latent mental states from digital Activity. We trained MAILA on 18,200 cursor and touchscreen recordings labeled with 1.3 million mental-health self-reports collected from 9,500 participants. MAILA tracks dynamic mental states along 13 clinically relevant dimensions, resolves circadian fluctuations and experimental manipulations of arousal and valence, achieves near-ceiling accuracy at the group level, captures information that is only partially reflected in verbal self-report, and improves the ability of large language models to infer user mental health. By extracting signatures of psychological function that have so far remained untapped, MAILA establishes human-computer interactions as a new modality for scalable digital phenotyping and a foundation for context-aware artificial intelligence.", "date": "2025-11-25", "categories": []}
{"id": "2601.07961", "title": "Language Markers of Emotion Flexibility Predict Depression and Anxiety Treatment Outcomes", "abstract": "Predicting treatment non-response for anxiety and depression is challenging, in part because of sparse symptom assessments in real-world care. We examined whether passively captured, fine-grained emotions serve as linguistic markers of treatment outcomes by analyzing 12 weeks of de-identified teletherapy transcripts from 12,043 U.S. patients with moderate-to-severe anxiety and depression symptoms. A transformer-based small language model extracted patients' emotions at the talk-turn level; a state-space model (VISTA-SSM) clustered subgroups based on emotion dynamics over time and produced temporal networks. Two groups emerged: an improving group (n=8,230) and a non-response group (n=3,813) showing increased odds of symptom deterioration, and lower likelihood of clinically significant improvement. Temporal networks indicated that sadness and fear exerted most influence on emotion dynamics in non-responders, whereas improving patients showed balanced joy, sadness, and neutral expressions. Findings suggest that linguistic markers of emotional inflexibility can serve as scalable, interpretable, and theoretically grounded indicators for treatment risk stratification.", "date": "2026-01-12", "categories": []}
{"id": "2602.07608", "title": "HistoMet: A Pan-Cancer Deep Learning Framework for Prognostic Prediction of Metastatic Progression and Site Tropism from Primary Tumor Histopathology", "abstract": "Metastatic Progression remains the leading cause of cancer-related mortality, yet predicting whether a primary tumor will metastasize and where it will disseminate directly from histopathology remains a fundamental challenge. Although whole-slide images (WSIs) provide rich morphological information, prior computational pathology approaches typically address metastatic status or site prediction as isolated tasks, and do not explicitly model the clinically sequential decision process of metastatic risk assessment followed by downstream site-specific evaluation. To address this research gap, we present a decision-aware, concept-aligned MIL framework, HistoMet, for prognostic metastatic outcome prediction from primary tumor WSIs. Our proposed framework adopts a two-module prediction pipeline in which the likelihood of metastatic progression from the primary tumor is first estimated, followed by conditional prediction of metastatic site for high-risk cases. To guide representation learning and improve clinical interpretability, our framework integrates linguistically defined and data-adaptive metastatic concepts through a pretrained pathology vision-language model. We evaluate HistoMet on a multi-institutional pan-cancer cohort of 6504 patients with metastasis follow-up and site annotations. Under clinically relevant high-sensitivity screening settings (95 percent sensitivity), HistoMet significantly reduces downstream workload while maintaining high metastatic risk recall. Conditional on metastatic cases, HistoMet achieves a macro F1 of 74.6 with a standard deviation of 1.3 and a macro one-vs-rest AUC of 92.1. These results demonstrate that explicitly modeling clinical decision structure enables robust and deployable prognostic prediction of metastatic progression and site tropism directly from primary tumor histopathology.", "date": "2026-02-07", "categories": []}
{"id": "2605.02740", "title": "Foundation Models to Unlock Real-World Evidence from Nationwide Medical Claims", "abstract": "Evidence derived from large-scale real-world data (RWD) is increasingly informing regulatory evaluation and healthcare decision-making. Administrative claims provide population-scale, longitudinal records of healthcare utilization, expenditure, and detailed coding of diagnoses, procedures, and medications, yet their potential as a substrate for healthcare foundation models remains largely unexplored. Here we present ReClaim, a generative transformer trained from scratch on 43.8 billion medical events from more than 200 million enrollees in the MarketScan claims data spanning 2008-2022. ReClaim models longitudinal trajectories across diagnoses, procedures, medications, and expenditure, and was scaled to 140 million, 700 million, and 1.7 billion parameters. Across over 1,000 disease-onset prediction tasks, ReClaim achieved a mean AUC of 75.6%, substantially outperforming disease-specific LightGBM (66.3%) and the transformer-based Delphi model (69.4%), with the largest gains for rare diseases. These advantages held across retrospective and prospective evaluations and in external validation on two independent datasets. Performance improved monotonically with scale, and post-training added 13.8 percentage points over pre-training alone. Beyond disease prediction, ReClaim captured financial outcomes and improved real-world evidence (RWE) analyses: for healthcare expenditure forecasting it increased explained variance from 0.28 to 0.37 relative to LightGBM, and in a target trial emulation it reduced systematic bias by 72% on average relative to Delphi. Together, these results establish administrative claims as a scalable substrate for healthcare foundation models and show that learned representations generalize across time periods and data sources, supporting disease surveillance, expenditure forecasting, and RWE generation.", "date": "2026-05-04", "categories": []}
{"id": "2605.04063", "title": "Investigating Trustworthiness of Nonparametric Deep Survival Models for Alzheimer's Disease Progression Analysis", "abstract": "Alzheimer's Dementia (AD) is a progressive neurodegenerative disease marked by irreversible decline, making reliable modeling of its progression essential for effective patient care. Progression-aware methods such as survival analysis are therefore crucial tools for the early detection and monitoring of AD. Recent advancements in deep learning have demonstrated remarkable performance in survival tasks, but alarmingly fewer studies have been conducted in the domain of AD. Further, the studies that do exist do not consider learned bias within the model itself, which could result in unfair and unreliable predictions toward certain marginalized groups. As such, we conduct a rigorous study of fairness in AD progression analysis along with a thorough feature importance study to determine the characteristics which are most important for reliable AD predictions. Furthermore, we propose two novel fairness metrics, called Time-Dependent Concordance Impurity and Kaplan-Meier Fairness, to quantify bias with respect to sensitive attributes such as sex, race, and education in nonparametric survival models. Our study demonstrates that while deep learning powered survival models are robust tools which can aid clinicians in AD care decisions, they often exhibit considerable bias, representing important avenues for future research.", "date": "2026-04-10", "categories": []}
{"id": "2605.04071", "title": "FlatASCEND: Autoregressive Clinical Sequence Generation with Continuous Time Prediction and Association-Based Pharmacological Testing", "abstract": "Autoregressive models can predict clinical events, but generating patient-conditioned multi-step trajectories that respond to intervention tokens and testing whether those responses preserve known pharmacological associations has received limited attention. We present FlatASCEND, a 14.5M-parameter autoregressive clinical sequence model using flat composite tokens and a zero-inflated log-normal time head. Standard distributional metrics (Jaccard 0.889-0.954) do not distinguish FlatASCEND from trivial baselines; the model's value lies in conditional generation from patient-specific prefixes. A prompt-shuffle ablation shows patient-specific conditioning amplifies mechanistic pharmacological effects (2.0-2.2x for steroid to glucose, diuretic to potassium) while leaving confounding-driven associations unchanged (0.9x for insulin to glucose). An incident-user framework assesses directional consistency against prior pharmacological knowledge on MIMIC-IV (N=500 per comparison): 4/10 recover correct mechanistic directions, 2 reproduce treatment-context associations, 4 are incorrect (9/10 significant, Wilcoxon p<0.05). This pattern - partial recovery under residual confounding - is consistent with learned observational associations without causal distinction. Direct preference optimisation with surrogate reward destroys all correct associations (3/3 to 0/3), illustrating reward exploitation when reward and evaluation share an outcome domain. Generative evidence is strongest for short-horizon ICU data; outpatient temporal fidelity is weaker (median 10 vs 154 days on INSPECT), and zero-shot cross-site transfer degrades without adaptation.", "date": "2026-04-13", "categories": []}
{"id": "2605.04079", "title": "Efficient Handwriting-Based Alzheimer,s Disease Diagnosis Using a Low-Rank Mixture of Experts Deep Learning Framework", "abstract": "Early and reliable detection of Alzheimer's disease (AD) is crucial for timely clinical intervention and improved patient management. It also supports the evaluation of emerging therapeutic strategies. In this paper, we propose a Low-Rank Mixture of Experts (LoRA-MoE) deep learning framework for Alzheimer's disease diagnosis based on handwriting analysis. Handwriting signals provide a non-invasive and scalable digital biomarker that captures subtle cognitive-motor impairments associated with early AD progression. The proposed architecture allows multiple experts to specialize in different handwriting patterns while sharing a common base network. This design enables efficient learning of general representations while reducing interference between experts. Each expert is equipped with lightweight low-rank adapters. This mechanism significantly reduces the number of trainable parameters compared with standard Mixture of Experts (MoE) models and improves training stability. The proposed framework is evaluated on the Diagnosis AlzheimeR WIth haNdwriting (DARWIN) dataset. Extensive experiments are conducted, including ablation studies on key architectural parameters such as hidden dimension size, number of experts, and LoRA rank. The method is compared with multilayer perceptron (MLP) and conventional MoE architectures. In addition, stacking ensemble strategies (StackMean and StackMax) are investigated to improve robustness and predictive performance. Experimental results show that the LoRA-MoE framework achieves powerful diagnostic performance while activating significantly fewer parameters during inference. These results highlight the potential of the proposed approach as an accurate and computationally efficient solution for handwriting-based Alzheimer's disease screening and digital health applications.", "date": "2026-04-14", "categories": []}
{"id": "2605.04666", "title": "Feature importance analysis for patient management decisions", "abstract": "The objective of this paper is to understand what characteristics and features of clinical data influence physician's decision about ordering laboratory tests or prescribing medications the most. We conduct our analysis on data and decisions extracted from electronic health records of 4486 post-surgical cardiac patients. The summary statistics for 335 different lab order decisions and 407 medication decisions are reported. We show that in many cases, physician's lab-order and medication decisions can be well predicted from a small subset of all features.", "date": "2026-05-06", "categories": []}
{"id": "2605.05082", "title": "External Validation of Deep Learning Models for BI-RADS Breast Density Prediction from Ultrasound Images", "abstract": "We externally validated three deep learning models (DenseNet121, ViT-B/32, and ResNet50) for predicting mammographic breast density from breast ultrasound exams on an independent cohort. The external validation set comprised 2,000 ultrasound exams, including 500 cancer cases defined by an initial negative exam (BI-RADS 1 or 2) followed by a cancer diagnosis within 6 months to 10 years, and 1,500 negative controls matched by manufacturer and study year. Performance was measured using patient-level AUROC across four density categories: A (fatty), B (scattered), C (heterogeneous), and D (extremely dense). As a downstream assessment, we also evaluated 10-year risk prediction by incorporating age and AI-derived density into the Tyrer-Cuzick model and comparing performance against a reference model using age and mammography-reported density. All three models performed best in extremely dense breasts (AUROC 0.868-0.899), with strong performance in fatty (0.814-0.838) and scattered density (0.764-0.799), and lower performance in heterogeneously dense breasts (0.699-0.729). DenseNet121 achieved the highest overall performance (micro-averaged AUROC 0.885), and performance across categories was comparable between internal and external testing. For risk modeling, age combined with AI-derived density yielded a lower AUROC than age combined with mammography-reported density (0.541 vs. 0.570; p = 0.23), with no statistically significant difference. These findings indicate that deep learning models generalize well to external data with different racial composition for breast density assessment. While performance is strongest in extremely dense breasts, heterogeneously dense remains more challenging, highlighting the need for targeted optimization.", "date": "2026-05-06", "categories": []}
{"id": "2605.05124", "title": "Conditional outlier detection for clinical alerting", "abstract": "We develop and evaluate a data-driven approach for detecting unusual (anomalous) patient-management actions using past patient cases stored in an electronic health record (EHR) system. Our hypothesis is that patient-management actions that are unusual with respect to past patients may be due to a potential error and that it is worthwhile to raise an alert if such a condition is encountered. We evaluate this hypothesis using data obtained from the electronic health records of 4,486 post-cardiac surgical patients. We base the evaluation on the opinions of a panel of experts. The results support that anomaly-based alerting can have reasonably low false alert rates and that stronger anomalies are correlated with higher alert rates.", "date": "2026-05-06", "categories": []}
{"id": "2605.05125", "title": "Joint Treatment Effect Estimation from Incomplete Healthcare Data: Temporal Causal Normalizing Flows with LLM-driven Evolutionary MNAR Imputation", "abstract": "Target trial emulation (TTE) enables causal questions to be studied with observational data when randomized controlled trials (RCTs) are infeasible. Yet treatment-effect methods often address causal estimation, missingness, and temporal structure separately, limiting their robustness in electronic health records (EHRs), where time-varying confounding and missing-not-at-random (MNAR) biomarkers can reach 50%--80%. We propose a two-stage pipeline for treatment effect estimation from incomplete longitudinal EHRs. First, CausalFlow-T, a directed acyclic graph (DAG)-constrained normalizing flow with long short-term memory (LSTM)-encoded patient history, performs exact invertible counterfactual inference, avoiding approximation errors from variational inference and separating confounding through explicit causal structure. Ablations on four synthetic and one semi-synthetic benchmark with known counterfactuals show that DAG constraints and exact inference address distinct failure modes: neither compensates for the other. Second, because CausalFlow-T requires completed inputs, we introduce an LLM-driven evolutionary imputer that proposes executable imputation operators rather than individual entries, and evaluate it with three large language model (LLM) backends, including two open-source models. Across 30%--80% MNAR missingness, this imputer achieves the best pooled rank over biomarker and causal metrics, leading in point-wise accuracy and temporal extrapolation while preserving average treatment effect (ATE) recovery as statistical baselines degrade. On Swiss primary-care EHRs from adults with type 2 diabetes initiating a GLP-1 receptor agonist or SGLT-2 inhibitor, the pipeline estimates a per-protocol weight-loss difference of -0.98 kg [95% CI -1.01, -0.96] favoring GLP-1 receptor agonists, consistent with randomized evidence and obtained from realistically incomplete real-world EHRs.", "date": "2026-05-06", "categories": []}
{"id": "2604.22535", "title": "An Integrated Framework for Explainable, Fair, and Observable Hospital Readmission Prediction: Development and Validation on MIMIC-IV", "abstract": "Objective: To propose and retrospectively validate an integrated framework addressing three barriers to clinical translation of readmission prediction: lack of explainability, absence of deployment reliability infrastructure, and inadequate demographic fairness evaluation.\n  Materials and Methods: We constructed a cohort of 415231 adult admissions from the MIMIC-IV database (30-day readmission prevalence 18.0%), split 70/15/15. Logistic regression, XGBoost, and LightGBM models were trained on 26 features. SHAP provided per-patient explanations. Fairness was evaluated across 16 subgroups using AUC-ROC, false negative rate (FNR), and positive predictive value (PPV). Calibration was assessed using Brier scores and calibration curves.\n  Results: XGBoost achieved AUC-ROC 0.696 (95% CI 0.691-0.701), outperforming or matching the LACE baseline (AUC 0.60-0.68). LightGBM achieved best calibration (Brier 0.146). Prior admissions were the dominant predictor. All subgroups met equity thresholds (delta AUC <= 0.05, delta FNR <= 0.10).\n  Conclusion: This framework delivers competitive performance, clinically actionable explanations, and strong demographic equity. Code is publicly available at https://github.com/Tomisin92/readmission-prediction.", "date": "2026-04-24", "categories": []}
{"id": "2605.03560", "title": "Enhance the after-discharge mortality rate prediction via learning from the medical notes", "abstract": "With the increase of the Electronic Health Records (EHR) data, more and more researchers are developing machine learning models to learn from the medical notes. These unstructured text data pose significant challenges on the learning process as the quality of data is low. These data are often messy, repetitive and redundant. We have shown these notes data to be informative by conducting the after-discharge mortality rate prediction task. The AUC-ROC for models using the medical note information is generally 0.1 higher than those without the medical notes. Furthermore, we propose the Deep Neural Network(DNN) model with 'pooling' mechanism to enhance the mortality prediction. Based on the experimental results, we demonstrate that the proposed model outperforms the traditional machine learning models like the tree-based models. The proposed method learns from the most informative medical notes and improves the prediction accuracy significantly. The AUC-ROC for the proposed model is 2% to 14% higher than the traditional ones in 15-days, 30-days, 60-days, 365-days after-discharge mortality prediction tasks. Moreover, we can discover some interesting knowledge through the traditional and proposed models. These knowledge are inspiring but also consistent with the previous findings. The models are able to reveal the relationships between the informative keywords and documents from the medical notes and the severity of the patients.", "date": "2026-05-05", "categories": []}
{"id": "2605.03618", "title": "BIT.UA-AAUBS at ArchEHR-QA 2026: Evaluating Open-Source and Proprietary LLMs via Prompting in Low-Resource QA", "abstract": "This paper presents the joint participation of the BIT.UA and AAUBS groups in the ArchEHR-QA 2026 shared task, which focuses on clinical question answering and evidence grounding in a low-resource setting. Due to the absence of training data and the strict data privacy constraints inherent to the healthcare domain (e.g. GDPR), we investigate the capabilities of Large Language Models (LLMs) without weight updates. We evaluate several state-of-the-art proprietary models and locally deployable open-source alternatives using various prompt engineering strategies, including task decomposition, Chain-of-Thought, and in-context learning. Furthermore, we explore majority voting and LLM-as-a-judge ensembling techniques to maximize predictive robustness. Our results demonstrate that while proprietary models exhibit strong resilience to prompt variations, domain-adapted open-source models (such as MedGemma 3 27B) achieve highly competitive performance when paired with the right prompt. Overall, our prompt-based approach proved highly effective, securing 1st place in Subtask 4 (evidence citation alignment) and 3rd place in Subtask 3 (patient-friendly answer generation). All code, results, and prompts are available on our GitHub repository: https://github.com/bioinformatics-ua/ArchEHR-QA-2026.", "date": "2026-05-05", "categories": []}
{"id": "2604.18058", "title": "Sonata: A Hybrid World Model for Inertial Kinematics under Clinical Data Scarcity", "abstract": "We introduce Sonata, a compact latent world model for six-axis trunk IMU representation learning under clinical data scarcity. Clinical cohorts typically comprise tens to hundreds of patients, making web-scale masked-reconstruction objectives poorly matched to the problem. Sonata is a 3.77 M-parameter hybrid model, pre-trained on a harmonised corpus of nine public datasets (739 subjects, 190k windows) with a latent world-model objective that predicts future state rather than reconstructing raw sensor traces. In a controlled comparison against a matched autoregressive forecasting baseline (MAE) on the same backbone, Sonata yields consistently stronger frozen-probe clinical discrimination, prospective fall-risk prediction, and cross-cohort transfer across a 14-arm evaluation suite, while producing higher-rank, more structured latent representations. At 3.77 M parameters the model is compatible with on-device wearable inference, offering a step toward general kinematic world models for neurological assessment.", "date": "2026-04-20", "categories": []}
{"id": "2604.18967", "title": "CXRMate-2: Structured Multimodal Temporal Embeddings and Tractable Reinforcement Learning for Clinically Acceptable Chest X-ray Radiology Report Generation", "abstract": "Chest X-ray (CXR) radiology report generation (RRG) models have shown rapid progress on automated metrics, yet their clinical utility remains uncertain due to limited qualitative evaluation by radiologists. We present CXRMate-2, a state-of-the-art CXR RRG model that enables tractable reinforcement learning (RL) through structured multimodal temporal embeddings and high-resolution visual feature compression, for efficient, unified conditioning of an LLM decoder on visual, textual, and temporal context from a study and its prior. This enables group relative policy optimisation (GRPO), where a proposed reward function is used to improve semantic alignment with radiologist reports. Across the MIMIC-CXR, CheXpert Plus, and ReXgradient datasets, CXRMate-2 achieves statistically significant improvements over strong benchmarks, including gains of 11.2% and 24.4% in GREEN and RadGraph-XL, respectively, on MIMIC-CXR relative to MedGemma 1.5 (4B).\n  To directly compare CXRMate-2 against radiologist reporting, we conduct a blinded, randomised qualitative retrospective evaluation. Three consultant radiologists compare generated and radiologist reports across 120 studies from the MIMIC-CXR test set. Generated reports were deemed acceptable (defined as preferred or rated equally to radiologist reports) in 45% of ratings, with no statistically significant difference in preference rates for seven of the eight analysed findings. Preferences for radiologist reports were driven primarily by higher recall, while generated reports were consistently preferred for readability.\n  Together, these results define a clear pathway to clinically acceptable CXR RRG. Improving recall and the detection of subtle findings represents the primary remaining barrier to non-inferiority with radiologist reporting, positioning CXR RRG for prospective evaluation in assistive, radiologist-led workflows.", "date": "2026-04-21", "categories": []}
{"id": "2605.00858", "title": "A Hybrid Windkessel-Neural Approach for Improved Noninvasive Blood Pressure Monitoring", "abstract": "Owing to the recent advancements in wearable devices for health care, the importance of BP estimation without cuffs increases. Cuff technologies are inappropriate for continuous BP measurement due to their inconvenient usage, invasive character, necessity of calibration, large size, and inability to perform long-term monitoring. Normally, the algorithm used for cuffless BP prediction employs machine learning models that operate according to the data-driven approach. However, although they show high numerical accuracy, ML models do not provide any interpretability, resulting in poor physiological validity and clinical applicability. We propose a combination of Windkessel and ML models that incorporates the physical aspects into the latter one. It is performed by reformulating Windkessel into a form that will allow employing ML models. The result is a system of ODEs which can be used in the neural network. Thus, the inclusion of physical constraints improves the data-driven approach by making models consistent with physics, understandable, and robust. For illustration, we apply the described technique using a publicly available MIMIC-II database that we obtain from the UCI Machine Learning Repository.", "date": "2026-04-21", "categories": []}
{"id": "2605.00872", "title": "Multi-View Hierarchical Representation Learning of Fetal Hemodynamics for Maternal Hypertension Detection at the Edge", "abstract": "Hypertensive disorders of pregnancy remain a leading cause of maternal and fetal morbidity worldwide, yet diagnosis relies on intermittent cuff-based blood pressure measurements that are prone to bias and fail to capture continuous physiological dynamics. Growing evidence suggests that fetal cardiovascular activity is associated with maternal-placental hemodynamics and may encode markers of maternal hypertension. To analyze this, we collected a large-scale dataset of fetal one-dimensional Doppler ultrasound recordings paired with maternal blood pressure from 3,255 pregnant women across 8,170 antenatal visits in rural Guatemala. We developed AutoHyPE, a hierarchical attention network that models short- and long-term signal structure, incorporating a novel prototype-based contrastive learning and multi-view strategy to enhance representation robustness under long-tailed class distribution and biological variability. AutoHyPE achieved an AUROC of 0.80 for maternal hypertension detection, outperforming baseline approaches while maintaining balanced performance across classes, with no performance degradation in an edge deployment scenario. Our findings demonstrated that fetal cardiac mechanical activity contains hemodynamic features indicative of maternal hypertension status. This supports a promising paradigm shift toward continuous, objective monitoring of maternal health using existing, low-cost ultrasound technology and introduces a complementary approach to traditional methods based on blood pressure measurements, advancing scalable prenatal care.", "date": "2026-04-24", "categories": []}
{"id": "2605.00933", "title": "CGM-JEPA: Learning Consistent Continuous Glucose Monitor Representations via Predictive Self-Supervised Pretraining", "abstract": "Continuous Glucose Monitoring (CGM) can detect early metabolic subphenotypes (insulin resistance, IR; $\\beta$-cell dysfunction), but population-scale deployment faces two coupled problems. First, the same physiological state appears through multiple views (CGM time series, venous OGTT, Glucodensity summaries), so single-view representations fail to transfer when deployment shifts the modality or setting. Second, baselines perform inconsistently across these shifts. Both problems point to one remedy: representations that abstract away from any single view to capture higher-level temporal and distributional structure. We propose CGM-JEPA, a self-supervised pretraining framework which predicts masked latent representations rather than raw values, yielding abstraction that transfers across modalities. X-CGM-JEPA adds a masked Glucodensity cross-view objective for complementary distributional information. We pretrain on $\\sim$389k unlabeled CGM readings from 228 subjects and evaluate on two clinical cohorts ($N=27$ and $N=17$ public-release subsets) across three regimes (cohort generalization, venous-to-CGM transfer, home CGM) under 20-iteration $\\times$ 2-fold cross-validation. X-CGM-JEPA ranks first or second on AUROC for both endpoints across all three regimes while no baseline does, exceeding the strongest baseline by up to $+6.5$ pp in cohort generalization and $+3.6$ pp in venous-to-CGM transfer (paired Wilcoxon, $p<0.001$). Under modality shift, it matches mean AUROC while redistributing toward weaker subgroups (ethnicity AUROC gap shrinks 25-54%); on sparse in-domain venous data, the distributional view lifts label-aware clustering (ARI $+39\\%$, NMI $+40\\%$). Code and weights: https://github.com/cruiseresearchgroup/CGM-JEPA", "date": "2026-05-01", "categories": []}
{"id": "2605.01189", "title": "NEURON: A Neuro-symbolic System for Grounded Clinical Explainability", "abstract": "Clinical AI adoption is hindered by the black-box/grey-box nature of high-performing models, which lack the ontological grounding and narrative transparency required for professional-level explainability. We present NEURON, a neuro-symbolic system designed to enhance both predictive reliability and clinical interpretability. NEURON integrates SNOMED CT ontology-informed structural representations with machine learning models to bridge the gap between raw data and medical nomenclature. To facilitate human-aligned interaction, the system utilizes a Retrieval-Augmented Generation (RAG) grounded LLM layer to synthesize SHAP feature attributions and patient-specific clinical notes into coherent, natural-language explanations. Validated on the MIMIC-IV dataset for Acute Heart Failure mortality prediction, NEURON improved the AUC from 0.74-0.77 to 0.84-0.88 and significantly outperformed raw SHAP visualizations in human-aligned metrics (0.85 vs. 0.50). Our results demonstrate that NEURON offers a robust, scalable engineering solution for deploying trustworthy, human-centered connected health applications.", "date": "2026-05-02", "categories": []}
{"id": "2605.02266", "title": "Reliability-Oriented Multilingual Orthopedic Diagnosis: A Domain-Adaptive Modeling and a Conceptual Validation Framework", "abstract": "Large Language Models (LLMs) are increasingly proposed for clinical decision support including multilingual diagnosis in low-resource settings. However, their reliability, calibration and safety characteristics remain insufficiently understood for structured, high-risk tasks. We present a system-level analysis of multilingual orthopedic diagnosis from free-text clinical notes in English, Hindi and Punjabi. We evaluate three modeling regimes: (i) task-aligned multilingual transformer encoders, (ii) a task-fine-tuned baseline (DistilBERT), and (iii) a domain-adaptive architecture tailored to orthopedic text (IndicBERT-HPA). These models are compared with zero-shot, instruction-tuned LLMs to assess suitability for structured diagnostic classification. Results indicate that while LLMs exhibit strong linguistic fluency, they show unstable calibration and reduced reliability under structured multilingual conditions, particularly in low-resource languages. These findings are specific to zero-shot evaluation and do not imply limitations of fine-tuned models. Domain-adaptive specialization substantially improves cross-lingual discrimination and confidence behavior. IndicBERT-HPA, with language-specific orthopedic adapter heads achieves consistently strong performance across six diagnostic categories and more predictable deployment characteristics than task-only adaptation. Building on these observations, we outline a conceptual deterministic agent-based validation framework for future implementation, formalizing evidence checks, language-sensitive validation and conservative human-in-the-loop gating. Reliable multilingual clinical decision support requires specialized architecture, explicit reliability analysis, and structured validation for safety-critical systems.", "date": "2026-05-04", "categories": []}
{"id": "2605.02614", "title": "Validation of an AI-based end-to-end model for prostate pathology using long-term archived routine samples", "abstract": "Artificial intelligence (AI) is becoming a clinical tool for prostate pathology, but generalization across variations in sample preparation and preservation over prolonged time periods remains poorly understood. We evaluated GleasonAI, an end-to-end attention-based multiple instance learning model, on an independent validation cohort comprising 10,366 biopsy cores from 1,028 patients across 14 Swedish regions, using archival diagnostic specimens from the ProMort cohorts collected between 1998-2015. The model achieved an overall quadratic-weighted kappa of 0.86 for core-level ISUP grading, comparable to several experienced pathologists and consistent across geographic regions. Notably, performance remained stable across the 17-year collection period, demonstrating robustness to time-related variation in archival material, a property not consistently observed with foundation model-based approaches, with exploratory analysis demonstrating a significant prognostic gradient across AI-assigned grade groups for prostate cancer-specific mortality. These findings support the generalizability of the AI grading model and demonstrate the potential of pathology archives as a large-scale resource for AI development, validation, and retrospective prognostic research.", "date": "2026-05-04", "categories": []}
{"id": "2605.00350", "title": "CURE-OOD: Benchmarking Out-of-Distribution Detection for Survival Prediction", "abstract": "``How long can I live and remain free of cancer?'' is often the first question a patient asks after receiving a cancer diagnosis and treatment. Accurate survival prediction helps alleviate psychological distress and supports risk stratification and personalized treatment planning. Recent survival prediction frameworks have shown strong performance using computed tomography (CT) images. However, variations in imaging acquisition introduce out-of-distribution (OOD) samples caused by covariate shifts that undermine model reliability. Despite this challenge, to our knowledge, no existing benchmark systematically studies OOD detection in cancer survival prediction. To address this gap, we introduce the Cancer sURvival bEnchmark for OOD Detection (CURE-OOD), the first benchmark for systematically evaluating OOD detection in survival prediction under controlled acquisition-induced distribution shifts. CURE-OOD defines scanner-parameter-based training, in-distribution (ID), and OOD test splits across four survival prediction tasks. Our experiments show that covariate shifts notably reduce survival prediction performance. It also shows that mainstream classification-oriented OOD detectors can fail in survival prediction. Finally, we include HazardDev as a simple survival-aware reference baseline for OOD detection. CURE-OOD enables systematic analysis of how distribution shifts affect both downstream survival performance and OOD detectability.", "date": "2026-05-01", "categories": []}
{"id": "2605.00645", "title": "From Prediction to Practice: A Task-Aware Evaluation Framework for Blood Glucose Forecasting", "abstract": "Clinical time-series forecasting is increasingly studied for decision support, yet standard aggregate metrics can obscure whether a model is actually useful for the task it is meant to serve. In safety-critical settings, low average error can coexist with dangerous failures in exactly the high-risk regimes that matter most. We present a task-aware evaluation framework for blood glucose forecasting built around two downstream uses: hypoglycemia early warning and insulin dosing decision support. For early warning, we evaluate on real data from three clinical cohorts using event-level recall and false alarms per patient-day, metrics that reflect operational alarm burden rather than aggregate accuracy. We show that models appearing acceptable overall, with recall above 0.9 on the full test set, can fail badly in the post-bolus slice, where insulin-on-board is elevated and missed warnings carry the greatest clinical consequences. Standard forecasting evaluation, however, does not test whether a model can reason about the effects of actions, a requirement for supporting insulin dosing decisions. We therefore add a second, interventional arm using the FDA-accepted UVA/Padova simulator, where we evaluate whether forecasters can predict glucose responses to altered insulin plans in paired factual/counterfactual scenarios. We show that models that look strong on real-data forecasting often fail to predict the direction, magnitude, or ranking of intervention effects, and choose poor insulin doses when evaluated under a clinically motivated cost. Taken together, the two arms reveal a consistent gap between forecasting accuracy and task-relevant usefulness. We release the benchmark, the standardized preprocessing pipeline for public cohorts, and the simulator-based interventional dataset as a reproducible toolkit.", "date": "2026-05-01", "categories": []}
{"id": "2605.00708", "title": "Deep Kernel Learning for Stratifying Glaucoma Trajectories", "abstract": "Effectively stratifying patient risk in chronic diseases like glaucoma is a major clinical challenge. Clinicians need tools to identify patients at high risk of progression from sparse and irregularly-sampled electronic health records (EHRs). We propose a novel deep kernel learning (DKL) architecture that leverages a Gaussian Process (GP) backend. The GP's kernel is defined by a transformer-based feature extractor applied to clinical-BERT embeddings to model glaucoma patient trajectories from multimodal EHR data. Our method successfully identifies three clinically distinct patient subgroups. Crucially, the model learns to decouple disease progression from current severity, identifying a high-risk group with a worsening trajectory despite having better average visual acuity than a second, stably poor group. This reveals that the model learns to identify progression risk rather than just the current disease state. This ability to stratify patients based on their risk trajectory progression offers a powerful tool for clinical decision support, enabling targeted interventions for high-risk individuals and improving the management of glaucoma care.", "date": "2026-05-01", "categories": []}
{"id": "2605.00738", "title": "Temporal Data Requirement for Predicting Unplanned Hospital Readmissions", "abstract": "With the proliferation of Electronic Health Records (EHRs), a critical challenge in building predictive models is determining the optimal historical data time window to maximize accuracy. This study investigates the impact of various observation windows ranging from the day of surgery to three years prior on predicting 30-day readmission following hip and knee arthroplasties. The dataset encompasses both structured encounter records (over 4 million) and unstructured clinical notes (80,000) from 7,174 patients. To extract meaning from the clinical notes, we employed a suite of non neural (BOW, count BOW, TF IDF, LDA) and neural encoders (BERT, 1D CNN, BiLSTM, Average). We subsequently evaluated models utilizing clinical notes alone, structured data alone, and a combination of both modalities. Our results demonstrate that the optimal time window for unstructured clinical notes is significantly shorter than for structured data, maximum predictive performance was achieved using notes from just three to six months prior to surgery. In contrast, performance using structured data improved as the time window lengthened, but strictly plateaued after twelve months. These modality-specific temporal patterns remained consistent regardless of model complexity or encoder type. Ultimately, these findings challenge the general assumption that more historical data inherently yields better machine learning predictions, establishing targeted time-window guidelines for optimizing readmission prediction models.", "date": "2026-05-01", "categories": []}
{"id": "2502.14270", "title": "Predicting Fetal Birthweight from High Dimensional Data using Advanced Machine Learning", "abstract": "Birth weight serves as a fundamental indicator of neonatal health, closely linked to both early medical interventions and long-term developmental risks. Traditional predictive models, often constrained by limited feature selection and incomplete datasets, struggle to achieve overlooking complex maternal and fetal interactions in diverse clinical settings. This research explores machine learning to address these limitations, utilizing a structured methodology that integrates advanced imputation strategies, supervised feature selection techniques, and predictive modeling. Given the constraints of the dataset, the research strengthens the role of data preprocessing in improving the model performance. Among the various methodologies explored, tree-based feature selection methods demonstrated superior capability in identifying the most relevant predictors, while ensemble-based regression models proved highly effective in capturing non-linear relationships and complex maternal-fetal interactions within the data. Beyond model performance, the study highlights the clinical significance of key physiological determinants, offering insights into maternal and fetal health factors that influence birth weight, offering insights that extend over statistical modeling. By bridging computational intelligence with perinatal research, this work underscores the transformative role of machine learning in enhancing predictive accuracy, refining risk assessment and informing data-driven decision-making in maternal and neonatal care. Keywords: Birth weight prediction, maternal-fetal health, MICE, BART, Gradient Boosting, neonatal outcomes, Clinipredictive.", "date": "2025-02-20", "categories": []}
{"id": "2508.12022", "title": "AI Models for Depressive Disorder Detection and Diagnosis: A Review", "abstract": "Major Depressive Disorder is one of the leading causes of disability worldwide, yet its diagnosis still depends largely on subjective clinical assessments. Integrating Artificial Intelligence (AI) holds promise for developing objective, scalable, and timely diagnostic tools. In this paper, we present a comprehensive survey of state-of-the-art AI methods for depression detection and diagnosis, based on a systematic review of 55 key studies. We introduce a novel hierarchical taxonomy that structures the field by primary clinical task (diagnosis vs. prediction), data modality (text, speech, neuroimaging, multimodal), and computational model class (e.g., graph neural networks, large language models, hybrid approaches). Our in-depth analysis reveals three major trends: the predominance of graph neural networks for modeling brain connectivity, the rise of large language models for linguistic and conversational data, and an emerging focus on multimodal fusion, explainability, and algorithmic fairness. Alongside methodological insights, we provide an overview of prominent public datasets and standard evaluation metrics as a practical guide for researchers. By synthesizing current advances and highlighting open challenges, this survey offers a comprehensive roadmap for future innovation in computational psychiatry.", "date": "2025-08-16", "categories": []}
{"id": "2603.18239", "title": "Impact of automatic speech recognition quality on Alzheimer's disease detection from spontaneous speech: a reproducible benchmark study with lexical modeling and statistical validation", "abstract": "Early detection of Alzheimer's disease from spontaneous speech has emerged as a promising non-invasive screening approach. However, the influence of automatic speech recognition (ASR) quality on downstream clinical language modeling remains insufficiently understood. In this study, we investigate Alzheimer's disease detection using lexical features derived from Whisper ASR transcripts on the ADReSSo 2021 diagnosis dataset. We evaluate interpretable machine-learning models, including Logistic Regression and Linear Support Vector Machines, using TF-IDF text representations under repeated 5x5 stratified cross-validation.\n  Our results demonstrate that transcript quality has a statistically significant impact on classification performance. Models trained on Whisper-small transcripts consistently outperform those using Whisper-base transcripts, achieving balanced accuracy above 0.7850 with Linear SVM. Paired statistical testing confirms that the observed improvements are significant. Importantly, classifier complexity contributes less to performance variation than ASR transcription quality. Feature analysis reveals that cognitively normal speakers produce more semantically precise object- and scene-descriptive language, whereas Alzheimer's speech is characterized by vagueness, discourse markers, and increased hesitation patterns.\n  These findings suggest that high-quality ASR can enable simple, interpretable lexical models to achieve competitive Alzheimer's detection performance without explicit acoustic modeling. The study provides a reproducible benchmark pipeline and highlights ASR selection as a critical modeling decision in clinical speech-based artificial intelligence systems.", "date": "2026-03-18", "categories": []}
{"id": "2604.26959", "title": "CareGuardAI: Context-Aware Multi-Agent Guardrails for Clinical Safety & Hallucination Mitigation in Patient-Facing LLMs", "abstract": "Integrating large language models (LLMs) into patient-facing healthcare systems offers significant potential to improve access to medical information. However, ensuring clinical safety and factual reliability remains a critical challenge. In practice, AI-generated responses may be conditionally correct yet medically inappropriate, as models often fail to interpret patient context and tend to produce agreeable responses rather than challenge unsafe assumptions. Unlike clinicians, who infer risk from incomplete information, LLMs frequently lack contextual awareness. Moreover, real-world patient interactions are open-ended and underspecified, unlike structured benchmark settings. We present CareGuardAI, a risk-aware safety framework for patient-facing medical question answering that addresses two key failure modes: clinical safety risk and hallucination risk. The framework introduces Clinical Safety Risk Assessment (SRA), inspired by ISO 14971, and Hallucination Risk Assessment (HRA) to evaluate medical risk and factual reliability. At inference time, CareGuardAI employs a multi-stage pipeline consisting of a controller agent, safety-constrained generation, and dual risk evaluation, followed by iterative refinement when necessary. Responses are released only when both SRA and HRA are less than or equal to 2, ensuring clinically acceptable outputs with bounded latency. We evaluate CareGuardAI on PatientSafeBench, MedSafetyBench, and MedHallu, covering both safety and hallucination detection. Across these benchmarks, the framework consistently outperforms strong baseline models, including GPT-4o-mini, demonstrating the importance of context-aware, risk-based, inference-time safety mechanisms for reliable deployment in healthcare.", "date": "2026-04-07", "categories": []}
{"id": "2604.27309", "title": "End-to-End Evaluation and Governance of an EHR-Embedded AI Agent for Clinicians", "abstract": "Clinical AI systems require not just point-in-time evaluation but continuous governance: the ongoing practice of monitoring, evaluating, iterating, and re-evaluating performance throughout deployment. We present an end-to-end framework of governance that integrates rubric validation, live deployment feedback, technical performance monitoring, and cost tracking, with controlled experimentation gating system changes before deployment. Applied to Hyperscribe, an EHR-embedded agent that converts ambient audio into structured chart updates, twenty clinicians authored 1,646 validated rubrics across 823 cases. Seven Hyperscribe versions were evaluated through controlled experiments, with median scores improving from 84% to 95%. Analysis of 107 live feedback entries over three months showed feedback composition shifting from 79% error reports and 14% positive observations to 30% errors and 45% positive observations as engineering interventions resolved failures. Median processing time per audio segment was 8.1 seconds with a 99.6% effective completion rate after retry mechanisms absorbed transient model errors. These results demonstrate that continuous, multi-channel governance of deployed clinical AI is both achievable and effective.", "date": "2026-04-30", "categories": []}
{"id": "2604.27899", "title": "Simulating clinical interventions with a generative multimodal model of human physiology", "abstract": "Understanding how human health changes over time, and why responses to interventions vary between individuals, remains a central challenge in medicine. Here we present HealthFormer, a decoder-only transformer that models the human physiological trajectory generatively, by training on data from the Human Phenotype Project, a multi-visit cohort of over 15,000 deeply phenotyped individuals. We tokenise each participant's health trajectory across 667 measurements spanning seven domains: blood biomarkers, body composition, sleep physiology, continuous glucose monitoring, gut microbiome, wearable-derived physiology, and behaviour and medication exposure. We train HealthFormer to forecast individual physiological trajectories across these domains, and from this single generative objective a range of clinically relevant tasks can be expressed as queries on the model. We show that, without task-specific training, HealthFormer transfers to four independent cohorts and improves prediction for 27 of 30 incident-disease and mortality endpoints, exceeding established clinical risk scores in every comparison. We further show that the model can simulate interventions in silico: in a held-out personalised-nutrition trial, intervention-conditioned predictions recover individual six-month biomarker changes (e.g., Pearson r = 0.78 for diastolic blood pressure). Across 41 randomised intervention-outcome comparisons drawn from published trials, our results show that the predicted direction of effect agrees in every case, and the predicted mean falls within the reported 95% confidence interval in 30 cases. We position HealthFormer as an initial health world model, from which forecasting, risk stratification, and intervention-conditioned simulation arise as queries, providing a basis for clinical digital twins.", "date": "2026-04-30", "categories": []}
{"id": "2604.27967", "title": "Differentiable latent structure discovery for interpretable forecasting in clinical time series", "abstract": "Background: Timely, uncertainty-aware forecasting from irregular electronic health records (EHR) can support critical-care decisions, yet most approaches either impute to a grid or sacrifice interpretability. We introduce StructGP, a continuous-time multi-task Gaussian process that couples process convolutions with differentiable structure learning to uncover a sparse, ordered directed acyclic graph (DAG) of inter-variable dependencies while preserving principled uncertainty. We further propose LP-StructGP, which augments StructGP with latent pathways-shared, temporally shifted trajectories inferred via subject-specific coupling filters and a softmax gating mechanism-to capture cross-patient progression patterns. Both models are trained under sparsity and acyclicity constraints (augmented Lagrangian, Adam) using scalable low-rank updates. Results: In simulations, the approach reliably recovers ground-truth graphs (Structural Hamming Distance approaching 0 as cohorts grow) and pathway assignments (high Adjusted Rand Index). On a MIMIC-IV septic shock cohort (n=1,008; norepinephrine, creatinine, mean arterial pressure), StructGP improves short-horizon (6 h) forecasting over independent-task baselines (average RMSE 0.68 [95%CI: 0.63--0.74] vs. 0.88 [0.83-0.94]) and, with 15 additional inputs, markedly outperforms unstructured kernels (0.63 [0.58-0.69] vs. 3.02 [2.85-3.18]) with superior calibration (coverage 0.96 vs. 0.84). On the PhysioNet Challenge (12k patients, 41 variables), StructGP attains competitive accuracy (MAE 3.72e-2) relative to a state-of-the-art graph neural model while maintaining calibrated uncertainty. Conclusion: These results show that structured process convolutions with latent pathways deliver interpretable, scalable, and well-calibrated forecasting for irregular clinical time series.", "date": "2026-04-30", "categories": []}
{"id": "2604.28055", "title": "PROMISE-AD: Progression-aware Multi-horizon Survival Estimation for Alzheimer's Disease Progression and Dynamic Tracking", "abstract": "Individualized Alzheimer's disease (AD) progression prediction requires models that use irregular visits, account for censoring, avoid diagnostic leakage, and provide calibrated horizon risks. We propose PROgression-aware MultI-horizon Survival Estimation for Alzheimer's Disease (PROMISE-AD), a leakage-safe survival framework for predicting conversion from cognitively normal (CN) to mild cognitive impairment (MCI) and from MCI to AD dementia using ADNI/TADPOLE tabular histories. PROMISE-AD converts pre-index visits into tokens with standardized measurements, missingness masks, longitudinal changes, time-normalized slopes, visit timing, and non-diagnostic categorical attributes. A temporal Transformer fuses global, attention-pooled, and latest-visit representations to estimate a progression score and latent discrete-time mixture hazards. Training combines survival likelihood, horizon-specific focal risk loss, progression ranking, hazard smoothness, and mixture-balance regularization, followed by validation-set isotonic calibration for 1-, 2-, 3-, and 5-year risks. In held-out testing across three seeds, PROMISE-AD achieved an integrated Brier score (IBS) of 0.085 $\\pm$ 0.012, C-index of 0.808 $\\pm$ 0.015, and mean time-dependent AUC of 0.840 $\\pm$ 0.081 for CN-to-MCI conversion, yielding the lowest IBS among compared methods. For MCI-to-AD conversion, PROMISE-AD achieved the highest C-index (0.894 $\\pm$ 0.018) and near-ceiling 5-year discrimination (AUROC 0.997 $\\pm$ 0.003; AUPRC 0.999 $\\pm$ 0.001), although some baselines had lower IBS. Ablations and interpretability supported longitudinal change features, fused temporal representations, mixture hazards, cognitive and functional measures, APOE4 status, and recent conversion-proximal visits. These findings suggest that progression-aware survival modeling can provide interpretable multi-horizon AD conversion risk estimates.", "date": "2026-04-30", "categories": []}
{"id": "2401.02458", "title": "Data-Centric Foundation Models in Computational Healthcare: A Survey", "abstract": "The advent of foundation models (FMs) as an emerging suite of AI techniques has struck a wave of opportunities in computational healthcare. The interactive nature of these models, guided by pre-training data and human instructions, has ignited a data-centric AI paradigm that emphasizes better data characterization, quality, and scale. In healthcare AI, obtaining and processing high-quality clinical data records has been a longstanding challenge, encompassing data quantity, annotation, patient privacy, and ethics. In this survey, we investigate a wide range of data-centric approaches in the FM era (from model pre-training to inference) towards improving the healthcare workflow. We discuss key perspectives in AI security, assessment, and alignment with human values. Finally, we offer a promising outlook on FM-based analytics to enhance patient outcomes and clinical workflows in the evolving landscape of healthcare and medicine. We provide an up-to-date list of healthcare-related foundation models and datasets at https://github.com/Yunkun-Zhang/Data-Centric-FM-Healthcare.", "date": "2024-01-04", "categories": []}
{"id": "2412.14736", "title": "Advances in Artificial Intelligence forDiabetes Prediction: Insights from a Systematic Literature Review", "abstract": "This systematic review explores the use of machine learning (ML) in predicting diabetes, focusing on datasets, algorithms, training methods, and evaluation metrics. It examines datasets like the Singapore National Diabetic Retinopathy Screening program, REPLACE-BG, National Health and Nutrition Examination Survey, and Pima Indians Diabetes Database. The review assesses the performance of ML algorithms like CNN, SVM, Logistic Regression, and XGBoost in predicting diabetes outcomes. The study emphasizes the importance of interdisciplinary collaboration and ethical considerations in ML-based diabetes prediction models.", "date": "2024-12-19", "categories": []}
{"id": "2602.01297", "title": "RE-MCDF: Closed-Loop Multi-Expert LLM Reasoning for Knowledge-Grounded Clinical Diagnosis", "abstract": "Electronic medical records (EMRs), particularly in neurology, are inherently heterogeneous, sparse, and noisy, which poses significant challenges for large language models (LLMs) in clinical diagnosis. In such settings, single-agent systems are vulnerable to self-reinforcing errors, as their predictions lack independent validation and can drift toward spurious conclusions. Although recent multi-agent frameworks attempt to mitigate this issue through collaborative reasoning, their interactions are often shallow and loosely structured, failing to reflect the rigorous, evidence-driven processes used by clinical experts. More fundamentally, existing approaches largely ignore the rich logical dependencies among diseases, such as mutual exclusivity, pathological compatibility, and diagnostic confusion. This limitation prevents them from ruling out clinically implausible hypotheses, even when sufficient evidence is available. To overcome these, we propose RE-MCDF, a relation-enhanced multi-expert clinical diagnosis framework. RE-MCDF introduces a generation--verification--revision closed-loop architecture that integrates three complementary components: (i) a primary expert that generates candidate diagnoses and supporting evidence, (ii) a laboratory expert that dynamically prioritizes heterogeneous clinical indicators, and (iii) a multi-relation awareness and evaluation expert group that explicitly enforces inter-disease logical constraints. Guided by a medical knowledge graph (MKG), the first two experts adaptively reweight EMR evidence, while the expert group validates and corrects candidate diagnoses to ensure logical consistency. Extensive experiments on the neurology subset of CMEMR (NEEMRs) and on our curated dataset (XMEMRs) demonstrate that RE-MCDF consistently outperforms state-of-the-art baselines in complex diagnostic scenarios (https://github.com/shenshaowei/RE-MCDF).", "date": "2026-02-01", "categories": []}
{"id": "2602.06603", "title": "The hidden risks of temporal resampling in clinical reinforcement learning", "abstract": "Reinforcement learning (RL) is a type of artificial intelligence for making optimal choices. In healthcare, researchers generally use offline RL (ORL), where models are trained and evaluated from retrospective observational data. To accommodate inherently irregular clinical records, researchers often resample the data into uniform time intervals before training (known as binning). However, discretised data presents the model with a fictional representation of clinical scenarios, especially where unpredictable decision timings are common. As these models lack robust trial evidence, we chose to explore the effects of this further by conducting an in silico clinical trial using 30 virtual patients with type 1 diabetes from the FDA-approved UVA/Padova simulator. The simulator was modified to include stochastic intervals between decisions and used to generate a training dataset for offline RL. We trained three ORL algorithms on both the unprocessed dataset and equivalent datasets resampled at 10-minute, 2-hour, and 4-hour intervals. When deployed back into the simulated environment, temporal resampling was found to reduce model performance by up to 60% relative to unprocessed data, with 4-hour binning causing all agents to perform worse than the dataset's baseline. Retrospective evaluation on resampled data actively obscured this effect, predicting 1.5-3x better returns than agents achieved in practice. We recommend that future research in this area prioritises datasets with natural clinical timings between decisions, which may be a necessary step before these models can be safely deployed into patient care.", "date": "2026-02-06", "categories": []}
{"id": "2604.05190", "title": "Retrieval-Augmented LLMs for Evidence Localization in Clinical Trial Recruitment from Longitudinal EHR Narratives", "abstract": "Screening patients for enrollment is a well-known, labor-intensive bottleneck that leads to under-enrollment and, ultimately, trial failures. Recent breakthroughs in large language models (LLMs) offer a promising opportunity to use artificial intelligence to improve screening. This study systematically explored both encoder- and decoder-based generative LLMs for screening clinical narratives to facilitate clinical trial recruitment. We examined both general-purpose LLMs and medical-adapted LLMs and explored three strategies to alleviate the \"Lost in the Middle\" issue when handling long documents, including 1) Original long-context: using the default context windows of LLMs, 2) NER-based extractive summarization: converting the long document into summarizations using named entity recognition, 3) RAG: dynamic evidence retrieval based on eligibility criteria. The 2018 N2C2 Track 1 benchmark dataset is used for evaluation. Our experimental results show that the MedGemma model with the RAG strategy achieved the best micro-F1 score of 89.05%, outperforming other models. Generative LLMs have remarkably improved trial criteria that require long-term reasoning across long documents, whereas trial criteria that span a short piece of context (e.g., lab tests) show incremental improvements. The real-world adoption of LLMs for trial recruitment must consider specific criteria for selecting among rule-based queries, encoder-based LLMs, and generative LLMs to maximize efficiency within reasonable computing costs.", "date": "2026-04-06", "categories": []}
{"id": "2604.22063", "title": "Reliability Auditing for Downstream LLM tasks in Psychiatry: LLM-Generated Hospitalization Risk Scores", "abstract": "Large language models (LLMs) are increasingly utilized in clinical reasoning and risk assessment. However, their interpretive reliability in critical and indeterminate domains such as psychiatry remains unclear. Prior work has identified algorithmic biases and prompt sensitivity in these systems, raising concerns about how contextual information may influence model outputs, but there remains no systematic way to assess these, especially in the psychiatric domain. We propose an approach for reliability auditing downstream LLM tasks by structuring evaluation around the impact of prompt design and the inclusion of medically insignificant inputs on predicted hospitalization risk scores, which is often the first downstream AI clinical-decision-making task. In our audit, a cohort of synthetic patient profiles (n = 50) is generated, each consisting of 15 clinically relevant features and up to 50 clinically insignificant features, across four prompt reframings (neutral, logical, human impact, clinical judgment). We audit four LLMs (Gemini 2.5 Flash, LLaMa 3.3 70b, Claude Sonnet 4.6, GPT-4o mini), and our results show that including medically insignificant variables resulted in a statistically significant increase in the absolute mean predicted hospitalization risk and output variability across all models and prompts, indicating reduced predictive stability as contextual noise increased. Clinically insignificant features had an effect on instability across many model-prompt conditions, and prompt variations independently affected the trajectory of instability in a model-dependent manner. These findings quantify how LLM-based psychiatric risk assessments are sensitive to non-clinical information, highlighting the need for systematic evaluations of attributional stability and uncertainty behavior like this before clinical deployments.", "date": "2026-04-23", "categories": []}
{"id": "2604.25942", "title": "A Multimodal and Explainable Machine Learning Approach to Diagnosing Multi-Class Ejection Fraction from Electrocardiograms", "abstract": "Left ventricular ejection fraction (LVEF) assessment depends on echocardiography, limiting access in primary care and resource-constrained settings. We developed a multimodal machine-learning framework that combines engineered 12-lead ECG timeseries features with structured EHR variables to classify LVEF into four clinically used strata: normal (>50%), mildly reduced (40-50%), moderately reduced (30-40%), and severely reduced (<30%). To support model explainability, we identified the most influential ECG and EHR features via SHAP attributions. Using retrospective data from Hartford HealthCare, we trained XGBoost models on 36,784 ECG-echocardiogram pairs from 30,952 outpatients and evaluated temporal generalizability on 19,966 ECGs from a subsequent period. The multimodal model achieved one-vs-rest AUROCs of 0.95 (severe), 0.92 (moderate), 0.82 (mild), and 0.91 (normal), outperforming ECG-only and EHR-only baselines, and maintained performance under temporal validation. This work supports ECG-based, multimodal LVEF stratification as a practical screening and triage aid to prioritize confirmatory imaging where resources are limited.", "date": "2026-04-17", "categories": []}
{"id": "2604.26242", "title": "Recurrence-Based Nonlinear Vocal Dynamics as Digital Biomarkers for Depression Detection from Conversational Speech", "abstract": "Digital biomarkers for depression have largely relied on static acoustic descriptors, pooled summary statistics, or conventional machine learning representations. Such approaches may miss nonlinear temporal organization embedded in conversational vocal dynamics. We hypothesized that depression is associated with altered recurrence structure in vocal state trajectories, reflecting changes in how the vocal system revisits acoustic states over time. Using the depression subset of the DAIC-WOZ corpus with 142 labeled participants, we modeled frame-level COVAREP trajectories as nonlinear dynamical systems and derived recurrence-based biomarkers from 74 vocal channels. Logistic regression with feature selection and stratified cross-validation evaluated classification performance. Recurrence-based biomarkers achieved a mean cross-validated AUC of 0.689, exceeding static acoustic baselines, entropy-dynamics features, Hurst exponent features, determinism features, and Lyapunov-like instability proxies. Permutation testing indicated statistical significance with $p=0.004$. Pooled cross-validated predictions yielded AUC 0.665 with a 95\\% bootstrap confidence interval of [0.568, 0.758]. These findings suggest that depression may be characterized by altered recurrence structure in conversational vocal dynamics and support nonlinear state-space analysis as a promising direction for digital psychiatric biomarkers.", "date": "2026-04-29", "categories": []}
{"id": "2604.26671", "title": "From Black-Box Confidence to Measurable Trust in Clinical AI: A Framework for Evidence, Supervision, and Staged Autonomy", "abstract": "Trust in clinical artificial intelligence (AI) cannot be reduced to model accuracy, fluency of generation, or overall positive user impression. In medicine, trust must be engineered as a measurable system property grounded in evidence, supervision, and operational boundaries of AI autonomy. This article proposes a practical framework for trustworthy clinical AI built around three principles: evidence, supervision, and staged autonomy. Rather than replacing deterministic clinical logic wholesale with end-to-end black-box models, the proposed approach combines a deterministic core, a patient-specific AI assistant for contextual validation, a multi-tier model escalation mechanism, and a human supervision layer for verification, escalation, and risk control. We demonstrate that trust also depends on selective verification of clinically critical findings, bounded clinical context, disciplined prompt architecture, and careful evaluation on realistic cases. Classifier-driven modular prompting is examined as an incremental path to scaling clinical depth without sacrificing prompt performance and without waiting for complete rule-based coverage. To operationalize trust, a set of trust metrics is proposed, built on metrological principles -- measurement uncertainty, calibration, traceability -- enabling quantitative rather than subjective assessment of each architectural layer. In this perspective, trustworthy clinical AI emerges not as a property of an individual model, but as an architectural outcome of a system into which evidence trails, human oversight, tiered escalation, and graduated action rights are embedded from the outset.", "date": "2026-04-29", "categories": []}
{"id": "2604.26766", "title": "Domain-Adapted Small Language Models for Reliable Clinical Triage", "abstract": "Accurate and consistent Emergency Severity Index (ESI) assignment remains a persistent challenge in emergency departments, where highly variable free-text triage documentation contributes to mistriage and workflow inefficiencies. This study evaluates whether open-source small language models (SLMs) can serve as reliable, privacy-preserving decision-support tools for clinical triage. We systematically compared multiple SLMs across diverse prompting pipelines and found that clinical vignettes, concise summaries of triage narratives, yielded the most accurate predictions. The SLM, Qwen2.5-7B, demonstrated the strongest balance of accuracy, stability, and computational efficiency. Through large-scale domain adaptation using expert-curated and silver-standard pediatric triage data, fine-tuned Qwen2.5-7B models substantially reduced discordance and clinically significant errors, outperforming all baseline SLMs and advanced proprietary large language models (LLMs, e.g., GPT-4o). These findings highlight the feasibility of institution-specific SLMs for reliable, privacy-preserving ESI decision support and underscore the importance of targeted fine-tuning over more complex inference strategies.", "date": "2026-04-29", "categories": []}
{"id": "2309.15039", "title": "Can-SAVE: Deploying Low-Cost and Population-Scale Cancer Screening via Survival Analysis Variables and EHR", "abstract": "Conventional medical cancer screening methods are costly, labor-intensive, and extremely difficult to scale. Although AI can improve cancer detection, most systems rely on complex or specialized medical data, making them impractical for large-scale screening. We introduce Can-SAVE, a lightweight AI system that ranks population-wide cancer risks solely based on medical history events. By integrating survival model outputs into a gradient-boosting framework, our approach detects subtle, long-term patient risk patterns - often well before clinical symptoms manifest. Can-SAVE was rigorously evaluated on a real-world dataset of 2.5 million adults spanning five Russian regions, marking the study as one of the largest and most comprehensive deployments of AI-driven cancer risk assessment. In a retrospective oncologist-supervised study over 1.9M patients, Can-SAVE achieves a 4-10x higher detection rate at identical screening volumes and an Average Precision (AP) of 0.228 vs. 0.193 for the best baseline (LoRA-tuned Qwen3-Embeddings via DeepSeek-R1 summarization). In a year-long prospective pilot (426K patients), our method almost doubled the cancer detection rate (+91%) and increased population coverage by 36% over the national screening protocol. The system demonstrates practical scalability: a city-wide population of 1 million patients can be processed in under three hours using standard hardware, enabling seamless clinical integration. This work proves that Can-SAVE achieves nationally significant cancer detection improvements while adhering to real-world public healthcare constraints, offering immediate clinical utility and a replicable framework for population-wide screening. Code for training and feature engineering is available at https://github.com/sb-ai-lab/Can-SAVE.", "date": "2023-09-26", "categories": []}
{"id": "2604.24796", "title": "A multi-stage soft computing framework for complex disease modelling and decision support: A liver cirrhosis case study", "abstract": "Liver cirrhosis is a major global health problem causing millions of deaths annually, and timely detection with aggressive treatment can significantly improve patients' quality of life. Modelling complex diseases from biomedical data is computationally challenging due to high dimensionality, strong feature correlations, noise, and limited labelled samples. Conventional Machine Learning (ML) pipelines often struggle with robustness, interpretability, and generalisation under such conditions. In this study, we propose an ML-driven multi-stage decision framework for complex disease modelling and therapeutic exploration. The framework integrates single-cell transcriptomic profiling, high-dimensional network-based feature stabilisation, multi-model learning, deep representation construction, and post-hoc decision support. Specifically, single-cell sequencing data were analysed to identify key cellular subpopulations, followed by high-dimensional weighted gene co-expression network analysis (hdWGCNA) to stabilise gene modules under sparsity and noise. To enhance non-linear feature interaction modelling, tabular molecular features were restructured into two-dimensional disease maps and analysed using a CNN. Finally, molecular docking was incorporated as a decision-support module to evaluate candidate therapeutic compounds. Using liver cirrhosis as a representative case, the framework identified a disease-associated endothelial subpopulation and extracted seven robust signature genes (HSPB1, GADD45A, CLDN5, ATP1B3, C1QBP, ENPP2, and PARL). The CNN-based representation learning module outperformed conventional pipelines in classification. The framework is disease-agnostic and readily extends to other omics-driven biomedical applications involving uncertainty, heterogeneity, and limited samples.", "date": "2026-04-26", "categories": []}
{"id": "2507.20058", "title": "Modeling Parkinson's Disease Progression Using Longitudinal Voice Biomarkers: A Comparative Study of Statistical and Neural Mixed-Effects Models", "abstract": "Longitudinal voice biomarkers provide a non-invasive source of information for monitoring Parkinson's disease progression, but their statistical analysis is difficult because repeated measurements from the same subject are correlated, clinical cohorts are often small, and disease trajectories can vary substantially across individuals. This study evaluates statistical and neural mixed-effects approaches for modeling Parkinson's disease progression from telemonitoring voice data. Using the Oxford Parkinson's telemonitoring dataset (N=42), we compare Neural Mixed Effects (NME) models, Generalized Neural Network Mixed Models (GNMMs), and semi-parametric Generalized Additive Mixed Models (GAMMs) under the same longitudinal prediction setting. The results show that neural mixed-effects models provide flexible nonlinear representations but can overfit severely in this small-sample setting, whereas GAMMs achieve stronger predictive performance and retain interpretable smooth effects and subject-level structure. In particular, the GAMM-based approach attains the lowest prediction error (MSE 6.56), while the neural baselines have substantially larger errors (MSE > 90). These findings support the use of interpretable statistical mixed-effects models for small longitudinal telemonitoring studies and suggest that larger and more diverse cohorts are needed before highly flexible neural mixed-effects models can be reliably assessed in this application.", "date": "2025-07-26", "categories": []}
{"id": "2510.22609", "title": "CLIN-LLM: A Safety-Constrained Hybrid Framework for Clinical Diagnosis and Treatment Generation", "abstract": "Accurate symptom-to-disease classification and clinically grounded treatment recommendations remain challenging, particularly in heterogeneous patient settings with high diagnostic risk. Existing large language model (LLM)-based systems often lack medical grounding and fail to quantify uncertainty, resulting in unsafe outputs. We propose CLIN-LLM, a safety-constrained hybrid pipeline that integrates multimodal patient encoding, uncertainty-calibrated disease classification, and retrieval-augmented treatment generation. The framework fine-tunes BioBERT on 1,200 clinical cases from the Symptom2Disease dataset and incorporates Focal Loss with Monte Carlo Dropout to enable confidence-aware predictions from free-text symptoms and structured vitals. Low-certainty cases (18%) are automatically flagged for expert review, ensuring human oversight. For treatment generation, CLIN-LLM employs Biomedical Sentence-BERT to retrieve top-k relevant dialogues from the 260,000-sample MedDialog corpus. The retrieved evidence and patient context are fed into a fine-tuned FLAN-T5 model for personalized treatment generation, followed by post-processing with RxNorm for antibiotic stewardship and drug-drug interaction (DDI) screening. CLIN-LLM achieves 98% accuracy and F1 score, outperforming ClinicalBERT by 7.1% (p < 0.001), with 78% top-5 retrieval precision and a clinician-rated validity of 4.2 out of 5. Unsafe antibiotic suggestions are reduced by 67% compared to GPT-5. These results demonstrate CLIN-LLM's robustness, interpretability, and clinical safety alignment. The proposed system provides a deployable, human-in-the-loop decision support framework for resource-limited healthcare environments. Future work includes integrating imaging and lab data, multilingual extensions, and clinical trial validation.", "date": "2025-10-26", "categories": []}
{"id": "2511.16839", "title": "Predicting one-year clinical instability and mortality in heart failure patients using sequence modeling", "abstract": "Heart failure (HF) discharge planning depends on identifying patients at risk of deterioration or death, yet accurate prediction from routinely collected electronic health records (EHRs) remains challenging. We developed and validated sequence models for three one-year prediction tasks in a Swedish HF cohort (N = 42,820): clinical instability (a rehospitalization phenotype) and mortality after the initial in-hospital HF diagnosis, and mortality after the latest hospitalization. A modular three-component framework transforms structured EHRs into patient sequences by specifying tokenization strategies, temporal representations, and model configurations. Patient data included diagnoses, vital signs, laboratories, medications, and procedures. Autoregressive next-token prediction models consistently outperformed alternative objectives in short-context settings (<= 512 tokens). The best model (Llama) achieved AUPRCs (95% CI) of 0.555 (0.535-0.575), 0.582 (0.558-0.608), and 0.854 (0.842-0.865), with robust calibration. Ablations show Llama and Mamba variants learn efficient patient representations, with tiny configurations surpassing larger conventional baselines, indicating that model size alone does not improve performance. With limited clinical concepts or training data, Llama maintains strong performance, frequently surpassing full-data baselines. Combining clinical instability and mortality predictions defines four distinct care pathways, from standard primary care to intensive home care, supporting patient-centered decisions at discharge. These findings demonstrate accurate risk prediction from routine hospital data, provide actionable development guidance, and support post-discharge risk stratification.", "date": "2025-11-20", "categories": []}
{"id": "2602.03886", "title": "Prenatal Stress Detection from Electrocardiography Using Self-Supervised Deep Learning: Development and External Validation", "abstract": "Prenatal psychological stress affects 15-25% of pregnancies and increases risks of preterm birth, low birth weight, and adverse neurodevelopmental outcomes. Current screening relies on subjective questionnaires (PSS-10), limiting continuous monitoring. We developed deep learning models for stress detection from electrocardiography (ECG) using the FELICITy 1 cohort (151 pregnant women, 32-38 weeks gestation). A ResNet-34 encoder was pretrained via SimCLR contrastive learning on 40,692 ECG segments per subject. Multi-layer feature extraction enabled binary classification and continuous PSS prediction across maternal (mECG), fetal (fECG), and abdominal ECG (aECG). External validation used the FELICITy 2 RCT (28 subjects, different ECG device, yoga intervention vs. control). On FELICITy 1 (5-fold CV): mECG 98.6% accuracy (R2=0.88, MAE=1.90), fECG 99.8% (R2=0.95, MAE=1.19), aECG 95.5% (R2=0.75, MAE=2.80). External validation on FELICITy 2: mECG 77.3% accuracy (R2=0.62, MAE=3.54, AUC=0.826), aECG 63.6% (R2=0.29, AUC=0.705). Signal quality-based channel selection outperformed all-channel averaging (+12% R2 improvement). Mixed-effects models detected a significant intervention response (p=0.041). Self-supervised deep learning on pregnancy ECG enables accurate, objective stress assessment, with multi-layer feature extraction substantially outperforming single embedding approaches.", "date": "2026-02-03", "categories": []}
{"id": "2603.05212", "title": "Early Warning of Intraoperative Adverse Events via Transformer-Driven Multi-Label Learning", "abstract": "Early warning of intraoperative adverse events plays a vital role in reducing surgical risk and improving patient safety. While deep learning has shown promise in predicting the single adverse event, several key challenges remain: overlooking adverse event dependencies, underutilizing heterogeneous clinical data, and suffering from the class imbalance inherent in medical datasets. To address these issues, we construct the first Multi-label Adverse Events dataset (MuAE) for intraoperative adverse events prediction, covering six critical events. Next, we propose a novel Transformerbased multi-label learning framework (IAENet) that combines an improved Time-Aware Feature-wise Linear Modulation (TAFiLM) module for static covariates and dynamic variables robust fusion and complex temporal dependencies modeling. Furthermore, we introduce a Label-Constrained Reweighting Loss (LCRLoss) with co-occurrence regularization to effectively mitigate intra-event imbalance and enforce structured consistency among frequently co-occurring events. Extensive experiments demonstrate that IAENet consistently outperforms strong baselines on 5, 10, and 15-minute early warning tasks, achieving improvements of +5.05%, +2.82%, and +7.57% on average F1 score. These results highlight the potential of IAENet for supporting intelligent intraoperative decision-making in clinical practice.", "date": "2026-03-05", "categories": []}
{"id": "2604.23465", "title": "Machine learning models for estimating counterfactuals in a single-arm inflammatory bowel disease study", "abstract": "Single-arm trials accelerate study timelines by reducing the number of patients that must be recruited for a concurrent control group. However, these designs require an alternative comparator to estimate treatment effects. One approach is to construct a virtual control arm using a machine learning (ML) model trained on external control data to predict the counterfactual outcomes of the treatment arm. Our aim in this study was to leverage virtual controls by developing and evaluating ML-based counterfactual outcome models trained on IFX-treated patients to predict 1-year steroid-free clinical remission (SFCR ) and a composite of C-reactive protein remission plus steroid-free clinical remission (CRP-SFCR) for ADA-treated pediatric Crohn's disease patients, and to compare the resulting IFX-versus-ADA treatment effect estimates with those obtained using propensity score matching to external controls. Five ML models were used to train counterfactual models on the observed IFX cohort data. The resulting models were used to predict the counterfactual outcomes for the ADA arm patients. LGBM yields the best OR closest to the propensity score matched reference, and all 95% CI results align with the conclusion from the reference study that no statistical difference in the primary and secondary outcomes has been observed between the patients treated with ADA or IFX. Our study supports virtual controls as a viable and effective substitute for expensive, lengthy or unethical patient recruitment in an inflammatory bowel disease (IBD) trial. The developed gradient boosted prediction model can be used as a pretrained model to generate IFX counterfactual predictions in future studies, pending external validation and assessment of transportability.", "date": "2026-04-25", "categories": []}
{"id": "2604.23732", "title": "Impact of Age Specialized Models for Hypoglycemia Classification", "abstract": "Disease progression varies with age and is influenced by underlying genetic, biochemical, and hormonal etiologies, suggesting the need for tailored monitoring, care, and medication beyond standard clinical guidelines. Specifically, in autoimmune diseases like type 1 diabetes (T1D), where patients depend on exogenous insulin to compensate for insulin deficiency, medication dosing and the physiological response reflected in vital signs can differ. Insulin therapy can lead to hypoglycemia, a dangerous condition characterized by decreased blood glucose levels ($\\leq$70). This risk can be mitigated through improved diabetes management supported by data analytics. Notably, leveraging data from continuous glucose monitoring (CGM) devices, hypoglycemia onset can be predicted. However, while glucose variability, auto-antibody levels, and hypoglycemia occurrence differ across age groups, hypoglycemia classification most often only relies on population-based models specialized in specific age ranges. In this work, we classify hypoglycemia 0, 5-15, 20-45, and 50-120 minutes before onset using DiaData, a large CGM dataset of patients with T1D ranging from children to seniors. In particular, we investigate: 1) the generalizability of a population-based model including all age groups, 2) the impact of age-segmented models trained separately per age group, and 3) the effect of model individualization through transfer learning. The results show that a global population-based model yields similar or superior performance compared to age-segmented models. These findings suggest that data from children, teenagers, and adults can be combined for training models on hypoglycemia classification. While glucose variation differs across age groups, short-term hypoglycemic patterns are similar. However, data of children obtain their best recall with age specialized model.", "date": "2026-04-26", "categories": []}
{"id": "2604.23854", "title": "Does Machine Unlearning Preserve Clinical Safety? A Risk Analysis for Medical Image Classification", "abstract": "The application of Deep Learning in medical diagnosis must balance patient safety with compliance with data protection regulations. Machine Unlearning enables the selective removal of training data from deployed models. However, most methods are validated primarily through efficiency and privacy-oriented metrics, with limited attention to clinically asymmetric error costs. In this work, we investigate how unlearning affects clinical risk in binary medical image classification. We show that standard unlearning strategies (Fine-Tuning, Random Labeling, and SalUn) may reduce test utility while increasing false-negative rates, thereby amplifying clinical risk. To mitigate this, we propose SalUn-CRA (Clinical Risk-Aware), a variant of SalUn that replaces random relabeling with entropy-based forgetting for malignant samples in the forget set, preventing the model from learning harmful benign associations. We evaluate on DermaMNIST and PathMNIST medical image datasets under 20% and 50% data removal. Using Global Risk metrics with asymmetric costs, SalUn-CRA achieves lower or comparable clinical risk to full retraining while preserving unlearning effectiveness. These results suggest that clinical risk should be an integral component of unlearning validation in medical systems.", "date": "2026-04-26", "categories": []}
{"id": "2604.23933", "title": "Robust and Clinically Reliable EEG Biomarkers: A Cross Population Framework for Generalizable Parkinson's Disease Detection", "abstract": "Developing robust and clinically reliable EEG biomarkers requires evaluation frameworks that explicitly address cross population generalization in multi site settings such as Parkinsons disease (PD) detection. Models trained under i.i.d. assumptions often capture population specific artifacts rather than disease relevant neural structure, leading to poor generalization across clinical cohorts. EEG further amplifies this challenge due to low signal to noise ratio and heterogeneous acquisition conditions. We propose a population aware evaluation framework to assess the robustness and clinical reliability of EEG biomarkers under distribution shift. Using an n gram expansion strategy, we enumerate all cross population train test configurations across five independent cohorts, resulting in 75 directional evaluations. A nested cross validation design with integrated channel selection ensures prospective biomarker identification without population leakage. Results show that cross population transfer is asymmetric and that both accuracy and biomarker stability improve with increasing training population diversity, achieving up to 94.1% accuracy on held out cohorts. A theoretical analysis based on mixture risk optimization and hypothesis space contraction explains these trends, showing that multi population training promotes population robust representations. This work establishes a principled framework for learning robust, generalizable, and clinically reliable EEG biomarkers for multi site biomedical applications.", "date": "2026-04-27", "categories": []}
{"id": "2604.23949", "title": "Context-Aware Hospitalization Forecasting Evaluations for Decision Support using LLMs", "abstract": "Medical and public health experts must make real-time resource decisions, such as expanding hospital bed capacity, based on projected hospitalization trends during large-scale healthcare disruptions (e.g., operational failures or pandemics). Forecasting models can assist in this task by analyzing large volumes of resource-related data at the facility level, but they must be reliable for decision-making under real-world data conditions. Recent work shows that large language models (LLMs) can incorporate richer forms of context into numerical forecasting. Whereas traditional models rely primarily on temporal context (i.e., past observations), LLMs can also leverage non-temporal public health context such as demographic, geographic, and population-level features. However, it remains unclear how these models should be used to produce stable or decision-relevant predictions in real-world healthcare settings. To evaluate how LLMs can be effectively used in this setting, we evaluate three approaches across 60 counties with low-,mid-, and high-hospitalization intensities in the United States: direct LLM-based forecasting, classical time-series models, and a context-augmented hybrid pipeline (HybridARX) that incorporates LLM-derived signals into structured models. Because the goal is operational decision-making rather than error minimization alone, we evaluate performance with bias and lead-lag alignment in addition to standard forecasting metrics. Our results show that HybridARX improves over classical ARX by yielding more stable and better-calibrated forecasts, particularly when incorporating noisy contextual signals into structured time-series models. These findings suggest that, in non-stationary healthcare resource forecasting, LLMs are most useful when embedded within structured hybrid models.", "date": "2026-04-27", "categories": []}
{"id": "2604.23954", "title": "An empirical evaluation of the risks of AI model updates using clinical data: stability, arbitrariness, and fairness", "abstract": "Artificial Intelligence and Machine Learning (AI/ML) models used in clinical settings are increasingly deployed to support clinical decision-making. However, when training data become stale due to changes in demographics, environment, or patient behaviors, model performance can degrade substantially. While updating models with new training data is necessary, such updates may also introduce new risks. We evaluated the proposed monitoring framework on four publicly available U.S.-based Type 1 Diabetes datasets containing high-resolution continuous glucose monitoring (CGM) data, comprising approximately 11,300 weekly observations from 496 participants under 20 years of age. All datasets included structured sociodemographic information. Using the prediction of severe hyperglycemia events in children with type 1 diabetes as a case study, we examine how different model update strategies can adversely affect model stability (e.g., by causing predictions to \"flip\" for a large number of cases after an update), increase arbitrariness in predictions, or worsen accuracy equity and the balance of error rates across subpopulations. We propose multiple dimensions for continuous monitoring to detect these issues and argue that such monitoring is essential for the development of trustworthy clinical decision support systems.", "date": "2026-04-27", "categories": []}
{"id": "2604.24126", "title": "Psychologically-Grounded Graph Modeling for Interpretable Depression Detection", "abstract": "Automatic depression detection from conversational interactions holds significant promise for scalable screening but remains hindered by severe data scarcity and a lack of clinical interpretability. Existing approaches typically rely on black-box deep learning architectures that struggle to model the subtle, temporal evolution of depressive symptoms or account for participant-specific heterogeneity. In this work, we propose PsyGAT (Psychological Graph Attention Network), a psychologically grounded framework that models conversational sessions as dynamic temporal graphs. We introduce Psychological Expression Units (PEUs) to explicitly encode utterance-level clinical evidence, structuring the session graph to capture transitions in psychological states rather than mere semantic dependencies. To address the critical class imbalance in depression datasets, we employ clinically approved persona-based data augmentation, enable robust model learning. Additionally, we integrate session-level personality context directly into the graph structure to disentangle trait-based behavior from acute depressive symptoms. PsyGAT achieves state-of-the-art performance, surpassing both strong graph-based baselines and closed-source LLMs like GPT-5, achieving 89.99 and 71.37 Macro F1 scores in DAIC-WoZ and E-DAIC, respectively. We further introduce Causal-PsyGAT, an interpretability module that identifies symptom triggers. Experiments show a 20% improvement in MRR for identifying causal indicators, effectively bridging the gap between depression monitoring and clinical explainability. The full augmented dataset is publicly available at https://doi.org/10.6084/m9.figshare.31801921.", "date": "2026-04-27", "categories": []}
{"id": "2604.24376", "title": "Learning Evidence of Depression Symptoms via Prompt Induction", "abstract": "Depression places substantial pressure on mental health services, and many people describe their experiences outside clinical settings in high-volume user-generated text (e.g., online forums and social media). Automatically identifying clinical symptom evidence in such text can therefore complement limited clinical capacity and scale to large populations. We address this need through sentence-level classification of 21 depression symptoms from the BDI-II questionnaire, using BDI-Sen, a dataset annotated for symptom relevance. This task is fine-grained and highly imbalanced, and we find that common LLM approaches (zero-shot, in-context learning, and fine-tuning) struggle to apply consistent relevance criteria for most symptoms. We propose Symptom Induction (SI), a novel approach which compresses labeled examples into short, interpretable guidelines that specify what counts as evidence for each symptom and uses these guidelines to condition classification. Across four LLM families and eight models, SI achieves the best overall weighted F1 on BDI-Sen, with especially large gains for infrequent symptoms. Cross-domain evaluation on an external dataset further shows that induced guidelines generalize across other diseases shared symptomatology (bipolar and eating disorders).", "date": "2026-04-27", "categories": []}
{"id": "2604.24473", "title": "Agentic clinical reasoning over longitudinal myeloma records: a retrospective evaluation against expert consensus", "abstract": "Multiple myeloma is managed through sequential lines of therapy over years to decades, with each decision depending on cumulative disease history distributed across dozens to hundreds of heterogeneous clinical documents. Whether LLM-based systems can synthesise this evidence at a level approaching expert agreement has not been established. A retrospective evaluation was conducted on longitudinal clinical records of 811 myeloma patients treated at a tertiary centre (2001-2026), covering 44,962 documents and 1,334,677 laboratory values, with external validation on MIMIC-IV. An agentic reasoning system was compared against single-pass retrieval-augmented generation (RAG), iterative RAG, and full-context input on 469 patient-question pairs from 48 templates at three complexity levels. Reference labels came from double annotation by four oncologists with senior haematologist adjudication. Iterative RAG and full-context input converged on a shared ceiling (75.4% vs 75.8%, p = 1.00). The agentic system reached 79.6% concordance (95% CI 76.4-82.8), exceeding both baselines (+3.8 and +4.2 pp; p = 0.006 and 0.007). Gains rose with question complexity, reaching +9.4 pp on criteria-based synthesis (p = 0.032), and with record length, reaching +13.5 pp in the top decile (n = 10). The system error rate (12.2%) was comparable to expert disagreement (13.6%), but severity was inverted: 57.8% of system errors were clinically significant versus 18.8% of expert disagreements. Agentic reasoning was the only approach to exceed the shared ceiling, with gains concentrated on the most complex questions and longest records. The greater clinical consequence of residual system errors indicates that prospective evaluation in routine care is required before these findings translate into patient benefit.", "date": "2026-04-27", "categories": []}
{"id": "2604.24679", "title": "Benchmarking Pathology Foundation Models for Breast Cancer Survival Prediction", "abstract": "Pathology foundation models (PFMs) have recently emerged as powerful pretrained encoders for computational pathology, enabling transfer learning across a wide range of downstream tasks. However, systematic comparisons of these models for clinically meaningful prediction problems remain limited, especially in the context of survival prediction under external validation. In this study, we benchmark widely used and recently proposed PFMs for breast cancer survival prediction from whole-slide histopathology images. Using a standardized pipeline based on patch-level feature extraction and a unified survival modeling framework, we evaluate model representations across three independent clinical cohorts comprising more than 5,400 patients with long-term follow-up. Models are trained on one cohort and evaluated on two independent external cohorts, enabling a rigorous assessment of cross-dataset generalization. Overall, H-optimus-1 achieves the strongest survival prediction performance. More broadly, we observe consistent generational improvements across model families, with second-generation PFMs outperforming their first-generation counterparts. However, absolute performance differences between many recent PFMs remain modest, suggesting diminishing returns from further scaling of pretraining data or model size alone. Notably, the compact distilled model H0-mini slightly outperforms its larger teacher model H-optimus-0, despite using fewer than 8% of the parameters and enabling significantly faster feature extraction. Together, these results provide the first large-scale, externally validated benchmark of PFMs for breast cancer survival prediction, and offer practical guidance for efficient deployment of PFMs in clinical workflows.", "date": "2026-04-27", "categories": []}
{"id": "2604.24710", "title": "Case-Specific Rubrics for Clinical AI Evaluation: Methodology, Validation, and LLM-Clinician Agreement Across 823 Encounters", "abstract": "Objective. Clinical AI documentation systems require evaluation methodologies that are clinically valid, economically viable, and sensitive to iterative changes. Methods requiring expert review per scoring instance are too slow and expensive for safe, iterative deployment. We present a case-specific, clinician-authored rubric methodology for clinical AI evaluation and examine whether LLM-generated rubrics can approximate clinician agreement.\n  Materials and Methods. Twenty clinicians authored 1,646 rubrics for 823 clinical cases (736 real-world, 87 synthetic) across primary care, psychiatry, oncology, and behavioral health. Each rubric was validated by confirming that an LLM-based scoring agent consistently scored clinician-preferred outputs higher than rejected ones. Seven versions of an EHR-embedded AI agent for clinicians were evaluated across all cases.\n  Results. Clinician-authored rubrics discriminated effectively between high- and low-quality outputs (median score gap: 82.9%) with high scoring stability (median range: 0.00%). Median scores improved from 84% to 95%. In later experiments, clinician-LLM ranking agreement (tau: 0.42-0.46) matched or exceeded clinician-clinician agreement (tau: 0.38-0.43), attributable to both ceiling compression and LLM rubric improvement.\n  Discussion. This convergence supports incorporating LLM rubrics alongside clinician-authored ones. At roughly 1,000 times lower cost, LLM rubrics enable substantially greater evaluation coverage, while continued clinical authorship grounds evaluation in expert judgment. Ceiling compression poses a methodological challenge for future inter-rater agreement studies.\n  Conclusion. Case-specific rubrics offer a path for clinical AI evaluation that preserves expert judgment while enabling automation at three orders lower cost. Clinician-authored rubrics establish the baseline against which LLM rubrics are validated.", "date": "2026-04-27", "categories": []}
{"id": "2604.14651", "title": "CURA: Clinical Uncertainty Risk Alignment for Language Model-Based Risk Prediction", "abstract": "Clinical language models (LMs) are increasingly applied to support clinical risk prediction from free-text notes, yet their uncertainty estimates often remain poorly calibrated and clinically unreliable. In this work, we propose Clinical Uncertainty Risk Alignment (CURA), a framework that aligns clinical LM-based risk estimates and uncertainty with both individual error likelihoods and cohort-level ambiguities. CURA first fine-tunes domain-specific clinical LMs to obtain task-adapted patient embeddings, and then performs uncertainty fine-tuning of a multi-head classifier using a bi-level uncertainty objective. Specifically, an individual-level calibration term aligns predictive uncertainty with each patient's likelihood of error, while a cohort-aware regularizer pulls risk estimates toward event rates in their local neighborhoods in the embedding space and places extra weight on ambiguous cohorts near the decision boundary. We further show that this cohort-aware term can be interpreted as a cross-entropy loss with neighborhood-informed soft labels, providing a label-smoothing view of our method. Extensive experiments on MIMIC-IV clinical risk prediction tasks across various clinical LMs show that CURA consistently improves calibration metrics without substantially compromising discrimination. Further analysis illustrates that CURA reduces overconfident false reassurance and yields more trustworthy uncertainty estimates for downstream clinical decision support.", "date": "2026-04-16", "categories": []}
{"id": "2604.22061", "title": "Lightweight Retrieval-Augmented Generation and Large Language Model-Based Modeling for Scalable Patient-Trial Matching", "abstract": "Patient-trial matching requires reasoning over long, heterogeneous electronic health records (EHRs) and complex eligibility criteria, posing significant challenges for scalability, generalization, and computational efficiency. Existing approaches either rely on full-document processing with large language models (LLMs), which is computationally expensive, or use traditional machine learning methods that struggle to capture unstructured clinical narratives. In this work, we propose a lightweight framework that combines retrieval-augmented generation and large language model-based modeling for scalable patient-trial matching. The framework explicitly separates two key components: retrieval-augmented generation is used to identify clinically relevant segments from long EHRs, reducing input complexity, while large language models are used to encode these selected segments into informative representations. These representations are further refined through dimensionality reduction and modeled using lightweight predictors, enabling efficient and scalable downstream classification. We evaluate the proposed approach on multiple public benchmarks (n2c2, SIGIR, TREC 2021/2022) and a real-world multimodal dataset from Mayo Clinic (MCPMD). Results show that retrieval-based information selection significantly reduces computational burden while preserving clinically meaningful signals. We further demonstrate that frozen LLMs provide strong representations for structured clinical data, whereas fine-tuning is essential for modeling unstructured clinical narratives. Importantly, the proposed lightweight pipeline achieves performance comparable to end-to-end LLM approaches with substantially lower computational cost.", "date": "2026-04-23", "categories": []}
{"id": "2604.22348", "title": "A Nationwide Japanese Medical Claims Foundation Model: Balancing Model Scaling and Task-Specific Computational Efficiency", "abstract": "Clinical risk prediction using longitudinal medical data supports individualized care. Self-supervised foundation models have emerged as a promising approach for leveraging large-scale unlabeled healthcare records. In natural language processing, scaling laws suggest that larger models achieve predictably lower pretraining losses, supporting the foundation model paradigm. However, for structured medical data, characterized by a limited vocabulary and sparse observations, whether increasing model size consistently improves downstream predictions is unclear, as most studies evaluate only a single model scale. In this study, we evaluated the relationship between model scale and downstream task performance for structured medical foundation models. Using a random sample (2.3 million patients, 32 hospitals) from a nationwide 519-hospital Japanese claims database, we pretrained encoder-only Transformers at five scales (2.2M-101M parameters) for disease incidence and medication prediction. Downstream performance saturated at task-dependent thresholds: disease prediction benefited from larger models (32M-101M), whereas medication prediction saturated at 11M, reducing pretraining time by 178 h. Across all tasks, the best-performing model consistently outperformed a Light Gradient Boosting Machine baseline in the area under the precision-recall curve. These findings indicate that, unlike the monotonically decreasing pretraining loss, the optimal model size varied depending on task characteristics. This task-dependent saturation provides practical guidance for balancing predictive performance and computational cost in structured medical foundation models.", "date": "2026-04-24", "categories": []}
{"id": "2604.22428", "title": "CognitiveTwin: Robust Multi-Modal Digital Twins for Predicting Cognitive Decline in Alzheimer's Disease", "abstract": "Predicting individual cognitive decline in Alzheimer's disease (AD) is difficult due to the heterogeneity of disease progression. Reliable clinical tools require not only high accuracy but also fairness across demographics and robustness to missing data. We present CognitiveTwin, a digital twin framework that predicts patient-specific cognitive trajectories. The model integrates multi-modal longitudinal data (cognitive scores, magnetic resonance imaging, positron emission tomography, cerebrospinal fluid biomarkers, and genetics). We use a Transformer-based architecture to fuse these modalities and a Deep Markov Model to capture temporal dynamics. We trained and evaluated the framework using data from 1,666 patients in the TADPOLE (Alzheimer's Disease Neuroimaging Initiative) dataset. We assessed the model for prediction error, demographic fairness, and robustness to missing-not-at-random (MNAR) data patterns. ognitiveTwin provides accurate and personalized predictions of cognitive decline. Its demonstrated fairness across patient demographics and resilience to clinical dropout make it a reliable tool for clinical trial enrichment and personalized care planning.", "date": "2026-04-24", "categories": []}
{"id": "2604.22534", "title": "FeatEHR-LLM: Leveraging Large Language Models for Feature Engineering in Electronic Health Records", "abstract": "Feature engineering for Electronic Health Records (EHR) is complicated by irregular observation intervals, variable measurement frequencies, and structural sparsity inherent to clinical time series. Existing automated methods either lack clinical domain awareness or assume clean, regularly sampled inputs, limiting their applicability to real-world EHR data. We present \\textbf{FeatEHR-LLM}, a framework that leverages Large Language Models (LLMs) to generate clinically meaningful tabular features from irregularly sampled EHR time series. To limit patient privacy exposure, the LLM operates exclusively on dataset schemas and task descriptions rather than raw patient records. A tool-augmented generation mechanism equips the LLM with specialized routines for querying irregular temporal data, enabling it to produce executable feature-extraction code that explicitly handles uneven observation patterns and informative sparsity. FeatEHR-LLM supports both univariate and multivariate feature generation through an iterative, validation-in-the-loop pipeline. Evaluated on eight clinical prediction tasks across four ICU datasets, our framework achieves the highest mean AUROC on 7 out of 8 tasks, with improvements of up to 6 percentage points over strong baselines. Code is available at github.com/hojjatkarami/FeatEHR-LLM.", "date": "2026-04-24", "categories": []}
{"id": "2604.22618", "title": "Beyond Patient Invariance: Learning Cardiac Dynamics via Action-Conditioned JEPAs", "abstract": "Self-supervised learning in healthcare has largely relied on invariance-based objectives, which maximize similarity between different views of the same patient. While effective for static anatomy, this paradigm is fundamentally misaligned with clinical diagnosis, as it mathematically compels the model to suppress the transient pathological changes it is intended to detect. We propose a shift towards Action-Conditioned World Models that learn to simulate the dynamics of disease progression, or Event-Conditioned. Adapting the LeJEPA framework to physiological time-series, we define pathology not as a static label, but as a transition vector acting on a patient's latent state. By predicting the future electrophysiological state of the heart given a disease onset, our model explicitly disentangles stable anatomical features from dynamic pathological forces. Evaluated on the MIMIC-IV-ECG dataset, our approach outperforms fully supervised baselines on the critical triage task. Crucially, we demonstrate superior sample efficiency: in low-resource regimes, our world model outperforms supervised learning by over 0.05 AUROC. These results suggest that modeling biological dynamics provides a dense supervision signal that is far more robust than static classification. Source code is available at https://github.com/cljosegfer/lesaude-dynamics", "date": "2026-04-24", "categories": []}
{"id": "2604.20869", "title": "Clinical Reasoning AI for Oncology Treatment Planning: A Multi-Specialty Case-Based Evaluation", "abstract": "Background: More than 80% of U.S. cancer care is delivered in community settings, where survival remains worse than at academic centers. Clinicians must integrate genomics, staging, radiology, pathology, and changing guidelines, creating cognitive burden. We evaluated OncoBrain, an AI clinical reasoning platform for oncology treatment-plan generation, as an early step toward OGI.\n  Methods: OncoBrain combines general-purpose LLMs with a cancer-specific graph retrieval-augmented generation layer, a gold-standard treatment-plan corpus as long-term memory, and a model-agnostic safety layer (CHECK) for hallucination detection and suppression. We evaluated clinician-enriched case summaries across gynecologic, genitourinary, neuro-oncology, gastrointestinal/hepatobiliary, and hematologic malignancies. Three clinician groups completed structured evaluations of 173 cases using a common 16-item instrument: subspecialist oncologists reviewed 50 cases, physician reviewers 78, and advanced practice providers 45.\n  Results: Ratings were highest for scientific accuracy, evidence support, and safety, with lower but favorable scores for workflow integration and time savings. On a 5-point scale, mean alignment with evidence and guidelines was 4.60, 4.56, and 4.70 across subspecialists, physician reviewers, and advanced practice providers. Mean scores for absence of safety or misinformation concerns were 4.80, 4.40, and 4.60. Workflow integration averaged 4.50, 3.94, and 4.00; perceived time savings averaged 5.00, 3.89, and 3.60.\n  Conclusions: In this multi-specialty vignette-based evaluation, OncoBrain generated oncology treatment plans judged guideline-concordant, clinically acceptable, and easy to supervise. These findings support the potential of a carefully engineered AI reasoning platform to assist oncology treatment planning and justify prospective real-world evaluation in community settings.", "date": "2026-03-27", "categories": []}
{"id": "2604.20921", "title": "Validating a Deep Learning Algorithm to Identify Patients with Glaucoma using Systemic Electronic Health Records", "abstract": "We evaluated whether a glaucoma risk assessment (GRA) model trained on All of Us national data can identify patients at high probability of glaucoma using only systemic electronic health records (EHR) at an independent institution. In this cross-sectional study, 20,636 Stanford patients seen from November 2013 to January 2024 were included (15% with glaucoma). A pretrained GRA model was fine-tuned on the Stanford cohort and tested on a held-out set using demographics, systemic diagnoses, medications, laboratory results, and physical examination measurements as inputs. The best model achieved AUROC 0.883 and PPV 0.657. Calibration was consistent with clinical risk: the highest prediction decile showed the greatest glaucoma diagnosis rate (65.7%) and treatment rate (57.0%). Performance improved with more trainable layers up to 15 and with additional data. An EHR-only GRA model may enable scalable and accessible pre-screening without specialized imaging.", "date": "2026-04-22", "categories": []}
{"id": "2604.20924", "title": "Clinically Interpretable Sepsis Early Warning via LLM-Guided Simulation of Temporal Physiological Dynamics", "abstract": "Timely and interpretable early warning of sepsis remains a major clinical challenge due to the complex temporal dynamics of physiological deterioration. Traditional data-driven models often provide accurate yet opaque predictions, limiting physicians' confidence and clinical applicability. To address this limitation, we propose a Large Language Model (LLM)-guided temporal simulation framework that explicitly models physiological trajectories prior to disease onset for clinically interpretable prediction. The framework consists of a spatiotemporal feature extraction module that captures dynamic dependencies among multivariate vital signs, a Medical Prompt-as-Prefix module that embeds clinical reasoning cues into LLMs, and an agent-based post-processing component that constrains predictions within physiologically plausible ranges. By first simulating the evolution of key physiological indicators and then classifying sepsis onset, our model offers transparent prediction mechanisms that align with clinical judgment. Evaluated on the MIMIC-IV and eICU databases, the proposed method achieves superior AUC scores (0.861-0.903) across 24-4-hour pre-onset prediction tasks, outperforming conventional deep learning and rule-based approaches. More importantly, it provides interpretable trajectories and risk trends that can assist clinicians in early intervention and personalized decision-making in intensive care environments.", "date": "2026-04-22", "categories": []}
{"id": "2604.21235", "title": "Learning Dynamic Representations and Policies from Multimodal Clinical Time-Series with Informative Missingness", "abstract": "Multimodal clinical records contain structured measurements and clinical notes recorded over time, offering rich temporal information about the evolution of patient health. Yet these observations are sparse, and whether they are recorded depends on the patient's latent condition. Observation patterns also differ across modalities, as structured measurements and clinical notes arise under distinct recording processes. While prior work has developed methods that accommodate missingness in clinical time series, how to extract and use the information carried by the observation process itself remains underexplored. We therefore propose a patient representation learning framework for multimodal clinical time series that explicitly leverages informative missingness. The framework combines (1) a multimodal encoder that captures signals from structured and textual data together with their observation patterns, (2) a Bayesian filtering module that updates a latent patient state over time from observed multimodal signals, and (3) downstream modules for offline treatment policy learning and patient outcome prediction based on the learned patient state. We evaluate the framework on ICU sepsis cohorts from MIMIC-III, MIMIC-IV, and eICU. It improves both offline treatment policy learning and adverse outcome prediction, achieving FQE 0.679 versus 0.528 for clinician behavior and AUROC 0.886 for post-72-hour mortality prediction on MIMIC-III.", "date": "2026-04-23", "categories": []}
{"id": "2501.03624", "title": "LLAMADRS: Evaluating Open-Source LLMs on Real Clinical Interviews--To Reason or Not to Reason?", "abstract": "Large language models (LLMs) excel on many NLP benchmarks, but their behavior on real-world, semi-structured prediction remains underexplored. We present LlaMADRS, a benchmark for structured clinical assessment from dialogue built on the CAMI corpus of psychiatric interviews, comprising 5,804 expert annotations across 541 sessions. We evaluate 25 open-source models (standard and reasoning-augmented; 0.6B--400B parameters) and generate over 400,000 predictions. Our results demonstrate that strong open-source LLMs achieve item-level accuracy with residual error below clinically substantial thresholds. Additionally, an Item-then-Sum (ItS) strategy, assessing symptoms individually through discrete LLM calls before synthesizing final scores, significantly reduces error relative to Direct Total Score (DTS) prediction across most model architectures and scales, despite reasoning models attempting similar decomposition in the reasoning traces of their DTS predictions. In fact, we find that performance gains attributed to \"reasoning\" depend fundamentally on prompt design: standard models equipped with structured task definitions and examples match reasoning-augmented counterparts. Among the latter, longer reasoning traces correlate with reduced error; while higher model scale does across both architectures. Our results clarify when and why reasoning helps and offer actionable guidance for deploying LLMs in semi-structured clinical assessment.", "date": "2025-01-07", "categories": []}
{"id": "2511.02849", "title": "Benchmarking ResNet for Short-Term Hypoglycemia Classification with DiaData", "abstract": "Individualized therapy is driven forward by medical data analysis, which provides insight into the patient's context. In particular, for Type 1 Diabetes (T1D), which is an autoimmune disease, relationships between demographics, sensor data, and context can be analyzed. However, outliers, noisy data, and small data volumes cannot provide a reliable analysis. Hence, the research domain requires large volumes of high-quality data. Moreover, missing values can lead to information loss. To address this limitation, this study improves the data quality of DiaData, an integration of 15 separate datasets containing glucose values from 2510 subjects with T1D. Notably, we make the following contributions: 1) Outliers are identified with the interquartile range (IQR) approach and treated by replacing them with missing values. 2) Small gaps ($\\le$ 25 min) are imputed with linear interpolation and larger gaps ($\\ge$ 30 and $<$ 120 min) with Stineman interpolation. Based on a visual comparison, Stineman interpolation provides more realistic glucose estimates than linear interpolation for larger gaps. 3) After data cleaning, the correlation between glucose and heart rate is analyzed, yielding a moderate relation between 15 and 60 minutes before hypoglycemia ($\\le$ 70 mg/dL). 4) Finally, a benchmark for hypoglycemia classification is provided with a state-of-the-art ResNet model. The model is trained with the Maindatabase and Subdatabase II of DiaData to classify hypoglycemia onset up to 2 hours in advance. Training with more data improves performance by 7% while using quality-refined data yields a 2-3% gain compared to raw data.", "date": "2025-10-26", "categories": []}
{"id": "2604.18570", "title": "A multimodal and temporal foundation model for virtual patient representations at healthcare system scale", "abstract": "Modern medicine generates vast multimodal data across siloed systems, yet no existing model integrates the full breadth and temporal depth of the clinical record into a unified patient representation. We introduce Apollo, a multimodal temporal foundation model trained and evaluated on over three decades of longitudinal hospital records from a major US hospital system, composed of 25 billion records from 7.2 million patients, representing 28 distinct medical modalities and 12 major medical specialties. Apollo learns a unified representation space integrating over 100 thousand unique medical events in our clinical vocabulary as well as images and clinical text. This \"atlas of medical concepts\" forms a computational substrate for modeling entire patient care journeys comprised of sequences of structured and unstructured events, which are compressed by Apollo into virtual patient representations. To assess the potential of these whole-patient representations, we created 322 prognosis and retrieval tasks from a held-out test set of 1.4 million patients. We demonstrate the generalized clinical forecasting potential of Apollo embeddings, including predicting new disease onset risk up to five years in advance (95 tasks), disease progression (78 tasks), treatment response (59 tasks), risk of treatment-related adverse events (17 tasks), and hospital operations endpoints (12 tasks). Using feature attribution techniques, we show that model predictions align with clinically-interpretable multimodal biomarkers. We evaluate semantic similarity search on 61 retrieval tasks, and moreover demonstrate the potential of Apollo as a multimodal medical search engine using text and image queries. Together, these modeling capabilities establish the foundation for computable medicine, where the full context of patient care becomes accessible to computational reasoning.", "date": "2026-04-20", "categories": []}
{"id": "2604.19759", "title": "Automated Detection of Dosing Errors in Clinical Trial Narratives: A Multi-Modal Feature Engineering Approach with LightGBM", "abstract": "Clinical trials require strict adherence to medication protocols, yet dosing errors remain a persistent challenge affecting patient safety and trial integrity. We present an automated system for detecting dosing errors in unstructured clinical trial narratives using gradient boosting with comprehensive multi-modal feature engineering. Our approach combines 3,451 features spanning traditional NLP (TF-IDF, character n-grams), dense semantic embeddings (all-MiniLM-L6v2), domain-specific medical patterns, and transformer-based scores (BiomedBERT, DeBERTa-v3), used to train a LightGBM model. Features are extracted from nine complementary text fields (median 5,400 characters per sample) ensuring complete coverage across all 42,112 clinical trial narratives. On the CT-DEB benchmark dataset with severe class imbalance (4.9% positive rate), we achieve 0.8725 test ROC-AUC through 5-fold ensemble averaging (cross-validation: 0.8833 + 0.0091 AUC). Systematic ablation studies reveal that removing sentence embeddings causes the largest performance degradation (2.39%), demonstrating their critical role despite contributing only 37.07% of total feature importance. Feature efficiency analysis demonstrates that selecting the top 500-1000 features yields optimal performance (0.886-0.887 AUC), outperforming the full 3,451-feature set (0.879 AUC) through effective noise reduction. Our findings highlight the importance of feature selection as a regularization technique and demonstrate that sparse lexical features remain complementary to dense representations for specialized clinical text classification under severe class imbalance.", "date": "2026-03-25", "categories": []}
{"id": "2604.20256", "title": "RADS: Reinforcement Learning-Based Sample Selection Improves Transfer Learning in Low-resource and Imbalanced Clinical Settings", "abstract": "A common strategy in transfer learning is few shot fine-tuning, but its success is highly dependent on the quality of samples selected as training examples. Active learning methods such as uncertainty sampling and diversity sampling can select useful samples. However, under extremely low-resource and class-imbalanced conditions, they often favor outliers rather than truly informative samples, resulting in degraded performance. In this paper, we introduce RADS (Reinforcement Adaptive Domain Sampling), a robust sample selection strategy using reinforcement learning (RL) to identify the most informative samples. Experimental evaluations on several real world clinical datasets show our sample selection strategy enhances model transferability while maintaining robust performance under extreme class imbalance compared to traditional methods.", "date": "2026-04-22", "categories": []}
{"id": "2604.20259", "title": "Causal-Transformer with Adaptive Mutation-Locking for Early Prediction of Acute Kidney Injury", "abstract": "Accurate early prediction of Acute Kidney Injury (AKI) is critical for timely clinical intervention. However, existing deep learning models struggle with irregularly sampled data and suffer from the opaque \"black-box\" nature of sequential architectures, strictly limiting clinical trust. To address these challenges, we propose CT-Former, integrating continuous-time modeling with a Causal-Transformer. To handle data irregularity without biased artificial imputation, our framework utilizes a continuous-time state evolution mechanism to naturally track patient temporal trajectories. To resolve the black-box problem, our Causal-Attention module abandons uninterpretable hidden state aggregation. Instead, it generates a directed structural causal matrix to identify and trace the exact historical onset of severe physiological shocks. By establishing clear causal pathways between historical anomalies and current risk predictions, CT-Former provides native clinical interpretability. Training follows a decoupled two-stage protocol to optimize the causal-fusion process independently. Extensive experiments on the MIMIC-IV cohort (N=18,419) demonstrate that CT-Former significantly outperforms state-of-the-art baselines. The results confirm that our explicitly transparent architecture offers an accurate and trustworthy tool for clinical decision-making.", "date": "2026-04-22", "categories": []}
{"id": "2412.17842", "title": "Canine EEG Helps Human: Cross-Species and Cross-Modality Epileptic Seizure Detection via Multi-Space Alignment", "abstract": "Epilepsy significantly impacts global health, affecting about 65 million people worldwide, along with various animal species. The diagnostic processes of epilepsy are often hindered by the transient and unpredictable nature of seizures. Here we propose a multi-space alignment approach based on cross-species and cross-modality electroencephalogram (EEG) data to enhance the detection capabilities and understanding of epileptic seizures. By employing deep learning techniques, including domain adaptation and knowledge distillation, our framework aligns cross-species and cross-modality EEG signals to enhance the detection capability beyond traditional within-species and with-modality models. Experiments on multiple surface and intracranial EEG datasets of humans and canines demonstrated substantial improvements in the detection accuracy, achieving over 90% AUC scores for cross-species and cross-modality seizure detection with extremely limited labeled data from the target species/modality. To our knowledge, this is the first study that demonstrates the effectiveness of integrating heterogeneous data from different species and modalities to improve EEG-based seizure detection performance. The approach may also be generalizable to different brain-computer interface paradigms, and suggests the possibility to combine data from different species/modalities to increase the amount of training data for large EEG models.", "date": "2024-12-18", "categories": []}
{"id": "2507.20993", "title": "Annotation-Assisted Learning of Treatment Policies From Multimodal Electronic Health Records", "abstract": "We study how to learn treatment policies from multimodal electronic health records (EHRs) that consist of tabular data and clinical text. These policies can help physicians make better treatment decisions and allocate healthcare resources more efficiently. Causal policy learning methods prioritize patients with the largest expected treatment benefit. Yet, existing estimators are designed for tabular covariates under causal assumptions that may be hard to justify in the multimodal setting. A pragmatic alternative is to apply causal estimators directly to multimodal representations, but this can produce biased treatment effect estimates when the representations do not preserve the relevant confounding information. As a result, predictive models of baseline risk are commonly used in practice to guide treatment decisions, although they are not designed to identify which patients benefit most from treatment. We propose AACE (Annotation-Assisted Coarsened Effects), an annotation-assisted approach to causal policy learning for multimodal EHRs. The method uses expert-provided annotations during training to support confounding adjustment, and then predicts treatment benefit from only multimodal representations at inference. We show that the proposed method achieves strong empirical performance across synthetic, semi-synthetic, and real-world EHR datasets, outperforming risk-based and representation-based causal baselines, and offering practical insights for applying causal machine learning in clinical practice.", "date": "2025-07-28", "categories": []}
{"id": "2510.15218", "title": "Ensemble Deep Learning Models for Early Detection of Meningitis in ICU: Multi-center Study", "abstract": "The stacking ensemble combining RF, LightGBM, and DNN performed well on internal test sets, exhibiting an NPV greater than 99.9% even with substantial class imbalance. While performance was lower on the external eICU cohort compared to the internal test sets, sensitivity remained robust. Therefore, the stacking ensemble may serve as a rule-out screening option for ERs and ICUs after additional prospective multi-site validation studies for its efficacy in real-world.", "date": "2025-10-17", "categories": []}
{"id": "2601.22516", "title": "SCOPE-PD: Explainable AI on Subjective and Clinical Objective Measurements of Parkinson's Disease for Precision Decision-Making", "abstract": "Parkinson's disease (PD) is a chronic and complex neurodegenerative disorder influenced by genetic, clinical, and lifestyle factors. Predicting this disease early is challenging because it depends on traditional diagnostic methods that face issues of subjectivity, which commonly delay diagnosis. Several objective analyses are currently in practice to help overcome the challenges of subjectivity; however, a proper explanation of these analyses is still lacking. While machine learning (ML) has demonstrated potential in supporting PD diagnosis, existing approaches often rely on subjective reports only and lack interpretability for individualized risk estimation. This study proposes SCOPE-PD, an explainable AI-based prediction framework, by integrating subjective and objective assessments to provide personalized health decisions. Subjective and objective clinical assessment data are collected from the Parkinson's Progression Markers Initiative (PPMI) study to construct a multimodal prediction framework. Several ML techniques are applied to these data, and the best ML model is selected to interpret the results. Model interpretability is examined using SHAP-based analysis. The Random Forest algorithm achieves the highest accuracy of 98.66 percent using combined features from both subjective and objective test data. Tremor, bradykinesia, and facial expression are identified as the top three contributing features from the MDS-UPDRS test in the prediction of PD.", "date": "2026-01-30", "categories": []}
{"id": "2602.03562", "title": "NPCNet: Navigator-Driven Pseudo Text for Deep Clustering of Early Sepsis Phenotyping", "abstract": "Electronic Health Records (EHRs) provide high-dimensional temporal data essential for patient modeling; however, conventional algorithmic approaches often rely on data aggregation or imputation, which distorts temporal disease trajectories. Such computational limitations are particularly critical in sepsis, a heterogeneous syndrome where clustering-based stratification plays a key role in identifying clinically distinct phenotypes for precise treatment strategies. Furthermore, existing clustering processes seldom incorporate domain-driven constraints, often resulting in phenotypes that lack clear clinical distinction. We propose a novel clustering network, NPCNet, that comprises a text embedding generator, a clustering operator, and a target navigator. We first transform EHRs into pseudo texts by discretizing continuous clinical measurements, then integrate them with static variables to construct the embeddings. The target navigator then infuses clinical knowledge into the embeddings through auxiliary tasks, constraining clustering results to better align sepsis phenotypes with clinical significance. Finally, the clustering operator employs an iterative refinement mechanism to jointly optimize phenotype centroids and patient representations under domain-driven constraints. Extensive experiments on public datasets validate that NPCNet achieves superior performance on both internal clustering benchmarks and clinical validity metrics, offering a viable pathway for precision treatment strategies in the management of sepsis.", "date": "2026-02-03", "categories": []}
{"id": "2603.23987", "title": "Can we generate portable representations for clinical time series data using LLMs?", "abstract": "Deploying clinical ML is slow and brittle: models that work at one hospital often degrade under distribution shifts at the next. In this work, we study a simple question -- can large language models (LLMs) create portable patient embeddings i.e. representations of patients enable a downstream predictor built on one hospital to be used elsewhere with minimal-to-no retraining and fine-tuning. To do so, we map from irregular ICU time series onto concise natural language summaries using a frozen LLM, then embed each summary with a frozen text embedding model to obtain a fixed length vector capable of serving as input to a variety of downstream predictors. Across three cohorts (MIMIC-IV, HIRID, PPICU), on multiple clinically grounded forecasting and classification tasks, we find that our approach is simple, easy to use and competitive with in-distribution with grid imputation, self-supervised representation learning, and time series foundation models, while exhibiting smaller relative performance drops when transferring to new hospitals. We study the variation in performance across prompt design, with structured prompts being crucial to reducing the variance of the predictive models without altering mean accuracy. We find that using these portable representations improves few-shot learning and does not increase demographic recoverability of age or sex relative to baselines, suggesting little additional privacy risk. Our work points to the potential that LLMs hold as tools to enable the scalable deployment of production grade predictive models by reducing the engineering overhead.", "date": "2026-03-25", "categories": []}
{"id": "2603.24434", "title": "The Gait Signature of Frailty: Transfer Learning based Deep Gait Models for Scalable Frailty Assessment", "abstract": "Frailty is a condition in aging medicine characterized by diminished physiological reserve and increased vulnerability to stressors. However, frailty assessment remains subjective, heterogeneous, and difficult to scale in clinical practice. Gait is a sensitive marker of biological aging, capturing multisystem decline before overt disability. Yet the application of modern computer vision to gait-based frailty assessment has been limited by small, imbalanced datasets and a lack of clinically representative benchmarks. In this work, we introduce a publicly available silhouette-based frailty gait dataset collected in a clinically realistic setting, spanning the full frailty spectrum and including older adults who use walking aids. Using this dataset, we evaluate how pretrained gait recognition models can be adapted for frailty classification under limited data conditions. We study both convolutional and hybrid attention-based architectures and show that predictive performance depends primarily on how pretrained representations are transferred rather than architectural complexity alone. Across models, selectively freezing low-level gait representations while allowing higher-level features to adapt yields more stable and generalizable performance than either full fine-tuning or rigid freezing. Conservative handling of class imbalance further improves training stability, and combining complementary learning objectives enhances discrimination between clinically adjacent frailty states. Interpretability analyses reveal consistent model attention to lower-limb and pelvic regions, aligning with established biomechanical correlates of frailty. Together, these findings establish gait-based representation learning as a scalable, non-invasive, and interpretable framework for frailty assessment and support the integration of modern biometric modeling approaches into aging research and clinical practice.", "date": "2026-03-25", "categories": []}
{"id": "2603.24562", "title": "Scaling Recurrence-aware Foundation Models for Clinical Records via Next-Visit Prediction", "abstract": "While large-scale pretraining has revolutionized language modeling, its potential remains underexplored in healthcare with structured electronic health records (EHRs). We present RAVEN, a novel generative pretraining strategy for sequential EHR data based on Recurrence-Aware next-Visit EveNt prediction. Leveraging a dataset of over one million unique individuals, our model learns to autoregressively generate tokenized clinical events for the next visit conditioned on patient history. We introduce regularization on predicting repeated events and highlight a key pitfall in EHR-based foundation model evaluations: repeated event tokens can inflate performance metrics when new onsets are not distinguished from subsequent occurrences. Furthermore, we empirically investigate the scaling behaviors in a data-constrained, compute-saturated regime, showing that simply increasing model size is suboptimal without commensurate increases in data volume. We evaluate our model via zero-shot prediction for forecasting the incidence of a diverse set of diseases, where it rivals fully fine-tuned representation-based Transformer models and outperforms both standard simulation-based next-token approaches and a prompted medical large language model baseline. Finally, without additional parameter updates, we show that RAVEN can generalize to an external patient cohort under lossy clinical code mappings and feature coverage gaps.", "date": "2026-03-25", "categories": []}
{"id": "2603.26475", "title": "Foundation Model for Cardiac Time Series via Masked Latent Attention", "abstract": "Electrocardiograms (ECGs) are among the most widely available clinical signals and play a central role in cardiovascular diagnosis. While recent foundation models (FMs) have shown promise for learning transferable ECG representations, most existing pretraining approaches treat leads as independent channels and fail to explicitly leverage their strong structural redundancy. We introduce the latent attention masked autoencoder (LAMAE) FM that directly exploits this structure by learning cross-lead connection mechanisms during self-supervised pretraining. Our approach models higher-order interactions across leads through latent attention, enabling permutation-invariant aggregation and adaptive weighting of lead-specific representations. We provide empirical evidence on the Mimic-IV-ECG database that leveraging the cross-lead connection constitutes an effective form of structural supervision, improving representation quality and transferability. Our method shows strong performance in predicting ICD-10 codes, outperforming independent-lead masked modeling and alignment-based baselines.", "date": "2026-03-27", "categories": []}
{"id": "2604.16333", "title": "A Discordance-Aware Multimodal Framework with Multi-Agent Clinical Reasoning", "abstract": "Knee osteoarthritis frequently exhibits discordance between structural damage observed in imaging and patient-reported symptoms such as pain. This mismatch complicates clinical interpretation and patient stratification and remains insufficiently modeled in existing decision support systems. We propose a discordance aware multimodal framework that combines machine learning prediction models with a tool grounded multi agent reasoning system. Using baseline data from the FNIH Osteoarthritis Biomarkers Consortium, we trained multimodal models to predict two progression tasks, joint space loss only progression versus non progression, and pain only progression versus non progression. The predictive system integrates three modality specific experts: a CatBoost tabular model using demographic, radiographic, MRI-derived scalar, and biomarker features; MRI image embeddings extracted using a ResNet18 backbone; and Xray embeddings derived from the same architecture. Expert predictions are fused using a stacking ensemble. Residual based models estimate expected pain from structural features, enabling the computation of a pain structure discordance score between observed and expected symptoms. A multi-agent reasoning layer interprets these signals to assign clinically interpretable OA phenotypes and generate phenotype specific management recommendations.", "date": "2026-03-12", "categories": []}
{"id": "2604.16437", "title": "Sampling Matters: The Effect of ECG Frequency on Deep Learning-Based Atrial Fibrillation Detection", "abstract": "Deep learning models for atrial fibrillation (AF) detection are increasingly trained on heterogeneous electrocardiogram (ECG) datasets with varying sampling frequencies, yet the specific consequences of these discrepancies on model performance, calibration, and robustness remain insufficiently characterized. To address this, we conducted a systematic benchmark using 12-lead, 10-second recordings from the PTB-XL dataset, resampled to target frequencies of 62, 100, 250, and 500 Hz, to evaluate a standard 1-D Convolutional Neural Network (CNN) and a hybrid CNN-Long Short-Term Memory (LSTM) architecture under a rigorous patient-safe cross-validation framework. Our analysis reveals that sampling frequency significantly impacts detection metrics in an architecture-dependent manner; the hybrid CNN-LSTM model demonstrated optimal performance and consistent calibration at intermediate frequencies (100-250 Hz), whereas the 1-D CNN baseline exhibited marked degradation in accuracy and sensitivity at 500 Hz, suggesting increased susceptibility to high-frequency noise. We conclude that ECG sampling frequency is a critical, underappreciated factor in arrhythmia detection, and future foundation models must explicitly control for temporal resolution to ensure clinical reliability and reproducibility.", "date": "2026-04-06", "categories": []}
{"id": "2604.16537", "title": "Robustifying and Selecting Cohort-Appropriate Prognostic Models under Distributional Shifts", "abstract": "External validation is widely regarded as the gold standard for prognostic model evaluation. In this study, we challenge the assumption that successful external calibration guarantees model generalizability and propose two complementary strategies to improve transportability of prognostic models across cohorts.\n  Using six real-world surgical cohorts from tertiary academic centers, we tested whether successful external calibration depends largely on similarity in covariates and outcomes between training and validation cohorts, quantified using Kullback-Leibler (KL) divergence, with calibration assessed by the Integrated Calibration Index (ICI). From the model-developer's perspective, we trained the \"best-on-average\" prognostic model by tuning toward a meta-analysis-derived covariate and outcome distribution as an approximation of the broader target population. From the end-user perspective, we proposed a simple measure for cohort outcome similarity to identify, among published models, the one most suitable for a given target cohort in terms of both calibration and clinical utility.\n  External calibration worsened as distributional mismatch increased. Higher KL divergence was associated with higher ICI in both surgery-alone (Spearman $\\rho=0.614$, $p=0.004$) and surgery + adjuvant chemotherapy cohorts (Spearman $\\rho=0.738$, $p<0.001$). Meta-analysis-informed weighting improved calibration in most settings without materially affecting discrimination, with the clearest benefit when evaluated on the aggregated external population ($p=0.037$). Models developed in more similar cohorts achieved lower ICI in surgery-alone (Spearman $\\rho=0.803$, $p<0.001$) and surgery + adjuvant chemotherapy cohorts (Spearman $\\rho=0.737$, $p<0.001$), and provided greater clinical utility on DCA.", "date": "2026-04-16", "categories": []}
{"id": "2604.16742", "title": "CT Open: An Open-Access, Uncontaminated, Live Platform for the Open Challenge of Clinical Trial Outcome Prediction", "abstract": "Scientists have long sought to accurately predict outcomes of real-world events before they happen. Can AI systems do so more reliably? We study this question through clinical trial outcome prediction, a high-stakes open challenge even for domain experts. We introduce CT Open, an open-access, live platform that will run four challenge every year. Anyone can submit predictions for each challenge. CT Open evaluates those submissions on trials whose outcomes were not yet public at the time of submission but were made public afterwards. Determining if a trial's outcome is public on the internet before a certain date is surprisingly difficult. Outcomes posted on official registries may lag behind by years, while the first mention may appear in obscure articles. To address this, we propose a novel, fully automated decontamination pipeline that uses iterative LLM-powered web search to identify the earliest mention of trial outcomes. We validate the pipeline's quality and accuracy by human expert's annotations. Since CT Open's pipeline ensures that every evaluated trial had no publicly reported outcome when the prediction was made, it allows participants to use any methodology and any data source. In this paper, we release a training set and two time-stamped test benchmarks, Winter 2025 and Summer 2025. We believe CT Open can serve as a central hub for advancing AI research on forecasting real-world outcomes before they occur, while also informing biomedical research and improving clinical trial design. CT Open Platform is hosted at $\\href{https://ct-open.net/}{https://ct-open.net/}$", "date": "2026-04-17", "categories": []}
{"id": "2604.16775", "title": "Representation Before Training: A Fixed-Budget Benchmark for Generative Medical Event Models", "abstract": "Every prediction from a generative medical event model is bounded by how clinical events are tokenized, yet input representation is rarely isolated from other system and architectural choices. We evaluate how representation decisions affect downstream prediction after a shared one-epoch pretraining budget. We train 28 matched transformers on MIMIC-IV and evaluate them on 30 clinical outcomes in three experiments: (1) quantization granularity, reference-range anchoring, and code-value fusion; (2) value encoding (hard bins, soft discretization, code-normalized xVal) crossed with temporal encoding (event order, time tokens, admission-relative RoPE); and (3) native MIMIC laboratory/vital codes versus the Common Longitudinal ICU Format (CLIF)-remapped laboratory/vital codes with compression-preserving perturbation arms. In Experiment 1, fused code-value tokenization improves mortality AUROC from 0.891 to 0.915 (BH-adjusted p < 0.001), hospital length-of-stay AUROC from 0.763 to 0.788 (BH-adjusted p < 0.001), and, for the decile fused-vs-unfused comparison, mean regression Spearman rho across the 13 regression outcomes from 0.414 to 0.494. Across the three temporal encodings, event order only and admission-relative RoPE match or exceed inserting time tokens on average while shortening sequences by 11%. CLIF remapping preserves downstream performance in our single-site setting while yielding a smaller, clinically interpretable token set compatible with multi-site use. Finer-than-decile quantization, reference-range anchoring, and soft discretization help in selective outcomes, while code-normalized xVal remains well below the discrete and soft families, consistent with near-median suppression that persists after the affine variant.", "date": "2026-04-18", "categories": []}
{"id": "2604.16878", "title": "OC-Distill: Ontology-aware Contrastive Learning with Cross-Modal Distillation for ICU Risk Prediction", "abstract": "Early prediction of severe clinical deterioration and remaining length of stay can enable timely intervention and better resource allocation in high-acuity settings such as the ICU. This has driven the development of machine learning models that leverage continuous streams of vital signs and other physiological signals for real-time risk prediction. Despite their promise, existing methods have important limitations. Contrastive pretraining treats all patients as equally strong negatives, failing to capture clinically meaningful similarity between patients with related diagnoses. Meanwhile, downstream fine-tuning typically ignores complementary modalities such as clinical notes, which provide rich contextual information unavailable in physiological signals alone. To address these challenges, we propose OC-Distill, a two-stage framework that leverages multimodal supervision during training while requiring only vital signs at inference. In the first stage, we introduce an ontology-aware contrastive objective that exploits the ICD hierarchy to quantify patient similarity and learn clinically grounded representations. In the second stage, we fine-tune the pretrained encoder via cross-modal knowledge distillation, transferring complementary information from clinical notes into the model. Across multiple ICU prediction tasks on MIMIC, OC-Distill demonstrates improved label efficiency and achieves state-of-the-art performance among methods that use only vital signs at inference.", "date": "2026-04-18", "categories": []}
{"id": "2604.16884", "title": "Bias-constrained multimodal intelligence for equitable and reliable clinical AI", "abstract": "The integration of medical imaging and clinical text has enabled the emergence of generalist artificial intelligence (AI) systems for healthcare. However, pervasive biases, such as imbalanced disease prevalence, skewed anatomical region distributions, heterogeneous imaging protocols, and demographic disparities, pose significant challenges to the fairness and reliability of vision-language systems in real-world clinical settings. Here we present BiasCareVL, a bias-aware multimodal learning framework that introduces bias control directly into model design, rather than treating it as a post hoc correction. BiasCareVL incorporates adaptive uncertainty modeling with optional human-in-the-loop refinement to regulate the influence of dominant data patterns and to promote equitable reasoning under distributional imbalance. Trained on 3.44 million samples spanning over 15 imaging modalities, the framework supports diverse clinical tasks, including visual question answering, disease classification, segmentation, and report generation within a unified representation space. Across eight public benchmarks covering dermatology, oncology, radiology, and pathology, BiasCareVL consistently outperforms 20 state-of-the-art methods, with pronounced gains in clinically challenging scenarios, including over 10% accuracy improvement in multi-class skin lesion diagnosis and more than 20% Dice improvement in small tumor segmentation. Furthermore, BiasCareVL achieves diagnostic performance exceeding human accuracy with substantially reduced time requirements when evaluated with board-certified radiologists. By open-sourcing BiasCareVL, we aim to promote a transparent, reproducible, and equitable future for AI in healthcare, paving the way for general-purpose, trustworthy, and clinically reliable AI systems.", "date": "2026-04-18", "categories": []}
{"id": "2604.17611", "title": "STEP-PD: Stage-Aware and Explainable Parkinson's Disease Severity Classification Using Multimodal Clinical Assessments", "abstract": "Parkinson's disease (PD) is a progressive disorder in which symptom burden and functional impairment evolve over time, making severity staging essential for clinical monitoring and treatment planning. However, many computational studies emphasize binary PD detection and do not fully use repeated follow-up clinical assessments for stage-aware prediction. This study proposes STEP-PD, a severity-aware machine learning framework to classify PD severity using clinically interpretable boundaries. It leverages all available visits from the Parkinson's Progression Markers Initiative (PPMI) and integrates routinely collected subjective questionnaires and objective clinician-assessed measures. Disease severity is defined using Hoehn and Yahr staging and grouped into three clinically meaningful categories: Healthy, Mild PD (stages 1-2), and Moderate-to-Severe PD (stages 3-5). Three binary classification problems and a three-class severity task were evaluated using stratified cross-validation with imbalance-aware training. To enhance interpretability, SHAP was used to provide global explanations and local patient-level waterfall explanations. Across all tasks, XGBoost achieved the strongest and most stable performance, with accuracies of 95.48% (Healthy vs. Mild), 99.44% (Healthy vs. Moderate-to-Severe), and 96.78% (Mild vs. Moderate-to-Severe), and 94.14% accuracy with 0.8775 Macro-F1 for three-class severity classification. Explainability results highlight a shift from early motor features to progression-related axial and balance impairments. These findings show that multimodal clinical assessments within the PPMI cohort can support accurate and interpretable visit-level PD severity stratification.", "date": "2026-04-19", "categories": []}
{"id": "2604.17725", "title": "RePrompT: Recurrent Prompt Tuning for Integrating Structured EHR Encoders with Large Language Models", "abstract": "Large Language Models (LLMs) have shown strong promise for mining Electronic Health Records (EHRs) by reasoning over longitudinal clinical information to capture context-rich patient trajectories. However, leveraging LLMs for structured EHRs (e.g., standardized diagnosis and medication codes) presents two key challenges. First, translating time-stamped EHR sequences into plain text can obscure both temporal structure and code identities, weakening the ability to capture code co-occurrence and longitudinal regularities. Second, unlike cohort-trained predictive models that learn a shared, task-aligned representation space across patients, LLMs are often applied in a case-isolated inference setting where each patient is processed independently without leveraging population-level patterns. To address these challenges, we introduce RePrompT, a time-aware LLM framework that integrates structured EHR encoders through prompt tuning, without modifying underlying architectures. Specifically, RePrompT recurrently incorporates latent states from prior visits to preserve longitudinal information, and injects population-level information through trainable prompt tokens derived from a cohort-trained, task-aligned EHR encoder. Experiments on MIMIC-III and MIMIC-IV demonstrate that RePrompT consistently outperforms both EHR-based and LLM-based baselines across multiple clinical prediction tasks.", "date": "2026-04-20", "categories": []}
{"id": "2604.18302", "title": "Toward Zero-Egress Psychiatric AI: On-Device LLM Deployment for Privacy-Preserving Mental Health Decision Support", "abstract": "Privacy represents one of the most critical yet underaddressed barriers to AI adoption in mental healthcare -- particularly in high-sensitivity operational environments such as military, correctional, and remote healthcare settings, where the risk of patient data exposure can deter help-seeking behavior entirely. Existing AI-enabled psychiatric decision support systems predominantly rely on cloud-based inference pipelines, requiring sensitive patient data to leave the device and traverse external servers, creating unacceptable privacy and security risks in these contexts. In this paper, we propose a zero-egress, on-device AI platform for privacy-preserving psychiatric decision support, deployed as a cross-platform mobile application. The proposed system extends our prior work on fine-tuned LLM consortiums for psychiatric diagnosis standardization by fundamentally re-architecting the inference pipeline for fully local execution -- ensuring that no patient data is transmitted to, processed by, or stored on any external server at any stage. The platform integrates a consortium of three lightweight, fine-tuned, and quantized open-source LLMs -- Gemma, Phi-3.5-mini, and Qwen2 -- selected for their compact architectures and proven efficiency on resource-constrained mobile hardware. An on-device orchestration layer coordinates ensemble inference and consensus-based diagnostic reasoning, producing DSM-5-aligned assessments for conditions. The platform is designed to assist clinicians with differential diagnosis and evidence-linked symptom mapping, as well as to support patient-facing self-screening with appropriate clinical safeguards. Initial evaluation demonstrates that the proposed zero-egress deployment achieves diagnostic accuracy comparable to its server-side predecessor while sustaining real-time inference latency on commodity mobile hardware.", "date": "2026-04-20", "categories": []}
{"id": "2604.18372", "title": "Parkinson's Disease Detection via Self-Supervised Dual-Channel Cross-Attention on Bilateral Wrist-Worn IMU Signals", "abstract": "Parkinson's disease (PD) is a chronic neurodegenerative disease. It shows multiple motor symptoms such as tremor, bradykinesia, postural instability, freezing of gait (FoG). PD is currently diagnosed clinically through physical exam by health-care professionals, which can be time consuming and highly subjective. Wearable IMU sensors has become a promising gateway for passive monitoring of PD patients. We propose a self-supervised cross-attention encoder that processes bilateral wrist-worn IMU signals from a public dataset called PADS, consisting of three groups, PD (Parkinson Disease), HC (Healthy Control) and DD (Differential Diagnosis) of a total of 469 subjects. We have achieved a mean accuracy of 93.12% for HC vs. PD classification and 87.04% for PD vs. DD classification. The results emphasize the clinical challenge of distinguishing Parkinson's from other neurodegenerative diseases. Self-supervised representation learning using contrastive infoNCE loss gained an accuracy of 93.56% for HC vs. PD and 92.50% for PD vs. DD using only 20% of labelled data. This demonstrates the effectiveness of our method in transfer learning for clinical use with minimal labels. The real-time applicability was tested by deploying the optimized model with a mean inference time of 48.32 ms per window on a Raspberry Pi CPU.", "date": "2026-04-20", "categories": []}
{"id": "2510.21934", "title": "Joint Score-Threshold Optimization for Interpretable Risk Assessment", "abstract": "Risk assessment tools in healthcare commonly employ point-based scoring systems that map patients to ordinal risk categories via thresholds. While electronic health record (EHR) data presents opportunities for data-driven optimization of these tools, two fundamental challenges impede standard supervised learning: (1) labels are often available only for extreme risk categories due to intervention-censored outcomes, and (2) misclassification cost is asymmetric and increases with ordinal distance. We propose a mixed-integer programming (MIP) framework that jointly optimizes scoring weights and category thresholds in the face of these challenges. Our approach prevents label-scarce category collapse via threshold constraints, and utilizes an asymmetric, distance-aware objective. The MIP framework supports governance constraints, including sign restrictions, sparsity, and minimal modifications to incumbent tools, ensuring practical deployability in clinical workflows. We further develop a continuous relaxation of the MIP problem to provide warm-start solutions for more efficient MIP optimization. We apply the proposed score optimization framework to a case study of inpatient falls risk assessment using the Johns Hopkins Fall Risk Assessment Tool.", "date": "2025-10-24", "categories": []}
{"id": "2604.15555", "title": "CXR-LT 2026 Challenge: Multi-Center Long-Tailed and Zero Shot Chest X-ray Classification", "abstract": "Chest X-ray (CXR) interpretation is hindered by the long-tailed distribution of pathologies and the open-world nature of clinical environments. Existing benchmarks often rely on closed-set classes from a single institution, failing to capture the prevalence of rare diseases or the appearance of novel findings. To address this, we present the CXR-LT challenge. The first event, CXR-LT 2023, established a large-scale benchmark for long-tailed multi-label CXR classification and identified key challenges in rare disease recognition. CXR-LT 2024 further expanded the label space and introduced a zero-shot task to study generalization to unseen findings. Building on the success of CXR-LT 2023 and 2024, this third iteration of the benchmark introduces a multi-center dataset comprising over 145,000 images from PadChest and NIH Chest X-ray datasets. Additionally, all development and test sets in CXR-LT 2026 are annotated by radiologists, providing a more reliable and clinically grounded evaluation than report-derived labels. The challenge defines two core tasks this year: (1) Robust Multi-Label Classification on 30 known classes and (2) Open-World Generalization to 6 unseen (out-of-distribution) rare disease classes. This paper summarizes the overview of the CXR-LT 2026 challenge. We describe the data collection and annotation procedures, analyze solution strategies adopted by participating teams, and evaluate head-versus-tail performance, calibration, and cross-center generalization gaps. Our results show that vision-language foundation models improve both in-distribution and zero-shot performance, but detecting rare findings under multi-center shift remains challenging. Our study provides a foundation for developing and evaluating AI systems in realistic long-tailed and open-world clinical conditions.", "date": "2026-04-16", "categories": []}
{"id": "2601.18675", "title": "Learning temporal embeddings from electronic health records of chronic kidney disease patients", "abstract": "We investigate whether temporal embedding models trained on longitudinal electronic health records can learn clinically meaningful representations without compromising predictive performance, and how architectural choices affect embedding quality. Model-guided medicine requires representations that capture disease dynamics while remaining transparent and task agnostic, whereas most clinical prediction models are optimised for a single task. Representation learning facilitates learning embeddings that generalise across downstream tasks, and recurrent architectures are well-suited for modelling temporal structure in observational clinical data. Using the MIMIC-IV dataset, we study patients with chronic kidney disease (CKD) and compare three recurrent architectures: a vanilla LSTM, an attention-augmented LSTM, and a time-aware LSTM (T-LSTM). All models are trained both as embedding models and as direct end-to-end predictors. Embedding quality is evaluated via CKD stage clustering and in-ICU mortality prediction. The T-LSTM produces more structured embeddings, achieving a lower Davies-Bouldin Index (DBI = 9.91) and higher CKD stage classification accuracy (0.74) than the vanilla LSTM (DBI = 15.85, accuracy = 0.63) and attention-augmented LSTM (DBI = 20.72, accuracy = 0.67). For in-ICU mortality prediction, embedding models consistently outperform end-to-end predictors, improving accuracy from 0.72-0.75 to 0.82-0.83, which indicates that learning embeddings as an intermediate step is more effective than direct end-to-end learning.", "date": "2026-01-26", "categories": []}
{"id": "2604.14175", "title": "QU-NLP at ArchEHR-QA 2026: Two-Stage QLoRA Fine-Tuning of Qwen3-4B for Patient-Oriented Clinical Question Answering and Evidence Sentence Alignment", "abstract": "We present a unified system addressing both Subtask 3 (answer generation) and Subtask 4 (evidence sentence alignment) of the ArchEHR-QA Shared Task. For Subtask 3, we apply two-stage Quantised Low-Rank Adaptation (QLoRA) to Qwen3-4B loaded in 4-bit NF4 quantisation: first on 30,000 samples from the emrQA-MedSQuAD corpus to establish clinical domain competence, then on the 20 annotated development cases to learn the task-specific output style. Our system achieves an overall score of 32.87 on the official test-2026 split (BLEU = 9.42, ROUGE-L = 27.04, SARI = 55.42, BERTScore = 43.00, AlignScore = 25.28, MEDCON = 37.04). For Subtask 4, we develop a weighted ensemble of three retrieval methods - BM25 with relative thresholding, TF-IDF cosine similarity, and a fine-tuned cross-encoder - to identify note sentences supporting a given gold answer, achieving a micro-F1 of 67.16 on the 100-case test set. Experiments reveal that both subtasks expose the same fundamental challenge: 20 annotated training cases are insufficient to distinguish relevant from irrelevant clinical sentences, pointing to data augmentation as the highest-leverage future direction.", "date": "2026-03-26", "categories": []}
{"id": "2604.14216", "title": "Neuro-Oracle: A Trajectory-Aware Agentic RAG Framework for Interpretable Epilepsy Surgical Prognosis", "abstract": "Predicting post-surgical seizure outcomes in pharmacoresistant epilepsy is a clinical challenge. Conventional deep-learning approaches operate on static, single-timepoint pre-operative scans, omitting longitudinal morphological changes. We propose \\emph{Neuro-Oracle}, a three-stage framework that: (i) distils pre-to-post-operative MRI changes into a compact 512-dimensional trajectory vector using a 3D Siamese contrastive encoder; (ii) retrieves historically similar surgical trajectories from a population archive via nearest-neighbour search; and (iii) synthesises a natural-language prognosis grounded in the retrieved evidence using a quantized Llama-3-8B reasoning agent. Evaluations are conducted on the public EPISURG dataset ($N{=}268$ longitudinally paired cases) using five-fold stratified cross-validation. Since ground-truth seizure-freedom scores are unavailable, we utilize a clinical proxy label based on the resection type. We acknowledge that the network representations may potentially learn the anatomical features of the resection cavities (i.e., temporal versus non-temporal locations) rather than true prognostic morphometry. Our current evaluation thus serves mainly as a proof-of-concept for the trajectory-aware retrieval architecture. Trajectory-based classifiers achieve AUC values between 0.834 and 0.905, compared with 0.793 for a single-timepoint ResNet-50 baseline. The Neuro-Oracle agent (M5) matches the AUC of purely discriminative trajectory classifiers (0.867) while producing structured justifications with zero observed hallucinations under our audit protocol. A Siamese Diversity Ensemble (M6) of trajectory-space classifiers attains an AUC of 0.905 without language-model overhead.", "date": "2026-04-10", "categories": []}
{"id": "2604.14532", "title": "CSRA: Controlled Spectral Residual Augmentation for Robust Sepsis Prediction", "abstract": "Accurate prediction of future risk and disease progression in sepsis is clinically important for early warning and timely intervention in intensive care. However, short-window sepsis prediction remains challenging, because shorter observation windows provide limited historical evidence, whereas longer prediction horizons reduce the number of patient trajectories with valid future supervision. To address this problem, we propose CSRA, a Controlled Spectral Residual Augmentation framework for short-window multi-system ICU time series. CSRA first groups variables by clinical systems and extracts system-level and global representations. It then performs input-adaptive residual perturbation in the spectral domain to generate structured and clinically plausible trajectory variations. To improve augmentation stability and controllability, CSRA is trained end-to-end with the downstream predictor under a unified objective, together with anchor consistency loss and controller regularization. Experiments on a MIMIC-IV sepsis cohort across multiple downstream models show that CSRA is consistently competitive and often superior, reducing regression error by 10.2\\% in MSE and 3.7\\% in MAE over the non-augmentation baseline, while also yielding consistent gains on classification. CSRA further maintains more favorable performance under shorter observation windows, longer prediction horizons, and smaller training data scales, while also remaining effective on an external clinical dataset~(ZiGongICUinfection), indicating stronger robustness and generalizability in clinically constrained settings.", "date": "2026-04-16", "categories": []}
{"id": "2604.14547", "title": "Predicting Post-Traumatic Epilepsy from Clinical Records using Large Language Model Embeddings", "abstract": "Objective: Post-traumatic epilepsy (PTE) is a debilitating neurological disorder that develops after traumatic brain injury (TBI). Early prediction of PTE remains challenging due to heterogeneous clinical data, limited positive cases, and reliance on resource-intensive neuroimaging data. We investigate whether routinely collected acute clinical records alone can support early PTE prediction using language model-based approaches. Methods: Using a curated subset of the TRACK-TBI cohort, we developed an automated PTE prediction framework that implements pretrained large language models (LLMs) as fixed feature extractors to encode clinical records. Tabular features, LLM-generated embeddings, and hybrid feature representations were evaluated using gradient-boosted tree classifiers under stratified cross-validation. Results: LLM embeddings achieved performance improvements by capturing contextual clinical information compared to using tabular features alone. The best performance was achieved by a modality-aware feature fusion strategy combining tabular features and LLM embeddings, achieving an AUC-ROC of 0.892 and AUPRC of 0.798. Acute post-traumatic seizures, injury severity, neurosurgical intervention, and ICU stay are key contributors to the predictive performance. Significance: These findings demonstrate that routine acute clinical records contain information suitable for early PTE risk prediction using LLM embeddings in conjunction with gradient-boosted tree classifiers. This approach represents a promising complement to imaging-based prediction.", "date": "2026-04-16", "categories": []}
{"id": "2604.14738", "title": "Personalized and Context-Aware Transformer Models for Predicting Post-Intervention Physiological Responses from Wearable Sensor Data", "abstract": "Consumer wearables enable continuous measurement of physiological data related to stress and recovery, but turning these streams into actionable, personalized stress-management recommendations remains a challenge. In practice, users often do not know how a given intervention, defined as an activity intended to reduce stress, will affect heart rate (HR), heart rate variability (HRV), or inter-beat intervals (BBI) over the next 15 to 120 minutes. We present a framework that predicts post-intervention trajectories and the direction of change for these physiological indicators across time windows. Our methodology combines a Transformer model for multi-horizon trajectories of percent change relative to a pre-intervention baseline, direction-of-change calls (positive, negative, or neutral) at each horizon, and an empirical study using wearable sensor data overlaid with user-tagged events and interventions. This proof of concept shows that personalized post-intervention prediction is feasible. We encourage future integration into stress-management tools for personalized intervention recommendations tailored to each person's day following further validation in larger studies and, where applicable, appropriate regulatory review.", "date": "2026-04-16", "categories": []}
{"id": "2411.10703", "title": "Hybrid Attention Model Using Feature Decomposition and Knowledge Distillation for Glucose Forecasting", "abstract": "The availability of continuous glucose monitors as over-the-counter commodities have created a unique opportunity to monitor a person's blood glucose levels, forecast blood glucose trajectories and provide automated interventions to prevent devastating chronic complications that arise from poor glucose control. However, forecasting blood glucose levels is challenging because blood glucose changes consistently in response to food intake, medication intake, physical activity, sleep, and stress. It is particularly difficult to accurately predict BGL from multimodal and irregularly sampled data and over long prediction horizons. Furthermore, these forecasting models must operate in real-time on edge devices to provide in-the-moment interventions. To address these challenges, we propose GlucoNet, an AI-powered sensor system for continuously monitoring behavioral and physiological health and robust forecasting of blood glucose patterns. GlucoNet devises a feature decomposition-based transformer model that incorporates patients' behavioral and physiological data and transforms sparse and irregular patient data (e.g., diet and medication intake data) into continuous features using a mathematical model, facilitating better integration with the BGL data. Given the non-linear and non-stationary nature of BG signals, we propose a decomposition method to extract both low and high-frequency components from the BGL signals, thus providing accurate forecasting. To reduce the computational complexity, we also propose to employ knowledge distillation to compress the transformer model. GlucoNet achieves a 60% improvement in RMSE and a 21% reduction in the number of parameters, improving RMSE and MAE by 51% and 57%, using data obtained involving 12 participants with T1-Diabetes. These results underscore GlucoNet's potential as a compact and reliable tool for real-world diabetes prevention and management.", "date": "2024-11-16", "categories": []}
{"id": "2603.00192", "title": "Diagnostics for Individual-Level Prediction Instability in Machine Learning for Healthcare", "abstract": "In healthcare, predictive models increasingly inform patient-level decisions, yet little attention is paid to the variability in individual risk estimates and its impact on treatment decisions. For overparameterized models, now standard in machine learning, a substantial source of variability often goes undetected. Even when the data and model architecture are held fixed, randomness introduced by optimization and initialization can lead to materially different risk estimates for the same patient. This problem is largely obscured by standard evaluation practices, which rely on aggregate performance metrics (e.g., log-loss, accuracy) that are agnostic to individual-level stability. As a result, models with indistinguishable aggregate performance can nonetheless exhibit substantial procedural arbitrariness, which can undermine clinical trust. We propose an evaluation framework that quantifies individual-level prediction instability by using two complementary diagnostics: empirical prediction interval width (ePIW), which captures variability in continuous risk estimates, and empirical decision flip rate (eDFR), which measures instability in threshold-based clinical decisions. We apply these diagnostics to simulated data and GUSTO-I clinical dataset. Across observed settings, we find that for flexible machine-learning models, randomness arising solely from optimization and initialization can induce individual-level variability comparable to that produced by resampling the entire training dataset. Neural networks exhibit substantially greater instability in individual risk predictions compared to logistic regression models. Risk estimate instability near clinically relevant decision thresholds can alter treatment recommendations. These findings that stability diagnostics should be incorporated into routine model validation for assessing clinical reliability.", "date": "2026-02-27", "categories": []}
{"id": "2502.17403", "title": "Large Language Models are Powerful Electronic Health Record Encoders", "abstract": "Electronic Health Records (EHRs) offer considerable potential for clinical prediction, but their complexity and heterogeneity challenge traditional machine learning. Domain-specific EHR foundation models trained on unlabeled EHR data have shown improved predictive accuracy and generalization. However, their development is constrained by limited data access and site-specific vocabularies. We convert EHR data into plain text by replacing medical codes with natural-language descriptions, enabling general-purpose Large Language Models (LLMs) to produce high-dimensional embeddings for downstream prediction tasks without access to private medical training data. LLM-based embeddings perform on par with a specialized EHR foundation model, CLMBR-T-Base, across 15 clinical tasks from the EHRSHOT benchmark. In an external validation using the UK Biobank, an LLM-based model shows statistically significant improvements for some tasks, which we attribute to higher vocabulary coverage and slightly better generalization. Overall, we reveal a trade-off between the computational efficiency of specialized EHR models and the portability and data independence of LLM-based embeddings.", "date": "2025-02-24", "categories": []}
{"id": "2509.02648", "title": "Optimizing Prognostic Biomarker Discovery in Pancreatic Cancer Through Hybrid Ensemble Feature Selection and Multi-Omics Data", "abstract": "Prediction of patient survival using high-dimensional multi-omics data requires systematic feature selection methods that ensure predictive performance, sparsity, and reliability for prognostic biomarker discovery. We developed a hybrid ensemble feature selection (hEFS) approach that combines data subsampling with multiple prognostic models, integrating both embedded and wrapper-based strategies for survival prediction. Omics features are ranked using a voting-theory-inspired aggregation mechanism across models and subsamples, while the optimal number of features is selected via a Pareto front, balancing predictive accuracy and model sparsity without any user-defined thresholds. When applied to multi-omics datasets from three pancreatic cancer cohorts, hEFS identifies significantly fewer and more stable biomarkers compared to the conventional, late-fusion CoxLasso models, while maintaining comparable discrimination performance. Implemented within the open-source mlr3fselect R package, hEFS offers a robust, interpretable, and clinically valuable tool for prognostic modelling and biomarker discovery in high-dimensional survival settings.", "date": "2025-09-02", "categories": []}
{"id": "2604.06390", "title": "MorphDistill: Distilling Unified Morphological Knowledge from Pathology Foundation Models for Colorectal Cancer Survival Prediction", "abstract": "Background: Colorectal cancer (CRC) remains a leading cause of cancer-related mortality worldwide. Accurate survival prediction is essential for treatment stratification, yet existing pathology foundation models often overlook organ-specific features critical for CRC prognostication.\n  Methods: We propose MorphDistill, a two-stage framework that distills complementary knowledge from multiple pathology foundation models into a compact CRC-specific encoder. In Stage I, a student encoder is trained using dimension-agnostic multi-teacher relational distillation with supervised contrastive regularization on large-scale colorectal datasets. This preserves inter-sample relationships from ten foundation models without explicit feature alignment. In Stage II, the encoder extracts patch-level features from whole-slide images, which are aggregated via attention-based multiple instance learning to predict five-year survival.\n  Results: On the Alliance/CALGB 89803 cohort (n=424, stage III CRC), MorphDistill achieves an AUC of 0.68 (SD 0.08), an approximately 8% relative improvement over the strongest baseline (AUC 0.63). It also attains a C-index of 0.661 and a hazard ratio of 2.52 (95% CI: 1.73-3.65), outperforming all baselines. On an external TCGA cohort (n=562), it achieves a C-index of 0.628, demonstrating strong generalization across datasets and robustness across clinical subgroups.\n  Conclusion: MorphDistill enables task-specific representation learning by integrating knowledge from multiple foundation models into a unified encoder. This approach provides an efficient strategy for prognostic modeling in computational pathology, with potential for broader oncology applications. Further validation across additional cohorts and disease stages is warranted.", "date": "2026-04-07", "categories": []}
{"id": "2604.11971", "title": "Classification of Epileptic iEEG using Topological Machine Learning", "abstract": "Epileptic seizure detection from EEG signals remains challenging due to the high dimensionality and nonlinear, potentially stochastic, dynamics of neural activity. In this work, we investigate whether features derived from topological data analysis (TDA) can improve the classification of brain states in preictal, ictal and interictal iEEG recordings from epilepsy patients using multichannel data. We analyze data from 55 patients, significantly larger than many previous studies that rely on patient-specific models. Persistence diagrams derived from iEEG signals are vectorized using several TDA representations, including Carlsson coordinates, persistence images, and template functions. To understand how topological representations interact with modern machine learning pipelines, we conduct a large-scale ablation study across multiple iEEG frequency bands, dimensionality reduction techniques, feature representations, and classifier architectures. Our experiments show that dimension-reduced topological representations achieve up to 80\\% balanced accuracy for three-class classification. Interestingly, classical machine learning models perform comparably to deep learning models, achieving up to 79.17\\% balanced accuracy, suggesting that carefully designed topological features can substantially reduce model complexity requirements. In contrast, pipelines preserving the full multichannel feature structure exhibit severe overfitting due to the high-dimensional feature space. These findings highlight the importance of structure-preserving dimensionality reduction when applying topology-based representations to multichannel neural data.", "date": "2026-04-13", "categories": []}
{"id": "2601.01119", "title": "Community-Based Early-Stage Chronic Kidney Disease Screening using Explainable Machine Learning for Low-Resource Settings", "abstract": "Early detection of chronic kidney disease (CKD) is essential for preventing progression to end-stage renal disease. However, existing screening tools - primarily developed using populations from high-income countries - often underperform in Bangladesh and South Asia, where risk profiles differ. Most of these tools rely on simple additive scoring functions and are based on data from patients with advanced-stage CKD. Consequently, they fail to capture complex interactions among risk factors and are limited in predicting early-stage CKD. Our objective was to develop and evaluate an explainable machine learning (ML) framework for community-based early-stage CKD screening for low-resource settings, tailored to the Bangladeshi and South Asian population context. A community-based CKD dataset from Bangladesh was used to develop predictive models. Variables were organized into clinically meaningful feature groups, and ten complementary feature selection methods were applied to identify robust predictor subsets. Twelve ML classifiers were evaluated using nested cross-validation. Model performance was benchmarked against established CKD screening tools and externally validated on three independent datasets from India, the UAE, and Bangladesh. SHAP was used to interpret model predictions. An ML model trained on an RFECV-selected feature subset achieved a balanced accuracy of 90.40%, whereas minimal non-pathology-test features demonstrated excellent predictive capability with a balanced accuracy of 89.23%, often outperforming larger or full feature sets. Compared with existing screening tools, the proposed models achieved substantially higher accuracy and sensitivity while requiring fewer and more accessible inputs. External validation confirmed strong generalizability with 78% to 98% sensitivity.", "date": "2026-01-03", "categories": []}
{"id": "2604.06214", "title": "Unsupervised Neural Network for Automated Classification of Surgical Urgency Levels in Medical Transcriptions", "abstract": "Efficient classification of surgical procedures by urgency is paramount to optimize patient care and resource allocation within healthcare systems. This study introduces an unsupervised neural network approach to automatically categorize surgical transcriptions into three urgency levels: immediate, urgent, and elective. Leveraging BioClinicalBERT, a domain-specific language model, surgical transcripts are transformed into high-dimensional embeddings that capture their semantic nuances. These embeddings are subsequently clustered using both K-means and Deep Embedding Clustering (DEC) algorithms, in which DEC demonstrates superior performance in the formation of cohesive and well-separated clusters. To ensure clinical relevance and accuracy, the clustering results undergo validation through the Modified Delphi Method, which involves expert review and refinement. Following validation, a neural network that integrates Bidirectional Long Short-Term Memory (BiLSTM) layers with BioClinicalBERT embeddings is developed for classification tasks. The model is rigorously evaluated using cross-validation and metrics such as accuracy, precision, recall, and F1-score, which achieve robust performance and demonstrate strong generalization capabilities on unseen data. This unsupervised framework not only addresses the challenge of limited labeled data but also provides a scalable and reliable solution for real-time surgical prioritization, which ultimately enhances operational efficiency and patient outcomes in dynamic medical environments.", "date": "2026-03-16", "categories": []}
{"id": "2604.07717", "title": "Detecting HIV-Related Stigma in Clinical Narratives Using Large Language Models", "abstract": "Human immunodeficiency virus (HIV)-related stigma is a critical psychosocial determinant of health for people living with HIV (PLWH), influencing mental health, engagement in care, and treatment outcomes. Although stigma-related experiences are documented in clinical narratives, there is a lack of off-the-shelf tools to extract and categorize them. This study aims to develop a large language model (LLM)-based tool for identifying HIV stigma from clinical notes. We identified clinical notes from PLWH receiving care at the University of Florida (UF) Health between 2012 and 2022. Candidate sentences were identified using expert-curated stigma-related keywords and iteratively expanded via clinical word embeddings. A total of 1,332 sentences were manually annotated across four stigma subscales: Concern with Public Attitudes, Disclosure Concerns, Negative Self-Image, and Personalized Stigma. We compared GatorTron-large and BERT as encoder-based baselines, and GPT-OSS-20B, LLaMA-8B, and MedGemma-27B as generative LLMs, under zero-shot and few-shot prompting. GatorTron-large achieved the best overall performance (Micro F1 = 0.62). Few-shot prompting substantially improved generative model performance, with 5-shot GPT-OSS-20B and LLaMA-8B achieving Micro-F1 scores of 0.57 and 0.59, respectively. Performance varied by stigma subscale, with Negative Self-Image showing the highest predictability and Personalized Stigma remaining the most challenging. Zero-shot generative inference exhibited non-trivial failure rates (up to 32%). This study develops the first practical NLP tool for identifying HIV stigma in clinical notes.", "date": "2026-04-09", "categories": []}
{"id": "2604.09782", "title": "Biomarker-Based Pretraining for Chagas Disease Screening in Electrocardiograms", "abstract": "Chagas disease screening via ECGs is limited by scarce and noisy labels in existing datasets. We propose a biomarker-based pretraining approach, where an ECG feature extractor is first trained to predict percentile-binned blood biomarkers from the MIMIC-IV-ECG dataset. The pretrained model is then fine-tuned on Brazilian datasets for Chagas detection. Our 5-model ensemble, developed by the Ahus AIM team, achieved a challenge score of 0.269 on the hidden test set, ranking 5th in Detection of Chagas Disease from the ECG: The George B. Moody PhysioNet Challenge 2025. Source code and the model are shared on GitHub: github.com/Ahus-AIM/physionet-challenge-2025", "date": "2026-04-10", "categories": []}
{"id": "2604.09905", "title": "Improving Pediatric Emergency Department Triage with Modality Dropout in Late Fusion Multimodal EHR Models", "abstract": "Emergency department triage relies heavily on both quantitative vital signs and qualitative clinical notes, yet multimodal machine learning models predicting triage acuity often suffer from modality collapse by over-relying on structured tabular data. This limitation severely hinders demographic generalizability, particularly for pediatric patients where developmental variations in vital signs make unstructured clinical narratives uniquely crucial. To address this gap, we propose a late-fusion multimodal architecture that processes tabular vitals via XGBoost and unstructured clinical text via Bio_ClinicalBERT, combined through a Logistic Regression meta-classifier to predict the 5-level Emergency Severity Index. To explicitly target the external validity problem, we train our model exclusively on adult encounters from the MIMIC-IV and NHAMCS datasets and evaluate its zero-shot generalization on a traditionally overlooked pediatric cohort. Furthermore, we employ symmetric modality dropout during training to prevent the ensemble from overfitting to adult-specific clinical correlations. Our results demonstrate that the multimodal framework significantly outperforms single-modality baselines. Most notably, applying a 30-40% symmetric modality dropout rate yielded steep performance improvements in the unseen pediatric cohort, elevating the Quadratic Weighted Kappa to 0.351. These findings highlight modality dropout as a critical regularization technique for mitigating modality collapse and enhancing cross-demographic generalization in clinical AI.", "date": "2026-04-10", "categories": []}
{"id": "2604.10386", "title": "TrajOnco: a multi-agent framework for temporal reasoning over longitudinal EHR for multi-cancer early detection", "abstract": "Accurate estimation of cancer risk from longitudinal electronic health records (EHRs) could support earlier detection and improved care, but modeling such complex patient trajectories remains challenging. We present TrajOnco, a training-free, multi-agent large language model (LLM) framework designed for scalable multi-cancer early detection. Using a chain-of-agents architecture with long-term memory, TrajOnco performs temporal reasoning over sequential clinical events to generate patient-level summaries, evidence-linked rationales, and predicted risk scores. We evaluated TrajOnco on de-identified Truveta EHR data across 15 cancer types using matched case-control cohorts, predicting risk of cancer diagnosis at 1 year. In zero-shot evaluation, TrajOnco achieved AUROCs of 0.64-0.80, performing comparably to supervised machine learning in a lung cancer benchmark while demonstrating better temporal reasoning than single-agent LLMs. The multi-agent design also enabled effective temporal reasoning with smaller-capacity models such as GPT-4.1-mini. The fidelity of TrajOnco's output was validated through human evaluation. Furthermore, TrajOnco's interpretable reasoning outputs can be aggregated to reveal population-level risk patterns that align with established clinical knowledge. These findings highlight the potential of multi-agent LLMs to execute interpretable temporal reasoning over longitudinal EHRs, advancing both scalable multi-cancer early detection and clinical insight generation.", "date": "2026-04-12", "categories": []}
{"id": "2604.10765", "title": "Lung Cancer Detection Using Deep Learning", "abstract": "Lung cancer, the second leading cause of cancer-related deaths, is primarily linked to long-term tobacco smoking (85% of cases). Surprisingly, 10-15% of cases occur in non-smokers. In 2020, approximately 2 million people were affected globally, resulting in 1.5 million deaths. The survival rate, at around 20%, lags behind other cancers, partly due to late-stage symptom manifestation. Necessitates early and accurate detection for effective treatment. Performance metrics such as accuracy, precision, recall (sensitivity), and F1-score are computed to provide a comprehensive evaluation of each model's capabilities. By comparing these metrics, this study offers insights into the strengths and limitations of each approach, contributing to the advancement of lung cancer detection techniques. In this paper, we are going to discuss the methodologies of lung cancer detection using different deep learning algorithms - InceptionV3, MobileNetV2, VGG16, ResNet152 - are explored for their efficacy in classifying lung cancer cases. Our Proposed Model algorithm based is a 16 layers architecture based on CNN model. Our Proposed model exhibits several key highlights that contribute to its novelty. By integrating multiple layer types such as convolutional, pooling, flatten, dropout, fully connected and dense layers, the model leverages the strengths of each layer to enhance its predictive capabilities. Novelty of our proposed model is that its accuracy is increasing consistently with the increasing no of epochs. We have tested the model performance up to epoch no 30. Our proposed model also overcome the overfitting problem.", "date": "2026-04-12", "categories": []}
{"id": "2510.22293", "title": "Predicting Metabolic Dysfunction-Associated Steatotic Liver Disease using Machine Learning Methods: A Retrospective Cohort Study", "abstract": "Background: Metabolic dysfunction-associated steatotic liver disease (MASLD) affects 30-40% of US adults and is the most common chronic liver disease. Although often asymptomatic, progression can lead to cirrhosis. The objective of the study was to develop and evaluate an electronic health record (EHR) based prediction model to support early detection of MASLD in primary care settings. Methods: We evaluated LASSO logistic regression, random forest, XGBoost, and a neural network model for MASLD prediction using clinical feature subsets from a large EHR database, including the top 10 ranked features. To reduce disparities in true positive rates across racial and ethnic subgroups, we applied an equal opportunity postprocessing method in a prediction model called MASLD EHR Static Risk Prediction (MASER). Results: This retrospective cohort study included 59,492 participants in the training data, 24,198 in the validating data, and 25,188 in the testing data. The LASSO logistic regression model with the top 10 features was selected for its interpretability and comparable performance. Before fairness adjustment, the model achieved AUROC of 0.84, accuracy of 78%, sensitivity of 72%, specificity of 79%, and F1-score of 0.617. After equal opportunity postprocessing, accuracy modestly increased to 81% and specificity to 94%, while sensitivity decreased to 41% and F1-score to 0.515, reflecting the fairness trade-off. Conclusions: MASER achieved competitive performance for MASLD prediction, comparable to previously reported ensemble and tree-based models, while using a limited and routinely collected feature set and a diverse study population. The model is designed to support early detection and potential integration into primary care workflows. MASER demonstrates EHR-ready MASLD prediction with fairness adjustments, supporting future primary care implementation pending prospective validation.", "date": "2025-10-25", "categories": []}
{"id": "2511.14603", "title": "A Method for Characterizing Disease Progression from Acute Kidney Injury to Chronic Kidney Disease", "abstract": "Patients with acute kidney injury (AKI) are at high risk of developing chronic kidney disease (CKD), but identifying those at greatest risk remains challenging. We used electronic health record (EHR) data to dynamically track AKI patients' clinical evolution and characterize AKI-to-CKD progression. Post-AKI clinical states were identified by clustering patient vectors derived from longitudinal medical codes and creatinine measurements. Transition probabilities between states and progression to CKD were estimated using multi-state modeling. After identifying common post-AKI trajectories, CKD risk factors in AKI subpopulations were identified through survival analysis. Of 20,699 patients with AKI at admission, 3,491 (17%) developed CKD. We identified fifteen distinct post-AKI states, each with different probabilities of CKD development. Most patients (75%, n=15,607) remained in a single state or made only one transition during the study period. Both established (e.g., AKI severity, diabetes, hypertension, heart failure, liver disease) and novel CKD risk factors, with their impact varying across these clinical states. This study demonstrates a data-driven approach for identifying high-risk AKI patients, supporting the development of decision-support tools for early CKD detection and intervention.", "date": "2025-11-18", "categories": []}
{"id": "2604.08902", "title": "Using Synthetic Data for Machine Learning-based Childhood Vaccination Prediction in Narok, Kenya", "abstract": "Background: Limited data utilization in low-resource settings poses a barrier to the vaccine delivery ecosystem, undermining efforts to achieve equitable immunization coverage. In nomadic populations, individuals face an increased risk of missing crucial vaccination doses as children. One such population is the Maasai in Narok County, Kenya, where the absence of high-volume, quality data hampers accurate coverage estimates, impedes efficient resource allocation, and weakens the ability to deliver timely interventions. Additionally, data privacy concerns are heightened in groups with limited sensitive data. Objectives: First, we aim to identify children at risk of missing key vaccines across a large population to provide timely, evidence-based interventions that support increased vaccination coverage. Second, we aim to better protect the privacy of sensitive health data in a vulnerable population. Methods: We digitized 8 years of child vaccination records from the MOH 510 registry (n=6,913) and applied machine learning models (Logistic Regression and XGBoost) to identify children at risk. Additionally, we utilize a novel approach to tabular diffusion-based synthetic data generation (TabSyn) to protect patient privacy within the models. Results: Our findings show that classification techniques can reliably and successfully predict children at risk of missing a vaccine, with recall, precision, and F1-scores exceeding 90% for some vaccines modeled. Additionally, training these models with synthetic data rather than real data, thus preserving the privacy of individuals within the original dataset, does not lead to a loss in predictive performance. Conclusion: These results support the use of synthetic data implementation in health informatics strategies for clinics with limited digital infrastructure, enabling privacy-preserving, scalable forecasting for childhood immunization coverage.", "date": "2026-04-10", "categories": []}
{"id": "2604.09280", "title": "AMO-ENE: Attention-based Multi-Omics Fusion Model for Outcome Prediction in Extra Nodal Extension and HPV-associated Oropharyngeal Cancer", "abstract": "Extranodal extension (ENE) is an emerging prognostic factor in human papillomavirus (HPV)-associated oropharyngeal cancer (OPC), although it is currently omitted as a clinical staging criteria. Recent works have advocated for the inclusion of iENE as a prognostic marker in HPV-positive OPC staging. However, several practical limitations continue to hinder its clinical integration, including inconsistencies in segmentation, low contrast in the periphery of metastatic lymph nodes on CT imaging, and laborious manual annotations. To address these limitations, we propose a fully automated end-to-end pipeline that uses computed tomography (CT) images with clinical data to assess the status of nodal ENE and predict treatment outcomes. Our approach includes a hierarchical 3D semi-supervised segmentation model designed to detect and delineate relevant iENE from radiotherapy planning CT scans. From these segmentations, a set of radiomics and deep features are extracted to train an imaging-detected ENE grading classifier. The predicted ENE status is then evaluated for its prognostic value and compared with existing staging criteria. Furthermore, we integrate these nodal features with primary tumor characteristics in a multimodal, attention-based outcome prediction model, providing a dynamic framework for outcome prediction. Our method is validated in an internal cohort of 397 HPV-positive OPC patients treated with radiation therapy or chemoradiotherapy between 2009 and 2020. For outcome prediction at the 2-year mark, our pipeline surpassed baseline models with 88.2% (4.8) in AUC for metastatic recurrence, 79.2% (7.4) for overall survival, and 78.1% (8.6) for disease-free survival. We also obtain a concordance index of 83.3% (6.5) for metastatic recurrence, 71.3% (8.9) for overall survival, and 70.0% (8.1) for disease-free survival, making it feasible for clinical decision making.", "date": "2026-04-10", "categories": []}
{"id": "2409.02136", "title": "Large Language Models versus Classical Machine Learning: Performance in COVID-19 Mortality Prediction Using High-Dimensional Tabular Data", "abstract": "This study compared the performance of classical feature-based machine learning models (CMLs) and large language models (LLMs) in predicting COVID-19 mortality using high-dimensional tabular data from 9,134 patients across four hospitals. Seven CML models, including XGBoost and random forest (RF), were evaluated alongside eight LLMs, such as GPT-4 and Mistral-7b, which performed zero-shot classification on text-converted structured data. Additionally, Mistral- 7b was fine-tuned using the QLoRA approach. XGBoost and RF demonstrated superior performance among CMLs, achieving F1 scores of 0.87 and 0.83 for internal and external validation, respectively. GPT-4 led the LLM category with an F1 score of 0.43, while fine-tuning Mistral-7b significantly improved its recall from 1% to 79%, yielding a stable F1 score of 0.74 during external validation. Although LLMs showed moderate performance in zero-shot classification, fine-tuning substantially enhanced their effectiveness, potentially bridging the gap with CML models. However, CMLs still outperformed LLMs in handling high-dimensional tabular data tasks. This study highlights the potential of both CMLs and fine-tuned LLMs in medical predictive modeling, while emphasizing the current superiority of CMLs for structured data analysis.", "date": "2024-09-02", "categories": []}
{"id": "2509.16750", "title": "Interpretable Clinical Classification with Kolmogorov-Arnold Networks", "abstract": "The increasing use of machine learning in clinical decision support has been limited by the lack of transparency of many high-performing models. In clinical settings, predictions must be interpretable, auditable, and actionable. This study investigates Kolmogorov-Arnold Networks (KANs) as intrinsically interpretable alternatives to conventional black-box models for clinical classification of tabular health data, aiming to balance predictive performance with clinically meaningful transparency. We introduce two KAN-based models: the Logistic KAN, a flexible generalization of logistic regression, and the Kolmogorov-Arnold Additive Model (KAAM), an additive variant that yields transparent symbolic representations through feature-wise decomposability. Both models are evaluated on multiple public clinical datasets and compared with standard linear, tree-based, and neural baselines. Across all datasets, the proposed models achieve predictive performance comparable to or exceeding that of commonly used baselines while remaining fully interpretable. Logistic-KAN obtains the highest overall ranking across evaluation metrics, with a mean reciprocal rank of 0.76, indicating consistently strong performance across tasks. KAAM provides competitive accuracy while offering enhanced transparency through feature-wise decomposability, patient-level visualizations, and nearest-patient retrieval, enabling direct inspection of individual predictions. KAN-based models provide a practical and trustworthy alternative to black-box models for clinical classification, offering a strong balance between predictive performance and interpretability for clinical decision support. By enabling transparent, patient-level reasoning and clinically actionable insights, the proposed models represent a promising step toward trustworthy AI in healthcare (code: https://github.com/Patricia-A-Apellaniz/classification_with_kans).", "date": "2025-09-20", "categories": []}
{"id": "2505.03123", "title": "A Dynamic Prognostic Prediction Method for Colorectal Cancer Liver Metastasis", "abstract": "Colorectal cancer liver metastasis (CRLM) exhibits high postoperative recurrence and pronounced prognostic heterogeneity, challenging individualized management. Existing prognostic approaches often rely on static representations from a single postoperative snapshot, and fail to jointly capture tumor spatial distribution, longitudinal disease dynamics, and multimodal clinical information, limiting predictive accuracy. We propose DyPro, a deep learning framework that infers postoperative latent trajectories via residual dynamic evolution. Starting from an initial patient representation, DyPro generates a 12-step sequence of trajectory snapshots through autoregressive residual updates and integrates them to predict recurrence and survival outcomes. On the MSKCC CRLM dataset, DyPro achieves strong discrimination under repeated stratified 5-fold cross-validation, reaching a C-index of 0.755 for OS and 0.714 for DFS, with OS AUC@1y of 0.920 and OS IBS of 0.143. DyPro provides quantitative risk cues to support adjuvant therapy planning and follow-up scheduling.", "date": "2025-05-06", "categories": []}
{"id": "2604.04614", "title": "A Clinical Point Cloud Paradigm for In-Hospital Mortality Prediction from Multi-Level Incomplete Multimodal EHRs", "abstract": "Deep learning-based modeling of multimodal Electronic Health Records (EHRs) has become an important approach for clinical diagnosis and risk prediction. However, due to diverse clinical workflows and privacy constraints, raw EHRs are inherently multi-level incomplete, including irregular sampling, missing modalities, and sparse labels. These issues cause temporal misalignment, modality imbalance, and limited supervision. Most existing multimodal methods assume relatively complete data, and even methods designed for incompleteness usually address only one or two of these issues in isolation. As a result, they often rely on rigid temporal/modal alignment or discard incomplete data, which may distort raw clinical semantics. To address this problem, we propose HealthPoint (HP), a unified clinical point cloud paradigm for multi-level incomplete EHRs. HP represents heterogeneous clinical events as points in a continuous 4D space defined by content, time, modality, and case. To model interactions between arbitrary point pairs, we introduce a Low-Rank Relational Attention mechanism that efficiently captures high-order dependencies across these four dimensions. We further develop a hierarchical interaction and sampling strategy to balance fine-grained modeling and computational efficiency. Built on this framework, HP enables flexible event-level interaction and fine-grained self-supervision, supporting robust modality recovery and effective use of unlabeled data. Experiments on large-scale EHR datasets for risk prediction show that HP consistently achieves state-of-the-art performance and strong robustness under varying degrees of incompleteness.", "date": "2026-04-06", "categories": []}
{"id": "2604.06193", "title": "Depression Detection at the Point of Care: Automated Analysis of Linguistic Signals from Routine Primary Care Encounters", "abstract": "Depression is underdiagnosed in primary care, yet timely identification remains critical. Recorded clinical encounters, increasingly common with digital scribing technologies, present an opportunity to detect depression from naturalistic dialogue. We investigated automated depression detection from 1,108 audio-recorded primary care encounters in the Establishing Focus study, with depression defined by PHQ-9 (n=253 depressed, n=855 non-depressed). We compared three supervised approaches, Sentence-BERT + Logistic Regression (LR), LIWC+LR and ModernBERT, against a zero-shot GPT-OSS. GPT-OSS achieved the strongest performance (AUPRC=0.510, AUROC=0.774), with LIWC+LR competitive among supervised models (AUPRC=0.500, AUROC=0.742). Combined dyadic transcripts outperformed single-speaker configurations, with providers linguistically mirroring patients in depression encounters, an additive signal not captured by either speaker alone. Meaningful detection is achievable from the first 128 patient tokens (AUPRC=0.356, AUROC=0.675), supporting in-the-moment clinical decision support. These findings argue for passively collected clinical audio as a low-burden complement to existing screening workflows.", "date": "2026-03-11", "categories": []}
{"id": "2604.06262", "title": "From Exposure to Internalization: Dual-Stream Calibration for In-context Clinical Reasoning", "abstract": "Contextual clinical reasoning demands robust inference grounded in complex, heterogeneous clinical records. While state-of-the-art fine-tuning, in-context learning (ICL), and retrieval-augmented generation (RAG) enable knowledge exposure, they often fall short of genuine contextual internalization: dynamically adjusting a model's internal representations to the subtle nuances of individual cases at inference time. To address this, we propose Dual-Stream Calibration (DSC), a test-time training framework that transcends superficial knowledge exposure to achieve deep internalization during inference. DSC facilitates input internalization by synergistically aligning two calibration streams. Unlike passive context exposure, the Semantic Calibration Stream enforces a deliberative reflection on core evidence, internalizing semantic anchors by minimizing entropy to stabilize generative trajectories. Simultaneously, the Structural Calibration Stream assimilates latent inferential dependencies through an iterative meta-learning objective. By training on specialized support sets at test-time, this stream enables the model to bridge the gap between external evidence and internal logic, synthesizing fragmented data into a coherent response. Our approach shifts the reasoning paradigm from passive attention-based matching to an active refinement of the latent inferential space. Validated against thirteen clinical datasets, DSC demonstrates superiority across three distinct task paradigms, consistently outstripping state-of-the-art baselines ranging from training-dependent models to test-time learning frameworks.", "date": "2026-04-07", "categories": []}
{"id": "2604.06469", "title": "Predicting Alzheimer's disease progression using rs-fMRI and a history-aware graph neural network", "abstract": "Alzheimer's disease (AD) is a neurodegenerative disorder that affects more than seven million people in the United States alone. AD currently has no cure, but there are ways to potentially slow its progression if caught early enough. In this study, we propose a graph neural network (GNN)-based model for predicting whether a subject will transition to a more severe stage of cognitive impairment at their next clinical visit. We consider three stages of cognitive impairment in order of severity: cognitively normal (CN), mild cognitive impairment (MCI), and AD. We use functional connectivity graphs derived from resting-state functional magnetic resonance imaging (rs-fMRI) scans of 303 subjects, each with a different number of visits. Our GNN-based model incorporates a recurrent neural network (RNN) block, enabling it to process data from the subject's entire visit history. It can also work with irregular time gaps between visits by incorporating visit distance information into our input features. Our model demonstrates robust predictive performance, even with missing visits in the subjects' visit histories. It achieves an accuracy of 82.9%, with an especially impressive accuracy of 68.8% on CN to MCI conversions - a task that poses a substantial challenge in the field. Our results highlight the effectiveness of rs-fMRI in predicting the onset of MCI or AD and, in conjunction with other modalities, could offer a viable method for enabling timely interventions to slow the progression of cognitive impairment.", "date": "2026-04-07", "categories": []}
{"id": "2604.06985", "title": "Frailty Estimation in Elderly Oncology Patients Using Multimodal Wearable Data and Multi-Instance Learning", "abstract": "Frailty and functional decline strongly influence treatment tolerance and outcomes in older patients with cancer, yet assessment is typically limited to infrequent clinic visits. We propose a multimodal wearable framework to estimate frailty-related functional change between visits in elderly breast cancer patients enrolled in the multicenter CARDIOCARE study. Free-living smartwatch physical activity and sleep features are combined with ECG-derived heart rate variability (HRV) features from a chest strap and organized into patient-horizon bags aligned to month 3 (M3) and month 6 (M6) follow-ups. Our innovation is an attention-based multiple instance learning (MIL) formulation that fuses irregular, multimodal wearable instances under real-world missingness and weak supervision. An attention-based MIL model with modality-specific multilayer perceptron (MLP) encoders with embedding dimension 128 aggregates variable-length and partially missing longitudinal instances to predict discretized change-from-baseline classes (worsened, stable, improved) for FACIT-F and handgrip strength. Under subject-independent leave-one-subject-out (LOSO) evaluation, the full multimodal model achieved balanced accuracy/F1 of 0.68 +/- 0.08/0.67 +/- 0.09 at M3 and 0.70 +/- 0.10/0.69 +/- 0.08 at M6 for handgrip, and 0.59 +/- 0.04/0.58 +/- 0.06 at M3 and 0.64 +/- 0.05/0.63 +/- 0.07 at M6 for FACIT-F. Ablation results indicated that smartwatch activity and sleep provide the strongest predictive information for frailty-related functional changes, while HRV contributes complementary information when fused with smartwatch streams.", "date": "2026-04-08", "categories": []}
{"id": "2604.06990", "title": "Stress Estimation in Elderly Oncology Patients Using Visual Wearable Representations and Multi-Instance Learning", "abstract": "Psychological stress is clinically relevant in cardio-oncology, yet it is typically assessed only through patient-reported outcome measures (PROMs) and is rarely integrated into continuous cardiotoxicity surveillance. We estimate perceived stress in an elderly, multicenter breast cancer cohort (CARDIOCARE) using multimodal wearable data from a smartwatch (physical activity and sleep) and a chest-worn ECG sensor. Wearable streams are transformed into heterogeneous visual representations, yielding a weakly supervised setting in which a single Perceived Stress Scale (PSS) score corresponds to many unlabeled windows. A lightweight pretrained mixture-of-experts backbone (Tiny-BioMoE) embeds each representation into 192-dimensional vectors, which are aggregated via attention-based multiple instance learning (MIL) to predict PSS at month 3 (M3) and month 6 (M6). Under leave-one-subject-out (LOSO) evaluation, predictions showed moderate agreement with questionnaire scores (M3: R^2=0.24, Pearson r=0.42, Spearman rho=0.48; M6: R^2=0.28, Pearson r=0.49, Spearman rho=0.52), with global RMSE/MAE of 6.62/6.07 at M3 and 6.13/5.54 at M6.", "date": "2026-04-08", "categories": []}
{"id": "2604.07269", "title": "Joint Optimization of Reasoning and Dual-Memory for Self-Learning Diagnostic Agent", "abstract": "Clinical expertise improves not only by acquiring medical knowledge, but by accumulating experience that yields reusable diagnostic patterns. Recent LLMs-based diagnostic agents have shown promising progress in clinical reasoning for decision support. However, most approaches treat cases independently, limiting experience reuse and continual adaptation. We propose SEA, a self-learning diagnostic agent with cognitively inspired dual-memory module. We design a reinforcement training framework tailored to our designed agent for joint optimization of reasoning and memory management. We evaluate SEA in two complementary settings. On standard evaluation with MedCaseReasoning dataset, SEA achieves 92.46% accuracy, outperforming the strongest baseline by +19.6%, demonstrating the benefit of jointly optimizing reasoning and memory. On the long-horizon with ER-Reason dataset, SEA attains the best final accuracy (0.7214) and the largest improvement (+0.35 Acc@100), while baseline methods show limited or unstable gains. Expert evaluation further indicates that rules consolidated from SEA show strong clinical correctness, usefulness and trust, suggesting that the induced rules in dual-memory module are reliable and practically meaningful. Overall, SEA improves both diagnostic reasoning ability and continual learning by effectively transforming experience into reusable knowledge.", "date": "2026-04-08", "categories": []}
{"id": "2509.00946", "title": "Ultrasound-based detection and malignancy prediction of breast lesions eligible for biopsy: A multi-center clinical-scenario study using nomograms, large language models, and radiologist evaluation", "abstract": "To develop and externally validate integrated ultrasound nomograms combining BIRADS features and quantitative morphometric characteristics, and to compare their performance with expert radiologists and state of the art large language models in biopsy recommendation and malignancy prediction for breast lesions. In this retrospective multicenter, multinational study, 1747 women with pathologically confirmed breast lesions underwent ultrasound across three centers in Iran and Turkey. A total of 10 BIRADS and 26 morphological features were extracted from each lesion. A BIRADS, morphometric, and fused nomogram integrating both feature sets was constructed via logistic regression. Three radiologists (one senior, two general) and two ChatGPT variants independently interpreted deidentified breast lesion images. Diagnostic performance for biopsy recommendation (BIRADS 4,5) and malignancy prediction was assessed in internal and two external validation cohorts. In pooled analysis, the fused nomogram achieved the highest accuracy for biopsy recommendation (83.0%) and malignancy prediction (83.8%), outperforming the morphometric nomogram, three radiologists and both ChatGPT models. Its AUCs were 0.901 and 0.853 for the two tasks, respectively. In addition, the performance of the BIRADS nomogram was significantly higher than the morphometric nomogram, three radiologists and both ChatGPT models for biopsy recommendation and malignancy prediction. External validation confirmed the robust generalizability across different ultrasound platforms and populations. An integrated BIRADS morphometric nomogram consistently outperforms standalone models, LLMs, and radiologists in guiding biopsy decisions and predicting malignancy. These interpretable, externally validated tools have the potential to reduce unnecessary biopsies and enhance personalized decision making in breast imaging.", "date": "2025-08-31", "categories": []}
{"id": "2604.05116", "title": "Uncertainty-Guided Latent Diagnostic Trajectory Learning for Sequential Clinical Diagnosis", "abstract": "Clinical diagnosis requires sequential evidence acquisition under uncertainty. However, most Large Language Model (LLM) based diagnostic systems assume fully observed patient information and therefore do not explicitly model how clinical evidence should be sequentially acquired over time. Even when diagnosis is formulated as a sequential decision process, it is still challenging to learn effective diagnostic trajectories. This is because the space of possible evidence-acquisition paths is relatively large, while clinical datasets rarely provide explicit supervision information for desirable diagnostic paths. To this end, we formulate sequential diagnosis as a Latent Diagnostic Trajectory Learning (LDTL) framework based on a planning LLM agent and a diagnostic LLM agent. For the diagnostic LLM agent, diagnostic action sequences are treated as latent paths and we introduce a posterior distribution that prioritizes trajectories providing more diagnostic information. The planning LLM agent is then trained to follow this distribution, encouraging coherent diagnostic trajectories that progressively reduce uncertainty. Experiments on the MIMIC-CDM benchmark demonstrate that our proposed LDTL framework outperforms existing baselines in diagnostic accuracy under a sequential clinical diagnosis setting, while requiring fewer diagnostic tests. Furthermore, ablation studies highlight the critical role of trajectory-level posterior alignment in achieving these improvements.", "date": "2026-04-06", "categories": []}
{"id": "2604.05844", "title": "Modeling Patient Care Trajectories with Transformer Hawkes Processes", "abstract": "Patient healthcare utilization consists of irregularly time-stamped events, such as outpatient visits, inpatient admissions, and emergency encounters, forming individualized care trajectories. Modeling these trajectories is crucial for understanding utilization patterns and predicting future care needs, but is challenging due to temporal irregularity and severe class imbalance. In this work, we build on the Transformer Hawkes Process framework to model patient trajectories in continuous time. By combining Transformer-based history encoding with Hawkes process dynamics, the model captures event dependencies and jointly predicts event type and time-to-event. To address extreme imbalance, we introduce an imbalance-aware training strategy using inverse square-root class weighting. This improves sensitivity to rare but clinically important events without altering the data distribution. Experiments on real-world data demonstrate improved performance and provide clinically meaningful insights for identifying high-risk patient populations.", "date": "2026-04-07", "categories": []}
{"id": "2603.04755", "title": "KindSleep: Knowledge-Informed Diagnosis of Obstructive Sleep Apnea from Oximetry", "abstract": "Obstructive sleep apnea (OSA) is a sleep disorder that affects nearly one billion people globally and significantly elevates cardiovascular risk. Traditional diagnosis through polysomnography is resource-intensive and limits widespread access, creating a critical need for accurate and efficient alternatives. In this paper, we introduce KindSleep, a deep learning framework that integrates clinical knowledge with single-channel patient-specific oximetry signals and clinical data for precise OSA diagnosis. KindSleep first learns to identify clinically interpretable concepts, such as desaturation indices and respiratory disturbance events, directly from raw oximetry signals. It then fuses these AI-derived concepts with multimodal clinical data to estimate the Apnea-Hypopnea Index (AHI). We evaluate KindSleep on three large, independent datasets from the National Sleep Research Resource (SHHS, CFS, MrOS; total n = 9,815). KindSleep demonstrates excellent performance in estimating AHI scores (R2 = 0.917, ICC = 0.957) and consistently outperforms existing approaches in classifying OSA severity, achieving weighted F1-scores from 0.827 to 0.941 across diverse populations. By grounding its predictions in a layer of clinically meaningful concepts, KindSleep provides a more transparent and trustworthy diagnostic tool for sleep medicine practices.", "date": "2026-03-05", "categories": []}
{"id": "2603.28532", "title": "Detecting low left ventricular ejection fraction from ECG using an interpretable and scalable predictor-driven framework", "abstract": "Low left ventricular ejection fraction (LEF) frequently remains undetected until progression to symptomatic heart failure, underscoring the need for scalable screening strategies. Although artificial intelligence-enabled electrocardiography (AI-ECG) has shown promise, existing approaches rely solely on end-to-end black-box models with limited interpretability or on tabular systems dependent on commercial ECG measurement algorithms with suboptimal performance. We introduced ECG-based Predictor-Driven LEF (ECGPD-LEF), a structured framework that integrates foundation model-derived diagnostic probabilities with interpretable modeling for detecting LEF from ECG. Trained on the benchmark EchoNext dataset comprising 72,475 ECG-echocardiogram pairs and evaluated in predefined independent internal (n=5,442) and external (n=16,017) cohorts, our framework achieved robust discrimination for moderate LEF (internal AUROC 88.4%, F1 64.5%; external AUROC 86.8%, F1 53.6%), consistently outperforming the official end-to-end baseline provided with the benchmark across demographic and clinical subgroups. Interpretability analyses identified high-impact predictors, including normal ECG, incomplete left bundle branch block, and subendocardial injury in anterolateral leads, driving LEF risk estimation. Notably, these predictors independently enabled zero-shot-like inference without task-specific retraining (internal AUROC 75.3-81.0%; external AUROC 71.6-78.6%), indicating that ventricular dysfunction is intrinsically encoded within structured diagnostic probability representations. This framework reconciles predictive performance with mechanistic transparency, supporting scalable enhancement through additional predictors and seamless integration with existing AI-ECG systems.", "date": "2026-03-30", "categories": []}
{"id": "2604.03320", "title": "Robust Multi-Source Covid-19 Detection in CT Images", "abstract": "Deep learning models for COVID-19 detection from chest CT scans generally perform well when the training and test data originate from the same institution, but they often struggle when scans are drawn from multiple centres with differing scanners, imaging protocols, and patient populations. One key reason is that existing methods treat COVID-19 classification as the sole training objective, without accounting for the data source of each scan. As a result, the learned representations tend to be biased toward centres that contribute more training data. To address this, we propose a multi-task learning approach in which the model is trained to predict both the COVID-19 diagnosis and the originating data centre. The two tasks share an EfficientNet-B7 backbone, which encourages the feature extractor to learn representations that hold across all four participating centres. Since the training data is not evenly distributed across sources, we apply a logit-adjusted cross-entropy loss [1] to the source classification head to prevent underrepresented centres from being overlooked. Our pre-processing follows the SSFL framework with KDS [2], selecting eight representative slices per scan. Our method achieves an F1 score of 0.9098 and an AUC-ROC of 0.9647 on a validation set of 308 scans. The code is publicly available at https://github.com/Purdue-M2/-multisource-covid-ct.", "date": "2026-04-02", "categories": []}
{"id": "2604.03498", "title": "Resource-Conscious Modeling for Next- Day Discharge Prediction Using Clinical Notes", "abstract": "Timely discharge prediction is essential for optimizing bed turnover and resource allocation in elective spine surgery units. This study evaluates the feasibility of lightweight, fine-tuned large language models (LLMs) and traditional text-based models for predicting next-day discharge using postoperative clinical notes. We compared 13 models, including TF-IDF with XGBoost and LGBM, and compact LLMs (DistilGPT-2, Bio_ClinicalBERT) fine-tuned via LoRA. TF-IDF with LGBM achieved the best balance, with an F1-score of 0.47 for the discharge class, a recall of 0.51, and the highest AUC-ROC (0.80). While LoRA improved recall in DistilGPT2, overall transformer-based and generative models underperformed. These findings suggest interpretable, resource-efficient models may outperform compact LLMs in real-world, imbalanced clinical prediction tasks.", "date": "2026-04-03", "categories": []}
{"id": "2604.04078", "title": "BAAI Cardiac Agent: An intelligent multimodal agent for automated reasoning and diagnosis of cardiovascular diseases from cardiac magnetic resonance imaging", "abstract": "Cardiac magnetic resonance (CMR) is a cornerstone for diagnosing cardiovascular disease. However, it remains underutilized due to complex, time-consuming interpretation across multi-sequences, phases, quantitative measures that heavily reliant on specialized expertise. Here, we present BAAI Cardiac Agent, a multimodal intelligent system designed for end-to-end CMR interpretation. The agent integrates specialized cardiac expert models to perform automated segmentation of cardiac structures, functional quantification, tissue characterization and disease diagnosis, and generates structured clinical reports within a unified workflow. Evaluated on CMR datasets from two hospitals (2413 patients) spanning 7-types of major cardiovascular diseases, the agent achieved an area under the receiver-operating-characteristic curve exceeding 0.93 internally and 0.81 externally. In the task of estimating left ventricular function indices, the results generated by this system for core parameters such as ejection fraction, stroke volume, and left ventricular mass are highly consistent with clinical reports, with Pearson correlation coefficients all exceeding 0.90. The agent outperformed state-of-the-art models in segmentation and diagnostic tasks, and generated clinical reports showing high concordance with expert radiologists (six readers across three experience levels). By dynamically orchestrating expert models for coordinated multimodal analysis, this agent framework enables accurate, efficient CMR interpretation and highlights its potentials for complex clinical imaging workflows. Code is available at https://github.com/plantain-herb/Cardiac-Agent.", "date": "2026-04-05", "categories": []}
{"id": "2604.04175", "title": "Uncertainty-Aware Foundation Models for Clinical Data", "abstract": "Healthcare foundation models have largely followed paradigms from natural language processing and computer vision, emphasizing large scale pretraining and deterministic representations over heterogeneous clinical data. However, clinical observations are inherently incomplete, reflecting sparse, irregular, and modality dependent measurements of an underlying physiologic state. In this work, we propose a framework for uncertainty aware foundation modeling that represents each patient not as a point embedding, but as a distribution over plausible latent states. By learning set valued representations and enforcing consistency across partial views of the same patient, the model captures what is invariantly inferable while explicitly encoding epistemic uncertainty. We integrate this formulation with multimodal encoders and scalable self supervised objectives, combining reconstruction, contrastive alignment, and distributional regularization. Across diverse clinical tasks, our approach improves predictive performance, robustness under missing data, and uncertainty calibration relative to strong baselines. These results suggest that modeling what is not observed rather than only what is constitutes a critical inductive bias for healthcare foundation models.", "date": "2026-04-05", "categories": []}
{"id": "2604.04239", "title": "Good Rankings, Wrong Probabilities: A Calibration Audit of Multimodal Cancer Survival Models", "abstract": "Multimodal deep learning models that fuse whole-slide histopathology images with genomic data have achieved strong discriminative performance for cancer survival prediction, as measured by the concordance index. Yet whether the survival probabilities derived from these models - either directly from native outputs or via standard post-hoc reconstruction - are calibrated remains largely unexamined.\n  We conduct, to our knowledge, the first systematic fold-level 1-calibration audit of multimodal WSI-genomics survival architectures, evaluating native discrete-time survival outputs (Experiment A: 3 models on TCGA-BRCA) and Breslow-reconstructed survival curves from scalar risk scores (Experiment B: 11 architectures across 5 TCGA cancer types). In Experiment A, all three models fail 1-calibration on a majority of folds (12 of 15 fold-level tests reject after Benjamini-Hochberg correction). Across the full 290 fold-level tests, 166 reject the null of correct calibration at the median event time after Benjamini-Hochberg correction (FDR = 0.05). MCAT achieves C-index 0.817 on GBMLGG yet fails 1-calibration on all five folds.\n  Gating-based fusion is associated with better calibration; bilinear and concatenation fusion are not. Post-hoc Platt scaling reduces miscalibration at the evaluated horizon (e.g., MCAT: 5/5 folds failing to 2/5) without affecting discrimination. The concordance index alone is insufficient for evaluating survival models intended for clinical use.", "date": "2026-04-05", "categories": []}
{"id": "2604.04698", "title": "Explainable Machine Learning for Sepsis Outcome Prediction Using a Novel Romanian Electronic Health Record Dataset", "abstract": "We develop and analyze explainable machine learning (ML) models for sepsis outcome prediction using a novel Electronic Health Record (EHR) dataset from 12,286 hospitalizations at a large emergency hospital in Romania. The dataset includes demographics, International Classification of Diseases (ICD-10) diagnostics, and 600 types of laboratory tests. This study aims to identify clinically strong predictors while achieving state-of-the-art results across three classification tasks: (1)deceased vs. discharged, (2)deceased vs. recovered, and (3)recovered vs. ameliorated. We trained five ML models to capture complex distributions while preserving clinical interpretability. Experiments explored the trade-off between feature richness and patient coverage, using subsets of the 10--50 most frequent laboratory tests. Model performance was evaluated using accuracy and area under the curve (AUC), and explainability was assessed using SHapley Additive exPlanations (SHAP). The highest performance was obtained for the deceased vs. recovered case study (AUC=0.983, accuracy=0.93). SHAP analysis identified several strong predictors such as cardiovascular comorbidities, urea levels, aspartate aminotransferase, platelet count, and eosinophil percentage. Eosinopenia emerged as a top predictor, highlighting its value as an underutilized marker that is not included in current assessment standards, while the high performance suggests the applicability of these models in clinical settings.", "date": "2026-04-06", "categories": []}
{"id": "2604.02337", "title": "Generating Counterfactual Patient Timelines from Real-World Data", "abstract": "Counterfactual simulation - exploring hypothetical consequences under alternative clinical scenarios - holds promise for transformative applications such as personalized medicine and in silico trials. However, it remains challenging due to methodological limitations. Here, we show that an autoregressive generative model trained on real-world data from over 300,000 patients and 400 million patient timeline entries can generate clinically plausible counterfactual trajectories. As a validation task, we applied the model to patients hospitalized with COVID-19 in 2023, modifying age, serum C-reactive protein (CRP), and serum creatinine to simulate 7-day outcomes. Increased in-hospital mortality was observed in counterfactual simulations with older age, elevated CRP, and elevated serum creatinine. Remdesivir prescriptions increased in simulations with higher CRP values and decreased in those with impaired kidney function. These counterfactual trajectories reproduced known clinical patterns. These findings suggest that autoregressive generative models trained on real-world data in a self-supervised manner can establish a foundation for counterfactual clinical simulation.", "date": "2026-01-24", "categories": []}
{"id": "2604.01398", "title": "Benchmark Problems and Benchmark Datasets for the evaluation of Machine and Deep Learning methods on Photoplethysmography signals: the D4 report from the QUMPHY project", "abstract": "This report is part of the Qumphy project (22HLT01 Qumphy) that is funded by the European Union and is dedicated to the development of measures to quantify the uncertainties associated with Machine Learning algorithms applied to medical problems, in particular the analysis and processing of Photoplethysmography (PPG) signals. In this report, a list of six medical problems that are related to PPG signals and serve as Benchmark Problems is given. Suitable Benchmark datasets and their usage are described also.", "date": "2026-04-01", "categories": []}
{"id": "2604.01727", "title": "MATA-Former & SIICU: Semantic Aware Temporal Alignment for High-Fidelity ICU Risk Prediction", "abstract": "Forecasting evolving clinical risks relies on intrinsic pathological dependencies rather than mere chronological proximity, yet current methods struggle with coarse binary supervision and physical timestamps. To align predictive modeling with clinical logic, we propose the Medical-semantics Aware Time-ALiBi Transformer (MATA-Former), utilizing event semantics to dynamically parameterize attention weights to prioritize causal validity over time lags. Furthermore, we introduce Plateau-Gaussian Soft Labeling (PSL), reformulating binary classification into continuous multi-horizon regression for full-trajectory risk modeling. Evaluated on SIICU -- a newly constructed dataset featuring over 506k events with rigorous expert-verified, fine-grained annotations -- and the MIMIC-IV dataset, our framework demonstrates superior efficacy and robust generalization in capturing risks from text-intensive, irregular clinical time series.", "date": "2026-04-02", "categories": []}
{"id": "2604.01924", "title": "Is Clinical Text Enough? A Multimodal Study on Mortality Prediction in Heart Failure Patients", "abstract": "Accurate short-term mortality prediction in heart failure (HF) remains challenging, particularly when relying on structured electronic health record (EHR) data alone. We evaluate transformer-based models on a French HF cohort, comparing text-only, structured-only, multimodal, and LLM-based approaches. Our results show that enriching clinical text with entity-level representations improves prediction over CLS embeddings alone, and that supervised multimodal fusion of text and structured variables achieves the best overall performance. In contrast, large language models perform inconsistently across modalities and decoding strategies, with text-only prompts outperforming structured or multimodal inputs. These findings highlight that entity-aware multimodal transformers offer the most reliable solution for short-term HF outcome prediction, while current LLM prompting remains limited for clinical decision support.", "date": "2026-04-02", "categories": []}
{"id": "2604.02002", "title": "How and why does deep ensemble coupled with transfer learning increase performance in bipolar disorder and schizophrenia classification?", "abstract": "Transfer learning (TL) and deep ensemble learning (DE) have recently been shown to outperform simple machine learning in classifying psychiatric disorders. However, there is still a lack of understanding as to why that is. This paper aims to understand how and why DE and TL reduce the variability of single-subject classification models in bipolar disorder (BD) and schizophrenia (SCZ). To this end, we investigated the training stability of TL and DE models. For the two classification tasks under consideration, we compared the results of multiple trainings with the same backbone but with different initializations. In this way, we take into account the epistemic uncertainty associated with the uncertainty in the estimation of the model parameters. It has been shown that the performance of classifiers can be significantly improved by using TL with DE. Based on these results, we investigate i) how many models are needed to benefit from the performance improvement of DE when classifying BD and SCZ from healthy controls, and ii) how TL induces better generalization, with and without DE. In the first case, we show that DE reaches a plateau when 10 models are included in the ensemble. In the second case, we find that using a pre-trained model constrains TL models with the same pre-training to stay in the same basin of the loss function. This is not the case for DL models with randomly initialized weights.", "date": "2026-04-02", "categories": []}
{"id": "2604.00027", "title": "Multi-lingual Multi-institutional Electronic Health Record based Predictive Model", "abstract": "Large-scale EHR prediction across institutions is hindered by substantial heterogeneity in schemas and code systems. Although Common Data Models (CDMs) can standardize records for multi-institutional learning, the manual harmonization and vocabulary mapping are costly and difficult to scale. Text-based harmonization provides an alternative by converting raw EHR into a unified textual form, enabling pooled learning without explicit standardization. However, applying this paradigm to multi-national datasets introduces an additional layer of heterogeneity, which is \"language\" that must be addressed for truly scalable EHRs learning. In this work, we investigate multilingual multi-institutional learning for EHR prediction, aiming to enable pooled training across multinational ICU datasets without manual standardization. We compare two practical strategies for handling language barriers: (i) directly modeling multilingual records with multilingual encoders, and (ii) translating non-English records into English via LLM-based word-level translation. Across seven public ICU datasets, ten clinical tasks with multiple prediction windows, translation-based lingual alignment yields more reliable cross-dataset performance than multilingual encoders. The multi-institutional learning model consistently outperforms strong baselines that require manual feature selection and harmonization, and also surpasses single-dataset training. We further demonstrate that text-based framework with lingual alignment effectively performs transfer learning via few-shot fine-tuning, with additional gains. To our knowledge, this is the first study to aggregate multilingual multinational ICU EHR datasets into one predictive model, providing a scalable path toward language-agnostic clinical prediction and future global multi-institutional EHR research.", "date": "2026-03-11", "categories": []}
{"id": "2604.00308", "title": "Vocal Prognostic Digital Biomarkers in Monitoring Chronic Heart Failure: A Longitudinal Observational Study", "abstract": "Objective: This study aimed to evaluate which voice features can predict health deterioration in patients with chronic HF.\n  Background: Heart failure (HF) is a chronic condition with progressive deterioration and acute decompensations, often requiring hospitalization and imposing substantial healthcare and economic burdens. Current standard-of-care (SoC) home monitoring, such as weight tracking, lacks predictive accuracy and requires high patient engagement. Voice is a promising non-invasive biomarker, though prior studies have mainly focused on acute HF stages.\n  Methods: In a 2-month longitudinal study, 32 patients with HF collected daily voice recordings and SoC measures of weight and blood pressure at home, with biweekly questionnaires for health status. Acoustic analysis generated detailed vowel and speech features. Time-series features were extracted from aggregated lookback windows (e.g., 7 days) to predict next-day health status. Explainable machine learning with nested cross-validation identified top vocal biomarkers, and a case study illustrated model application.\n  Results: A total of 21,863 recordings were analyzed. Acoustic vowel features showed strong correlations with health status. Time-series voice features within the lookback window outperformed corresponding standard care measures, achieving peak sensitivity and specificity of 0.826 and 0.782 versus 0.783 and 0.567 for SoC metrics. Key prognostic voice features identifying deterioration included delayed energy shift, low energy variability, and higher shimmer variability in vowels, along with reduced speaking and articulation rate, lower phonation ratio, decreased voice quality, and increased formant variability in speech.\n  Conclusion: Voice-based monitoring offers a non-invasive approach to detect early health changes in chronic HF, supporting proactive and personalized care.", "date": "2026-03-31", "categories": []}
{"id": "2604.00493", "title": "A Reasoning-Enabled Vision-Language Foundation Model for Chest X-ray Interpretation", "abstract": "Chest X-rays (CXRs) are among the most frequently performed imaging examinations worldwide, yet rising imaging volumes increase radiologist workload and the risk of diagnostic errors. Although artificial intelligence (AI) systems have shown promise for CXR interpretation, most generate only final predictions, without making explicit how visual evidence is translated into radiographic findings and diagnostic predictions. We present CheXOne, a reasoning-enabled vision-language model for CXR interpretation. CheXOne jointly generates diagnostic predictions and explicit, clinically grounded reasoning traces that connect visual evidence, radiographic findings, and these predictions. The model is trained on 14.7 million instruction and reasoning samples curated from 30 public datasets spanning 36 CXR interpretation tasks, using a two-stage framework that combines instruction tuning with reinforcement learning to improve reasoning quality. We evaluate CheXOne in zero-shot settings across visual question answering, report generation, visual grounding and reasoning assessment, covering 17 evaluation settings. CheXOne outperforms existing medical and general-domain foundation models and achieves strong performance on independent public benchmarks. A clinical reader study demonstrates that CheXOne-drafted reports are comparable to or better than resident-written reports in 55% of cases, while effectively addressing clinical indications and enhancing both report writing and CXR interpretation efficiency. Further analyses involving radiologists reveal that the generated reasoning traces show high clinical factuality and provide causal support for the final predictions, offering a plausible explanation for the performance gains. These results suggest that explicit reasoning can improve model performance, interpretability and clinical utility in AI-assisted CXR interpretation.", "date": "2026-04-01", "categories": []}
{"id": "2604.00545", "title": "Neuropsychiatric Deviations From Normative Profiles: An MRI-Derived Marker for Early Alzheimer's Disease Detection", "abstract": "Neuropsychiatric symptoms (NPS) such as depression and apathy are common in Alzheimer's disease (AD) and often precede cognitive decline. NPS assessments hold promise as early detection markers due to their correlation with disease progression and their non-invasive nature. Yet current tools cannot distinguish whether NPS are part of aging or early signs of AD, limiting their utility. We present a deep learning-based normative modelling framework to identify atypical NPS burden from structural MRI. A 3D convolutional neural network was trained on cognitively stable participants from the Alzheimer's Disease Neuroimaging Initiative, learning the mapping between brain anatomy and Neuropsychiatric Inventory Questionnaire (NPIQ) scores. Deviations between predicted and observed scores defined the Divergence from NPIQ scores (DNPI). Higher DNPI was associated with future AD conversion (adjusted OR=2.5; p < 0.01) and achieved predictive accuracy comparable to cerebrospinal fluid AB42 (AUC=0.74 vs 0.75). Our approach supports scalable, non-invasive strategies for early AD detection.", "date": "2026-04-01", "categories": []}
{"id": "2604.00739", "title": "BioCOMPASS: Integrating Biomarkers into Transformer-Based Immunotherapy Response Prediction", "abstract": "Datasets used in immunotherapy response prediction are typically small in size, as well as diverse in cancer type, drug administered, and sequencer used. Models often drop in performance when tested on patient cohorts that are not included in the training process. Recent work has shown that transformer-based models along with self-supervised learning show better generalisation performance than threshold-based biomarkers, but is still suboptimal. We present BioCOMPASS, an extension of a transformer-based model called COMPASS, that integrates biomarkers and treatment information to further improve its generalisability. Instead of feeding biomarker data as input, we built loss components to align them with the model's intermediate representations. We found that components such as treatment gating and pathway consistency loss improved generalisability when evaluated with Leave-one-cohort-out, Leave-one-cancer-type-out and Leave-one-treatment-out strategies. Results show that building components that exploit biomarker and treatment information can help in generalisability of immunotherapy response prediction. Careful curation of additional components that leverage complementary clinical information and domain knowledge represents a promising direction for future research.", "date": "2026-04-01", "categories": []}
{"id": "2604.01113", "title": "CARE: Privacy-Compliant Agentic Reasoning with Evidence Discordance", "abstract": "Large language model (LLM) systems are increasingly used to support high-stakes decision-making, but they typically perform worse when the available evidence is internally inconsistent. Such a scenario exists in real-world healthcare settings, with patient-reported symptoms contradicting medical signs. To study this problem, we introduce MIMIC-DOS, a dataset for short-horizon organ dysfunction worsening prediction in the intensive care unit (ICU) setting. We derive this dataset from the widely recognized MIMIC-IV, a publicly available electronic health record dataset, and construct it exclusively from cases in which discordance between signs and symptoms exists. This setting poses a substantial challenge for existing LLM-based approaches, with single-pass LLMs and agentic pipelines often struggling to reconcile such conflicting signals. To address this problem, we propose CARE: a multi-stage privacy-compliant agentic reasoning framework in which a remote LLM provides guidance by generating structured categories and transitions without accessing sensitive patient data, while a local LLM uses these categories and transitions to support evidence acquisition and final decision-making. Empirically, CARE achieves stronger performance across all key metrics compared to multiple baseline settings, showing that CARE can more robustly handle conflicting clinical evidence while preserving privacy.", "date": "2026-04-01", "categories": []}
{"id": "2510.04923", "title": "REN: Anatomically-Informed Mixture-of-Experts for Interstitial Lung Disease Diagnosis", "abstract": "Mixture-of-Experts (MoE) architectures achieve scalable learning by routing inputs to specialized subnetworks through conditional computation. However, conventional MoE designs assume homogeneous expert capability and domain-agnostic routing-assumptions that are fundamentally misaligned with medical imaging, where anatomical structure and regional disease heterogeneity govern pathological patterns. We introduce Regional Expert Networks (REN), the first anatomically-informed MoE framework for medical image classification. REN encodes anatomical priors by training seven specialized experts, each dedicated to a distinct lung lobe or bilateral lung combination, enabling precise modeling of region-specific pathological variation. Multi-modal gating mechanisms dynamically integrate radiomics biomarkers with deep learning (DL) features extracted by convolutional (CNN), Transformer (ViT), and state-space (Mamba) architectures to weight expert contributions at inference. Applied to interstitial lung disease (ILD) classification on a 597-patient, 1,898-scan longitudinal cohort, REN achieves consistently superior performance: the radiomics-guided ensemble attains an average AUC of 0.8646 +- 0.0467, a +12.5 % improvement over the SwinUNETR single-model baseline (AUC 0.7685, p=0.031). Lower-lobe experts reach AUCs of 0.88-0.90, outperforming DL baselines (CNN: 0.76-0.79) and mirroring known patterns of basal ILD progression. Evaluated under rigorous patient-level cross-validation, REN demonstrates strong generalizability and clinical interpretability, establishing a scalable, anatomically-guided framework potentially extensible to other structured medical imaging tasks. Code is available on our GitHub https://github.com/NUBagciLab/MoE-REN.", "date": "2025-10-06", "categories": []}
{"id": "2601.11691", "title": "Explainable histomorphology-based survival prediction of glioblastoma, IDH-wildtype", "abstract": "Glioblastoma, IDH-wildtype (GBM-IDHwt) is the most common malignant brain tumor. While histomorphology is a crucial component of GBM-IDHwt diagnosis, it is not further considered for prognosis. Here, we present an explainable artificial intelligence (AI) framework to identify and interpret histomorphological features associated with patient survival. The framework combines an explainable multiple instance learning (MIL) architecture that directly identifies prognostically relevant image tiles with a sparse autoencoder (SAE) that maps these tiles to interpretable visual patterns. The MIL model was trained and evaluated on a new real-world dataset of 720 GBM-IDHwt cases from three hospitals and four cancer registries across Germany. The SAE was trained on 1,878 whole-slide images from five independent public glioblastoma collections. Despite the many factors influencing survival time, our method showed some ability to discriminate between patients living less than 180 days or more than 360 days solely based on histomorphology (AUC: 0.67; 95% CI: 0.63-0.72). Cox proportional hazards regression confirmed a significant survival difference between predicted groups after adjustment for established prognostic factors (hazard ratio: 1.47; 95% CI: 1.26-1.72). Three neuropathologists categorized the identified visual patterns into seven distinct histomorphological groups, revealing both established prognostic features and unexpected associations, the latter being potentially attributable to surgery-related confounders. The presented explainable AI framework facilitates prognostic biomarker discovery in GBM-IDHwt and beyond, highlighting promising histomorphological features for further analysis and exposing potential confounders that would be hidden in black-box models.", "date": "2026-01-16", "categories": []}
{"id": "2601.21747", "title": "Temporal Sepsis Modeling: a Relational and Explainable-by-Design Framework", "abstract": "Sepsis remains one of the most complex and heterogeneous syndromes in intensive care, characterized by diverse physiological trajectories and variable responses to treatment. While deep learning models perform well in the early prediction of sepsis, they often lack interpretability and ignore latent patient sub-phenotypes. In this work, we propose a machine learning framework by opening up a new avenue for addressing this issue: a relational approach. Temporal data from electronic medical records (EMRs) are viewed as multivariate patient logs and represented in a relational data schema. Then, a propositionalisation technique (based on classic aggregation/selection functions from the field of relational data) is applied to construct interpretable features to \"flatten\" the data. Finally, the flattened data is classified using a selective naive Bayesian classifier. Experimental validation demonstrates the relevance of the suggested approach as well as its extreme interpretability. The interpretation is fourfold: univariate, global, local, and counterfactual.", "date": "2026-01-29", "categories": []}
{"id": "2603.29176", "title": "Predicting Neuromodulation Outcome for Parkinson's Disease with Generative Virtual Brain Model", "abstract": "Parkinson's disease (PD) affects over ten million people worldwide. Although temporal interference (TI) and deep brain stimulation (DBS) are promising therapies, inter-individual variability limits empirical treatment selection, increasing non-negligible surgical risk and cost. Previous explorations either resort to limited statistical biomarkers that are insufficient to characterize variability, or employ AI-driven methods which is prone to overfitting and opacity. We bridge this gap with a pretraining-finetuning framework to predict outcomes directly from resting-state fMRI. Critically, a generative virtual brain foundation model, pretrained on a collective dataset (2707 subjects, 5621 sessions) to capture universal disorder patterns, was finetuned on PD cohorts receiving TI (n=51) or DBS (n=55) to yield individualized virtual brains with high fidelity to empirical functional connectivity (r=0.935). By constructing counterfactual estimations between pathological and healthy neural states within these personalized models, we predicted clinical responses (TI: AUPR=0.853; DBS: AUPR=0.915), substantially outperforming baselines. External and prospective validations (n=14, n=11) highlight the feasibility of clinical translation. Moreover, our framework provides state-dependent regional patterns linked to response, offering hypothesis-generating mechanistic insights.", "date": "2026-03-31", "categories": []}
{"id": "2603.29793", "title": "Multimodal Machine Learning for Early Prediction of Metastasis in a Swedish Multi-Cancer Cohort", "abstract": "Multimodal Machine Learning offers a holistic view of a patient's status, integrating structured and unstructured data from electronic health records (EHR). We propose a framework to predict metastasis risk one month prior to diagnosis, using six months of clinical history from EHR data. Data from four cancer cohorts collected at Karolinska University Hospital (Stockholm, Sweden) were analyzed: breast (n = 743), colon (n = 387), lung (n = 870), and prostate (n = 1890). The dataset included demographics, comorbidities, laboratory results, medications, and clinical text. We compared traditional and deep learning classifiers across single modalities and multimodal combinations, using various fusion strategies and a Transparent Reporting of a multivariable prediction model for Individual Prognosis Or Diagnosis (TRIPOD) 2a design, with an 80-20 development-validation split to ensure a rigorous, repeatable evaluation. Performance was evaluated using AUROC, AUPRC, F1 score, sensitivity, and specificity. We then employed a multimodal adaptation of SHAP to analyze the classifiers' reasoning. Intermediate fusion achieved the highest F1 scores on breast (0.845), colon (0.786), and prostate cancer (0.845), demonstrating strong predictive performance. For lung cancer, the intermediate fusion achieved an F1 score of 0.819, while the text-only model achieved the highest, with an F1 score of 0.829. Deep learning classifiers consistently outperformed traditional models. Colon cancer, the smallest cohort, had the lowest performance, highlighting the importance of sufficient training data. SHAP analysis showed that the relative importance of modalities varied across cancer types. Fusion strategies offer distinct strengths and weaknesses. Intermediate fusion consistently delivered the best results, but strategy choices should align with data characteristics and organizational needs.", "date": "2026-03-31", "categories": []}
{"id": "2509.19315", "title": "Advancing Few-Shot Pediatric Arrhythmia Classification with a Novel Contrastive Loss and Multimodal Learning", "abstract": "Arrhythmias are a major cause of sudden cardiac death in children, making automated rhythm classification from electrocardiograms (ECGs) clinically important. However, pediatric arrhythmia analysis remains challenging because of age-dependent waveform variability, limited data availability, and a pronounced long-tailed class distribution that hinders recognition of rare but clinically important rhythms. To address these issues, we propose a multimodal end-to-end framework that integrates surface ECG and intracardiac electrogram (IEGM) signals for pediatric arrhythmia classification. The model combines dual-branch feature encoders, attention-based cross-modal fusion, and a lightweight Transformer classifier to learn complementary electrophysiological representations. We further introduce an Adaptive Global Class-Aware Contrastive Loss (AGCACL), which incorporates prototype-based alignment, class-frequency reweighting, and globally informed hard-class modulation to improve intra-class compactness and inter-class separability under class imbalance. We evaluate the proposed method on the pediatric subset of the Leipzig Heart Center ECG-Database and establish a reproducible preprocessing pipeline including rhythm-segment construction, denoising, and label grouping. The proposed approach achieves 96.22% Top-1 accuracy and improves macro precision, macro recall, macro F1 score, and macro F2 score by 4.48, 1.17, 6.98, and 7.34 percentage points, respectively, over the strongest baseline. These results indicate improved minority-sensitive classification performance on the current benchmark. However, further validation under subject-independent and multicenter settings is still required before clinical translation.", "date": "2025-09-10", "categories": []}
{"id": "2509.26351", "title": "LLM-Assisted Emergency Triage Benchmark: Bridging Hospital-Rich and MCI-Like Field Simulation", "abstract": "Research on emergency and mass casualty incident (MCI) triage has been limited by the absence of openly usable, reproducible benchmarks. Yet these scenarios demand rapid identification of the patients most in need, where accurate deterioration prediction can guide timely interventions. While the MIMIC-IV-ED database is openly available to credentialed researchers, transforming it into a triage-focused benchmark requires extensive preprocessing, feature harmonization, and schema alignment -- barriers that restrict accessibility to only highly technical users.\n  We address these gaps by first introducing an open, LLM-assisted emergency triage benchmark for deterioration prediction (ICU transfer, in-hospital mortality). The benchmark then defines two regimes: (i) a hospital-rich setting with vitals, labs, notes, chief complaints, and structured observations, and (ii) an MCI-like field simulation limited to vitals, observations, and notes. Large language models (LLMs) contributed directly to dataset construction by (i) harmonizing noisy fields such as AVPU and breathing devices, (ii) prioritizing clinically relevant vitals and labs, and (iii) guiding schema alignment and efficient merging of disparate tables.\n  We further provide baseline models and SHAP-based interpretability analyses, illustrating predictive gaps between regimes and the features most critical for triage. Together, these contributions make triage prediction research more reproducible and accessible -- a step toward dataset democratization in clinical AI.", "date": "2025-09-30", "categories": []}
{"id": "2602.20168", "title": "Benchmarking Early Deterioration Prediction Across Hospital-Rich and MCI-Like Emergency Triage Under Constrained Sensing", "abstract": "Emergency triage decisions are made under severe information constraints, yet most data-driven deterioration models are evaluated using signals unavailable during initial assessment. We present a leakage-aware benchmarking framework for early deterioration prediction that evaluates model performance under realistic, time-limited sensing conditions. Using a patient-deduplicated cohort derived from MIMIC-IV-ED, we compare hospital-rich triage with a vitals-only, MCI-like setting, restricting inputs to information available within the first hour of presentation. Across multiple modeling approaches, predictive performance declines only modestly when limited to vitals, indicating that early physiological measurements retain substantial clinical signal. Structured ablation and interpretability analyses identify respiratory and oxygenation measures as the most influential contributors to early risk stratification, with models exhibiting stable, graceful degradation as sensing is reduced. This work provides a clinically grounded benchmark to support the evaluation and design of deployable triage decision-support systems in resource-constrained settings.", "date": "2026-02-09", "categories": []}
{"id": "2603.04955", "title": "Uncertainty quantification in neural network-based glucose prediction for diabetes", "abstract": "In this work, we investigate uncertainty-aware neural network models for blood glucose prediction and adverse glycemic event identification in Type 1 diabetes. We consider three families of sequence models based on LSTM, GRU, and Transformer architectures, with uncertainty quantification enabled by either Monte Carlo dropout or through evidential output layers compatible with Deep Evidential Regression. Using the HUPA-UCM diabetes dataset for validation, we find that Transformer-based models equipped with evidential output heads provide the most effective uncertainty-aware framework, achieving consistently higher predictive accuracies and better-calibrated uncertainty estimates whose magnitudes significantly correlate with prediction errors. We further evaluate the clinical risk of each model using the recently proposed Diabetes Technology Society error grid, with risk categories defined by international expert consensus. Our results demonstrate the value of integrating principled uncertainty quantification into real-time machine-learning-based blood glucose prediction systems.", "date": "2026-03-05", "categories": []}
{"id": "2603.26809", "title": "Dictionary-based Pathology Mining with Hard-instance-assisted Classifier Debiasing for Genetic Biomarker Prediction from WSIs", "abstract": "Prediction of genetic biomarkers, e.g., microsatellite instability in colorectal cancer is crucial for clinical decision making. But, two primary challenges hamper accurate prediction: (1) It is difficult to construct a pathology-aware representation involving the complex interconnections among pathological components. (2) WSIs contain a large proportion of areas unrelated to genetic biomarkers, which make the model easily overfit simple but irrelative instances. We hereby propose a Dictionary-based hierarchical pathology mining with hard-instance-assisted classifier Debiasing framework to address these challenges, dubbed as D2Bio. Our first module, dictionary-based hierarchical pathology mining, is able to mine diverse and very fine-grained pathological contextual interaction without the limit to the distances between patches. The second module, hard-instance-assisted classfier debiasing, learns a debiased classifier via focusing on hard but task-related features, without any additional annotations. Experimental results on five cohorts show the superiority of our method, with over 4% improvement in AUROC compared with the second best on the TCGA-CRC-MSI cohort. Our analysis further shows the clinical interpretability of D2Bio in genetic biomarker diagnosis and potential clinical utility in survival analysis. Code will be available at https://github.com/DeepMed-Lab-ECNU/D2Bio.", "date": "2026-03-26", "categories": []}
{"id": "2603.26821", "title": "Epileptic Seizure Prediction Using Patient-Adaptive Transformer Networks", "abstract": "Epileptic seizure prediction from electroencephalographic (EEG) recordings remains challenging due to strong inter-patient variability and the complex temporal structure of neural signals. This paper presents a patient-adaptive transformer framework for short-horizon seizure forecasting. The proposed approach employs a two-stage training strategy: self-supervised pretraining is first used to learn general EEG temporal representations through autoregressive sequence modeling, followed by patient-specific fine-tuning for binary prediction of seizure onset within a 30-second horizon. To enable transformer-based sequence learning, multichannel EEG signals are processed using noise-aware preprocessing and discretized into tokenized temporal sequences. Experiments conducted on subjects from the TUH EEG dataset demonstrate that the proposed method achieves validation accuracies above 90% and F1 scores exceeding 0.80 across evaluated patients, supporting the effectiveness of combining self-supervised representation learning with patient-specific adaptation for individualized seizure prediction.", "date": "2026-03-26", "categories": []}
{"id": "2603.27325", "title": "Improving Automated Wound Assessment Using Joint Boundary Segmentation and Multi-Class Classification Models", "abstract": "Accurate wound classification and boundary segmentation are essential for guiding clinical decisions in both chronic and acute wound management. However, most existing AI models are limited, focusing on a narrow set of wound types or performing only a single task (segmentation or classification), which reduces their clinical applicability. This study presents a deep learning model based on YOLOv11 that simultaneously performs wound boundary segmentation (WBS) and wound classification (WC) across five clinically relevant wound types: burn injury (BI), pressure injury (PI), diabetic foot ulcer (DFU), vascular ulcer (VU), and surgical wound (SW). A wound-type balanced dataset of 2,963 annotated images was created to train the models for both tasks, with stratified five-fold cross-validation ensuring robust and unbiased evaluation. The models trained on the original non-augmented dataset achieved consistent performance across folds, though BI detection accuracy was relatively lower. Therefore, the dataset was augmented using rotation, flipping, and variations in brightness, saturation, and exposure to help the model learn more generalized and invariant features. This augmentation significantly improved model performance, particularly in detecting visually subtle BI cases. Among tested variants, YOLOv11x achieved the highest performance with F1-scores of 0.9341 (WBS) and 0.8736 (WC), while the lightweight YOLOv11n provided comparable accuracy at lower computational cost, making it suitable for resource-constrained deployments. Supported by confusion matrices and visual detection outputs, the results confirm the model's robustness against complex backgrounds and high intra-class variability, demonstrating the potential of YOLOv11-based architectures for accurate, real-time wound analysis in both clinical and remote care settings.", "date": "2026-03-28", "categories": []}
{"id": "2603.28167", "title": "Automating Early Disease Prediction Via Structured and Unstructured Clinical Data", "abstract": "This study presents a fully automated methodology for early prediction studies in clinical settings, leveraging information extracted from unstructured discharge reports. The proposed pipeline uses discharge reports to support the three main steps of early prediction: cohort selection, dataset generation, and outcome labeling. By processing discharge reports with natural language processing techniques, we can efficiently identify relevant patient cohorts, enrich structured datasets with additional clinical variables, and generate high-quality labels without manual intervention. This approach addresses the frequent issue of missing or incomplete data in codified electronic health records (EHR), capturing clinically relevant information that is often underrepresented. We evaluate the methodology in the context of predicting atrial fibrillation (AF) progression, showing that predictive models trained on datasets enriched with discharge report information achieve higher accuracy and correlation with true outcomes compared to models trained solely on structured EHR data, while also surpassing traditional clinical scores. These results demonstrate that automating the integration of unstructured clinical text can streamline early prediction studies, improve data quality, and enhance the reliability of predictive models for clinical decision-making.", "date": "2026-03-30", "categories": []}
{"id": "2603.28698", "title": "EpiScreen: Early Epilepsy Detection from Electronic Health Records with Large Language Models", "abstract": "Epilepsy and psychogenic non-epileptic seizures often present with similar seizure-like manifestations but require fundamentally different management strategies. Misdiagnosis is common and can lead to prolonged diagnostic delays, unnecessary treatments, and substantial patient morbidity. Although prolonged video-electroencephalography is the diagnostic gold standard, its high cost and limited accessibility hinder timely diagnosis. Here, we developed a low-cost, effective approach, EpiScreen, for early epilepsy detection by utilizing routinely collected clinical notes from electronic health records. Through fine-tuning large language models on labeled notes, EpiScreen achieved an AUC of up to 0.875 on the MIMIC-IV dataset and 0.980 on a private cohort of the University of Minnesota. In a clinician-AI collaboration setting, EpiScreen-assisted neurologists outperformed unaided experts by up to 10.9%. Overall, this study demonstrates that EpiScreen supports early epilepsy detection, facilitating timely and cost-effective screening that may reduce diagnostic delays and avoid unnecessary interventions, particularly in resource-limited regions.", "date": "2026-03-30", "categories": []}
{"id": "2512.08029", "title": "CLARITY: Medical World Model for Guiding Treatment Decisions by Modeling Context-Aware Disease Trajectories in Latent Space", "abstract": "Clinical decision-making in oncology requires predicting dynamic disease evolution, a task current static AI predictors cannot perform. While world models (WMs) offer a paradigm for generative prediction, existing medical applications remain limited. Existing methods often rely on stochastic diffusion models, focusing on visual reconstruction rather than causal, physiological transitions. Furthermore, in medical domain, models like MeWM typically ignore patient-specific temporal and clinical contexts and lack a feedback mechanism to link predictions to treatment decisions. To address these gaps, we introduce CLARITY, a medical world model that forecasts disease evolution directly within a structured latent space. It explicitly integrates time intervals (temporal context) and patient-specific data (clinical context) to model treatment-conditioned progression as a smooth, interpretable trajectory, and thus generate physiologically faithful, individualized treatment plans. Finally, CLARITY introduces a novel prediction-to-decision framework, translating latent rollouts into transparent, actionable recommendations. CLARITY demonstrates state-of-the-art performance in treatment planning. On the MU-Glioma-Post dataset, our approach outperforms recent MeWM by 12\\%, and significantly surpasses all other medical-specific large language models.", "date": "2025-12-08", "categories": []}
{"id": "2603.26007", "title": "Longitudinal Boundary Sharpness Coefficient Slopes Predict Time to Alzheimer's Disease Conversion in Mild Cognitive Impairment: A Survival Analysis Using the ADNI Cohort", "abstract": "Predicting whether someone with mild cognitive impairment (MCI) will progress to Alzheimer's disease (AD) is crucial in the early stages of neurodegeneration. This uncertainty limits enrollment in clinical trials and delays urgent treatment. The Boundary Sharpness Coefficient (BSC) measures how well-defined the gray-white matter boundary looks on structural MRI. This study measures how BSC changes over time, namely, how fast the boundary degrades each year works much better than looking at a single baseline scan for predicting MCI-to-AD conversion. This study analyzed 1,824 T1-weighted MRI scans from 450 ADNI subjects (95 converters, 355 stable; mean follow-up: 4.84 years). BSC voxel-wise maps were computed using tissue segmentation at the gray-white matter cortical ribbon. Previous studies have used CNN and RNN models that reached 96.0% accuracy for AD classification and 84.2% for MCI conversion, but those approaches disregard specific regions within the brain. This study focused specifically on the gray-white matter interface. The approach uses temporal slope features capturing boundary degradation rates, feeding them into Random Survival Forest, a non-parametric ensemble method for right-censored survival data. The Random Survival Forest trained on BSC slopes achieved a test C-index of 0.63, a 163% improvement over baseline parametric models (test C-index: 0.24). Structural MRI costs a fraction of PET imaging ($800--$1,500 vs. $5,000--$7,000) and does not require CSF collection. These temporal biomarkers could help with patient-centered safety screening as well as risk assessment.", "date": "2026-03-27", "categories": []}
{"id": "2603.26254", "title": "Improving Risk Stratification in Hypertrophic Cardiomyopathy: A Novel Score Combining Echocardiography, Clinical, and Medication Data", "abstract": "Hypertrophic cardiomyopathy (HCM) requires accurate risk stratification to inform decisions regarding ICD therapy and follow-up management. Current established models, such as the European Society of Cardiology (ESC) score, exhibit moderate discriminative performance. This study develops a robust, explainable machine learning (ML) risk score leveraging routinely collected echocardiographic, clinical, and medication data, typically contained within Electronic Health Records (EHRs), to predict a 5-year composite cardiovascular outcome in HCM patients. The model was trained and internally validated using a large cohort (N=1,201) from the SHARE registry (Florence Hospital) and externally validated on an independent cohort (N=382) from Rennes Hospital. The final Random Forest ensemble model achieved a high internal Area Under the Curve (AUC) of 0.85 +- 0.02, significantly outperforming the ESC score (0.56 +- 0.03). Critically, survival curve analysis on the external validation set showed superior risk separation for the ML score (Log-rank p = 8.62 x 10^(-4) compared to the ESC score (p = 0.0559). Furthermore, longitudinal analyses demonstrate that the proposed risk score remains stable over time in event-free patients. The model high interpretability and its capacity for longitudinal risk monitoring represent promising tools for the personalized clinical management of HCM.", "date": "2026-03-27", "categories": []}
{"id": "2603.26434", "title": "Automating Clinical Information Retrieval from Finnish Electronic Health Records Using Large Language Models", "abstract": "Clinicians often need to retrieve patient-specific information from electronic health records (EHRs), a task that is time-consuming and error-prone. We present a locally deployable Clinical Contextual Question Answering (CCQA) framework that answers clinical questions directly from EHRs without external data transfer. Open-source large language models (LLMs) ranging from 4B to 70B parameters were benchmarked under fully offline conditions using 1,664 expert-annotated question-answer pairs derived from records of 183 patients. The dataset consisted predominantly of Finnish clinical text. In free-text generation, Llama-3.1-70B achieved 95.3% accuracy and 97.3% consistency across semantically equivalent question variants, while the smaller Qwen3-30B-A3B-2507 model achieved comparable performance. In a multiple-choice setting, models showed similar accuracy but variable calibration. Low-precision quantization (4-bit and 8-bit) preserved predictive performance while reducing GPU memory requirements and improving deployment feasibility. Clinical evaluation identified clinically significant errors in 2.9% of outputs, and semantically equivalent questions occasionally yielded discordant responses, including instances where one formulation was correct and the other contained a clinically significant error (0.96% of cases). These findings demonstrate that locally hosted open-source LLMs can accurately retrieve patient-specific information from EHRs using natural-language queries, while highlighting the need for validation and human oversight in clinical deployment.", "date": "2026-03-27", "categories": []}
{"id": "2603.26510", "title": "Clinical named entity recognition in the Portuguese language: a benchmark of modern BERT models and LLMs", "abstract": "Clinical notes contain valuable unstructured information. Named entity recognition (NER) enables the automatic extraction of medical concepts; however, benchmarks for Portuguese remain scarce. In this study, we aimed to evaluate BERT-based models and large language models (LLMs) for clinical NER in Portuguese and to test strategies for addressing multilabel imbalance. We compared BioBERTpt, BERTimbau, ModernBERT, and mmBERT with LLMs such as GPT-5 and Gemini-2.5, using the public SemClinBr corpus and a private breast cancer dataset. Models were trained under identical conditions and evaluated using precision, recall, and F1-score. Iterative stratification, weighted loss, and oversampling were explored to mitigate class imbalance. The mmBERT-base model achieved the best performance (micro F1 = 0.76), outperforming all other models. Iterative stratification improved class balance and overall performance. Multilingual BERT models, particularly mmBERT, perform strongly for Portuguese clinical NER and can run locally with limited computational resources. Balanced data-splitting strategies further enhance performance.", "date": "2026-03-27", "categories": []}
{"id": "2510.13046", "title": "One Dimensional CNN ECG Mamba for Multilabel Abnormality Classification in 12 Lead ECG", "abstract": "Accurate detection of cardiac abnormalities from electrocardiogram recordings is regarded as essential for clinical diagnostics and decision support. Traditional deep learning models such as residual networks and transformer architectures have been applied successfully to this task, but their performance has been limited when long sequential signals are processed. Recently, state space models have been introduced as an efficient alternative. In this study, a hybrid framework named One Dimensional Convolutional Neural Network Electrocardiogram Mamba is introduced, in which convolutional feature extraction is combined with Mamba, a selective state space model designed for effective sequence modeling. The model is built upon Vision Mamba, a bidirectional variant through which the representation of temporal dependencies in electrocardiogram data is enhanced. Comprehensive experiments on the PhysioNet Computing in Cardiology Challenges of 2020 and 2021 were conducted, and superior performance compared with existing methods was achieved. Specifically, the proposed model achieved substantially higher AUPRC and AUROC scores than those reported by the best previously published algorithms on twelve lead electrocardiograms. These results demonstrate the potential of Mamba-based architectures to advance reliable ECG classification. This capability supports early diagnosis and personalized treatment, while enhancing accessibility in telemedicine and resource-constrained healthcare systems.", "date": "2025-10-14", "categories": []}
{"id": "2603.25229", "title": "An Image Dataset of Common Skin Diseases of Bangladesh and Benchmarking Performance with Machine Learning Models", "abstract": "Skin diseases are a major public health concern worldwide, and their detection is often challenging without access to dermatological expertise. In countries like Bangladesh, which is highly populated, the number of qualified skin specialists and diagnostic instruments is insufficient to meet the demand. Due to the lack of proper detection and treatment of skin diseases, that may lead to severe health consequences including death. Common properties of skin diseases are, changing the color, texture, and pattern of skin and in this era of artificial intelligence and machine learning, we are able to detect skin diseases by using image processing and computer vision techniques. In response to this challenge, we develop a publicly available dataset focused on common skin disease detection using machine learning techniques. We focus on five prevalent skin diseases in Bangladesh: Contact Dermatitis, Vitiligo, Eczema, Scabies, and Tinea Ringworm. The dataset consists of 1612 images (of which, 250 are distinct while others are augmented), collected directly from patients at the outpatient department of Faridpur Medical College, Faridpur, Bangladesh. The data comprises of 302, 381, 301, 316, and 312 images of Dermatitis, Eczema, Scabies, Tinea Ringworm, and Vitiligo, respectively. Although the data are collected regionally, the selected diseases are common across many countries especially in South Asia, making the dataset potentially valuable for global applications in machine learning-based dermatology. We also apply several machine learning and deep learning models on the dataset and report classification performance. We expect that this research would garner attention from machine learning and deep learning researchers and practitioners working in the field of automated disease diagnosis.", "date": "2026-03-26", "categories": []}
{"id": "2603.25322", "title": "AD-CARE: A Guideline-grounded, Modality-agnostic LLM Agent for Real-world Alzheimer's Disease Diagnosis with Multi-cohort Assessment, Fairness Analysis, and Reader Study", "abstract": "Alzheimer's disease (AD) is a growing global health challenge as populations age, and timely, accurate diagnosis is essential to reduce individual and societal burden. However, real-world AD assessment is hampered by incomplete, heterogeneous multimodal data and variability across sites and patient demographics. Although large language models (LLMs) have shown promise in biomedicine, their use in AD has largely been confined to answering narrow, disease-specific questions rather than generating comprehensive diagnostic reports that support clinical decision-making. Here we expand LLM capabilities for clinical decision support by introducing AD-CARE, a modality-agnostic agent that performs guideline-grounded diagnostic assessment from incomplete, heterogeneous inputs without imputing missing modalities. By dynamically orchestrating specialized diagnostic tools and embedding clinical guidelines into LLM-driven reasoning, AD-CARE generates transparent, report-style outputs aligned with real-world clinical workflows. Across six cohorts comprising 10,303 cases, AD-CARE achieved 84.9% diagnostic accuracy, delivering 4.2%-13.7% relative improvements over baseline methods. Despite cohort-level differences, dataset-specific accuracies remain robust (80.4%-98.8%), and the agent consistently outperforms all baselines. AD-CARE reduced performance disparities across racial and age subgroups, decreasing the average dispersion of four metrics by 21%-68% and 28%-51%, respectively. In a controlled reader study, the agent improved neurologist and radiologist accuracy by 6%-11% and more than halved decision time. The framework yielded 2.29%-10.66% absolute gains over eight backbone LLMs and converges their performance. These results show that AD-CARE is a scalable, practically deployable framework that can be integrated into routine clinical workflows for multimodal decision support in AD.", "date": "2026-03-26", "categories": []}
{"id": "2312.00357", "title": "A Generalizable Deep Learning System for Cardiac MRI", "abstract": "Cardiac MRI allows for a comprehensive assessment of myocardial structure, function and tissue characteristics. Here we describe a foundational vision system for cardiac MRI, capable of representing the breadth of human cardiovascular disease and health. Our deep-learning model is trained via self-supervised contrastive learning, in which visual concepts in cine-sequence cardiac MRI scans are learned from the raw text of the accompanying radiology reports. We train and evaluate our model on data from four large academic clinical institutions in the United States. We additionally showcase the performance of our models on the UK BioBank and two additional publicly available external datasets. We explore emergent capabilities of our system and demonstrate remarkable performance across a range of tasks, including the problem of left-ventricular ejection fraction regression and the diagnosis of 39 different conditions such as cardiac amyloidosis and hypertrophic cardiomyopathy. We show that our deep-learning system is capable of not only contextualizing the staggering complexity of human cardiovascular disease but can be directed towards clinical problems of interest, yielding impressive, clinical-grade diagnostic accuracy with a fraction of the training data typically required for such tasks.", "date": "2023-12-01", "categories": []}
{"id": "2506.04831", "title": "EHR2Path: Scalable Modeling of Longitudinal Patient Pathways from Multimodal Electronic Health Records", "abstract": "Forecasting how a patient's condition is likely to evolve, including possible deterioration, recovery, treatment needs, and care transitions, could support more proactive and personalized care, but requires modeling heterogeneous and longitudinal electronic health record (EHR) data. Yet, existing approaches typically focus on isolated prediction tasks, narrow feature spaces, or short context windows, limiting their ability to model full patient pathways. To address this gap, we introduce EHR2Path, a multimodal framework for forecasting and simulating full in-hospital patient pathways from routine EHRs. EHR2Path converts diverse clinical inputs into a unified temporal representation, enabling modeling of a substantially broader set of patient information, including radiology reports, physician notes, vital signs, medication and laboratory patterns, and dense bedside charting. To support long clinical histories and broad feature spaces, we introduce a Masked Summarization Bottleneck that compresses long-term history into compact, task-optimized summary tokens while preserving recent context, improving both performance and token efficiency. In retrospective experiments on MIMIC-IV, EHR2Path enables next-step pathway forecasting and iterative simulation of complete in-hospital trajectories, while outperforming strong baselines on directly comparable tasks. These results establish a foundation for scalable pathway-level modeling from routine EHRs supporting anticipatory clinical decision-making. Our code is available at https://github.com/ChantalMP/EHR2Path.", "date": "2025-06-05", "categories": []}
{"id": "2412.13152", "title": "Continuous Patient Monitoring with AI: Real-Time Analysis of Video in Hospital Care Settings", "abstract": "This study introduces an AI-driven platform for continuous and passive patient monitoring in hospital settings, developed by LookDeep Health. Leveraging advanced computer vision, the platform provides real-time insights into patient behavior and interactions through video analysis, securely storing inference results in the cloud for retrospective evaluation. The dataset, compiled in collaboration with 11 hospital partners, encompasses over 300 high-risk fall patients and over 1,000 days of inference, enabling applications such as fall detection and safety monitoring for vulnerable patient populations. To foster innovation and reproducibility, an anonymized subset of this dataset is publicly available. The AI system detects key components in hospital rooms, including individual presence and role, furniture location, motion magnitude, and boundary crossings. Performance evaluation demonstrates strong accuracy in object detection (macro F1-score = 0.92) and patient-role classification (F1-score = 0.98), as well as reliable trend analysis for the \"patient alone\" metric (mean logistic regression accuracy = 0.82 \\pm 0.15). These capabilities enable automated detection of patient isolation, wandering, or unsupervised movement-key indicators for fall risk and other adverse events. This work establishes benchmarks for validating AI-driven patient monitoring systems, highlighting the platform's potential to enhance patient safety and care by providing continuous, data-driven insights into patient behavior and interactions.", "date": "2024-12-17", "categories": []}
{"id": "2512.15762", "title": "Cross-Sample Augmented Test-Time Adaptation for Personalized Intraoperative Hypotension Prediction", "abstract": "Intraoperative hypotension (IOH) poses significant surgical risks, but accurate prediction remains challenging due to patient-specific variability. While test-time adaptation (TTA) offers a promising approach for personalized prediction, the rarity of IOH events often leads to unreliable test-time training. To address this, we propose CSA-TTA, a novel Cross-Sample Augmented Test-Time Adaptation framework that enhances training by incorporating hypotension events from other individuals. Specifically, we first construct a cross-sample bank by segmenting historical data into hypotensive and non-hypotensive samples. Then, we introduce a coarse-to-fine retrieval strategy for building test-time training data: we initially apply K-Shape clustering to identify representative cluster centers and subsequently retrieve the top-K semantically similar samples based on the current patient signal. Additionally, we integrate both self-supervised masked reconstruction and retrospective sequence forecasting signals during training to enhance model adaptability to rapid and subtle intraoperative dynamics. We evaluate the proposed CSA-TTA on both the VitalDB dataset and a real-world in-hospital dataset by integrating it with state-of-the-art time series forecasting models, including TimesFM and UniTS. CSA-TTA consistently enhances performance across settings-for instance, on VitalDB, it improves Recall and F1 scores by +1.33% and +1.13%, respectively, under fine-tuning, and by +7.46% and +5.07% in zero-shot scenarios-demonstrating strong robustness and generalization.", "date": "2025-12-12", "categories": []}
{"id": "2603.21597", "title": "Cerebra: A Multidisciplinary AI Board for Multimodal Dementia Characterization and Risk Assessment", "abstract": "Modern clinical practice increasingly depends on reasoning over heterogeneous, evolving, and incomplete patient data. Although recent advances in multimodal foundation models have improved performance on various clinical tasks, most existing models remain static, opaque, and poorly aligned with real-world clinical workflows. We present Cerebra, an interactive multi-agent AI team that coordinates specialized agents for EHR, clinical notes, and medical imaging analysis. These outputs are synthesized into a clinician-facing dashboard that combines visual analytics with a conversational interface, enabling clinicians to interrogate predictions and contextualize risk at the point of care. Cerebra supports privacy-preserving deployment by operating on structured representations and remains robust when modalities are incomplete. We evaluated Cerebra using a massive multi-institutional dataset spanning 3 million patients from four independent healthcare systems. Cerebra consistently outperformed both state-of-the-art single-modality models and large multimodal language model baselines. In dementia risk prediction, it achieved AUROCs up to 0.80, compared with 0.74 for the strongest single-modality model and 0.68 for language model baselines. For dementia diagnosis, it achieved an AUROC of 0.86, and for survival prediction, a C-index of 0.81. In a reader study with experienced physicians, Cerebra significantly improved expert performance, increasing accuracy by 17.5 percentage points in prospective dementia risk estimation. These results demonstrate Cerebra's potential for interpretable, robust decision support in clinical care.", "date": "2026-03-23", "categories": []}
{"id": "2603.22785", "title": "Exposure-Normalized Bed and Chair Fall Rates via Continuous AI Monitoring", "abstract": "This retrospective cohort study used continuous AI monitoring to estimate fall rates by exposure time rather than occupied bed-days. From August 2024 to December 2025, 3,980 eligible monitoring units contributed 292,914 hourly rows, yielding probability-weighted rates of 17.8 falls per 1,000 chair exposure-hours and 4.3 per 1,000 bed exposure-hours. Within the study window, 43 adjudicated falls matched the monitoring pipeline, and 40 linked to eligible exposure hours for the primary Poisson model, producing an adjusted chair-versus-bed rate ratio of 2.35 (95% confidence interval 0.87 to 6.33; p=0.0907). In a separate broader observation cohort (n=32 deduplicated events), 6 of 7 direct chair falls involved footrest-positioning failures. Because this was an observational study in a single health system, these findings remain hypothesis-generating and support testing safer chair setups rather than using chairs less.", "date": "2026-03-24", "categories": []}
{"id": "2410.05824", "title": "Multi-Session Client-Centered Treatment Outcome Evaluation in Psychotherapy", "abstract": "In psychotherapy, therapeutic outcome assessment, or treatment outcome evaluation, is essential to mental health care by systematically evaluating therapeutic processes and outcomes. Existing large language model approaches often focus on therapist-centered, single-session evaluations, neglecting the client's subjective experience and longitudinal progress across multiple sessions. To address these limitations, we propose IPAEval, a client-Informed Psychological Assessment-based Evaluation framework, which automates treatment outcome evaluations from the client's perspective using clinical interviews. It integrates cross-session client-contextual assessment and session-focused client-dynamics assessment for a comprehensive understanding of therapeutic progress. Specifically, IPAEval employs a two-stage prompt scheme that maps client information onto psychometric test items, enabling interpretable and structured psychological assessments. Experiments on our new TheraPhase dataset, comprising 400 paired initial and completion stage client records, demonstrate that IPAEval effectively tracks symptom severity and treatment outcomes over multiple sessions, outperforming baseline approaches across both closed-source and open-source models, and validating the benefits of items-aware reasoning mechanisms.", "date": "2024-10-08", "categories": []}
{"id": "2412.02868", "title": "PrecLLM: A Privacy-Preserving Framework for Efficient Clinical Annotation Extraction from Unstructured EHRs using Small-Scale LLMs", "abstract": "Large Language Models (LLMs) have demonstrated remarkable proficiency in automated text annotation within natural language processing. However, their deployment in clinical settings is severely constrained by strict privacy regulations and the prohibitive computational cost of processing voluminous unstructured Electronic Health Records (EHRs). In this study, we developed a resource-efficient preprocessing technique that can be adopted in existing LLM procedures. This approach is particularly useful for smaller LLMs, which are often more accuracy-challenged, and forms a compact LLM framework optimized for local deployment in computational environments with stringent privacy requirements and restricted access to high-performance GPUs (PrecLLM). The preprocessing step includes both regular expressions (regex) and Retrieval-Augmented Generation (RAG) to extract and highlight key information from unstructured clinical notes. Pre-filtering long and unstructured texts enhanced the performance of smaller LLMs on EHR-related tasks. Evaluation was performed on two distinct cohorts: a locally curated private EHR dataset from the EPIC system for a Head and Neck Cancer (HNC) cohort, and the publicly available EHR dataset (MIMIC-IV). Using MIMIC-IV, we further compared PrecLLM against fine-tuned LLMs. Results demonstrated that PrecLLM substantially enhanced the performance of the original smaller LLMs in terms of sensitivity, specificity, and F1 scores, making it well-suited for privacy-sensitive and resource-constrained applications. This study offers optimized LLM performance for local, secure, and efficient healthcare applications, and provides practical guidance for clinical LLM deployment while addressing challenges related to privacy, computational feasibility, and clinical applicability.", "date": "2024-12-03", "categories": []}
{"id": "2507.19408", "title": "On Arbitrary Predictions from Equally Valid Models", "abstract": "Model multiplicity refers to the existence of multiple machine learning models that describe the data equally well but may produce different predictions on individual samples. In medicine, these models can admit conflicting predictions for the same patient -- a risk that is poorly understood and insufficiently addressed.\n  In this study, we empirically analyze the extent, drivers, and ramifications of predictive multiplicity across diverse medical tasks and model architectures, and show that even small ensembles can mitigate/eliminate predictive multiplicity in practice. Our analysis reveals that (1) standard validation metrics fail to identify a uniquely optimal model and (2) a substantial amount of predictions hinges on arbitrary choices made during model development. Using multiple models instead of a single model reveals instances where predictions differ across equally plausible models -- highlighting patients that would receive arbitrary diagnoses if any single model were used. In contrast, (3) a small ensemble paired with an abstention strategy can effectively mitigate measurable predictive multiplicity in practice; predictions with high inter-model consensus may thus be amenable to automated classification. While accuracy is not a principled antidote to predictive multiplicity, we find that (4) higher accuracy achieved through increased model capacity reduces predictive multiplicity.\n  Our findings underscore the clinical importance of accounting for model multiplicity and advocate for ensemble-based strategies to improve diagnostic reliability. In cases where models fail to reach sufficient consensus, we recommend deferring decisions to expert review.", "date": "2025-07-25", "categories": []}
{"id": "2512.23743", "title": "Hybrid-Code v2: Zero-Hallucination Clinical ICD-10 Coding via Neuro-Symbolic Verification and Automated Knowledge Base Expansion", "abstract": "Automated clinical ICD-10 coding is a high-impact healthcare task requiring a balance between coverage, precision, and safety. While neural approaches achieve strong performance, they suffer from hallucination-generating invalid or unsupported codes-posing unacceptable risks in safety-critical clinical settings. Rule-based systems eliminate hallucination but lack scalability and coverage due to manual knowledge base (KB) curation.\n  We present Hybrid-Code v2, a neuro-symbolic framework that achieves zero Type-I hallucination by construction while maintaining competitive coverage and precision. The system integrates neural candidate generation with a symbolic KB verification layer that enforces validity constraints through multi-layer verification, including format, evidence grounding, negation detection, temporal consistency, and exclusion rules. In addition, we introduce an automated KB expansion mechanism that extracts and validates coding patterns from unlabeled clinical text, addressing the scalability limitations of rule-based systems.\n  Evaluated on the MIMIC-III dataset against ClinicalBERT, BioBERT, rule-based systems, and GPT-4, Hybrid-Code v2 achieves 85% coverage, 92% precision, and 0% Type-I hallucination, outperforming rule-based systems by +40% coverage while eliminating hallucination observed in neural baselines (6-18%). The proposed architecture provides a formal safety guarantee for syntactic validity while preserving strong empirical performance.\n  These results demonstrate that neuro-symbolic verification can enforce safety constraints in neural medical AI systems without sacrificing effectiveness, offering a generalizable design pattern for deploying trustworthy AI in safety-critical domains.", "date": "2025-12-26", "categories": []}
{"id": "2602.15677", "title": "CAMEL: An ECG Language Model for Forecasting Cardiac Events", "abstract": "Electrocardiograms (ECG) are electrical recordings of the heart that are critical for diagnosing cardiovascular conditions. ECG language models (ELMs) have recently emerged as a promising framework for ECG classification accompanied by report generation. However, current models cannot forecast future cardiac events despite the immense clinical value for planning earlier intervention. To address this gap, we propose CAMEL, the first ELM that is capable of inference over longer signal durations which enables its forecasting capability. Our key insight is a specialized ECG encoder which enables cross-understanding of ECG signals with text. We train CAMEL using established LLM training procedures, combining LoRA adaptation with a curriculum learning pipeline. Our curriculum includes ECG classification, metrics calculations, and multi-turn conversations to elicit reasoning. CAMEL demonstrates strong zero-shot performance across 6 tasks and 9 datasets, including ECGForecastBench, a new benchmark that we introduce for forecasting arrhythmias. CAMEL is on par with or surpasses ELMs and fully supervised baselines both in- and out-of-distribution, achieving SOTA results on ECGBench (+7.0% absolute average gain) as well as ECGForecastBench (+12.4% over fully supervised models and +21.1% over zero-shot ELMs).", "date": "2026-02-17", "categories": []}
{"id": "2603.13275", "title": "PREBA: Surgical Duration Prediction via PCA-Weighted Retrieval-Augmented LLMs and Bayesian Averaging Aggregation", "abstract": "Accurate prediction of surgical duration is pivotal for hospital resource management. Although recent supervised learning approaches-from machine learning (ML) to fine-tuned large language models (LLMs)-have shown strong performance, they remain constrained by the need for high-quality labeled data and computationally intensive training. In contrast, zero-shot LLM inference offers a promising training-free alternative but it lacks grounding in institution-specific clinical context (e.g., local demographics and case-mix distributions), making its predictions clinically misaligned and prone to instability. To address these limitations, we present PREBA, a retrieval-augmented framework that integrates PCA-weighted retrieval and Bayesian averaging aggregation to ground LLM predictions in institution-specific clinical evidence and statistical priors. The core of PREBA is to construct an evidence-based prompt for the LLM, comprising (1) the most clinically similar historical surgical cases and (2) clinical statistical priors. To achieve this, PREBA first encodes heterogeneous clinical features into a unified representation space enabling systematic retrieval. It then performs PCA-weighted retrieval to identify clinically relevant historical cases, which form the evidence context supplied to the LLM. Finally, PREBA applies Bayesian averaging to fuse multi-round LLM predictions with population-level statistical priors, yielding calibrated and clinically plausible duration estimates. We evaluate PREBA on two real-world clinical datasets using three state-of-the-art LLMs, including Qwen3, DeepSeek-R1, and HuatuoGPT-o1. PREBA significantly improves performance-for instance, reducing MAE by up to 40% and raising R^2 from -0.13 to 0.62 over zero-shot inference-and it achieves accuracy competitive with supervised ML methods, demonstrating strong effectiveness and generalization.", "date": "2026-02-27", "categories": []}
{"id": "2603.20341", "title": "Interpretable Multiple Myeloma Prognosis with Observational Medical Outcomes Partnership Data", "abstract": "Machine learning (ML) promises better clinical decision-making, yet opaque model behavior limits the adoption in healthcare. We propose two novel regularization techniques for ensuring the interpretability of ML models trained on real-world data. In particular, we consider the prediction of five-year survival for multiple myeloma patients using clinical data from Helsinki University Hospital. To ensure the interpretability of the trained models, we use two alternative constructions for a penalty term used for regularization. The first one penalizes deviations from the predictions obtained from an interpretable logistic regression method with two manually chosen features. The second construction requires consistency of model predictions with the revised international staging system (R-ISS). We verify the usefulness of the proposed regularization techniques in numerical experiments using data from 812 patients. They achieve an accuracy up to 0.721 on a test set and SHAP values show that the models rely on the selected important features.", "date": "2026-03-20", "categories": []}
{"id": "2603.20442", "title": "Detecting Neurovascular Instability from Multimodal Physiological Signals Using Wearable-Compatible Edge AI: A Responsible Computational Framework", "abstract": "We propose Melaguard, a multimodal ML framework (Transformer-lite, 1.2M parameters, 4-head self-attention) for detecting neurovascular instability (NVI) from wearable-compatible physiological signals prior to structural stroke pathology. The model fuses heart rate variability (HRV), peripheral perfusion index, SpO2, and bilateral phase coherence into a composite NVI Score, designed for edge inference (WCET <=4 ms on Cortex-M4). NVI - the pre-structural dysregulation of cerebrovascular autoregulation preceding overt stroke - remains undetectable by existing single-modality wearables. With 12.2 million incident strokes annually, continuous multimodal physiological monitoring offers a practical path to community-scale screening. Three-stage independent validation: (1) synthetic benchmark (n=10,000), AUC=0.88 [0.83-0.92]; (2) clinical cohort PhysioNet CVES (n=172; 84 stroke, 88 control) - Transformer-lite achieves AUC=0.755 [0.630-0.778], outperforming LSTM (0.643), Random Forest (0.665), SVM (0.472); HRV-SDNN discriminates stroke (p=0.011); (3) PPG pipeline PhysioNet BIDMC (n=53) -- pulse rate r=0.748 and HRV surrogate r=0.690 vs. ECG ground truth. Cross-modality validation on PPG-BP (n=219) confirms PPG morphology classifies cerebrovascular disease at AUC=0.923 [0.869-0.968]. Multimodal fusion consistently outperforms single-modality baselines. Code: https://github.com/ClevixLab/Melaguard", "date": "2026-03-20", "categories": []}
{"id": "2603.20536", "title": "Towards Practical Multimodal Hospital Outbreak Detection", "abstract": "Rapid identification of outbreaks in hospitals is essential for controlling pathogens with epidemic potential. Although whole genome sequencing (WGS) remains the gold standard in outbreak investigations, its substantial costs and turnaround times limit its feasibility for routine surveillance, especially in less-equipped facilities. We explore three modalities as rapid alternatives: matrix-assisted laser desorption ionization-time of flight (MALDI-TOF) mass spectrometry, antimicrobial resistance (AR) patterns, and electronic health records (EHR). We present a machine learning approach that learns discriminative features from these modalities to support outbreak detection. Multi-species evaluation shows that the integration of these modalities can boost outbreak detection performance. We also propose a tiered surveillance paradigm that can reduce the need for WGS through these alternative modalities. Further analysis of EHR information identifies potentially high-risk contamination routes linked to specific clinical procedures, notably those involving invasive equipment and high-frequency workflows, providing infection prevention teams with actionable targets for proactive risk mitigation", "date": "2026-03-20", "categories": []}
{"id": "2603.20921", "title": "Discriminative Representation Learning for Clinical Prediction", "abstract": "Foundation models in healthcare have largely adopted self supervised pretraining objectives inherited from natural language processing and computer vision, emphasizing reconstruction and large scale representation learning prior to downstream adaptation. We revisit this paradigm in outcome centric clinical prediction settings and argue that, when high quality supervision is available, direct outcome alignment may provide a stronger inductive bias than generative pretraining. We propose a supervised deep learning framework that explicitly shapes representation geometry by maximizing inter class separation relative to within class variance, thereby concentrating model capacity along clinically meaningful axes. Across multiple longitudinal electronic health record tasks, including mortality and readmission prediction, our approach consistently outperforms masked, autoregressive, and contrastive pretraining baselines under matched model capacity. The proposed method improves discrimination, calibration, and sample efficiency, while simplifying the training pipeline to a single stage optimization. These findings suggest that in low entropy, outcome driven healthcare domains, supervision can act as the statistically optimal driver of representation learning, challenging the assumption that large scale self supervised pretraining is a prerequisite for strong clinical performance.", "date": "2026-03-21", "categories": []}
{"id": "2603.21832", "title": "Deriving Health Metrics from the Photoplethysmogram: Benchmarks and Insights from MIMIC-III-Ext-PPG", "abstract": "Photoplethysmography (PPG) is one of the most widely captured biosignals for clinical prediction tasks, yet PPG-based algorithms are typically trained on small-scale datasets of uncertain quality, which hinders meaningful algorithm comparisons. We present a comprehensive benchmark for PPG-based clinical prediction using the \\dbname~dataset, establishing baselines across the full spectrum of clinically relevant applications: multi-class heart rhythm classification, and regression of physiological parameters including respiratory rate (RR), heart rate (HR), and blood pressure (BP). Most notably, we provide the first comprehensive assessment of PPG for general arrhythmia detection beyond atrial fibrillation (AF) and atrial flutter (AFLT), with performance stratified by BP, HR, and demographic subgroups. Using established deep learning architectures, we achieved strong performance for AF detection (AUROC = 0.96) and accurate physiological parameter estimation (RR MAE: 2.97 bpm; HR MAE: 1.13 bpm; SBP/DBP MAE: 16.13/8.70 mmHg). Cross-dataset validation demonstrates excellent generalizability for AF detection (AUROC = 0.97), while clinical subgroup analysis reveals marked performance differences across subgroups by BP, HR, and demographic strata. These variations appear to reflect population-specific waveform differences rather than systematic bias in model behavior. This framework establishes the first integrated benchmark for multi-task PPG-based clinical prediction, demonstrating that PPG signals can effectively support multiple simultaneous monitoring tasks and providing essential baselines for future algorithm development.", "date": "2026-03-23", "categories": []}
{"id": "2603.22179", "title": "MARCUS: An agentic, multimodal vision-language model for cardiac diagnosis and management", "abstract": "Cardiovascular disease remains the leading cause of global mortality, with progress hindered by human interpretation of complex cardiac tests. Current AI vision-language models are limited to single-modality inputs and are non-interactive. We present MARCUS (Multimodal Autonomous Reasoning and Chat for Ultrasound and Signals), an agentic vision-language system for end-to-end interpretation of electrocardiograms (ECGs), echocardiograms, and cardiac magnetic resonance imaging (CMR) independently and as multimodal input. MARCUS employs a hierarchical agentic architecture comprising modality-specific vision-language expert models, each integrating domain-trained visual encoders with multi-stage language model optimization, coordinated by a multimodal orchestrator. Trained on 13.5 million images (0.25M ECGs, 1.3M echocardiogram images, 12M CMR images) and our novel expert-curated dataset spanning 1.6 million questions, MARCUS achieves state-of-the-art performance surpassing frontier models (GPT-5 Thinking, Gemini 2.5 Pro Deep Think). Across internal (Stanford) and external (UCSF) test cohorts, MARCUS achieves accuracies of 87-91% for ECG, 67-86% for echocardiography, and 85-88% for CMR, outperforming frontier models by 34-45% (P<0.001). On multimodal cases, MARCUS achieved 70% accuracy, nearly triple that of frontier models (22-28%), with 1.7-3.0x higher free-text quality scores. Our agentic architecture also confers resistance to mirage reasoning, whereby vision-language models derive reasoning from unintended textual signals or hallucinated visual content. MARCUS demonstrates that domain-specific visual encoders with an agentic orchestrator enable multimodal cardiac interpretation. We release our models, code, and benchmark open-source.", "date": "2026-03-23", "categories": []}
{"id": "2510.02835", "title": "Subject-Adaptive Sparse Linear Models for Interpretable Personalized Health Prediction from Multimodal Lifelog Data", "abstract": "Improved prediction of personalized health outcomes -- such as sleep quality and stress -- from multimodal lifelog data could have meaningful clinical and practical implications. However, state-of-the-art models, primarily deep neural networks and gradient-boosted ensembles, sacrifice interpretability and fail to adequately address the significant inter-individual variability inherent in lifelog data. To overcome these challenges, we propose the Subject-Adaptive Sparse Linear (SASL) framework, an interpretable modeling approach explicitly designed for personalized health prediction. SASL integrates ordinary least squares regression with subject-specific interactions, systematically distinguishing global from individual-level effects. We employ an iterative backward feature elimination method based on nested $F$-tests to construct a sparse and statistically robust model. Additionally, recognizing that health outcomes often represent discretized versions of continuous processes, we develop a regression-then-thresholding approach specifically designed to maximize macro-averaged F1 scores for ordinal targets. For intrinsically challenging predictions, SASL selectively incorporates outputs from compact LightGBM models through confidence-based gating, enhancing accuracy without compromising interpretability. Evaluations conducted on the CH-2025 dataset -- which comprises roughly 450 daily observations from ten subjects -- demonstrate that the hybrid SASL-LightGBM framework achieves predictive performance comparable to that of sophisticated black-box methods, but with significantly fewer parameters and substantially greater transparency, thus providing clear and actionable insights for clinicians and practitioners.", "date": "2025-10-03", "categories": []}
{"id": "2511.07213", "title": "DETECT: Data-Driven Evaluation of Treatments Enabled by Classification Transformers", "abstract": "Chronic pain is a global health challenge affecting millions of individuals, making it essential for physicians to have reliable and objective methods to measure the functional impact of clinical treatments. Traditionally used methods, like the numeric rating scale, while personalized and easy to use, are subjective due to their self-reported nature. Thus, this paper proposes DETECT (Data-Driven Evaluation of Treatments Enabled by Classification Transformers), a data-driven framework that assesses treatment success by comparing patient activities of daily life before and after treatment. We use DETECT on public benchmark datasets and simulated patient data from smartphone sensors. Our results demonstrate that DETECT is objective yet lightweight, making it a significant and novel contribution to clinical decision-making. By using DETECT, independently or together with other self-reported metrics, physicians can improve their understanding of their treatment impacts, ultimately leading to more personalized and responsive patient care.", "date": "2025-11-10", "categories": []}
{"id": "2601.03018", "title": "Dementia-R1: Reinforced Pretraining and Reasoning from Unstructured Clinical Notes for Real-World Dementia Prognosis", "abstract": "While Large Language Models (LLMs) have shown strong performance on clinical text understanding, they struggle with longitudinal prediction tasks such as dementia prognosis, which require reasoning over complex, non-monotonic symptom trajectories across multiple visits. Standard supervised training lacks explicit annotations for symptom evolution, while direct Reinforcement Learning (RL) is hindered by sparse binary rewards. To address this challenge, we introduce Dementia-R1, an RL-based framework for longitudinal dementia prognosis from unstructured clinical notes. Our approach adopts a Cold-Start RL strategy that pre-trains the model to predict verifiable clinical indices extracted from patient histories, enhancing the capability to reason about disease progression before determining the final clinical status. Extensive experiments show that Dementia-R1 achieves the best overall performance on the AMC real-world unstructured cohort, reaching an AUROC of 84.02% and outperforming models up to 10x larger. The framework also generalizes to Parkinson's disease dementia prediction in an independent hospital cohort, achieving an AUROC of 78.37%. On the ADNI benchmark, our 7B model attains the highest AUROC among all LLM baselines at 83.17%, demonstrating strong longitudinal reasoning over fluctuating cognitive trajectories. Code is available at https://anonymous.4open.science/r/dementiar1-CDB5.", "date": "2026-01-06", "categories": []}
{"id": "2507.19530", "title": "When Validation Fails: Cross-Institutional Blood Pressure Prediction and the Limits of Electronic Health Record-Based Models", "abstract": "External validation remains rare in healthcare machine learning despite being critical for establishing real-world feasibility. We developed an ensemble framework to predict blood pressure from electronic health records, incorporating rigorous data leakage prevention. Internal validation on the MIMIC-III dataset yielded moderate performance for systolic (R^2 = 0.248, RMSE = 14.84 mmHg) and diastolic (R^2 = 0.297, RMSE = 8.27 mmHg) blood pressure. However, external validation on the eICU dataset revealed substantial generalization challenges. Baseline systolic performance dropped significantly from R^2 = 0.248 to -0.024, with RMSE increasing from 14.84 to 18.69 mmHg. To address potential confounding from feature imputation, we conducted an intersection-only experiment using 16 universally available features; this yielded worse external performance (R^2 = -0.115, RMSE = 17.32 mmHg), proving imputation artifacts were not the primary cause. Attempts at post-hoc correction, including linear and isotonic recalibration (R^2 ranging from -0.170 to 0.024) and domain adaptation via covariate shift reweighting (R^2 = -0.141), showed limited gains. This highlights fundamental cross-institutional barriers. Our root-cause analysis identified three primary obstacles to generalizability: (1) site-specific feature distributions, even among standard physiological variables; (2) underlying patient population differences with unique pathophysiologies; and (3) institutional variations in measurement protocols creating non-transferable learned patterns. These findings demonstrate that strong internal performance cannot guarantee cross-institutional deployment success. Transparent reporting of validation failures is essential for setting realistic expectations for predictive models. Code is available at https://github.com/mdbasit897/ehr-bp-ensemble.", "date": "2025-07-21", "categories": []}
{"id": "2603.18115", "title": "LLM-Augmented Computational Phenotyping of Long Covid", "abstract": "Phenotypic characterization is essential for understanding heterogeneity in chronic diseases and for guiding personalized interventions. Long COVID, a complex and persistent condition, yet its clinical subphenotypes remain poorly understood. In this work, we propose an LLM-augmented computational phenotyping framework ``Grace Cycle'' that iteratively integrates hypothesis generation, evidence extraction, and feature refinement to discover clinically meaningful subgroups from longitudinal patient data. The framework identifies three distinct clinical phenotypes, Protected, Responder, and Refractory, based on 13,511 Long Covid participants. These phenotypes exhibit pronounced separation in peak symptom severity, baseline disease burden, and longitudinal dose-response patterns, with strong statistical support across multiple independent dimensions.\n  This study illustrates how large language models can be integrated into a principled, statistically grounded pipeline for phenotypic screening from complex longitudinal data. Note that the proposed framework is disease-agnostic and offers a general approach for discovering clinically interpretable subphenotypes.", "date": "2026-03-18", "categories": []}
{"id": "2603.18124", "title": "Evaluating FrameNet-Based Semantic Modeling for Gender-Based Violence Detection in Clinical Records", "abstract": "Gender-based violence (GBV) is a major public health issue, with the World Health Organization estimating that one in three women experiences physical or sexual violence by an intimate partner during her lifetime. In Brazil, although healthcare professionals are legally required to report such cases, underreporting remains significant due to difficulties in identifying abuse and limited integration between public information systems. This study investigates whether FrameNet-based semantic annotation of open-text fields in electronic medical records can support the identification of patterns of GBV. We compare the performance of an SVM classifier for GBV cases trained on (1) frame-annotated text, (2) annotated text combined with parameterized data, and (3) parameterized data alone. Quantitative and qualitative analyses show that models incorporating semantic annotation outperform categorical models, achieving over 0.3 improvement in F1 score and demonstrating that domain-specific semantic representations provide meaningful signals beyond structured demographic data. The findings support the hypothesis that semantic analysis of clinical narratives can enhance early identification strategies and support more informed public health interventions.", "date": "2026-03-18", "categories": []}
{"id": "2603.19082", "title": "A Dataset and Resources for Identifying Patient Health Literacy Information from Clinical Notes", "abstract": "Health literacy is a critical determinant of patient outcomes, yet current screening tools are not always feasible and differ considerably in the number of items, question format, and dimensions of health literacy they capture, making documentation in structured electronic health records difficult to achieve. Automated detection from unstructured clinical notes offers a promising alternative, as these notes often contain richer, more contextual health literacy information, but progress has been limited by the lack of annotated resources. We introduce HEALIX, the first publicly available annotated health literacy dataset derived from real clinical notes, curated through a combination of social worker note sampling, keyword-based filtering, and LLM-based active learning. HEALIX contains 589 notes across 9 note types, annotated with three health literacy labels: low, normal, and high. To demonstrate its utility, we benchmarked zero-shot and few-shot prompting strategies across four open source large language models (LLMs).", "date": "2026-03-19", "categories": []}
{"id": "2512.00479", "title": "Aligning Probabilistic Beliefs under Informative Missingness: LLM Steerability in Clinical Reasoning", "abstract": "Large Language Models (LLMs) are increasingly deployed for clinical reasoning tasks, which inherently require eliciting calibrated probabilistic beliefs based on available evidence. However, real-world clinical data are frequently incomplete, with missingness patterns often informative of patient prognosis; for example, ordering a rare laboratory test reflects a clinician's latent suspicion. In this work, we investigate whether LLMs can be steered to leverage this informative missingness for prognostic inference. To evaluate how well LLMs align their verbalized probabilistic beliefs with an underlying target distribution, we analyze three common prompt-based interventions: explicit serialization, instruction steering, and in-context learning. We introduce a bias-variance decomposition of the log-loss to clarify the mechanisms driving gains in predictive performance. Using a real-world intensive care testbed, we find that while explicit structural steering and in-context learning can improve probabilistic alignment, the models do not natively leverage informative missingness without careful interventions.", "date": "2025-11-29", "categories": []}
{"id": "2603.16891", "title": "A Novel end-to-end Digital Health System Using Deep Learning-based ECG Analysis", "abstract": "This study presents AI-HEART, a cloud-based information system for managing and analysing long-duration ambulatory electrocardiogram (ECG) recordings and supporting clinician decision-making. The platform operationalises an end-to-end pipeline that ingests multi-day three-lead ECGs, normalises inputs, performs signal preprocessing, and applies dedicated deep neural networks for wave delineation, noise/quality detection, and beat- and rhythm-level multi-class arrhythmia classification. To address class imbalance and real-world signal variability, model development combines large clinically annotated datasets with expert-in-the-loop curation and generative augmentation for under-represented rhythms. Empirical evaluation on three-lead ambulatory ECG data shows that delineation accuracy is sufficient for automated interval measurement, noise detection reliably flags poor-quality segments, and arrhythmia classification achieves high specificity with clinically useful macro-averaged performance across common and rarer rhythms. Beyond predictive accuracy, AI-HEART provides a scalable deployment approach for integrating AI into routine ECG services, enabling traceable outputs, audit-friendly storage of recordings and derived annotations, and clinician review/editing that captures feedback for controlled model improvement. The findings demonstrate the technical feasibility and operational value of a noise-aware AI-ECG platform as a digital health information system.", "date": "2026-02-28", "categories": []}
{"id": "2603.17234", "title": "Deployment and Evaluation of an EHR-integrated, Large Language Model-Powered Tool to Triage Surgical Patients", "abstract": "Surgical co-management (SCM) is an evidence-based model in which hospitalists jointly manage medically complex perioperative patients alongside surgical teams. Despite its clinical and financial value, SCM is limited by the need to manually identify eligible patients. To determine whether SCM triage can be automated, we conducted a prospective, unblinded study at Stanford Health Care in which an LLM-based, electronic health record (EHR)-integrated triage tool (SCM Navigator) provided SCM recommendations followed by physician review.\n  Using pre-operative documentation, structured data, and clinical criteria for perioperative morbidity, SCM Navigator categorized patients as appropriate, not appropriate, or possibly appropriate for SCM. Faculty indicated their clinical judgment and provided free-text feedback when they disagreed. Sensitivity, specificity, positive predictive value, and negative predictive value were measured using physician determinations as a reference. Free-text reasons were thematically categorized, and manual chart review was conducted on all false-negative cases and 30 randomly selected cases from the largest false-positive category. Since deployment, 6,193 cases have been triaged, of which 1,582 (23%) were recommended for hospitalist consultation. SCM Navigator displayed high sensitivity (0.94, 95% CI 0.91-0.96) and moderate specificity (0.74, 95% CI 0.71-0.77). Post-hoc chart review suggested most discrepancies reflect modifiable gaps in clinical criteria, institutional workflow, or physician practice variability rather than LLM misclassification, which accounted for 2 of 19 (11%) false-negative cases. These findings demonstrate that an LLM-powered, EHR-integrated, human-in-the-loop AI system can accurately and safely triage surgical patients for SCM, and that AI-enabled screening tools can augment and potentially automate time-intensive clinical workflows.", "date": "2026-03-18", "categories": []}
{"id": "2603.17722", "title": "Predicting Trajectories of Long COVID in Adult Women: The Critical Role of Causal Disentanglement", "abstract": "Early prediction of Post-Acute Sequelae of SARS-CoV-2 severity is a critical challenge for women's health, particularly given the diagnostic overlap between PASC and common hormonal transitions such as menopause. Identifying and accounting for these confounding factors is essential for accurate long-term trajectory prediction. We conducted a retrospective study of 1,155 women (mean age 61) from the NIH RECOVER dataset. By integrating static clinical profiles with four weeks of longitudinal wearable data (monitoring cardiac activity and sleep), we developed a causal network based on a Large Language Model to predict future PASC scores. Our framework achieved a precision of 86.7\\% in clinical severity prediction. Our causal attribution analysis demonstrate the model's ability to differentiate between active pathology and baseline noise: direct indicators such as breathlessness and malaise reached maximum saliency (1.00), while confounding factors like menopause and diabetes were successfully suppressed with saliency scores below 0.27.", "date": "2026-03-18", "categories": []}
{"id": "2410.03385", "title": "From Epilepsy Seizures Classification to Detection: A Deep Learning-based Approach for Raw EEG Signals", "abstract": "Epilepsy represents the most prevalent neurological disease in the world. One-third of people suffering from mesial temporal lobe epilepsy (MTLE) exhibit drug resistance, urging the need to develop new treatments. A key part in anti-seizure medication (ASM) development is the capability of detecting and quantifying epileptic seizures occurring in electroencephalogram (EEG) signals, which is crucial for treatment efficacy evaluation. In this study, we introduced a seizure detection pipeline based on deep learning models applied to raw EEG signals. This pipeline integrates: a new pre-processing technique which segments continuous raw EEG signals without prior distinction between seizure and seizure-free activities; a post-processing algorithm developed to reassemble EEG segments and allow the identification of seizures start/end; and finally, a new evaluation procedure based on a strict seizure events comparison between predicted and real labels. Models training have been performed using a data splitting strategy which addresses the potential for data leakage. We demonstrated the fundamental differences between a seizure classification and a seizure detection task and showed the differences in performance between the two tasks. Finally, we demonstrated the generalization capabilities across species of our best architecture, combining a Convolutional Neural Network and a Transformer encoder. The model was trained on animal EEGs and tested on human EEGs with a F1-score of 93% on a balanced Bonn dataset.", "date": "2024-10-04", "categories": []}
{"id": "2508.03715", "title": "Detection of Autonomic Dysreflexia in Individuals With Spinal Cord Injury Using Multimodal Wearable Sensors", "abstract": "Autonomic Dysreflexia (AD) is a potentially life-threatening condition characterized by sudden, severe blood pressure (BP) spikes in individuals with spinal cord injury (SCI). Early, accurate detection is essential to prevent cardiovascular complications, yet current monitoring methods are either invasive or rely on subjective symptom reporting, limiting applicability in daily file. This study presents a non-invasive, explainable machine learning framework for detecting AD using multimodal wearable sensors. Data were collected from 27 individuals with chronic SCI during urodynamic studies, including electrocardiography (ECG), photoplethysmography (PPG), bioimpedance (BioZ), temperature, respiratory rate (RR), and heart rate (HR), across three commercial devices. Objective AD labels were derived from synchronized cuff-based BP measurements. Following signal preprocessing and feature extraction, BorutaSHAP was used for robust feature selection, and SHAP values for explainability. We trained modality- and device-specific weak learners and aggregated them using a stacked ensemble meta-model. Cross-validation was stratified by participants to ensure generalizability. HR- and ECG-derived features were identified as the most informative, particularly those capturing rhythm morphology and variability. The Nearest Centroid ensemble yielded the highest performance (Macro F1 = 0.77+/-0.03), significantly outperforming baseline models. Among modalities, HR achieved the highest area under the curve (AUC = 0.93), followed by ECG (0.88) and PPG (0.86). RR and temperature features contributed less to overall accuracy, consistent with missing data and low specificity. The model proved robust to sensor dropout and aligned well with clinical AD events. These results represent an important step toward personalized, real-time monitoring for individuals with SCI.", "date": "2025-07-23", "categories": []}
{"id": "2603.13297", "title": "Enhanced Atrial Fibrillation Prediction in ESUS Patients with Hypergraph-based Pre-training", "abstract": "Atrial fibrillation (AF) is a major complication following embolic stroke of undetermined source (ESUS), elevating the risk of recurrent stroke and mortality. Early identification is clinically important, yet existing tools face limitations in accuracy, scalability, and cost. Machine learning (ML) offers promise but is hindered by small ESUS cohorts and high-dimensional medical features. To address these challenges, we introduce supervised and unsupervised hypergraph-based pre-training strategies to improve AF prediction in ESUS patients. We first pre-train hypergraph-based patient embedding models on a large stroke cohort (7,780 patients) to capture salient features and higher-order interactions. The resulting embeddings are transferred to a smaller ESUS cohort (510 patients), reducing feature dimensionality while preserving clinically meaningful information, enabling effective prediction with lightweight models. Experiments show that both pre-training approaches outperform traditional models trained on raw data, improving accuracy and robustness. This framework offers a scalable and efficient solution for AF risk prediction after stroke.", "date": "2026-03-02", "categories": []}
{"id": "2603.14177", "title": "Artificial intelligence-enabled single-lead ECG for non-invasive hyperkalemia detection: development, multicenter validation, and proof-of-concept deployment", "abstract": "Hyperkalemia is a life-threatening electrolyte disorder that is common in patients with chronic kidney disease and heart failure, yet frequent monitoring remains difficult outside hospital settings. We developed and validated Pocket-K, a single-lead AI-ECG system initialized from the ECGFounder foundation model for non-invasive hyperkalemia screening and handheld deployment. In this multicentre observational study using routinely collected clinical ECG and laboratory data, 34,439 patients contributed 62,290 ECG--potassium pairs. Lead I data were used to fine-tune the model. Data from Peking University People's Hospital were divided into development and temporal validation sets, and data from The Second Hospital of Tianjin Medical University served as an independent external validation set. Hyperkalemia was defined as venous serum potassium > 5.5 mmol/L. Pocket-K achieved AUROCs of 0.936 in internal testing, 0.858 in temporal validation, and 0.808 in external validation. For KDIGO-defined moderate-to-severe hyperkalemia (serum potassium >= 6.0 mmol/L), AUROCs increased to 0.940 and 0.861 in the temporal and external sets, respectively. External negative predictive value exceeded 99.3%. Model-predicted high risk below the hyperkalemia threshold was more common in patients with chronic kidney disease and heart failure. A handheld prototype enabled near-real-time inference, supporting future prospective evaluation in native handheld and wearable settings.", "date": "2026-03-15", "categories": []}
{"id": "2603.14621", "title": "A Heterogeneous Ensemble for Multi-Center COVID-19 Classification from Chest CT Scans", "abstract": "The COVID-19 pandemic exposed critical limitations in diagnostic workflows: RT-PCR tests suffer from slow turnaround times and high false-negative rates, while CT-based screening offers faster complementary diagnosis but requires expert radiological interpretation. Deploying automated CT analysis across multiple hospital centres introduces further challenges, as differences in scanner hardware, acquisition protocols, and patient populations cause substantial domain shift that degrades single-model performance. To address these challenges, we present a heterogeneous ensemble of nine models spanning three inference paradigms: (1) a self-supervised DINOv2 Vision Transformer with slice-level sigmoid aggregation, (2) a RadImageNet-pretrained DenseNet-121 with slice-level sigmoid averaging, and (3) seven Gated Attention Multiple Instance Learning models using EfficientNet-B3, ConvNeXt-Tiny, and EfficientNetV2-S backbones with scan-level softmax classification. Ensemble diversity is further enhanced through random-seed variation and Stochastic Weight Averaging. We address severe overfitting, reducing the validation-to-training loss ratio from 35x to less than 3x, through a combination of Focal Loss, embedding-level Mixup, and domain-aware augmentation. Model outputs are fused via score-weighted probability averaging and calibrated with per-source threshold optimization. The final ensemble achieves an average macro F1 of 0.9280 across four hospital centres, outperforming the best single model (F1=0.8969) by +0.031, demonstrating that heterogeneous architectures combined with source-aware calibration are essential for robust multi-site medical image classification.", "date": "2026-03-15", "categories": []}
{"id": "2603.15143", "title": "Clinical Priors Guided Lung Disease Detection in 3D CT Scans", "abstract": "Accurate classification of lung diseases from chest CT scans plays an important role in computer-aided diagnosis systems. However, medical imaging datasets often suffer from severe class imbalance, which may significantly degrade the performance of deep learning models, especially for minority disease categories. To address this issue, we propose a gender-aware two-stage lung disease classification framework. The proposed approach explicitly incorporates gender information into the disease recognition pipeline. In the first stage, a gender classifier is trained to predict the patient's gender from CT scans. In the second stage, the input CT image is routed to a corresponding gender-specific disease classifier to perform final disease prediction. This design enables the model to better capture gender-related imaging characteristics and alleviate the influence of imbalanced data distribution. Experimental results demonstrate that the proposed method improves the recognition performance for minority disease categories, particularly squamous cell carcinoma, while maintaining competitive performance on other classes.", "date": "2026-03-16", "categories": []}
{"id": "2603.15644", "title": "Tokenization Tradeoffs in Structured EHR Foundation Models", "abstract": "Foundation models for structured electronic health records (EHRs) are pretrained on longitudinal sequences of timestamped clinical events to learn adaptable patient representations. Tokenization -- how these timelines are converted into discrete model inputs -- determines what information is preserved, how efficiently it is encoded, and which relationships must be learned versus precomputed. Yet the impact of tokenization design choices on downstream performance and computational efficiency remains largely unexplored. Here, we pretrained a transformer on pediatric EHR data under a factorial design, varying tokenization along event encoding, time encoding, and workflow annotation. We evaluated area-under-the-receiver-operating-characteristic curve across 74 clinical prediction tasks. Joint event encoding and positional time encoding outperformed their alternatives (73/74 and 71/74 tasks) while requiring 39.5% and 9.6% fewer pretraining floating-point operations, respectively. Targeted ablations traced the joint encoding advantage to local binding efficiency, that is, code-attribute pairs are combined into single tokens, rather than split across tokens that the model must learn to associate during pretraining. External evaluation on an adult intensive care unit cohort demonstrated that this advantage generalizes despite substantial vocabulary mismatch, while temporal and workflow effects remain institution-specific. These results establish tokenization as a tractable lever for improving both the performance and efficiency of EHR foundation models.", "date": "2026-03-03", "categories": []}
{"id": "2603.15688", "title": "PulmoVec: A Two-Stage Stacking Meta-Learning Architecture Built on the HeAR Foundation Model for Multi-Task Classification of Pediatric Respiratory Sounds", "abstract": "Background: Respiratory diseases are a leading cause of childhood morbidity and mortality, yet lung auscultation remains subjective and limited by inter-listener variability, particularly in pediatric populations. Existing AI approaches are further constrained by small datasets and single-task designs. We developed PulmoVec, a multi-task framework built on the Health Acoustic Representations (HeAR) foundation model for classification of pediatric respiratory sounds. Methods: In this retrospective analysis of the SPRSound database, 24,808 event-level annotated segments from 1,652 pediatric patients were analyzed. Three task-specific classifiers were trained for screening, sound-pattern recognition, and disease-group prediction. Their out-of-fold probability outputs were combined with demographic metadata in a LightGBM stacking meta-model, and event-level predictions were aggregated to the patient level using ensemble voting. Results: At the event level, the screening model achieved an ROC-AUC of 0.96 (95% CI, 0.95-0.97), the sound-pattern recognition model a macro ROC-AUC of 0.96 (95% CI, 0.96-0.97), and the disease-group prediction model a macro ROC-AUC of 0.94 (95% CI, 0.93-0.94). At the patient level, disease-group classification yielded an accuracy of 0.74 (95% CI, 0.71-0.77), a weighted F1-score of 0.73, and a macro ROC-AUC of 0.91 (95% CI, 0.90-0.93). Stacking improved performance across all tasks compared with base models alone. Conclusions: PulmoVec links event-level acoustic phenotyping with patient-level clinical classification, supporting the potential of foundation-model-based digital auscultation in pediatric respiratory medicine. Multi-center external validation across devices and real-world conditions remains essential.", "date": "2026-03-15", "categories": []}
{"id": "2603.15814", "title": "Longitudinal Risk Prediction in Mammography with Privileged History Distillation", "abstract": "Breast cancer remains a leading cause of cancer-related mortality worldwide. Longitudinal mammography risk prediction models improve multi-year breast cancer risk prediction based on prior screening exams. However, in real-world clinical practice, longitudinal histories are often incomplete, irregular, or unavailable due to missed screenings, first-time examinations, heterogeneous acquisition schedules, or archival constraints. The absence of prior exams degrades the performance of longitudinal risk models and limits their practical applicability. While substantial longitudinal history is available during training, prior exams are commonly absent at test time. In this paper, we address missing history at inference time and propose a longitudinal risk prediction method that uses mammography history as privileged information during training and distills its prognostic value into a student model that only requires the current exam at inference time. The key idea is a privileged multi-teacher distillation scheme with horizon-specific teachers: each teacher is trained on the full longitudinal history to specialize in one prediction horizon, while the student receives only a reconstructed history derived from the current exam. This allows the student to inherit horizon-dependent longitudinal risk cues without requiring prior screening exams at deployment. Our new Privileged History Distillation (PHD) method is validated on a large longitudinal mammography dataset with multi-year cancer outcomes, CSAW-CC, comparing full-history and no-history baselines to their distilled counterparts. Using time-dependent AUC across horizons, our privileged history distillation method markedly improves the performance of long-horizon prediction over no-history models and is comparable to that of full-history models, while using only the current exam at inference time.", "date": "2026-03-16", "categories": []}
{"id": "2603.16330", "title": "An Interpretable Machine Learning Framework for Non-Small Cell Lung Cancer Drug Response Analysis", "abstract": "Lung cancer is a condition where there is abnormal growth of malignant cells that spread in an uncontrollable fashion in the lungs. Some common treatment strategies are surgery, chemotherapy, and radiation which aren't the best options due to the heterogeneous nature of cancer. In personalized medicine, treatments are tailored according to the individual's genetic information along with lifestyle aspects. In addition, AI-based deep learning methods can analyze large sets of data to find early signs of cancer, types of tumor, and prospects of treatment. The paper focuses on the development of personalized treatment plans using specific patient data focusing primarily on the genetic profile. Multi-Omics data from Genomics of Drug Sensitivity in Cancer have been used to build a predictive model along with machine learning techniques. The value of the target variable, LN-IC50, determines how sensitive or resistive a drug is. An XGBoost regressor is utilized to predict the drug response focusing on molecular and cellular features extracted from cancer datasets. Cross-validation and Randomized Search are performed for hyperparameter tuning to further optimize the model's predictive performance. For explanation purposes, SHAP (SHapley Additive exPlanations) was used. SHAP values measure each feature's impact on an individual prediction. Furthermore, interpreting feature relationships was performed using DeepSeek, a large language model trained to verify the biological validity of the features. Contextual explanations regarding the most important genes or pathways were provided by DeepSeek alongside the top SHAP value constituents, supporting the predictability of the model.", "date": "2026-03-17", "categories": []}
{"id": "2411.16349", "title": "Machine learning for cerebral blood vessels' malformations", "abstract": "Cerebral aneurysms and arteriovenous malformations are life-threatening hemodynamic pathologies of the brain. While surgical intervention is often essential to prevent fatal outcomes, it carries significant risks both during the procedure and in the postoperative period, making the management of these conditions highly challenging. Parameters of cerebral blood flow, routinely monitored during medical interventions or with modern noninvasive high-resolution imaging methods, could potentially be utilized in machine learning-assisted protocols for risk assessment and therapeutic prognosis. To this end, we developed a linear oscillatory model of blood velocity and pressure for clinical data acquired from neurosurgical operations. Using the method of Sparse Identification of Nonlinear Dynamics (SINDy), the parameters of our model can be reconstructed online within milliseconds from a short time series of the hemodynamic variables. The identified parameter values enable automated classification of the blood-flow pathologies by means of logistic regression, achieving an accuracy of 73 \\%}. Our results demonstrate the potential of this model for both diagnostic and prognostic applications, providing a robust and interpretable framework for assessing cerebral blood vessel conditions.", "date": "2024-11-25", "categories": []}
{"id": "2509.13360", "title": "PREDICT-GBM: A multi-center platform to advance personalized glioblastoma radiotherapy planning", "abstract": "Glioblastoma recurrence is largely driven by diffuse infiltration beyond radiologically visible tumor margins, yet standard radiotherapy, the mainstay of glioblastoma treatment, relies on uniform expansions that ignore patient-specific biological and anatomical factors. While computational models promise to map this invisible growth and guide personalized treatment planning, their clinical translation is hindered by the lack of standardized, large-scale benchmarking and reproducible validation workflows. To bridge this gap, we present PREDICT-GBM, a comprehensive open-source platform that integrates a curated, longitudinal, multi-center dataset of 243 patients with a standardized evaluation pipeline, and fuels model development and validation. We demonstrate PREDICT-GBM's potential by training and benchmarking a novel U-Net-based recurrence prediction model against state-of-the-art biophysical and data-driven methods. Our results show that both biophysical and deep-learning approaches significantly outperform standard-of-care protocols in predicting future recurrence sites while maintaining iso-volumetric treatment constraints. Notably, our U-Net model achieved a superior coverage of enhancing recurrence (79.37 +/- 2.08 %), markedly surpassing the standard-of-care (paired Wilcoxon signed-rank test, p = 0.0000057). Furthermore, the biophysical model GliODIL reached 78.91 +/- 2.08 % (p = 0.00045), validating the platform's ability to compare diverse modeling paradigms. By providing the first rigorous, reproducible ecosystem for model training and validation, PREDICT-GBM eliminates a major bottleneck for personalized, computationally guided radiotherapy. This work establishes a new standard for developing computationally guided, personalized radiotherapy, with the platform, models, and data openly available at github.com/BrainLesion/PredictGBM", "date": "2025-09-15", "categories": []}
{"id": "2601.05276", "title": "Channel Selected Stratified Nested Cross Validation for Clinically Relevant EEG Based Parkinsons Disease Detection", "abstract": "The early detection of Parkinsons disease remains a critical challenge in clinical neuroscience, with electroencephalography offering a noninvasive and scalable pathway toward population level screening. While machine learning has shown promise in this domain, many reported results suffer from methodological flaws, most notably patient level data leakage, inflating performance estimates and limiting clinical translation. To address these modeling pitfalls, we propose a unified evaluation framework grounded in nested cross validation and incorporating three complementary safeguards: (i) patient level stratification to eliminate subject overlap and ensure unbiased generalization, (ii) multi layered windowing to harmonize heterogeneous EEG recordings while preserving temporal dynamics, and (iii) inner loop channel selection to enable principled feature reduction without information leakage. Applied across three independent datasets with a heterogeneous number of channels, a convolutional neural network trained under this framework achieved 80.6% accuracy and demonstrated state of the art performance under held out population block testing, comparable to other methods in the literature. This performance underscores the necessity of nested cross validation as a safeguard against bias and as a principled means of selecting the most relevant information for patient level decisions, providing a reproducible foundation that can extend to other biomedical signal analysis domains.", "date": "2025-12-28", "categories": []}
{"id": "2602.00009", "title": "Unlocking Electronic Health Records: A Hybrid Graph RAG Approach to Safe Clinical AI for Patient QA", "abstract": "Electronic health record (EHR) systems present clinicians with vast repositories of clinical information, creating a significant cognitive burden where critical details are easily overlooked. While Large Language Models (LLMs) offer transformative potential for data processing, they face significant limitations in clinical settings, particularly regarding context grounding and hallucinations. Current solutions typically isolate retrieval methods focusing either on structured data (SQL/Cypher) or unstructured semantic search but fail to integrate both simultaneously. This work presents MediGRAF (Medical Graph Retrieval Augmented Framework), a novel hybrid Graph RAG system that bridges this gap. By uniquely combining Neo4j Text2Cypher capabilities for structured relationship traversal with vector embeddings for unstructured narrative retrieval, MediGRAF enables natural language querying of the complete patient journey. Using 10 patients from the MIMIC-IV dataset (generating 5,973 nodes and 5,963 relationships), we generated enough nodes and data for patient level question answering (QA), and we evaluated this architecture across varying query complexities. The system demonstrated 100\\% recall for factual queries which means all relevant information was retrieved and in the output, while complex inference tasks achieved a mean expert quality score of 4.25/5 with zero safety violations. These results demonstrate that hybrid graph-grounding significantly advances clinical information retrieval, offering a safer, more comprehensive alternative to standard LLM deployments.", "date": "2025-11-27", "categories": []}
{"id": "2603.08448", "title": "A prospective clinical feasibility study of a conversational diagnostic AI in an ambulatory primary care clinic", "abstract": "Large language model (LLM)-based AI systems have shown promise for patient-facing diagnostic and management conversations in simulated settings. Translating these systems into clinical practice requires assessment in real-world workflows with rigorous safety oversight. We report a prospective, single-arm feasibility study of an LLM-based conversational AI, the Articulate Medical Intelligence Explorer (AMIE), conducting clinical history taking and presentation of potential diagnoses for patients to discuss with their provider at urgent care appointments at a leading academic medical center. 100 adult patients completed an AMIE text-chat interaction up to 5 days before their appointment. We sought to assess the conversational safety and quality, patient and clinician experience, and clinical reasoning capabilities compared to primary care providers (PCPs). Human safety supervisors monitored all patient-AMIE interactions in real time and did not need to intervene to stop any consultations based on pre-defined criteria. Patients reported high satisfaction and their attitudes towards AI improved after interacting with AMIE (p < 0.001). PCPs found AMIE's output useful with a positive impact on preparedness. AMIE's differential diagnosis (DDx) included the final diagnosis, per chart review 8 weeks post-encounter, in 90% of cases, with 75% top-3 accuracy. Blinded assessment of AMIE and PCP DDx and management (Mx) plans suggested similar overall DDx and Mx plan quality, without significant differences for DDx (p = 0.6) and appropriateness and safety of Mx (p = 0.1 and 1.0, respectively). PCPs outperformed AMIE in the practicality (p = 0.003) and cost effectiveness (p = 0.004) of Mx. While further research is needed, this study demonstrates the initial feasibility, safety, and user acceptance of conversational AI in a real-world setting, representing crucial steps towards clinical translation.", "date": "2026-03-09", "categories": []}
{"id": "2603.13231", "title": "Translational Gaps in Graph Transformers for Longitudinal EHR Prediction: A Critical Appraisal of GT-BEHRT", "abstract": "Transformer-based models have improved predictive modeling on longitudinal electronic health records through large-scale self-supervised pretraining. However, most EHR transformer architectures treat each clinical encounter as an unordered collection of codes, which limits their ability to capture meaningful relationships within a visit. Graph-transformer approaches aim to address this limitation by modeling visit-level structure while retaining the ability to learn long-term temporal patterns. This paper provides a critical review of GT-BEHRT, a graph-transformer architecture evaluated on MIMIC-IV intensive care outcomes and heart failure prediction in the All of Us Research Program. We examine whether the reported performance gains reflect genuine architectural benefits and whether the evaluation methodology supports claims of robustness and clinical relevance. We analyze GT-BEHRT across seven dimensions relevant to modern machine learning systems, including representation design, pretraining strategy, cohort construction transparency, evaluation beyond discrimination, fairness assessment, reproducibility, and deployment feasibility. GT-BEHRT reports strong discrimination for heart failure prediction within 365 days, with AUROC 94.37 +/- 0.20, AUPRC 73.96 +/- 0.83, and F1 64.70 +/- 0.85. Despite these results, we identify several important gaps, including the lack of calibration analysis, incomplete fairness evaluation, sensitivity to cohort selection, limited analysis across phenotypes and prediction horizons, and limited discussion of practical deployment considerations. Overall, GT-BEHRT represents a meaningful architectural advance in EHR representation learning, but more rigorous evaluation focused on calibration, fairness, and deployment is needed before such models can reliably support clinical decision-making.", "date": "2026-01-20", "categories": []}
{"id": "2603.13590", "title": "Opportunistic Cardiac Health Assessment: Estimating Phenotypes from Localizer MRI through Multi-Modal Representations", "abstract": "Cardiovascular diseases are the leading cause of death. Cardiac phenotypes (CPs), e.g., ejection fraction, are the gold standard for assessing cardiac health, but they are derived from cine cardiac magnetic resonance imaging (CMR), which is costly and requires high spatio-temporal resolution. Every magnetic resonance (MR) examination begins with rapid and coarse localizers for scan planning, which are discarded thereafter. Despite non-diagnostic image quality and lack of temporal information, localizers can provide valuable structural information rapidly. In addition to imaging, patient-level information, including demographics and lifestyle, influence the cardiac health assessment. Electrocardiograms (ECGs) are inexpensive, routinely ordered in clinical practice, and capture the temporal activity of the heart. Here, we introduce C-TRIP (Cardiac Tri-modal Representations for Imaging Phenotypes), a multi-modal framework that aligns localizer MRI, ECG signals, and tabular metadata to learn a robust latent space and predict CPs using localizer images as an opportunistic alternative to CMR. By combining these three modalities, we leverage cheap spatial and temporal information from localizers, and ECG, respectively while benefiting from patient-specific context provided by tabular data. Our pipeline consists of three stages. First, encoders are trained independently to learn uni-modal representations. The second stage fuses the pre-trained encoders to unify the latent space. The final stage uses the enriched representation space for CP prediction, with inference performed exclusively on localizer MRI. Proposed C-TRIP yields accurate functional CPs, and high correlations for structural CPs. Since localizers are inherently rapid and low-cost, our C-TRIP framework could enable better accessibility for CP estimation.", "date": "2026-03-13", "categories": []}
{"id": "2603.13673", "title": "LLM-MINE: Large Language Model based Alzheimer's Disease and Related Dementias Phenotypes Mining from Clinical Notes", "abstract": "Accurate extraction of Alzheimer's Disease and Related Dementias (ADRD) phenotypes from electronic health records (EHR) is critical for early-stage detection and disease staging. However, this information is usually embedded in unstructured textual data rather than tabular data, making it difficult to be extracted accurately. We therefore propose LLM-MINE, a Large Language Model-based phenotype mining framework for automatic extraction of ADRD phenotypes from clinical notes. Using two expert-defined phenotype lists, we evaluate the extracted phenotypes by examining their statistical significance across cohorts and their utility for unsupervised disease staging. Chi-square analyses confirm statistically significant phenotype differences across cohorts, with memory impairment being the strongest discriminator. Few-shot prompting with the combined phenotype lists achieves the best clustering performance (ARI=0.290, NMI=0.232), substantially outperforming biomedical NER and dictionary-based baselines. Our results demonstrate that LLM-based phenotype extraction is a promising tool for discovering clinically meaningful ADRD signals from unstructured notes.", "date": "2026-03-14", "categories": []}
{"id": "2603.13850", "title": "Fronto-parietal and fronto-temporal EEG coherence as predictive neuromarkers of transcutaneous auricular vagus nerve stimulation response in treatment-resistant schizophrenia: A machine learning study", "abstract": "Response variability limits the clinical utility of transcutaneous auricular vagus nerve stimulation (taVNS) for negative symptoms in treatment-resistant schizophrenia (TRS). This study aimed to develop an electroencephalography (EEG)-based machine learning (ML) model to predict individual response and explore associated neurophysiological mechanisms. We used ML to develop and validate predictive models based on pre-treatment EEG data features (power, coherence, and dynamic functional connectivity) from 50 TRS patients enrolled in the taVNS trial, within a nested cross-validation framework. Participants received 20 sessions of active or sham taVNS (n = 25 each) over two weeks, followed by a two-week follow-up. The prediction target was the percentage change in the positive and negative syndrome scale-factor score for negative symptoms (PANSS-FSNS) from baseline to post-treatment, with further evaluation of model specificity and neurophysiological relevance.The optimal model accurately predicted taVNS response in the active group, with predicted PANSS-FSNS changes strongly correlated with observed changes (r = 0.87, p < .001); permutation testing confirmed performance above chance (p < .001). Nine consistently retained features were identified, predominantly fronto-parietal and fronto-temporal coherence features. Negligible predictive performance in the sham group and failure to predict positive symptom change support the predictive specificity of this oscillatory signature for taVNS-related negative symptom improvement. Two coherence features within fronto-parietal-temporal networks showed post-taVNS changes significantly associated with symptom improvement, suggesting dual roles as predictors and potential therapeutic targets. EEG oscillatory neuromarkers enable accurate prediction of individual taVNS response in TRS, supporting mechanism-informed precision neuromodulation strategies.", "date": "2026-03-14", "categories": []}
{"id": "2603.14003", "title": "The Taxonomies, Training, and Applications of Event Stream Modelling for Electronic Health Records", "abstract": "The widespread adoption of electronic health records (EHRs) enables the acquisition of heterogeneous clinical data, spanning lab tests, vital signs, medications, and procedures, which offer transformative potential for artificial intelligence in healthcare. Although traditional modelling approaches have typically relied on multivariate time series, they often struggle to accommodate the inherent sparsity and irregularity of real-world clinical workflows. Consequently, research has shifted toward event stream representation, which treats patient records as continuous sequences, thereby preserving the precise temporal structure of the patient journey. However, the existing literature remains fragmented, characterised by inconsistent definitions, disparate modelling architectures, and varying training protocols. To address these gaps, this review establishes a unified definition of EHR event streams and introduces a novel taxonomy that categorises models based on their handling of event time, type, and value. We systematically review training strategies, ranging from supervised learning to self-supervised methods, and provide a comprehensive discussion of applications across clinical scenarios. Finally, we identify open critical challenges and future directions, with the aim of clarifying the current landscape and guiding the development of next-generation healthcare models.", "date": "2026-03-14", "categories": []}
{"id": "2603.14183", "title": "Selective Fine-Tuning of GPT Architectures for Parameter-Efficient Clinical Text Classification", "abstract": "The rapid expansion of electronic health record (EHR) systems has generated large volumes of unstructured clinical narratives that contain valuable information for disease identification, patient cohort discovery, and clinical decision support. Extracting structured knowledge from these free-text documents remains challenging because clinical language is highly specialized, labeled datasets are limited, and full fine-tuning of large pretrained language models can require substantial computational resources. Efficient adaptation strategies are therefore essential for practical clinical natural language processing applications. This study proposes a parameter-efficient selective fine-tuning framework for adapting GPT-2 to clinical text classification tasks. Instead of updating the entire pretrained model, the majority of network parameters are frozen, and only the final Transformer block, the final layer normalization module, and a lightweight classification head are updated during training. This design substantially reduces the number of trainable parameters while preserving the contextual representation capabilities learned during pretraining. The proposed approach is evaluated using radiology reports from the MIMIC-IV-Note dataset with automatically derived CheXpert-style labels. Experiments on 50,000 radiology reports demonstrate that selective fine-tuning achieves approximately 91% classification accuracy while updating fewer than 6% of the model parameters. Comparative experiments with head-only training and full-model fine-tuning show that the proposed method provides a favorable balance between predictive performance and computational efficiency. These results indicate that selective fine-tuning offers an efficient and scalable framework for clinical text classification.", "date": "2026-03-15", "categories": []}
{"id": "2603.14559", "title": "A comprehensive multimodal dataset and benchmark for ulcerative colitis scoring in endoscopy", "abstract": "Ulcerative colitis (UC) is a chronic mucosal inflammatory condition that places patients at increased risk of colorectal cancer. Colonoscopic surveillance remains the gold standard for assessing disease activity, and reporting typically relies on standardised endoscopic scoring metrics. The most widely used is the Mayo Endoscopic Score (MES), with some centres also adopting the Ulcerative Colitis Endoscopic Index of Severity (UCEIS). Both are descriptive assessments of mucosal inflammation (MES: 0 to 3; UCEIS: 0 to 8), where higher values indicate more severe disease. However, computational methods for automatically predicting these scores remain limited, largely due to the lack of publicly available expert-annotated datasets and the absence of robust benchmarking. There is also a significant research gap in generating clinically meaningful descriptions of UC images, despite image captioning being a well-established computer vision task. Variability in endoscopic systems and procedural workflows across centres further highlights the need for multi-centre datasets to ensure algorithmic robustness and generalisability. In this work, we introduce a curated multi-centre, multi-resolution dataset that includes expert-validated MES and UCEIS labels, alongside detailed clinical descriptions. To our knowledge, this is the first comprehensive dataset that combines dual scoring metrics for classification tasks with expert-generated captions describing mucosal appearance and clinically accepted reasoning for image captioning. This resource opens new opportunities for developing clinically meaningful multimodal algorithms. In addition to the dataset, we also provide benchmarking using convolutional neural networks, vision transformers, hybrid models, and widely used multimodal vision-language captioning algorithms.", "date": "2026-03-15", "categories": []}
{"id": "2603.14719", "title": "Multimodal Deep Learning for Early Prediction of Patient Deterioration in the ICU: Integrating Time-Series EHR Data with Clinical Notes", "abstract": "Early identification of patients at risk for clinical deterioration in the intensive care unit (ICU) remains a critical challenge. Delayed recognition of impending adverse events, including mortality, vasopressor initiation, and mechanical ventilation, contributes to preventable morbidity and mortality. We present a multimodal deep learning approach that combines structured time-series data (vital signs and laboratory values) with unstructured clinical notes to predict patient deterioration within 24 hours. Using the MIMIC-IV database, we constructed a cohort of 74,822 ICU stays and generated 5.7 million hourly prediction samples. Our architecture employs a bidirectional LSTM encoder for temporal patterns in physiologic data and ClinicalBERT embeddings for clinical notes, fused through a cross-modal attention mechanism. We also present a systematic review of existing approaches to ICU deterioration prediction, identifying 31 studies published between 2015 and 2024. Most existing models rely solely on structured data and achieve area under the curve (AUC) values between 0.70 and 0.85. Studies incorporating clinical notes remain rare but show promise for capturing information not present in structured fields. Our multimodal model achieves a test AUROC of 0.7857 and AUPRC of 0.1908 on 823,641 held-out samples, with a validation-to-test gap of only 0.6 percentage points. Ablation analysis validates the multimodal approach: clinical notes improve AUROC by 2.5 percentage points and AUPRC by 39.2% relative to a structured-only baseline, while deep learning models consistently outperform classical baselines (XGBoost AUROC: 0.7486, logistic regression: 0.7171). This work contributes both a thorough review of the field and a reproducible multimodal framework for clinical deterioration prediction.", "date": "2026-03-16", "categories": []}
{"id": "2603.14876", "title": "A Hybrid AI and Rule-Based Decision Support System for Disease Diagnosis and Management Using Labs", "abstract": "This research paper outlines the development and implementation of a novel Clinical Decision Support System (CDSS) that integrates AI predictive modeling with medical knowledge bases. It utilizes the quantifiable information elements in lab results for inferring likely diagnoses a patient might have. Subsequently, suggesting investigations to confirm the likely diagnoses -- an assistive tool for physicians. The system fuses knowledge contained in a rule-base expert system with inferences of data driven predictors based on the features in labs. The data for 593,055 patients was collected from 547 primary care centers across the US to model our decision support system and derive Real-Word Evidence (RWE) to make it relevant for a large demographic of patients. Our Rule-Base comprises clinically validated rules, modeling 59 health conditions that can directly confirm one or more of diseases and assign ICD-10 codes to them. The Likely Diagnosis system uses multi-class classification, covering 37 ICD-10 codes, which are grouped together into 11 categories based on the labs that physicians prescribe to confirm the diagnosis. This research offers a novel system that assists a physician by utilizing medical profile of a patient and routine lab investigations to predict a group of likely diseases and then confirm them, coupled with providing explanations for inferences, thereby assisting physicians to reduce misdiagnosis of patients in clinical decision-making.", "date": "2026-03-16", "categories": []}
{"id": "2603.14947", "title": "FairMed-XGB: A Bayesian-Optimised Multi-Metric Framework with Explainability for Demographic Equity in Critical Healthcare Data", "abstract": "Machine learning models deployed in critical care settings exhibit demographic biases, particularly gender disparities, that undermine clinical trust and equitable treatment. This paper introduces FairMed-XGB, a novel framework that systematically detects and mitigates gender-based prediction bias while preserving model performance and transparency. The framework integrates a fairness-aware loss function combining Statistical Parity Difference, Theil Index, and Wasserstein Distance, jointly optimised via Bayesian Search into an XGBoost classifier. Post-mitigation evaluation on seven clinically distinct cohorts derived from the MIMIC-IV-ED and eICU databases demonstrates substantial bias reduction: Statistical Parity Difference decreases by 40 to 51 percent on MIMIC-IV-ED and 10 to 19 percent on eICU; Theil Index collapses by four to five orders of magnitude to near-zero values; Wasserstein Distance is reduced by 20 to 72 percent. These gains are achieved with negligible degradation in predictive accuracy (AUC-ROC drop <0.02). SHAP-based explainability reveals that the framework diminishes reliance on gender-proxy features, providing clinicians with actionable insights into how and where bias is corrected. FairMed-XGB offers a robust, interpretable, and ethically aligned solution for equitable clinical decision-making, paving the way for trustworthy deployment of AI in high-stakes healthcare environments.", "date": "2026-03-16", "categories": []}
{"id": "2603.15100", "title": "Learning from Limited and Incomplete Data: A Multimodal Framework for Predicting Pathological Response in NSCLC", "abstract": "Major pathological response (pR) following neoadjuvant therapy is a clinically meaningful endpoint in non-small cell lung cancer, strongly associated with improved survival. However, accurate preoperative prediction of pR remains challenging, particularly in real-world clinical settings characterized by limited data availability and incomplete clinical profiles. In this study, we propose a multimodal deep learning framework designed to address these constraints by integrating foundation model-based CT feature extraction with a missing-aware architecture for clinical variables. This approach enables robust learning from small cohorts while explicitly modeling missing clinical information, without relying on conventional imputation strategies. A weighted fusion mechanism is employed to leverage the complementary contributions of imaging and clinical modalities, yielding a multimodal model that consistently outperforms both unimodal imaging and clinical baselines. These findings underscore the added value of integrating heterogeneous data sources and highlight the potential of multimodal, missing-aware systems to support pR prediction under realistic clinical conditions.", "date": "2026-03-16", "categories": []}
{"id": "2503.14550", "title": "Novel AI-Based Quantification of Breast Arterial Calcification to Predict Cardiovascular Risk", "abstract": "Women are underdiagnosed and undertreated for cardiovascular disease. Automatic quantification of breast arterial calcification on screening mammography can identify women at risk for cardiovascular disease and enable earlier treatment and management of disease. In this retrospective study of 116,135 women from two healthcare systems, a transformer-based neural network quantified BAC severity (no BAC, mild, moderate, and severe) on screening mammograms. Outcomes included major adverse cardiovascular events (MACE) and all-cause mortality. BAC severity was independently associated with MACE after adjusting for cardiovascular risk factors, with increasing hazard ratios from mild (HR 1.18-1.22), moderate (HR 1.38-1.47), to severe BAC (HR 2.03-2.22) across datasets (all p<0.001). This association remained significant across all age groups, with even mild BAC indicating increased risk in women under 50. BAC remained an independent predictor when analyzed alongside ASCVD risk scores, showing significant associations with myocardial infarction, stroke, heart failure, and mortality (all p<0.005). Automated BAC quantification enables opportunistic cardiovascular risk assessment during routine mammography without additional radiation or cost. This approach provides value beyond traditional risk factors, particularly in younger women, offering potential for early CVD risk stratification in the millions of women undergoing annual mammography.", "date": "2025-03-17", "categories": []}
{"id": "2602.19531", "title": "A Statistical Approach for Modeling Irregular Multivariate Time Series with Missing Observations", "abstract": "Irregular multivariate time series with missing values present significant challenges for predictive modeling in domains such as healthcare. While deep learning approaches often focus on temporal interpolation or complex architectures to handle irregularities, we propose a simpler yet effective alternative: extracting time-agnostic summary statistics to eliminate the temporal axis. Our method computes four key features per variable-mean and standard deviation of observed values, as well as the mean and variability of changes between consecutive observations to create a fixed-dimensional representation. These features are then utilized with standard classifiers, such as logistic regression and XGBoost. Evaluated on four biomedical datasets (PhysioNet Challenge 2012, 2019, PAMAP2, and MIMIC-III), our approach achieves state-of-the-art performance, surpassing recent transformer and graph-based models by 0.5-1.7% in AUROC/AUPRC and 1.1-1.7% in accuracy/F1-score, while reducing computational complexity. Ablation studies demonstrate that feature extraction-not classifier choice-drives performance gains, and our summary statistics outperform raw/imputed input in most benchmarks. In particular, we identify scenarios where missing patterns themselves encode predictive signals, as in sepsis prediction (PhysioNet, 2019), where missing indicators alone can achieve 94.2% AUROC with XGBoost, only 1.6% lower than using original raw data as input. Our results challenge the necessity of complex temporal modeling when task objectives permit time-agnostic representations, providing an efficient and interpretable solution for irregular time series classification.", "date": "2026-02-23", "categories": []}
{"id": "2212.14084", "title": "Multimodal Explainability via Latent Shift applied to COVID-19 stratification", "abstract": "We are witnessing a widespread adoption of artificial intelligence in healthcare. However, most of the advancements in deep learning in this area consider only unimodal data, neglecting other modalities. Their multimodal interpretation necessary for supporting diagnosis, prognosis and treatment decisions. In this work we present a deep architecture, which jointly learns modality reconstructions and sample classifications using tabular and imaging data. The explanation of the decision taken is computed by applying a latent shift that, simulates a counterfactual prediction revealing the features of each modality that contribute the most to the decision and a quantitative score indicating the modality importance. We validate our approach in the context of COVID-19 pandemic using the AIforCOVID dataset, which contains multimodal data for the early identification of patients at risk of severe outcome. The results show that the proposed method provides meaningful explanations without degrading the classification performance.", "date": "2022-12-28", "categories": []}
{"id": "2307.11465", "title": "A Deep Learning Approach for Overall Survival Prediction in Lung Cancer with Missing Values", "abstract": "In the field of lung cancer research, particularly in the analysis of overall survival (OS), artificial intelligence (AI) serves crucial roles with specific aims. Given the prevalent issue of missing data in the medical domain, our primary objective is to develop an AI model capable of dynamically handling this missing data. Additionally, we aim to leverage all accessible data, effectively analyzing both uncensored patients who have experienced the event of interest and censored patients who have not, by embedding a specialized technique within our AI model, not commonly utilized in other AI tasks. Through the realization of these objectives, our model aims to provide precise OS predictions for non-small cell lung cancer (NSCLC) patients, thus overcoming these significant challenges. We present a novel approach to survival analysis with missing values in the context of NSCLC, which exploits the strengths of the transformer architecture to account only for available features without requiring any imputation strategy. More specifically, this model tailors the transformer architecture to tabular data by adapting its feature embedding and masked self-attention to mask missing data and fully exploit the available ones. By making use of ad-hoc designed losses for OS, it is able to account for both censored and uncensored patients, as well as changes in risks over time. We compared our method with state-of-the-art models for survival analysis coupled with different imputation strategies. We evaluated the results obtained over a period of 6 years using different time granularities obtaining a Ct-index, a time-dependent variant of the C-index, of 71.97, 77.58 and 80.72 for time units of 1 month, 1 year and 2 years, respectively, outperforming all state-of-the-art methods regardless of the imputation method used.", "date": "2023-07-21", "categories": []}
{"id": "2412.14810", "title": "MARIA: a Multimodal Transformer Model for Incomplete Healthcare Data", "abstract": "In healthcare, the integration of multimodal data is pivotal for developing comprehensive diagnostic and predictive models. However, managing missing data remains a significant challenge in real-world applications. We introduce MARIA (Multimodal Attention Resilient to Incomplete datA), a novel transformer-based deep learning model designed to address these challenges through an intermediate fusion strategy. Unlike conventional approaches that depend on imputation, MARIA utilizes a masked self-attention mechanism, which processes only the available data without generating synthetic values. This approach enables it to effectively handle incomplete datasets, enhancing robustness and minimizing biases introduced by imputation methods. We evaluated MARIA against 10 state-of-the-art machine learning and deep learning models across 8 diagnostic and prognostic tasks. The results demonstrate that MARIA outperforms existing methods in terms of performance and resilience to varying levels of data incompleteness, underscoring its potential for critical healthcare applications.", "date": "2024-12-19", "categories": []}
{"id": "2603.06605", "title": "Structure-Aware Set Transformers: Temporal and Variable-Type Attention Biases for Asynchronous Clinical Time Series", "abstract": "Electronic health records (EHR) are irregular, asynchronous multivariate time series. As time-series foundation models increasingly tokenize events rather than discretizing time, the input layout becomes a key design choice. Grids expose time$\\times$variable structure but require imputation or missingness masks, risking error or sampling-policy shortcuts. Point-set tokenization avoids discretization but loses within-variable trajectories and time-local cross-variable context (Fig.1). We restore these priors in STructure-AwaRe (STAR) Set Transformer by adding parameter-efficient soft attention biases: a temporal locality penalty $-|\\Delta t|/\\tau$ with learnable timescales and a variable-type affinity $B_{s_i,s_j}$ from a learned feature-compatibility matrix. We benchmark 10 depth-wise fusion schedules (Fig.2). On three ICU prediction tasks, STAR-Set achieves AUC/APR of 0.7158/0.0026 (CPR), 0.9164/0.2033 (mortality), and 0.8373/0.1258 (vasopressor use), outperforming regular-grid, event-time grid, and prior set baselines. Learned $\\tau$ and $B$ provide interpretable summaries of temporal context and variable interactions, offering a practical plug-in for context-informed time-series models.", "date": "2026-02-18", "categories": []}
{"id": "2603.11594", "title": "Leveraging Large Language Models and Survival Analysis for Early Prediction of Chemotherapy Outcomes", "abstract": "Chemotherapy for cancer treatment is costly and accompanied by severe side effects, highlighting the critical need for early prediction of treatment outcomes to improve patient management and informed decision-making. Predictive models for chemotherapy outcomes using real-world data face challenges, including the absence of explicit phenotypes and treatment outcome labels such as cancer progression and toxicity. This study addresses these challenges by employing Large Language Models (LLMs) and ontology-based techniques for phenotypes and outcome label extraction from patient notes. We focused on one of the most frequently occurring cancers, breast cancer, due to its high prevalence and significant variability in patient response to treatment, making it a critical area for improving predictive modeling. The dataset included features such as vitals, demographics, staging, biomarkers, and performance scales. Drug regimens and their combinations were extracted from the chemotherapy plans in the EMR data and shortlisted based on NCCN guidelines, verified with NIH standards, and analyzed through survival modeling. The proposed approach significantly reduced phenotypes sparsity and improved predictive accuracy. Random Survival Forest was used to predict time-to-failure, achieving a C-index of 73%, and utilized as a classifier at a specific time point to predict treatment outcomes, with accuracy and F1 scores above 70%. The outcome probabilities were validated for reliability by calibration curves. We extended our approach to four other cancer types. This research highlights the potential of early prediction of treatment outcomes using LLM-based clinical data extraction enabling personalized treatment plans with better patient outcomes.", "date": "2026-03-12", "categories": []}
{"id": "2603.11598", "title": "Survival Meets Classification: A Novel Framework for Early Risk Prediction Models of Chronic Diseases", "abstract": "Chronic diseases are long-lasting conditions that require lifelong medical attention. Using big EMR data, we have developed early disease risk prediction models for five common chronic diseases: diabetes, hypertension, CKD, COPD, and chronic ischemic heart disease. In this study, we present a novel approach for disease risk models by integrating survival analysis with classification techniques. Traditional models for predicting the risk of chronic diseases predominantly focus on either survival analysis or classification independently. In this paper, we show survival analysis methods can be re-engineered to enable them to do classification efficiently and effectively, thereby making them a comprehensive tool for developing disease risk surveillance models. The results of our experiments on real-world big EMR data show that the performance of survival models in terms of accuracy, F1 score, and AUROC is comparable to or better than that of prior state-of-the-art models like LightGBM and XGBoost. Lastly, the proposed survival models use a novel methodology to generate explanations, which have been clinically validated by a panel of three expert physicians.", "date": "2026-03-12", "categories": []}
{"id": "2603.11644", "title": "IDRL: An Individual-Aware Multimodal Depression-Related Representation Learning Framework for Depression Diagnosis", "abstract": "Depression is a severe mental disorder, and reliable identification plays a critical role in early intervention and treatment. Multimodal depression detection aims to improve diagnostic performance by jointly modeling complementary information from multiple modalities. Recently, numerous multimodal learning approaches have been proposed for depression analysis; however, these methods suffer from the following limitations: 1) inter-modal inconsistency and depression-unrelated interference, where depression-related cues may conflict across modalities while substantial irrelevant content obscures critical depressive signals, and 2) diverse individual depressive presentations, leading to individual differences in modality and cue importance that hinder reliable fusion. To address these issues, we propose Individual-aware Multimodal Depression-related Representation Learning Framework (IDRL) for robust depression diagnosis. Specifically, IDRL 1) disentangles multimodal representations into a modality-common depression space, a modality-specific depression space, and a depression-unrelated space to enhance modality alignment while suppressing irrelevant information, and 2) introduces an individual-aware modality-fusion module (IAF) that dynamically adjusts the weights of disentangled depression-related features based on their predictive significance, thereby achieving adaptive cross-modal fusion for different individuals. Extensive experiments demonstrate that IDRL achieves superior and robust performance for multimodal depression detection.", "date": "2026-03-12", "categories": []}
{"id": "2507.20859", "title": "Leveraging Open-Source Large Language Models for Clinical Information Extraction in Resource-Constrained Settings", "abstract": "Medical reports contain rich clinical information but are often unstructured and written in domain-specific language, posing challenges for information extraction. While proprietary large language models (LLMs) have shown promise in clinical natural language processing, their lack of transparency and data privacy concerns limit their utility in healthcare. This study therefore evaluates nine open-source generative LLMs on the DRAGON benchmark, which includes 28 clinical information extraction tasks in Dutch. We developed \\texttt{llm\\_extractinator}, a publicly available framework for information extraction using open-source generative LLMs, and used it to assess model performance in a zero-shot setting. Several 14 billion parameter models, Phi-4-14B, Qwen-2.5-14B, and DeepSeek-R1-14B, achieved competitive results, while the bigger Llama-3.3-70B model achieved slightly higher performance at greater computational cost. Translation to English prior to inference consistently degraded performance, highlighting the need of native-language processing. These findings demonstrate that open-source LLMs, when used with our framework, offer effective, scalable, and privacy-conscious solutions for clinical information extraction in low-resource settings.", "date": "2025-07-28", "categories": []}
{"id": "2509.23719", "title": "PD-Diag-Net: Clinical-Priors guided Network on Brain MRI for Auxiliary Diagnosis of Parkinson's Disease", "abstract": "Parkinson's disease (PD) is a common neurodegenerative disorder that severely diminishes patients' quality of life. Its global prevalence has increased markedly in recent decades. Current diagnostic workflows are complex and heavily reliant on neurologists' expertise, often resulting in delays in early detection and missed opportunities for timely intervention. To address these issues, we propose an end-to-end automated diagnostic method for PD, termed PD-Diag-Net, which performs risk assessment and auxiliary diagnosis directly from raw MRI scans. This framework first introduces an MRI Pre-processing Module (MRI-Processor) to mitigate inter-subject and inter-scanner variability by flexibly integrating established medical imaging preprocessing tools. It then incorporates two forms of clinical prior knowledge: (1) Brain-Region-Relevance-Prior (Relevance-Prior), which specifies brain regions strongly associated with PD; and (2) Brain-Region-Aging-Prior (Aging-Prior), which reflects the accelerated aging typically observed in PD-associated regions. Building on these priors, we design two dedicated modules: the Relevance-Prior Guided Feature Aggregation Module (Aggregator), which guides the model to focus on PD-associated regions at the inter-subject level, and the Age-Prior Guided Diagnosis Module (Diagnoser), which leverages brain age gaps as auxiliary constraints at the intra-subject level to enhance diagnostic accuracy and clinical interpretability. Furthermore, we collected external test data from our collaborating hospital. Experimental results show that PD-Diag-Net achieves 86\\% accuracy on external tests and over 96% accuracy in early-stage diagnosis, outperforming existing advanced methods by more than 20%.", "date": "2025-09-28", "categories": []}
{"id": "2603.09638", "title": "Tracking Cancer Through Text: Longitudinal Extraction From Radiology Reports Using Open-Source Large Language Models", "abstract": "Radiology reports capture crucial longitudinal information on tumor burden, treatment response, and disease progression, yet their unstructured narrative format complicates automated analysis. While large language models (LLMs) have advanced clinical text processing, most state-of-the-art systems remain proprietary, limiting their applicability in privacy-sensitive healthcare environments. We present a fully open-source, locally deployable pipeline for longitudinal information extraction from radiology reports, implemented using the llm_extractinator framework. The system applies the qwen2.5-72b model to extract and link target, non-target, and new lesion data across time points in accordance with RECIST criteria. Evaluation on 50 Dutch CT Thorax/Abdomen report pairs yielded high extraction performance, with attribute-level accuracies of 93.7% for target lesions, 94.9% for non-target lesions, and 94.0% for new lesions. The approach demonstrates that open-source LLMs can achieve clinically meaningful performance in multi-timepoint oncology tasks while ensuring data privacy and reproducibility. These results highlight the potential of locally deployable LLMs for scalable extraction of structured longitudinal data from routine clinical text.", "date": "2026-03-10", "categories": []}
{"id": "2603.10004", "title": "Fine-Tune, Don't Prompt, Your Language Model to Identify Biased Language in Clinical Notes", "abstract": "Clinical documentation can contain emotionally charged language with stigmatizing or privileging valences. We present a framework for detecting and classifying such language as stigmatizing, privileging, or neutral. We constructed a curated lexicon of biased terms scored for emotional valence. We then used lexicon-based matching to extract text chunks from OB-GYN delivery notes (Mount Sinai Hospital, NY) and MIMIC-IV discharge summaries across multiple specialties. Three clinicians annotated all chunks, enabling characterization of valence patterns across specialties and healthcare systems.\n  We benchmarked multiple classification strategies (zero-shot prompting, in-context learning, and supervised fine-tuning) across encoder-only models (GatorTron) and generative large language models (Llama). Fine-tuning with lexically primed inputs consistently outperformed prompting approaches. GatorTron achieved an F1 score of 0.96 on the OB-GYN test set, outperforming larger generative models while requiring minimal prompt engineering and fewer computational resources. External validation on MIMIC-IV revealed limited cross-domain generalizability (F1 < 0.70, 44% drop). Training on the broader MIMIC-IV dataset improved generalizability when testing on OB-GYN (F1 = 0.71, 11% drop), but at the cost of reduced precision.\n  Our findings demonstrate that fine-tuning outperforms prompting for emotional valence classification and that models must be adapted to specific medical specialties to achieve clinically appropriate performance. The same terms can carry different emotional valences across specialties: words with clinical meaning in one context may be stigmatizing in another. For bias detection, where misclassification risks undermining clinician trust or perpetuating patient harm, specialty-specific fine-tuning is essential to capture these semantic shifts.\n  * Equal contribution.", "date": "2026-02-16", "categories": []}
{"id": "2603.10216", "title": "An Automated Radiomics Framework for Postoperative Survival Prediction in Colorectal Liver Metastases using Preoperative MRI", "abstract": "While colorectal liver metastasis (CRLM) is potentially curable via hepatectomy, patient outcomes remain highly heterogeneous. Postoperative survival prediction is necessary to avoid non-beneficial surgeries and guide personalized therapy. In this study, we present an automated AI-based framework for postoperative CRLM survival prediction using pre- and post-contrast MRI. We performed a retrospective study of 227 CRLM patients who had gadoxetate-enhanced MRI prior to curative-intent hepatectomy between 2013 and 2020. We developed a survival prediction framework comprising an anatomy-aware segmentation pipeline followed by a radiomics pipeline. The segmentation pipeline learns liver, CRLMs, and spleen segmentation from partially-annotated data, leveraging promptable foundation models to generate pseudo-labels. To support this pipeline, we propose SAMONAI, a prompt propagation algorithm that extends Segment Anything Model to 3D point-based segmentation. Predicted pre- and post-contrast segmentations are then fed into our radiomics pipeline, which extracts per-tumor features and predicts survival using SurvAMINN, an autoencoder-based multiple instance neural network for time-to-event survival prediction. SurvAMINN jointly learns dimensionality reduction and survival prediction from right-censored data, emphasizing high-risk metastases. We compared our framework against established methods and biomarkers using univariate and multivariate Cox regression. Our segmentation pipeline achieves median Dice scores of 0.96 (liver) and 0.93 (spleen), driving a CRLM segmentation Dice score of 0.78 and a detection F1-score of 0.79. Accurate segmentation enables our radiomics pipeline to achieve a survival prediction C-index of 0.69. Our results show the potential of integrating segmentation algorithms with radiomics-based survival analysis to deliver accurate and automated CRLM outcome prediction.", "date": "2026-03-10", "categories": []}
{"id": "2603.10494", "title": "VERI-DPO: Evidence-Aware Alignment for Clinical Summarization via Claim Verification and Direct Preference Optimization", "abstract": "Brief Hospital Course (BHC) narratives must be clinically useful yet faithful to fragmented EHR evidence. LLM-based clinical summarizers still introduce unsupported statements, and alignment can encourage omissions (\"say-less\" degeneration). We introduce VERI-DPO, which uses claim verification to mine preferences and distill them into the summarizer with Direct Preference Optimization (DPO). On MIMIC-III-Ext-VeriFact-BHC (100 ICU patients; patient-level splits), we train a retrieval-augmented verifier to label claim-evidence pairs as Supported, Not Supported, or Not Addressed via a single-token format. The verifier scores sentence-level claims from sampled BHC candidates and aggregates margins into a coverage-aware utility to mine length-controlled, contradiction-anchored preference pairs. On held-out patients, verifier-mined preferences separate candidates by contradiction density, and VERI-DPO reduces Not Supported claim rates from 10.7% to 1.9% (local verifier judge) and from 11.6% to 6.4% (GPT-4o judge), while improving validity from 76.7% to 82.5% and maintaining informative length.", "date": "2026-03-11", "categories": []}
{"id": "2603.10677", "title": "Emulating Clinician Cognition via Self-Evolving Deep Clinical Research", "abstract": "Clinical diagnosis is a complex cognitive process, grounded in dynamic cue acquisition and continuous expertise accumulation. Yet most current artificial intelligence (AI) systems are misaligned with this reality, treating diagnosis as single-pass retrospective prediction while lacking auditable mechanisms for governed improvement. We developed DxEvolve, a self-evolving diagnostic agent that bridges these gaps through an interactive deep clinical research workflow. The framework autonomously requisitions examinations and continually externalizes clinical experience from increasing encounter exposure as diagnostic cognition primitives. On the MIMIC-CDM benchmark, DxEvolve improved diagnostic accuracy by 11.2% on average over backbone models and reached 90.4% on a reader-study subset, comparable to the clinician reference (88.8%). DxEvolve improved accuracy on an independent external cohort by 10.2% (categories covered by the source cohort) and 17.1% (uncovered categories) compared to the competitive method. By transforming experience into a governable learning asset, DxEvolve supports an accountable pathway for the continual evolution of clinical AI.", "date": "2026-03-11", "categories": []}
{"id": "2603.10764", "title": "HeartAgent: An Autonomous Agent System for Explainable Differential Diagnosis in Cardiology", "abstract": "Heart diseases remain a leading cause of morbidity and mortality worldwide, necessitating accurate and trustworthy differential diagnosis. However, existing artificial intelligence-based diagnostic methods are often limited by insufficient cardiology knowledge, inadequate support for complex reasoning, and poor interpretability. Here we present HeartAgent, a cardiology-specific agent system designed to support a reliable and explainable differential diagnosis. HeartAgent integrates customized tools and curated data resources and orchestrates multiple specialized sub-agents to perform complex reasoning while generating transparent reasoning trajectories and verifiable supporting references. Evaluated on the MIMIC dataset and a private electronic health records cohort, HeartAgent achieved over 36% and 20% improvements over established comparative methods, in top-3 diagnostic accuracy, respectively. Additionally, clinicians assisted by HeartAgent demonstrated gains of 26.9% in diagnostic accuracy and 22.7% in explanatory quality compared with unaided experts. These results demonstrate that HeartAgent provides reliable, explainable, and clinically actionable decision support for cardiovascular care.", "date": "2026-03-11", "categories": []}
{"id": "2603.06758", "title": "Enhancing SHAP Explainability for Diagnostic and Prognostic ML Models in Alzheimer Disease", "abstract": "Alzheimer disease (AD) diagnosis and prognosis increasingly rely on machine learning (ML) models. Although these models provide good results, clinical adoption is limited by the need for technical expertise and the lack of trustworthy and consistent model explanations. SHAP (SHapley Additive exPlanations) is com-monly used to interpret AD models, but existing studies tend to focus on explanations for isolated tasks, providing little evidence about their robustness across disease stages, model architectures, or prediction objectives. This paper proposes a multi-level explainability framework that measures the coherence, stabil-ity and consistency of explanations by integrating: (1) within-model coherence metrics between feature importance and SHAP, (2) SHAP stability across AD boundaries, and (3) SHAP cross-task consistency be-tween diagnosis and prognosis. Using AutoML to optimize classifiers on the NACC dataset, we trained four diagnostic and four prognostic models covering the standard AD progression stages. Stability was then evaluated using correlation metrics, top-k feature overlap, SHAP sign consistency, and domain-level contribution ratios. Results show that cognitive and functional markers dominate SHAP explanations in both diagnosis and prognosis. SHAP-SHAP consistency between diagnostic and prognostic models was high across all classifiers, with 100% sign stability and minimal shifts in explanatory magnitude. Domain-level contributions also remained stable, with only minimal increases in genetic features for prognosis. These results demonstrate that SHAP explanations can be quantitatively vali-dated for robustness and transferability, providing clinicians with more reliable interpretations of ML pre-dictions.", "date": "2026-03-06", "categories": []}
{"id": "2603.09052", "title": "From Days to Minutes: An Autonomous AI Agent Achieves Reliable Clinical Triage in Remote Patient Monitoring", "abstract": "Background: Remote patient monitoring (RPM) generates vast data, yet landmark trials (Tele-HF, BEAT-HF) failed because data volume overwhelmed clinical staff. While TIM-HF2 showed 24/7 physician-led monitoring reduces mortality by 30%, this model remains prohibitively expensive and unscalable.\n  Methods: We developed Sentinel, an autonomous AI agent using Model Context Protocol (MCP) for contextual triage of RPM vitals via 21 clinical tools and multi-step reasoning. Evaluation included: (1) self-consistency (100 readings x 5 runs); (2) comparison against rule-based thresholds; and (3) validation against 6 clinicians (3 physicians, 3 NPs) using a connected matrix design. A leave-one-out (LOO) analysis compared the agent against individual clinicians; severe overtriage cases underwent independent physician adjudication.\n  Results: Against a human majority-vote standard (N=467), the agent achieved 95.8% emergency sensitivity and 88.5% sensitivity for all actionable alerts (85.7% specificity). Four-level exact accuracy was 69.4% (quadratic-weighted kappa=0.778); 95.9% of classifications were within one severity level. In LOO analysis, the agent outperformed every clinician in emergency sensitivity (97.5% vs. 60.0% aggregate) and actionable sensitivity (90.9% vs. 69.5%). While disagreements skewed toward overtriage (22.5%), independent adjudication of severe gaps (>=2 levels) validated agent escalation in 88-94% of cases; consensus resolution validated 100%. The agent showed near-perfect self-consistency (kappa=0.850). Median cost was $0.34/triage.\n  Conclusions: Sentinel triages RPM vitals with sensitivity exceeding individual clinicians. By automating systematic context synthesis, Sentinel addresses the core limitation of prior RPM trials, offering a scalable path toward the intensive monitoring shown to reduce mortality while maintaining a clinically defensible overtriage profile.", "date": "2026-03-10", "categories": []}
{"id": "2603.09685", "title": "Automatic Cardiac Risk Management Classification using large-context Electronic Patients Health Records", "abstract": "To overcome the limitations of manual administrative coding in geriatric Cardiovascular Risk Management, this study introduces an automated classification framework leveraging unstructured Electronic Health Records (EHRs). Using a dataset of 3,482 patients, we benchmarked three distinct modeling paradigms on longitudinal Dutch clinical narratives: classical machine learning baselines, specialized deep learning architectures optimized for large-context sequences, and general-purpose generative Large Language Models (LLMs) in a zero-shot setting. Additionally, we evaluated a late fusion strategy to integrate unstructured text with structured medication embeddings and anthropometric data. Our analysis reveals that the custom Transformer architecture outperforms both traditional methods and generative \\acs{llm}s, achieving the highest F1-scores and Matthews Correlation Coefficients. These findings underscore the critical role of specialized hierarchical attention mechanisms in capturing long-range dependencies within medical texts, presenting a robust, automated alternative to manual workflows for clinical risk stratification.", "date": "2026-03-10", "categories": []}
{"id": "2603.09940", "title": "SignalMC-MED: A Multimodal Benchmark for Evaluating Biosignal Foundation Models on Single-Lead ECG and PPG", "abstract": "Recent biosignal foundation models (FMs) have demonstrated promising performance across diverse clinical prediction tasks, yet systematic evaluation on long-duration multimodal data remains limited. We introduce SignalMC-MED, a benchmark for evaluating biosignal FMs on synchronized single-lead electrocardiogram (ECG) and photoplethysmogram (PPG) data. Derived from the MC-MED dataset, SignalMC-MED comprises 22,256 visits with 10-minute overlapping ECG and PPG signals, and includes 20 clinically relevant tasks spanning prediction of demographics, emergency department disposition, laboratory value regression, and detection of prior ICD-10 diagnoses. Using this benchmark, we perform a systematic evaluation of representative time-series and biosignal FMs across ECG-only, PPG-only, and ECG + PPG settings. We find that domain-specific biosignal FMs consistently outperform general time-series models, and that multimodal ECG + PPG fusion yields robust improvements over unimodal inputs. Moreover, using the full 10-minute signal consistently outperforms shorter segments, and larger model variants do not reliably outperform smaller ones. Hand-crafted ECG domain features provide a strong baseline and offer complementary value when combined with learned FM representations. Together, these results establish SignalMC-MED as a standardized benchmark and provide practical guidance for evaluating and deploying biosignal FMs.", "date": "2026-03-10", "categories": []}
{"id": "2503.03935", "title": "LLM-Powered Prediction of Hyperglycemia and Discovery of Behavioral Treatment Pathways from Wearables and Diet", "abstract": "Postprandial hyperglycemia, marked by the blood glucose level exceeding the normal range after consuming a meal, is a critical indicator of progression toward type 2 diabetes in people with prediabetes and in healthy individuals. A key metric for understanding blood glucose dynamics after eating is the postprandial area under the curve (AUC). Predicting postprandial AUC in advance based on a person's lifestyle factors, such as diet and physical activity level, and explaining the factors that affect postprandial blood glucose could allow an individual to adjust their lifestyle accordingly to maintain normal glucose levels. In this study, we developed an explainable machine learning solution, GlucoLens, that takes sensor-driven inputs and uses advanced data processing, large language models, and trainable machine learning models to predict postprandial AUC and hyperglycemia from diet, physical activity, and recent glucose patterns. We used data obtained from wearables in a five-week clinical trial of 10 adults who worked full-time to develop and evaluate the proposed computational model that integrates wearable sensing, multimodal data, and machine learning. Our machine learning model takes multimodal data from wearable activity and glucose monitoring sensors, along with food and work logs, and provides an interpretable prediction of the postprandial glucose pattern. Our GlucoLens system achieves a normalized root mean squared error (NRMSE) of 0.123 in its best configuration. On average, the proposed technology provides a 16% better performance level compared to the comparison models. Additionally, our technique predicts hyperglycemia with an accuracy of 73.3% and an F1 score of 0.716 and recommends different treatment options to help avoid hyperglycemia through diverse counterfactual explanations. Code available: https://github.com/ab9mamun/GlucoLens.", "date": "2025-03-05", "categories": []}
{"id": "2504.06987", "title": "Enhancing Metabolic Syndrome Prediction with Hybrid Data Balancing and Counterfactuals", "abstract": "Metabolic Syndrome (MetS) is a cluster of interrelated risk factors that significantly increases the risk of cardiovascular diseases and type 2 diabetes. Despite its global prevalence, accurate prediction of MetS remains challenging due to issues such as class imbalance, data scarcity, and methodological inconsistencies in existing studies. In this paper, we address these challenges by systematically evaluating and optimizing machine learning (ML) models for MetS prediction, leveraging advanced data balancing techniques and counterfactual analysis. Multiple ML models, including XGBoost, Random Forest, TabNet, etc., were trained and compared under various data balancing techniques such as random oversampling (ROS), SMOTE, ADASYN, and CTGAN. Additionally, we introduce MetaBoost, a novel hybrid framework that integrates SMOTE, ADASYN, and CTGAN, optimizing synthetic data generation through weighted averaging and iterative weight tuning to enhance the model's performance (achieving up to a 1.87% accuracy improvement over individual balancing techniques). A comprehensive counterfactual analysis is conducted to quantify the feature-level changes required to shift individuals from high-risk to low-risk categories. The results indicate that blood glucose (50.3%) and triglycerides (46.7%) were the most frequently modified features, highlighting their clinical significance in MetS risk reduction. Additionally, probabilistic analysis shows elevated blood glucose (85.5% likelihood) and triglycerides (74.9% posterior probability) as the strongest predictors. This study not only advances the methodological rigor of MetS prediction but also provides actionable insights for clinicians and researchers, highlighting the potential of ML in mitigating the public health burden of metabolic syndrome.", "date": "2025-04-09", "categories": []}
{"id": "2603.06888", "title": "Enhancing the Detection of Coronary Artery Disease Using Machine Learning", "abstract": "Coronary Artery Disease (CAD) remains a leading cause of morbidity and mortality worldwide. Early detection is critical to recover patient outcomes and decrease healthcare costs. In recent years, machine learning (ML) advancements have shown significant potential in enhancing the accuracy of CAD diagnosis. This study investigates the application of ML algorithms to improve the detection of CAD by analyzing patient data, including clinical features, imaging, and biomarker profiles. Bi-directional Long Short-Term Memory (Bi-LSTM), Gated Recurrent Units (GRU), and a hybrid of Bi-LSTM+GRU were trained on large datasets to predict the presence of CAD. Results demonstrated that these ML models outperformed traditional diagnostic methods in sensitivity and specificity, offering a robust tool for clinicians to make more informed decisions. The experimental results show that the hybrid model achieved an accuracy of 97.07%. By integrating advanced data preprocessing techniques and feature selection, this study ensures optimal learning and model performance, setting a benchmark for the application of ML in CAD diagnosis. The integration of ML into CAD detection presents a promising avenue for personalized healthcare and could play a pivotal role in the future of cardiovascular disease management.", "date": "2026-03-06", "categories": []}
{"id": "2603.07249", "title": "LF2L: Loss Fusion Horizontal Federated Learning Across Heterogeneous Feature Spaces Using External Datasets Effectively: A Case Study in Second Primary Cancer Prediction", "abstract": "Second primary cancer (SPC), a new cancer in patients different from previously diagnosed, is a growing concern due to improved cancer survival rates. Early prediction of SPC is essential to enable timely clinical interventions. This study focuses on lung cancer survivors treated in Taiwanese hospitals, where the limited size and geographic scope of local datasets restrict the effectiveness and generalizability of traditional machine learning approaches. To address this, we incorporate external data from the publicly available US-based Surveillance, Epidemiology, and End Results (SEER) program, significantly increasing data diversity and scale. However, the integration of multi-source datasets presents challenges such as feature inconsistency and privacy constraints. Rather than naively merging data, we proposed a loss fusion horizontal federated learning (LF2L) framework that can enable effective cross-institutional collaboration while preserving institutional privacy by avoiding data sharing. Using both common and unique features and balancing their contributions through a shared loss mechanism, our method demonstrates substantial improvements in the prediction performance of SPC. Experiment results show statistically significant improvements in AUROC and AUPRC when compared to localized, horizontal federated, and centralized learning baselines. This highlights the importance of not only acquiring external data but also leveraging it effectively to enhance model performance in real-world clinical model development.", "date": "2026-03-07", "categories": []}
{"id": "2603.07348", "title": "Learning Clinical Representations Under Systematic Distribution Shift", "abstract": "Clinical machine learning models are increasingly trained using large scale, multimodal foundation paradigms, yet deployment environments often differ systematically from the data generating settings used during training. Such shifts arise from heterogeneous measurement policies, documentation practices, and institutional workflows, leading to representation entanglement between physiologic signal and practice specific artifacts. In this work, we propose a practice invariant representation learning framework for multimodal clinical prediction. We model clinical observations as arising from latent physiologic factors and environment dependent processes, and introduce an objective that jointly optimizes predictive performance while suppressing environment predictive information in the learned embedding. Concretely, we combine supervised risk minimization with adversarial environment regularization and invariant risk penalties across hospitals. Across multiple longitudinal EHR prediction tasks and cross institution evaluations, our method improves out of distribution AUROC by up to 2 to 3 points relative to masked pretraining and standard supervised baselines, while maintaining in distribution performance and improving calibration. These results demonstrate that explicitly accounting for systematic distribution shift during representation learning yields more robust and transferable clinical models, highlighting the importance of structural invariance alongside architectural scale in healthcare AI.", "date": "2026-03-07", "categories": []}
{"id": "2603.08242", "title": "Optimising antibiotic switching via forecasting of patient physiology", "abstract": "Timely transition from intravenous (IV) to oral antibiotic therapy shortens hospital stays, reduces catheter-related infections, and lowers healthcare costs, yet one in five patients in England remain on IV antibiotics despite meeting switching criteria. Clinical decision support systems can improve switching rates, but approaches that learn from historical decisions reproduce the delays and inconsistencies of routine practice. We propose using neural processes to model vital sign trajectories probabilistically, predicting switch-readiness by comparing forecasts against clinical guidelines rather than learning from past actions, and ranking patients to prioritise clinical review. The design yields interpretable outputs, adapts to updated guidelines without retraining, and preserves clinical judgement. Validated on MIMIC-IV (US intensive care, 6,333 encounters) and UCLH (a large urban academic UK hospital group, 10,584 encounters), the system selects 2.2-3.2$\\times$ more relevant patients than random. Our results demonstrate that forecasting patient physiology offers a principled foundation for decision support in antibiotic stewardship.", "date": "2026-03-09", "categories": []}
{"id": "2603.08278", "title": "TA-RNN-Medical-Hybrid: A Time-Aware and Interpretable Framework for Mortality Risk Prediction", "abstract": "Accurate and interpretable mortality risk prediction in intensive care units (ICUs) remains a critical challenge due to the irregular temporal structure of electronic health records (EHRs), the complexity of longitudinal disease trajectories, and the lack of clinically grounded explanations in many data-driven models. To address these challenges, we propose \\textit{TA-RNN-Medical-Hybrid}, a time-aware and knowledge-enriched deep learning framework that jointly models longitudinal clinical sequences and irregular temporal dynamics through explicit continuous-time encoding, along with standardized medical concept representations. The proposed framework extends time-aware recurrent modeling by integrating explicit continuous-time embeddings that operate independently of visit indexing, SNOMED-based disease representations, and a hierarchical dual-level attention mechanism that captures both visit-level temporal importance and feature/concept-level clinical relevance. This design enables accurate mortality risk estimation while providing transparent and clinically meaningful explanations aligned with established medical knowledge. We evaluate the proposed approach on the MIMIC-III critical care dataset and compare it against strong time-aware and sequential baselines. Experimental results demonstrate that TA-RNN-Medical-Hybrid consistently improves predictive performance in terms of AUC, accuracy, and recall-oriented F$_2$-score. Moreover, qualitative analysis shows that the model effectively decomposes mortality risk across time and clinical concepts, yielding interpretable insights into disease severity, chronicity, and temporal progression. Overall, the proposed framework bridges the gap between predictive accuracy and clinical interpretability, offering a scalable and transparent solution for high-stakes ICU decision support systems.", "date": "2026-03-09", "categories": []}
{"id": "2603.08459", "title": "Data-Driven Priors for Uncertainty-Aware Deterioration Risk Prediction with Multimodal Data", "abstract": "Safe predictions are a crucial requirement for integrating predictive models into clinical decision support systems. One approach for ensuring trustworthiness is to enable models' ability to express their uncertainty about individual predictions. However, current machine learning models frequently lack reliable uncertainty estimation, hindering real-world deployment. This is further observed in multimodal settings, where the goal is to enable effective information fusion. In this work, we propose $\\texttt{MedCertAIn}$, a predictive uncertainty framework that leverages multimodal clinical data for in-hospital risk prediction to improve model performance and reliability. We design data-driven priors over neural network parameters using a hybrid strategy that considers cross-modal similarity in self-supervised latent representations and modality-specific data corruptions. We train and evaluate the models with such priors using clinical time-series and chest X-ray images from the publicly-available datasets MIMIC-IV and MIMIC-CXR. Our results show that $\\texttt{MedCertAIn}$ significantly improves predictive performance and uncertainty quantification compared to state-of-the-art deterministic baselines and alternative Bayesian methods. These findings highlight the promise of data-driven priors in advancing robust, uncertainty-aware AI tools for high-stakes clinical applications.", "date": "2026-03-09", "categories": []}
{"id": "2603.05535", "title": "Clinical-Injection Transformer with Domain-Adapted MAE for Lupus Nephritis Prognosis Prediction", "abstract": "Lupus nephritis (LN) is a severe complication of systemic lupus erythematosus that affects pediatric patients with significantly greater severity and worse renal outcomes compared to adults. Despite the urgent clinical need, predicting pediatric LN prognosis remains unexplored in computational pathology. Furthermore, the only existing histopathology-based approach for LN relies on multiple costly staining protocols and fails to integrate complementary clinical data. To address these gaps, we propose the first multimodal computational pathology framework for three-class treatment response prediction (complete remission, partial response, and no response) in pediatric LN, utilizing only routine PAS-stained biopsies and structured clinical data. Our framework introduces two key methodological innovations. First, a Clinical-Injection Transformer (CIT) embeds clinical features as condition tokens into patch-level self-attention, facilitating implicit and bidirectional cross-modal interactions within a unified attention space. Second, we design a decoupled representation-knowledge adaptation strategy using a domain-adapted Masked Autoencoder (MAE). This strategy explicitly separates self-supervised morphological feature learning from pathological knowledge extraction. Additionally, we introduce a multi-granularity morphological type injection mechanism to bridge distilled classification knowledge with downstream prognostic predictions at both the instance and patient levels. Evaluated on a cohort of 71 pediatric LN patients with KDIGO-standardized labels, our method achieves a three-class accuracy of 90.1% and an AUC of 89.4%, demonstrating its potential as a highly accurate and cost-effective prognostic tool.", "date": "2026-03-04", "categories": []}
{"id": "2603.05572", "title": "Machine Learning for analysis of Multiple Sclerosis cross-tissue bulk and single-cell transcriptomics data", "abstract": "Multiple Sclerosis (MS) is a chronic autoimmune disease of the central nervous system whose molecular mechanisms remain incompletely understood. In this study, we developed an end-to-end machine learning pipeline to analyze transcriptomic data from peripheral blood mononuclear cells and cerebrospinal fluid, integrating both bulk microarray and single-cell RNA sequencing datasets (concentrating on CD4+ and B-cells). After rigorous preprocessing, batch correction, and gene declustering, XGBoost classifiers were trained to distinguish MS patients from healthy controls. Explainable AI tools, namely SHapley Additive exPlanations (SHAP), were employed to identify key genes driving classification, and results were compared with Differential Expression Analysis (DEA). SHAP-prioritized genes were further investigated through interaction networks and pathway enrichment analyses. The models achieved strong performance, particularly in CSF B-cells (AUC=0.94) and microarray (AUC=0.86). SHAP gene selection proved to be complementary to classical DEA. Gene clusters identified across multiple datasets highlighted immune activation, non-canonical immune checkpoints (ITK, CLEC2D, KLRG1, CEACAM1), ribosomal and translational programs, ubiquitin-proteasome regulation, lipid trafficking, and Epstein-Barr virus-related pathways. Our integrative and explainable framework reveals complementary insights beyond conventional analysis and provides novel mechanistic hypotheses and potential biomarkers for MS pathogenesis.", "date": "2026-03-05", "categories": []}
{"id": "2603.06522", "title": "Artificial Intelligence for Detecting Fetal Orofacial Clefts and Advancing Medical Education", "abstract": "Orofacial clefts are among the most common congenital craniofacial abnormalities, yet accurate prenatal detection remains challenging due to the scarcity of experienced specialists and the relative rarity of the condition. Early and reliable diagnosis is essential to enable timely clinical intervention and reduce associated morbidity. Here we show that an artificial intelligence system, trained on over 45,139 ultrasound images from 9,215 fetuses across 22 hospitals, can diagnose fetal orofacial clefts with sensitivity and specificity exceeding 93% and 95% respectively, matching the performance of senior radiologists and substantially outperforming junior radiologists. When used as a medical copilot, the system raises junior radiologists' sensitivity by more than 6%. Beyond direct diagnostic assistance, the system also accelerates the development of clinical expertise. A pilot study involving 24 radiologists and trainees demonstrated that the model can improve the expertise development for rare conditions. This dual-purpose approach offers a scalable solution for improving both diagnostic accuracy and specialist training in settings where experienced radiologists are scarce.", "date": "2026-03-06", "categories": []}
{"id": "2512.07081", "title": "ClinNoteAgents: An LLM Multi-Agent System for Predicting and Interpreting Heart Failure 30-Day Readmission from Clinical Notes", "abstract": "Heart failure (HF) is one of the leading causes of rehospitalization among older adults in the United States. Although clinical notes contain rich, detailed patient information and make up a large portion of electronic health records (EHRs), they remain underutilized for HF readmission risk analysis. Traditional computational models for HF readmission often rely on expert-crafted rules, medical thesauri, and ontologies to interpret clinical notes, which are typically written under time pressure and may contain misspellings, abbreviations, and domain-specific jargon. We present ClinNoteAgents, an LLM-based multi-agent framework that transforms free-text clinical notes into (1) structured representations of clinical and social risk factors for association analysis and (2) clinician-style abstractions for HF 30-day readmission prediction. We evaluate ClinNoteAgents on 3,544 notes from 2,065 patients (readmission rate=35.16%), demonstrating high extraction fidelity for clinical variables (conditional accuracy >= 90% for multiple vitals), key risk factor identification, and preservation of predictive signal despite 60 to 90% text reduction. By reducing reliance on structured fields and minimizing manual annotation and model training, ClinNoteAgents provides a scalable and interpretable approach to note-based HF readmission risk modeling in data-limited healthcare systems.", "date": "2025-12-08", "categories": []}
{"id": "2603.04449", "title": "An Explainable Ensemble Framework for Alzheimer's Disease Prediction Using Structured Clinical and Cognitive Data", "abstract": "Early and accurate detection of Alzheimer's disease (AD) remains a major challenge in medical diagnosis due to its subtle onset and progressive nature. This research introduces an explainable ensemble learning Framework designed to classify individuals as Alzheimer's or Non-Alzheimer's using structured clinical, lifestyle, metabolic, and lifestyle features. The workflow incorporates rigorous preprocessing, advanced feature engineering, SMOTE-Tomek hybrid class balancing, and optimized modeling using five ensemble algorithms-Random Forest, XGBoost, LightGBM, CatBoost, and Extra Trees-alongside a deep artificial neural network. Model selection was performed using stratified validation to prevent leakage, and the best-performing model was evaluated on a fully unseen test set. Ensemble methods achieved superior performance over deep learning, with XGBoost, Random Forest, and Soft Voting showing the strongest accuracy, sensitivity, and F1-score profiles. Explainability techniques, including SHAP and feature importance analysis, highlighted MMSE, Functional Assessment Age, and several engineered interaction features as the most influential determinants.\n  The results demonstrate that the proposed framework provides a reliable and transparent approach to Alzheimer's disease prediction, offering strong potential for clinical decision support applications.", "date": "2026-02-26", "categories": []}
{"id": "2603.04589", "title": "ECG-MoE: Mixture-of-Expert Electrocardiogram Foundation Model", "abstract": "Electrocardiography (ECG) analysis is crucial for cardiac diagnosis, yet existing foundation models often fail to capture the periodicity and diverse features required for varied clinical tasks. We propose ECG-MoE, a hybrid architecture that integrates multi-model temporal features with a cardiac period-aware expert module. Our approach uses a dual-path Mixture-of-Experts to separately model beat-level morphology and rhythm, combined with a hierarchical fusion network using LoRA for efficient inference. Evaluated on five public clinical tasks, ECG-MoE achieves state-of-the-art performance with 40% faster inference than multi-task baselines.", "date": "2026-03-04", "categories": []}
{"id": "2603.05184", "title": "Logi-PAR: Logic-Infused Patient Activity Recognition via Differentiable Rule", "abstract": "Patient Activity Recognition (PAR) in clinical settings uses activity data to improve safety and quality of care. Although significant progress has been made, current models mainly identify which activity is occurring. They often spatially compose sub-sparse visual cues using global and local attention mechanisms, yet only learn logically implicit patterns due to their neural-pipeline. Advancing clinical safety requires methods that can infer why a set of visual cues implies a risk, and how these can be compositionally reasoned through explicit logic beyond mere classification. To address this, we proposed Logi-PAR, the first Logic-Infused Patient Activity Recognition Framework that integrates contextual fact fusion as a multi-view primitive extractor and injects neural-guided differentiable rules. Our method automatically learns rules from visual cues, optimizing them end-to-end while enabling the implicit emergence patterns to be explicitly labelled during training. To the best of our knowledge, Logi-PAR is the first framework to recognize patient activity by applying learnable logic rules to symbolic mappings. It produces auditable why explanations as rule traces and supports counterfactual interventions (e.g., risk would decrease by 65% if assistance were present). Extensive evaluation on clinical benchmarks (VAST and OmniFall) demonstrates state-of-the-art performance, significantly outperforming Vision-Language Models and transformer baselines. The code is available via: https://github.com/zararkhan985/Logi-PAR.git}", "date": "2026-03-05", "categories": []}
{"id": "2503.07638", "title": "Leveraging Taxonomy Similarity for Next Activity Prediction in Patient Treatment", "abstract": "The rapid progress in modern medicine presents physicians with complex challenges when planning patient treatment. Techniques from the field of Predictive Business Process Monitoring, like Next-activity-prediction (NAP) can be used as a promising technique to support physicians in treatment planning, by proposing a possible next treatment step. Existing patient data, often in the form of electronic health records, can be analyzed to recommend the next suitable step in the treatment process. However, the use of patient data poses many challenges due to its knowledge-intensive character, high variability and scarcity of medical data. To overcome these challenges, this article examines the use of the knowledge encoded in taxonomies to improve and explain the prediction of the next activity in the treatment process. This study proposes the TS4NAP approach, which uses medical taxonomies (ICD-10-CM and ICD-10-PCS) in combination with graph matching to assess the similarities of medical codes to predict the next treatment step. The effectiveness of the proposed approach will be evaluated using event logs that are derived from the MIMIC-IV dataset. The results highlight the potential of using domain-specific knowledge held in taxonomies to improve the prediction of the next activity, and thus can improve treatment planning and decision-making by making the predictions more explainable.", "date": "2025-03-05", "categories": []}
{"id": "2509.25095", "title": "Benchmarking ECG FMs: A Reality Check Across Clinical Tasks", "abstract": "The 12-lead electrocardiogram (ECG) is a long-standing diagnostic tool. Yet machine learning for ECG interpretation remains fragmented, often limited to narrow tasks or datasets. FMs promise broader adaptability, but fundamental questions remain: Which architectures generalize best? How do models scale with limited labels? What explains performance differences across model families? We benchmarked eight ECG FMs on 26 clinically relevant tasks using 12 public datasets comprising 1,650 regression and classification targets. Models were evaluated under fine-tuning and frozen settings, with scaling analyses across dataset sizes. Results show heterogeneous performance across domains: in adult ECG interpretation, three FMs consistently outperformed strong supervised baselines. In contrast, ECG-CPC, a compact structured state-space model, dominated 5 of 7 task categories, demonstrating that architecture matters more than scale. FMs improved label efficiency 3.3-9x over supervised baselines, though scaling behaviors varied across architectures. Representation analysis reveals that models with similar performance learn markedly different internal structures, suggesting multiple viable paths to effective ECG representation. Overall, while FMs show promise for adult ECG analysis, substantial gaps remain in cardiac structure, outcome prediction, and patient characterization. ECG-CPC's strong performance despite being orders of magnitude smaller challenges the assumption that FM quality requires massive scale, highlighting architectural inductive biases as an untapped opportunity.", "date": "2025-09-29", "categories": []}
{"id": "2307.04842", "title": "Predicting Tuberculosis from Real-World Cough Audio Recordings and Metadata", "abstract": "Tuberculosis (TB) is an infectious disease caused by the bacterium Mycobacterium tuberculosis and primarily affects the lungs, as well as other body parts. TB is spread through the air when an infected person coughs, sneezes, or talks. Medical doctors diagnose TB in patients via clinical examinations and specialized tests. However, coughing is a common symptom of respiratory diseases such as TB. Literature suggests that cough sounds coming from different respiratory diseases can be distinguished by both medical doctors and computer algorithms. Therefore, cough recordings associated with patients with and without TB seems to be a reasonable avenue of investigation. In this work, we utilize a very large dataset of TB and non-TB cough audio recordings obtained from the south-east of Africa, India, and the south-east of Asia using a fully automated phone-based application (Hyfe), without manual annotation. We fit statistical classifiers based on spectral and time domain features with and without clinical metadata. A stratified grouped cross-validation approach shows that an average Area Under Curve (AUC) of approximately 0.70 $\\pm$ 0.05 both for a cough-level and a participant-level classification can be achieved using cough sounds alone. The addition of demographic and clinical factors increases performance, resulting in an average AUC of approximately 0.81 $\\pm$ 0.05. Our results suggest mobile phone-based applications that integrate clinical symptoms and cough sound analysis could help community health workers and, most importantly, health service programs to improve TB case-finding efforts while reducing costs, which could substantially improve public health.", "date": "2023-07-10", "categories": []}
{"id": "2501.18731", "title": "Evaluating Spoken Language as a Biomarker for Automated Screening of Cognitive Impairment", "abstract": "Timely and accurate assessment of cognitive impairment remains a major unmet need. Speech biomarkers offer a scalable, non-invasive, cost-effective solution for automated screening. However, the clinical utility of machine learning (ML) remains limited by interpretability and generalisability to real-world speech datasets. We evaluate explainable ML for screening of Alzheimer's disease and related dementias (ADRD) and severity prediction using benchmark DementiaBank speech (N = 291, 64% female, 69.8 (SD = 8.6) years). We validate generalisability on pilot data collected in-residence (N = 22, 59% female, 76.2 (SD = 8.0) years). To enhance clinical utility, we stratify risk for actionable triage and assess linguistic feature importance. We show that a Random Forest trained on linguistic features for ADRD detection achieves a mean sensitivity of 69.4% (95% confidence interval (CI) = 66.4-72.5) and specificity of 83.3% (78.0-88.7). On pilot data, this model yields a mean sensitivity of 70.0% (58.0-82.0) and specificity of 52.5% (39.3-65.7). For prediction of Mini-Mental State Examination (MMSE) scores, a Random Forest Regressor achieves a mean absolute MMSE error of 3.7 (3.7-3.8), with comparable performance of 3.3 (3.1-3.5) on pilot data. Risk stratification improves specificity by 13% on the test set, offering a pathway for clinical triage. Linguistic features associated with ADRD include increased use of pronouns and adverbs, greater disfluency, reduced analytical thinking, lower lexical diversity, and fewer words that reflect a psychological state of completion. Our predictive modelling shows promise for integration with conversational technology at home to monitor cognitive health and triage higher-risk individuals, enabling early screening and intervention.", "date": "2025-01-30", "categories": []}
{"id": "2511.01275", "title": "Adversarial Spatio-Temporal Attention Networks for Epileptic Seizure Forecasting", "abstract": "Forecasting epileptic seizures from multivariate EEG signals represents a critical challenge in healthcare time series prediction, requiring high sensitivity, low false alarm rates, and subject-specific adaptability. We present STAN, an Adversarial Spatio-Temporal Attention Network that jointly models spatial brain connectivity and temporal neural dynamics through cascaded attention blocks with alternating spatial and temporal modules. Unlike existing approaches that assume fixed preictal durations or separately process spatial and temporal features, STAN captures bidirectional dependencies between spatial and temporal patterns through a unified cascaded architecture. Adversarial training with gradient penalty enables robust discrimination between interictal and preictal states learned from clearly defined 15-minute preictal windows. Continuous 90-minute pre-seizure monitoring reveals that the learned spatio-temporal attention patterns enable early detection: reliable alarms trigger at subject-specific times (typically 15-45 minutes before onset), reflecting the model's capacity to capture subtle preictal dynamics without requiring individualized training. Experiments on two benchmark EEG datasets (CHB-MIT scalp: 8 subjects, 46 events; MSSM intracranial: 4 subjects, 14 events) demonstrate state-of-the-art performance: 96.6% sensitivity with 0.011 false detections per hour and 94.2% sensitivity with 0.063 false detections per hour, respectively, while maintaining computational efficiency (2.3M parameters, 45 ms latency, 180 MB memory) for real-time edge deployment. Beyond epilepsy, the proposed framework provides a general paradigm for spatio-temporal forecasting in healthcare and other time series domains where individual heterogeneity and interpretability are crucial.", "date": "2025-11-03", "categories": []}
{"id": "2603.00221", "title": "A medical coding language model trained on clinical narratives from a population-wide cohort of 1.8 million patients", "abstract": "Medical coding translates clinical documentation into standardized codes for billing, research, and public health, but manual coding is time-consuming and error-prone. Existing automation efforts rely on small datasets that poorly represent real-world patient heterogeneity. We trained a language model on 5.8 million electronic health records from 1.8 million patients across nearly all specialties in Eastern Denmark (2006--2016) to predict ICD-10 codes from clinical notes, medications, and laboratory results. Evaluated on 270,000 held-out patients, the model achieved a micro F1 of 71.8% and a top-10 recall of 95.5%. Performance varied by specialty (F1: 53--91%), with higher scores in specialties with well-defined diagnostic criteria. Codes appearing predominantly as secondary diagnoses had markedly lower F1 scores. For three such codes (suicide-related behaviors, weight disorders, and hypertension), the model identified thousands of uncoded cases, of which 76-86% were confirmed valid upon manual review, suggesting systematic under-coding rather than model error. These findings suggest under-coding of secondary diagnoses in Eastern Denmark during this period, with potential implications for epidemiological research, public health surveillance, and understanding of multimorbidity. Similar time constraints and reimbursement structures in other healthcare systems suggest this may not be isolated to this dataset. The model can automate coding for approximately 50% of cases and provide accurate suggestions for most others, and may offer a practical solution to help capture missed secondary conditions.", "date": "2026-02-27", "categories": []}
{"id": "2603.02268", "title": "PRISM: Exploring Heterogeneous Pretrained EEG Foundation Model Transfer to Clinical Differential Diagnosis", "abstract": "EEG foundation models are typically pretrained on narrow-source clinical archives and evaluated on benchmarks from the same ecosystem, leaving unclear whether representations encode neural physiology or recording-distribution artifacts. We introduce PRISM (Population Representative Invariant Signal Model), a masked autoencoder ablated along two axes -- pretraining population and downstream adaptation -- with architecture and preprocessing fixed. We compare a narrow-source EU/US corpus (TUH + PhysioNet) against a geographically diverse pool augmented with multi-center South Asian clinical recordings across multiple EEG systems. Three findings emerge. First, narrow-source pretraining yields stronger linear probes on distribution-matched benchmarks, while diverse pretraining produces more adaptable representations under fine-tuning -- a trade-off invisible under single-protocol evaluation. Trained on three source corpora, PRISM matches or outperforms REVE (92 datasets, 60,000+ hours) on the majority of tasks, demonstrating that targeted diversity can substitute for indiscriminate scale and that dataset count is a confounding variable in model comparison. Second, on a clinically challenging and previously untested task -- distinguishing epilepsy from diagnostic mimickers via interictal EEG -- the diverse checkpoint outperforms the narrow-source checkpoint by +12.3 pp balanced accuracy, the largest gap across all evaluations. Third, systematic inconsistencies between EEG-Bench and EEG-FM-Bench reverse model rankings on identical datasets by up to 24 pp; we identify six concrete sources including split construction, checkpoint selection, segment length, and normalization, showing these factors compound non-additively.", "date": "2026-02-28", "categories": []}
{"id": "2603.02937", "title": "Bias and Fairness in Self-Supervised Acoustic Representations for Cognitive Impairment Detection", "abstract": "Speech-based detection of cognitive impairment (CI) offers a promising non-invasive approach for early diagnosis, yet performance disparities across demographic and clinical subgroups remain underexplored, raising concerns around fairness and generalizability. This study presents a systematic bias analysis of acoustic-based CI and depression classification using the DementiaBank Pitt Corpus. We compare traditional acoustic features (MFCCs, eGeMAPS) with contextualized speech embeddings from Wav2Vec 2.0 (W2V2), and evaluate classification performance across gender, age, and depression-status subgroups. For CI detection, higher-layer W2V2 embeddings outperform baseline features (UAR up to 80.6\\%), but exhibit performance disparities; specifically, females and younger participants demonstrate lower discriminative power (\\(AUC\\): 0.769 and 0.746, respectively) and substantial specificity disparities (\\(\\Delta_{spec}\\) up to 18\\% and 15\\%, respectively), leading to a higher risk of misclassifications than their counterparts. These disparities reflect representational biases, defined as systematic differences in model performance across demographic or clinical subgroups. Depression detection within CI subjects yields lower overall performance, with mild improvements from low and mid-level W2V2 layers. Cross-task generalization between CI and depression classification is limited, indicating that each task depends on distinct representations. These findings emphasize the need for fairness-aware model evaluation and subgroup-specific analysis in clinical speech applications, particularly in light of demographic and clinical heterogeneity in real-world applications.", "date": "2026-03-03", "categories": []}
{"id": "2506.13104", "title": "Equitable Electronic Health Record Prediction with FAME: Fairness-Aware Multimodal Embedding", "abstract": "Electronic Health Record (EHR) data encompass diverse modalities -- text, images, and medical codes -- that are vital for clinical decision-making. To process these complex data, multimodal AI (MAI) has emerged as a powerful approach for fusing such information. However, most existing MAI models optimize for better prediction performance, potentially reinforcing biases across patient subgroups. Although bias-reduction techniques for multimodal models have been proposed, the individual strengths of each modality and their interplay in both reducing bias and optimizing performance remain underexplored. In this work, we introduce FAME (Fairness-Aware Multimodal Embeddings), a framework that explicitly weights each modality according to its fairness contribution. FAME optimizes both performance and fairness by incorporating a combined loss function. We leverage the Error Distribution Disparity Index (EDDI) to measure fairness across subgroups and propose a sign-agnostic aggregation method to balance fairness across subgroups, ensuring equitable model outcomes. We evaluate FAME with BEHRT and BioClinicalBERT, combining structured and unstructured EHR data, and demonstrate its effectiveness in terms of performance and fairness compared with other baselines across multiple EHR prediction tasks.", "date": "2025-06-16", "categories": []}
{"id": "2507.21783", "title": "Domain Generalization and Adaptation in Intensive Care with Anchor Regression", "abstract": "The performance of predictive models in clinical settings often degrades when deployed in new hospitals due to distribution shifts. This paper presents a large-scale study of causality-inspired domain generalization on heterogeneous multi-center intensive care unit (ICU) data. We apply anchor regression and introduce anchor boosting, a novel, tree-based nonlinear extension, to a large dataset comprising 400,000 patients from nine distinct ICU databases. We find that anchor regularization yields improvements of out-of-distribution performance, particularly for the most dissimilar target domains. The methods appear robust to violations of theoretical assumptions, such as anchor exogeneity. Furthermore, we propose a novel conceptual framework to quantify the utility of large external data datasets. By evaluating performance as a function of available target-domain data, we identify three regimes: (i) a domain generalization regime, where only the external model should be used, (ii) a domain adaptation regime, where refitting the external model is optimal, and (iii) a data-rich regime, where external data provides no additional value.", "date": "2025-07-29", "categories": []}
{"id": "2509.03906", "title": "Toward Clinically Explainable AI for Medical Diagnosis: A Foundation Model with Human-Compatible Reasoning via Reinforcement Learning", "abstract": "The clinical adoption of artificial intelligence (AI) in medical diagnostics is critically hampered by its black-box nature, which prevents clinicians from verifying the rationale behind automated decisions. To overcome this fundamental barrier, we introduce DeepMedix-R1, a foundation model (FM) for chest X-ray (CXR) interpretation that generates not only accurate diagnoses but also a transparent, step-by-step reasoning process grounded in specific visual evidence. Our methodology employs a sequential training strategy, beginning with instruction fine-tuning, followed by a cold-start phase to elicit reasoning capabilities. Critically, we then implement reinforcement learning with grounded rewards to meticulously refine the model, aligning both its diagnostic outputs and its reasoning pathways with clinical plausibility. Quantitative assessments show that DeepMedix-R1 substantially outperforms advanced FMs, achieving improvements in report generation and visual question answering tasks. We also introduce Report Arena, a novel LLM-based benchmark that ranks DeepMedix-R1 first among competing models for output quality. Most significantly, a formal review by clinical experts reveals a profound preference for DeepMedix-R1's generated reasoning over the broadly adopted Qwen2.5-VL-7B model, confirming its superior interpretability and clinical utility.", "date": "2025-09-04", "categories": []}
{"id": "2512.01210", "title": "Knowledge Graph Augmented Large Language Models for Disease Prediction", "abstract": "Electronic health records (EHRs) enable strong clinical prediction, but explanations are often coarse and hard to use for patient-level decisions. We propose a knowledge graph (KG)-guided chain-of-thought (CoT) framework for visit-level disease prediction on MIMIC-III. We map ICD-9 codes to PrimeKG, mine disease-relevant nodes and paths, and use these paths to scaffold temporally consistent CoT rationales, retaining only samples whose conclusions match observed outcomes. We fine-tune lightweight instruction-tuned LLMs (LLaMA-3.1-Instruct-8B and Gemma-7B) on two small cohorts (400 and 1,000 index visits) across ten PrimeKG-mapped diseases. Our models outperform strong classical baselines, reaching AUROC 0.66-0.70 and macro-AUPR 0.40-0.47. Without additional training, the models transfer zero-shot to the CRADLE cohort, improving accuracy from 0.40-0.51 to 0.72-0.77. In a blinded clinician study, KG-guided CoT rationales are consistently preferred for clarity, relevance, and correctness. Code is available at: https://github.com/JonathanWry/KG-guided-LLM-pipeline", "date": "2025-12-01", "categories": []}
{"id": "2512.16235", "title": "AI-Powered Dermatological Diagnosis: From Interpretable Models to Clinical Implementation A Comprehensive Framework for Accessible and Trustworthy Skin Disease Detection", "abstract": "Dermatological conditions affect 1.9 billion people globally, yet accurate diagnosis remains challenging due to limited specialist availability and complex clinical presentations. Family history significantly influences skin disease susceptibility and treatment responses, but is often underutilized in diagnostic processes. This research addresses the critical question: How can AI-powered systems integrate family history data with clinical imaging to enhance dermatological diagnosis while supporting clinical trial validation and real-world implementation?\n  We developed a comprehensive multi-modal AI framework that combines deep learning-based image analysis with structured clinical data, including detailed family history patterns. Our approach employs interpretable convolutional neural networks integrated with clinical decision trees that incorporate hereditary risk factors. The methodology includes prospective clinical trials across diverse healthcare settings to validate AI-assisted diagnosis against traditional clinical assessment.\n  In this work, validation was conducted with healthcare professionals to assess AI-assisted outputs against clinical expectations; prospective clinical trials across diverse healthcare settings are proposed as future work. The integrated AI system demonstrates enhanced diagnostic accuracy when family history data is incorporated, particularly for hereditary skin conditions such as melanoma, psoriasis, and atopic dermatitis. Expert feedback indicates potential for improved early detection and more personalized recommendations; formal clinical trials are planned. The framework is designed for integration into clinical workflows while maintaining interpretability through explainable AI mechanisms.", "date": "2025-12-18", "categories": []}
{"id": "2601.21955", "title": "From Generative Modeling to Clinical Classification: A GPT-Based Architecture for EHR Notes", "abstract": "The increasing availability of unstructured clinical narratives in electronic health records (EHRs) has created new opportunities for automated disease characterization, cohort identification, and clinical decision support. However, modeling long, domain-specific clinical text remains challenging due to limited labeled data, severe class imbalance, and the high computational cost of adapting large pretrained language models.\n  This study presents a GPT-based architecture for clinical text classification that adapts a pretrained decoder-only Transformer using a selective fine-tuning strategy. Rather than updating all model parameters, the majority of the GPT-2 backbone is frozen, and training is restricted to the final Transformer block, the final layer normalization, and a lightweight classification head. This approach substantially reduces the number of trainable parameters while preserving the representational capacity required to model complex clinical language.\n  The proposed method is evaluated on radiology reports from the MIMIC-IV-Note dataset using uncertainty-aware CheXpert-style labels derived directly from report text. Experiments cover multiple problem formulations, including multi-label classification of radiographic findings, binary per-label classification under different uncertainty assumptions, and aggregate disease outcome prediction. Across varying dataset sizes, the model exhibits stable convergence behavior and strong classification performance, particularly in settings dominated by non-mention and negated findings.\n  Overall, the results indicate that selective fine-tuning of pretrained generative language models provides an efficient and effective pathway for clinical text classification, enabling scalable adaptation to real-world EHR data while significantly reducing computational complexity.", "date": "2026-01-29", "categories": []}
{"id": "2602.19661", "title": "PaReGTA: An LLM-based EHR Data Encoding Approach to Capture Temporal Information", "abstract": "Temporal information in structured electronic health records (EHRs) is often lost in sparse one-hot or count-based representations, while sequence models can be costly and data-hungry. We propose PaReGTA, an LLM-based encoding framework that (i) converts longitudinal EHR events into visit-level templated text with explicit temporal cues, (ii) learns domain-adapted visit embeddings via lightweight contrastive fine-tuning of a sentence-embedding model, and (iii) aggregates visit embeddings into a fixed-dimensional patient representation using hybrid temporal pooling that captures both recency and globally informative visits. Because PaReGTA does not require training from scratch but instead utilizes a pre-trained LLM, it can perform well even in data-limited cohorts. Furthermore, PaReGTA is model-agnostic and can benefit from future EHR-specialized sentence-embedding models. For interpretability, we introduce PaReGTA-RSS (Representation Shift Score), which quantifies clinically defined factor importance by recomputing representations after targeted factor removal and projecting representation shifts through a machine learning model. On 39,088 migraine patients from the All of Us Research Program, PaReGTA outperforms sparse baselines for migraine type classification while deep sequential models were unstable in our cohort.", "date": "2026-02-23", "categories": []}
{"id": "2602.22400", "title": "Predicting Multi-Drug Resistance in Bacterial Isolates Through Performance Comparison and LIME-based Interpretation of Classification Models", "abstract": "The rise of Antimicrobial Resistance, particularly Multi-Drug Resistance (MDR), presents a critical challenge for clinical decision-making due to limited treatment options and delays in conventional susceptibility testing. This study proposes an interpretable machine learning framework to predict MDR in bacterial isolates using clinical features and antibiotic susceptibility patterns. Five classification models were evaluated, including Logistic Regression, Random Forest, AdaBoost, XGBoost, and LightGBM. The models were trained on a curated dataset of 9,714 isolates, with resistance encoded at the antibiotic family level to capture cross-class resistance patterns consistent with MDR definitions. Performance assessment included accuracy, F1-score, AUC-ROC, and Matthews Correlation Coefficient. Ensemble models, particularly XGBoost and LightGBM, demonstrated superior predictive capability across all metrics. To address the clinical transparency gap, Local Interpretable Model-agnostic Explanations (LIME) was applied to generate instance-level explanations. LIME identified resistance to quinolones, Co-trimoxazole, Colistin, aminoglycosides, and Furanes as the strongest contributors to MDR predictions, aligning with known biological mechanisms. The results show that combining high-performing models with local interpretability provides both accuracy and actionable insights for antimicrobial stewardship. This framework supports earlier MDR identification and enhances trust in machine learning-assisted clinical decision support.", "date": "2026-02-25", "categories": []}
{"id": "2603.00022", "title": "Noise reduction in BERT NER models for clinical entity extraction", "abstract": "Precision is of utmost importance in the realm of clinical entity extraction from clinical notes and reports. Encoder Models fine-tuned for Named Entity Recognition (NER) are an efficient choice for this purpose, as they don't hallucinate. We pre-trained an in-house BERT over clinical data and then fine-tuned it for NER. These models performed well on recall but could not close upon the high precision range, needed for clinical models. To address this challenge, we developed a Noise Removal model that refines the output of NER. The NER model assigns token-level entity tags along with probability scores for each token. Our Noise Removal (NR) model then analyzes these probability sequences and classifies predictions as either weak or strong. A na\\\"ive approach might involve filtering predictions based on low probability values; however, this method is unreliable. Owing to the characteristics of the SoftMax function, Transformer based architectures often assign disproportionately high confidence scores even to uncertain or weak predictions, making simple thresholding ineffective. To address this issue, we adopted a supervised modeling strategy in which the NR model leverages advanced features such as the Probability Density Map (PDM). The PDM captures the Semantic-Pull effect observed within Transformer embeddings, an effect that manifests in the probability distributions of NER class predictions across token sequences. This approach enables the model to classify predictions as weak or strong with significantly improved accuracy. With these NR models we were able to reduce False Positives across various clinical NER models by 50\\% to 90\\%.", "date": "2026-02-03", "categories": []}
{"id": "2603.00067", "title": "A Representation-Consistent Gated Recurrent Framework for Robust Medical Time-Series Classification", "abstract": "Medical time-series data are characterized by irregular sampling, high noise levels, missing values, and strong inter-feature dependencies. Recurrent neural networks (RNNs), particularly gated architectures such as Long Short-Term Memory (LSTM) and Gated Recurrent Units (GRU), are widely used for modeling such data due to their ability to capture temporal dependencies. However, standard gated recurrent models do not explicitly constrain the evolution of latent representations over time, leading to representation drift and instability under noisy or incomplete inputs. In this work, we propose a representation-consistent gated recurrent framework (RC-GRF) that introduces a principled regularization strategy to enforce temporal consistency in hidden-state representations. The proposed framework is model-agnostic and can be integrated into existing gated recurrent architectures without modifying their internal gating mechanisms. We provide a theoretical analysis demonstrating how the consistency constraint bounds hidden-state divergence and improves stability. Extensive experiments on medical time-series classification benchmarks show that the proposed approach improves robustness, reduces variance, and enhances generalization performance, particularly in noisy and low-sample settings.", "date": "2026-02-10", "categories": []}
{"id": "2603.00201", "title": "AdURA-Net: Adaptive Uncertainty and Region-Aware Network", "abstract": "One of the common issues in clinical decision-making is the presence of uncertainty, which often arises due to ambiguity in radiology reports, which often reflect genuine diagnostic uncertainty or limitations of automated label extraction in various complex cases. Especially the case of multilabel datasets such as CheXpert, MIMIC-CXR, etc., which contain labels such as positive, negative, and uncertain. In clinical decision-making, the uncertain label plays a tricky role as the model should not be forced to provide a confident prediction in the absence of sufficient evidence. The ability of the model to say it does not understand whenever it is not confident is crucial, especially in the cases of clinical decision-making involving high risks. Here, we propose AdURA-Net, a geometry-driven adaptive uncertainty-aware framework for reliable thoracic disease classification. The key highlights of the proposed model are: a) Adaptive dilated convolution and multiscale deformable alignment coupled with the backbone Densenet architecture capturing the anatomical complexities of the medical images, and b) Dual Head Loss, which combines masked binary cross entropy with logit and a Dirichlet evidential learning objective.", "date": "2026-02-27", "categories": []}
{"id": "2603.00208", "title": "Tipping the Balance: Impact of Class Imbalance Correction on the Performance of Clinical Risk Prediction Models", "abstract": "Objective: ML-based clinical risk prediction models are increasingly used to support decision-making in healthcare. While class-imbalance correction techniques are commonly applied to improve model performance in settings with rare outcomes, their impact on probabilistic calibration remains insufficiently understood. This study evaluated the effect of widely used resampling strategies on both discrimination and calibration across real-world clinical prediction tasks. Methods: Ten clinical datasets spanning diverse medical domains and including 605,842 patients were analyzed. Multiple machine-learning model families, including linear models and several non-linear approaches, were evaluated. Models were trained on the original data and under three commonly used 1:1 class-imbalance correction strategies (SMOTE, RUS, ROS). Performance was assessed on held-out data using discrimination and calibration metrics. Results: Across all datasets and model families, resampling had no positive impact on predictive performance. Changes in the Receiver Operating Characteristic Area Under Curve (ROC-AUC) relative to models trained on the original data were small and inconsistent (ROS: -0.002, p<0.05; RUS: -0.004, p>0.05; SMOTE: -0.01, p<0.05), with no resampling strategy demonstrating a systematic improvement. In contrast, resampling in general degraded the calibration performance. Models trained using imbalance correction exhibited higher Brier scores (0.029 to 0.080, p<0.05), reflecting poorer probabilistic accuracy, and marked deviations in calibration intercept and slope, indicating systematic distortions of predicted risk despite preserved rank-based performance. Conclusion: In a diverse set of real-world clinical prediction tasks, commonly used class-imbalance correction techniques did not provide generalizable improvements in discrimination and were associated with degraded calibration.", "date": "2026-02-27", "categories": []}
{"id": "2603.00756", "title": "Stroke outcome and evolution prediction from CT brain using a spatiotemporal diffusion autoencoder", "abstract": "Stroke is a major cause of death and disability worldwide. Accurate outcome and evolution prediction has the potential to revolutionize stroke care by individualizing clinical decision-making leading to better outcomes. However, despite a plethora of attempts and the rich data provided by neuroimaging, modelling the ultimate fate of brain tissue remains a challenging task. In this work, we apply recent ideas in the field of diffusion probabilistic models to generate a self-supervised semantically meaningful stroke representation from Computed Tomography (CT) images. We then improve this representation by extending the method to accommodate longitudinal images and the time from stroke onset. The effectiveness of our approach is evaluated on a dataset consisting of 5,824 CT images from 3,573 patients across two medical centers with minimal labels. Comparative experiments show that our method achieves the best performance for predicting next-day severity and functional outcome at discharge.", "date": "2026-02-28", "categories": []}
{"id": "2603.00757", "title": "Identifying and Characterising Response in Clinical Trials: Development and Validation of a Machine Learning Approach in Colorectal Cancer", "abstract": "Precision medicine promises to transform health care by offering individualised treatments that dramatically improve clinical outcomes. A necessary prerequisite is to identify subgroups of patients who respond differently to different therapies. Current approaches are limited to static measures of treatment success, neglecting the repeated measures found in most clinical trials. Our approach combines the concept of partly conditional modelling with treatment effect estimation based on the Virtual Twins method. The resulting time-specific responses to treatment are characterised using survLIME, an extension of Local Interpretable Model-agnostic Explanations (LIME) to survival data. Performance was evaluated using synthetic data and applied to clinical trials examining the effectiveness of panitumumab to treat metastatic colorectal cancer. An area under the receiver operating characteristic curve (AUC) of 0.77 for identifying fixed responders was achieved in a 1000 patient simulation. When considering dynamic responders, partly conditional modelling increased the AUC from 0.597 to 0.685. Applying the approach to colorectal cancer trials found genetic mutations, sites of metastasis, and ethnicity as important factors for response to treatment. Our approach can accommodate a dynamic response to treatment while potentially providing better performance than existing methods in instances of a fixed response to treatment. When applied to clinical data we attain results consistent with the literature.", "date": "2026-02-28", "categories": []}
{"id": "2603.01557", "title": "Benchmarking LLM Summaries of Multimodal Clinical Time Series for Remote Monitoring", "abstract": "Large language models (LLMs) can generate fluent clinical summaries of remote therapeutic monitoring time series. However, it remains unclear whether these narratives faithfully capture clinically significant events, such as sustained abnormalities. Existing evaluation metrics primarily focus on semantic similarity and linguistic quality, leaving event-level correctness largely unmeasured.\n  To address this gap, we introduce an event-based evaluation framework for multimodal time-series summarization using the Technology-Integrated Health Management (TIHM)-1.5 dementia monitoring dataset. Clinically grounded daily events are derived through rule-based abnormal thresholds and temporal persistence criteria. Model-generated summaries are then aligned with these structured facts.\n  Our evaluation protocol measures abnormality recall, duration recall, measurement coverage, and hallucinated event mentions. We benchmark three approaches: zero-shot prompting, statistical prompting, and a vision-based pipeline that uses rendered time-series visualizations. The results reveal a striking decoupling between conventional metrics and clinical event fidelity. Models that achieve high semantic similarity scores often exhibit near-zero abnormality recall. In contrast, the vision-based approach demonstrates the strongest event alignment, achieving 45.7% abnormality recall and 100% duration recall.\n  These findings underscore the importance of event-aware evaluation to ensure reliable clinical time-series summarization.", "date": "2026-03-02", "categories": []}
{"id": "2603.01948", "title": "PreSight: Preoperative Outcome Prediction for Parkinson's Disease via Region-Prior Morphometry and Patient-Specific Weighting", "abstract": "Preoperative improvement rate prediction for Parkinson's disease surgery is clinically important yet difficult because imaging signals are subtle and patients are heterogeneous. We address this setting, where only information available before surgery is used, and the goal is to predict patient-specific postoperative motor benefit. We present PreSight, a presurgical outcome model that fuses clinical priors with preoperative MRI and deformation-based morphometry (DBM) and adapts regional importance through a patient-specific weighting module. The model produces end-to-end, calibrated, decision-ready predictions with patient-level explanations. We evaluate PreSight on a real-world two-center cohort of 400 subjects with multimodal presurgical inputs and postoperative improvement labels. PreSight outperforms strong clinical, imaging-only, and multimodal baselines. It attains 88.89% accuracy on internal validation and 85.29% on an external-center test for responder classification and shows better probability calibration and higher decision-curve net benefit. Ablations and analyses confirm the contribution of DBM and the patient-specific weighting module and indicate that the model emphasizes disease-relevant regions in a patient-specific manner. These results demonstrate that integrating clinical prior knowledge with region-adaptive morphometry enables reliable presurgical decision support in routine practice.", "date": "2026-03-02", "categories": []}
{"id": "2602.23507", "title": "Sample Size Calculations for Developing Clinical Prediction Models: Overview and pmsims R package", "abstract": "Background: Clinical prediction models are increasingly used to inform healthcare decisions, but determining the minimum sample size for their development remains a critical and unresolved challenge. Inadequate sample sizes can lead to overfitting, poor generalisability, and biased predictions. Existing approaches, such as heuristic rules, closed-form formulas, and simulation-based methods, vary in flexibility and accuracy, particularly for complex data structures and machine learning models. Methods: We review current methodologies for sample size estimation in prediction modelling and introduce a conceptual framework that distinguishes between mean-based and assurance-based criteria. Building on this, we propose a novel simulation-based approach that integrates learning curves, Gaussian Process optimisation, and assurance principles to identify sample sizes that achieve target performance with high probability. This approach is implemented in pmsims, an open-source, model-agnostic R package. Results: Through case studies, we demonstrate that sample size estimates vary substantially across methods, performance metrics, and modelling strategies. Compared to existing tools, pmsims provides flexible, efficient, and interpretable solutions that accommodate diverse models and user-defined metrics while explicitly accounting for variability in model performance. Conclusions: Our framework and software advance sample size methodology for clinical prediction modelling by combining flexibility with computational efficiency. Future work should extend these methods to hierarchical and multimodal data, incorporate fairness and stability metrics, and address challenges such as missing data and complex dependency structures.", "date": "2026-02-26", "categories": []}
{"id": "2602.22280", "title": "Integrating Machine Learning Ensembles and Large Language Models for Heart Disease Prediction Using Voting Fusion", "abstract": "Cardiovascular disease is the primary cause of death globally, necessitating early identification, precise risk classification, and dependable decision-support technologies. The advent of large language models (LLMs) provides new zero-shot and few-shot reasoning capabilities, even though machine learning (ML) algorithms, especially ensemble approaches like Random Forest, XGBoost, LightGBM, and CatBoost, are excellent at modeling complex, non-linear patient data and routinely beat logistic regression. This research predicts cardiovascular disease using a merged dataset of 1,190 patient records, comparing traditional machine learning models (95.78% accuracy, ROC-AUC 0.96) with open-source large language models via OpenRouter APIs. Finally, a hybrid fusion of the ML ensemble and LLM reasoning under Gemini 2.5 Flash achieved the best results (96.62% accuracy, 0.97 AUC), showing that LLMs (78.9 % accuracy) work best when combined with ML models rather than used alone. Results show that ML ensembles achieved the highest performance (95.78% accuracy, ROC-AUC 0.96), while LLMs performed moderately in zero-shot (78.9%) and slightly better in few-shot (72.6%) settings. The proposed hybrid method enhanced the strength in uncertain situations, illustrating that ensemble ML is considered the best structured tabular prediction case, but it can be integrated with hybrid ML-LLM systems to provide a minor increase and open the way to more reliable clinical decision-support tools.", "date": "2026-02-25", "categories": []}
{"id": "2602.22288", "title": "Reliable XAI Explanations in Sudden Cardiac Death Prediction for Chagas Cardiomyopathy", "abstract": "Sudden cardiac death (SCD) is unpredictable, and its prediction in Chagas cardiomyopathy (CC) remains a significant challenge, especially in patients not classified as high risk. While AI and machine learning models improve risk stratification, their adoption is hindered by a lack of transparency, as they are often perceived as \\textit{black boxes} with unclear decision-making processes. Some approaches apply heuristic explanations without correctness guarantees, leading to mistakes in the decision-making process. To address this, we apply a logic-based explainability method with correctness guarantees to the problem of SCD prediction in CC. This explainability method, applied to an AI classifier with over 95\\% accuracy and recall, demonstrated strong predictive performance and 100\\% explanation fidelity. When compared to state-of-the-art heuristic methods, it showed superior consistency and robustness. This approach enhances clinical trust, facilitates the integration of AI-driven tools into practice, and promotes large-scale deployment, particularly in endemic regions where it is most needed.", "date": "2026-02-25", "categories": []}
{"id": "2602.22902", "title": "A Data-Driven Approach to Support Clinical Renal Replacement Therapy", "abstract": "This study investigates a data-driven machine learning approach to predict membrane fouling in critically ill patients undergoing Continuous Renal Replacement Therapy (CRRT). Using time-series data from an ICU, 16 clinically selected features were identified to train predictive models. To ensure interpretability and enable reliable counterfactual analysis, the researchers adopted a tabular data approach rather than modeling temporal dependencies directly. Given the imbalance between fouling and non-fouling cases, the ADASYN oversampling technique was applied to improve minority class representation. Random Forest, XGBoost, and LightGBM models were tested, achieving balanced performance with 77.6% sensitivity and 96.3% specificity at a 10% rebalancing rate. Results remained robust across different forecasting horizons. Notably, the tabular approach outperformed LSTM recurrent neural networks, suggesting that explicit temporal modeling was not necessary for strong predictive performance. Feature selection further reduced the model to five key variables, improving simplicity and interpretability with minimal loss of accuracy. A Shapley value-based counterfactual analysis was applied to the best-performing model, successfully identifying minimal input changes capable of reversing fouling predictions. Overall, the findings support the viability of interpretable machine learning models for predicting membrane fouling during CRRT. The integration of prediction and counterfactual analysis offers practical clinical value, potentially guiding therapeutic adjustments to reduce fouling risk and improve patient management.", "date": "2026-02-26", "categories": []}
{"id": "2502.14183", "title": "Glycemic-Aware and Architecture-Agnostic Training Framework for Blood Glucose Forecasting in Type 1 Diabetes", "abstract": "Managing Type 1 Diabetes (T1D) demands constant vigilance as individuals strive to regulate their blood glucose levels and avoid dysglycemia, including hyperglycemia and hypoglycemia. Despite advances in automated insulin delivery (AID) systems, achieving optimal glycemic control remains challenging. These systems integrate data from wearable devices such as insulin pumps and continuous glucose monitors (CGMs), helping reduce variability and improve time in range. However, they often fail to prevent dysglycemia due to limitations in prediction algorithms that cannot accurately anticipate glycemic excursions. This limitation highlights the need for more advanced glucose forecasting methods. To address this need, we introduce GLIMMER (Glucose Level Indicator Model with Modified Error Rate), a modular and architecture-agnostic training framework for glucose forecasting. GLIMMER combines structured preprocessing, a region-aware loss formulation, and genetic algorithm-based weight optimization to emphasize prediction accuracy in dysglycemic regions. We evaluate GLIMMER using two datasets: the publicly available OhioT1DM dataset and a newly collected AZT1D dataset consisting of data from 25 individuals with T1D. Our analyses demonstrate that GLIMMER consistently improves forecasting performance across baseline architectures, reducing RMSE and MAE by up to 24.6% and 29.6%, respectively. Additionally, GLIMMER achieves a recall of 98.4% and an F1-score of 86.8% for dysglycemia prediction, highlighting strong performance in clinically high-risk regions. Compared with state-of-the-art models containing millions of parameters-such as TimesNet (18.7M), BG-BERT (2.1M), and Gluformer (11.2M)-GLIMMER attains comparable accuracy while using only 10K parameters, demonstrating its efficiency as a lightweight and architecture-agnostic solution for glycemic forecasting.", "date": "2025-02-20", "categories": []}
{"id": "2507.14206", "title": "A Comprehensive Benchmark for Electrocardiogram Time-Series", "abstract": "Electrocardiogram~(ECG), a key bioelectrical time-series signal, is crucial for assessing cardiac health and diagnosing various diseases. Given its time-series format, ECG data is often incorporated into pre-training datasets for large-scale time-series model training. However, existing studies often overlook its unique characteristics and specialized downstream applications, which differ significantly from other time-series data, leading to an incomplete understanding of its properties. In this paper, we present an in-depth investigation of ECG signals and establish a comprehensive benchmark, which includes (1) categorizing its downstream applications into four distinct evaluation tasks, (2) identifying limitations in traditional evaluation metrics for ECG analysis, and introducing a novel metric; (3) benchmarking state-of-the-art time-series models and proposing a new architecture. Extensive experiments demonstrate that our proposed benchmark is comprehensive and robust. The results validate the effectiveness of the proposed metric and model architecture, which establish a solid foundation for advancing research in ECG signal analysis.", "date": "2025-07-15", "categories": []}
{"id": "2602.10359", "title": "Beyond Calibration: Confounding Pathology Limits Foundation Model Specificity in Abdominal Trauma CT", "abstract": "Purpose: Translating foundation models into clinical practice requires evaluating their performance under compound distribution shift, where severe class imbalance coexists with heterogeneous imaging appearances. This challenge is relevant for traumatic bowel injury, a rare but high-mortality diagnosis. We investigated whether specificity deficits in foundation models are associated with heterogeneity in the negative class. Methods: This retrospective study used the multi-institutional, RSNA Abdominal Traumatic Injury CT dataset (2019-2023), comprising scans from 23 centres. Two foundation models (MedCLIP, zero-shot; RadDINO, linear probe) were compared against three task-specific approaches (CNN, Transformer, Ensemble). Models were trained on 3,147 patients (2.3% bowel injury prevalence) and evaluated on an enriched 100-patient test set. To isolate negative-class effects, specificity was assessed in patients without bowel injury who had concurrent solid organ injury (n=58) versus no abdominal pathology (n=50). Results: Foundation models achieved equivalent discrimination to task-specific models (AUC, 0.64-0.68 versus 0.58-0.64) with higher sensitivity (79-91% vs 41-74%) but lower specificity (33-50% vs 50-88%). All models demonstrated high specificity in patients without abdominal pathology (84-100%). When solid organ injuries were present, specificity declined substantially for foundation models (50-51 percentage points) compared with smaller reductions of 12-41 percentage points for task-specific models. Conclusion: Foundation models matched task-specific discrimination without task-specific training, but their specificity deficits were driven primarily by confounding negative-class heterogeneity rather than prevalence alone. Susceptibility to negative-class heterogeneity decreased progressively with labelled training, suggesting adaptation is required before clinical implementation.", "date": "2026-02-10", "categories": []}
{"id": "2602.19674", "title": "Continuous Telemonitoring of Heart Failure using Personalised Speech Dynamics", "abstract": "Remote monitoring of heart failure (HF) via speech signals provides a non-invasive and cost-effective solution for long-term patient management. However, substantial inter-individual heterogeneity in vocal characteristics often limits the accuracy of traditional cross-sectional classification models. To address this, we propose a Longitudinal Intra-Patient Tracking (LIPT) scheme designed to capture the trajectory of relative symptomatic changes within individuals. Central to this framework is a Personalised Sequential Encoder (PSE), which transforms longitudinal speech recordings into context-aware latent representations. By incorporating historical data at each timestamp, the PSE facilitates a holistic assessment of the clinical trajectory rather than modelling discrete visits independently. Experimental results from a cohort of 225 patients demonstrate that the LIPT paradigm significantly outperforms the classic cross-sectional approaches, achieving a recognition accuracy of 99.7% for clinical status transitions. The model's high sensitivity was further corroborated by additional follow-up data, confirming its efficacy in predicting HF deterioration and its potential to secure patient safety in remote, home-based settings. Furthermore, this work addresses the gap in existing literature by providing a comprehensive analysis of different speech task designs and acoustic features. Taken together, the superior performance of the LIPT framework and PSE architecture validates their readiness for integration into long-term telemonitoring systems, offering a scalable solution for remote heart failure management.", "date": "2026-02-23", "categories": []}
{"id": "2602.21648", "title": "Multimodal Survival Modeling and Fairness-Aware Clinical Machine Learning for 5-Year Breast Cancer Risk Prediction", "abstract": "Clinical risk prediction models often underperform in real-world settings due to poor calibration, limited transportability, and subgroup disparities. These challenges are amplified in high-dimensional multimodal cancer datasets characterized by complex feature interactions and a p >> n structure. We present a fully reproducible multimodal machine learning framework for 5-year overall survival prediction in breast cancer, integrating clinical variables with high-dimensional transcriptomic and copy-number alteration (CNA) features from the METABRIC cohort.\n  After variance- and sparsity-based filtering and dimensionality reduction, models were trained using stratified train/validation/test splits with validation-based hyperparameter tuning. Two survival approaches were compared: an elastic-net regularized Cox model (CoxNet) and a gradient-boosted survival tree model implemented using XGBoost. CoxNet provides embedded feature selection and stable estimation, whereas XGBoost captures nonlinear effects and higher-order interactions.\n  Performance was assessed using time-dependent area under the ROC curve (AUC), average precision (AP), calibration curves, Brier score, and bootstrapped 95 percent confidence intervals. CoxNet achieved validation and test AUCs of 98.3 and 96.6, with AP values of 90.1 and 80.4. XGBoost achieved validation and test AUCs of 98.6 and 92.5, with AP values of 92.5 and 79.9. Fairness diagnostics showed stable discrimination across age groups, estrogen receptor status, molecular subtypes, and menopausal state.\n  This work introduces a governance-oriented multimodal survival framework emphasizing calibration, fairness auditing, robustness, and reproducibility for high-dimensional clinical machine learning.", "date": "2026-02-25", "categories": []}
{"id": "2602.22018", "title": "Disease Progression and Subtype Modeling for Combined Discrete and Continuous Input Data", "abstract": "Disease progression modeling provides a robust framework to identify long-term disease trajectories from short-term biomarker data. It is a valuable tool to gain a deeper understanding of diseases with a long disease trajectory, such as Alzheimer's disease. A key limitation of most disease progression models is that they are specific to a single data type (e.g., continuous data), thereby limiting their applicability to heterogeneous, real-world datasets. To address this limitation, we propose the Mixed Events model, a novel disease progression model that handles both discrete and continuous data types. This model is implemented within the Subtype and Stage Inference (SuStaIn) framework, resulting in Mixed-SuStaIn, enabling subtype and progression modeling. We demonstrate the effectiveness of Mixed-SuStaIn through simulation experiments and real-world data from the Alzheimer's Disease Neuroimaging Initiative, showing that it performs well on mixed datasets. The code is available at: https://github.com/ucl-pond/pySuStaIn.", "date": "2026-02-25", "categories": []}
{"id": "2507.01080", "title": "Development and Comparative Evaluation of Three Artificial Intelligence Models (NLP, LLM, JEPA) for Predicting Triage in Emergency Departments: A 7-Month Retrospective Proof-of-Concept", "abstract": "Emergency departments struggle with persistent triage errors, especially undertriage and overtriage, which are aggravated by growing patient volumes and staff shortages. This study evaluated three AI models [TRIAGEMASTER (NLP), URGENTIAPARSE (LLM), and EMERGINET (JEPA)] against the FRENCH triage scale and nurse practice, using seven months of adult triage data from Roger Salengro Hospital in Lille, France. Among the models, the LLM-based URGENTIAPARSE consistently outperformed both AI alternatives and nurse triage, achieving the highest accuracy (F1-score 0.900, AUC-ROC 0.879) and superior performance in predicting hospitalization needs (GEMSA). Its robustness across structured data and raw transcripts highlighted the advantage of LLM architectures in abstracting patient information. Overall, the findings suggest that integrating LLM-based AI into emergency department workflows could significantly enhance patient safety and operational efficiency, though successful adoption will depend on addressing limitations and ensuring ethical transparency.", "date": "2025-07-01", "categories": []}
{"id": "2601.11283", "title": "Metabolomic Biomarker Discovery for ADHD Diagnosis Using Interpretable Machine Learning", "abstract": "Attention Deficit Hyperactivity Disorder (ADHD) is a prevalent neurodevelopmental disorder with limited objective diagnostic tools, highlighting the urgent need for objective, biology-based diagnostic frameworks in precision psychiatry. We integrate urinary metabolomics with an interpretable machine learning framework to identify biochemical signatures associated with ADHD. Targeted metabolomic profiles from 52 ADHD and 46 control participants were analyzed using a Closest Resemblance (CR) classifier with embedded feature selection. The CR model outperformed Random Forest and K-Nearest Neighbor classifiers, achieving an AUC > 0.97 based on a reduced panel of 14 metabolites. These metabolites including dopamine 4-sulfate, N-acetylaspartylglutamic acid, and citrulline map to dopaminergic neurotransmission and amino acid metabolism pathways, offering mechanistic insight into ADHD pathophysiology. The CR classifier's transparent decision boundaries and low computational cost support integration into targeted metabolomic assays and future point of care diagnostic platforms. Overall, this work demonstrates a translational framework combining metabolomics and interpretable machine learning to advance objective, biologically informed diagnostic strategies for ADHD.", "date": "2026-01-16", "categories": []}
{"id": "2602.20324", "title": "An artificial intelligence framework for end-to-end rare disease phenotyping from clinical notes using large language models", "abstract": "Phenotyping is fundamental to rare disease diagnosis, but manual curation of structured phenotypes from clinical notes is labor-intensive and difficult to scale. Existing artificial intelligence approaches typically optimize individual components of phenotyping but do not operationalize the full clinical workflow of extracting features from clinical text, standardizing them to Human Phenotype Ontology (HPO) terms, and prioritizing diagnostically informative HPO terms. We developed RARE-PHENIX, an end-to-end AI framework for rare disease phenotyping that integrates large language model-based phenotype extraction, ontology-grounded standardization to HPO terms, and supervised ranking of diagnostically informative phenotypes. We trained RARE-PHENIX using data from 2,671 patients across 11 Undiagnosed Diseases Network clinical sites, and externally validated it on 16,357 real-world clinical notes from Vanderbilt University Medical Center. Using clinician-curated HPO terms as the gold standard, RARE-PHENIX consistently outperformed a state-of-the-art deep learning baseline (PhenoBERT) across ontology-based similarity and precision-recall-F1 metrics in end-to-end evaluation (i.e., ontology-based similarity of 0.70 vs. 0.58). Ablation analyses demonstrated performance improvements with the addition of each module in RARE-PHENIX (extraction, standardization, and prioritization), supporting the value of modeling the full clinical phenotyping workflow. By modeling phenotyping as a clinically aligned workflow rather than a single extraction task, RARE-PHENIX provides structured, ranked phenotypes that are more concordant with clinician curation and has the potential to support human-in-the-loop rare disease diagnosis in real-world settings.", "date": "2026-02-23", "categories": []}
{"id": "2602.20442", "title": "Imputation of Unknown Missingness in Sparse Electronic Health Records", "abstract": "Machine learning holds great promise for advancing the field of medicine, with electronic health records (EHRs) serving as a primary data source. However, EHRs are often sparse and contain missing data due to various challenges and limitations in data collection and sharing between healthcare providers. Existing techniques for imputing missing values predominantly focus on known unknowns, such as missing or unavailable values of lab test results; most do not explicitly address situations where it is difficult to distinguish what is missing. For instance, a missing diagnosis code in an EHR could signify either that the patient has not been diagnosed with the condition or that a diagnosis was made, but not shared by a provider. Such situations fall into the paradigm of unknown unknowns. To address this challenge, we develop a general purpose algorithm for denoising data to recover unknown missing values in binary EHRs. We design a transformer-based denoising neural network where the output is thresholded adaptively to recover values in cases where we predict data are missing. Our results demonstrate improved accuracy in denoising medical codes within a real EHR dataset compared to existing imputation approaches and leads to increased performance on downstream tasks using the denoised data. In particular, when applying our method to a real world application, predicting hospital readmission from EHRs, our method achieves statistically significant improvement over all existing baselines.", "date": "2026-02-24", "categories": []}
{"id": "2602.21165", "title": "PVminer: A Domain-Specific Tool to Detect the Patient Voice in Patient Generated Data", "abstract": "Patient-generated text such as secure messages, surveys, and interviews contains rich expressions of the patient voice (PV), reflecting communicative behaviors and social determinants of health (SDoH). Traditional qualitative coding frameworks are labor intensive and do not scale to large volumes of patient-authored messages across health systems. Existing machine learning (ML) and natural language processing (NLP) approaches provide partial solutions but often treat patient-centered communication (PCC) and SDoH as separate tasks or rely on models not well suited to patient-facing language. We introduce PVminer, a domain-adapted NLP framework for structuring patient voice in secure patient-provider communication. PVminer formulates PV detection as a multi-label, multi-class prediction task integrating patient-specific BERT encoders (PV-BERT-base and PV-BERT-large), unsupervised topic modeling for thematic augmentation (PV-Topic-BERT), and fine-tuned classifiers for Code, Subcode, and Combo-level labels. Topic representations are incorporated during fine-tuning and inference to enrich semantic inputs. PVminer achieves strong performance across hierarchical tasks and outperforms biomedical and clinical pre-trained baselines, achieving F1 scores of 82.25% (Code), 80.14% (Subcode), and up to 77.87% (Combo). An ablation study further shows that author identity and topic-based augmentation each contribute meaningful gains. Pre-trained models, source code, and documentation will be publicly released, with annotated datasets available upon request for research use.", "date": "2026-02-24", "categories": []}
{"id": "2602.21168", "title": "Sequential Counterfactual Inference for Temporal Clinical Data: Addressing the Time Traveler Dilemma", "abstract": "Counterfactual inference enables clinicians to ask \"what if\" questions about patient outcomes, but standard methods assume feature independence and simultaneous modifiability -- assumptions violated by longitudinal clinical data. We introduce the Sequential Counterfactual Framework, which respects temporal dependencies in electronic health records by distinguishing immutable features (chronic diagnoses) from controllable features (lab values) and modeling how interventions propagate through time. Applied to 2,723 COVID-19 patients (383 Long COVID heart failure cases, 2,340 matched controls), we demonstrate that 38-67% of patients with chronic conditions would require biologically impossible counterfactuals under naive methods. We identify a cardiorenal cascade (CKD -> AKI -> HF) with relative risks of 2.27 and 1.19 at each step, illustrating temporal propagation that sequential -- but not naive -- counterfactuals can capture. Our framework transforms counterfactual explanation from \"what if this feature were different?\" to \"what if we had intervened earlier, and how would that propagate forward?\" --  yielding clinically actionable insights grounded in biological plausibility.", "date": "2026-02-24", "categories": []}
{"id": "2602.18496", "title": "A Patient-Specific Digital Twin for Adaptive Radiotherapy of Non-Small Cell Lung Cancer", "abstract": "Radiotherapy continues to become more precise and data dense, with current treatment regimens generating high frequency imaging and dosimetry streams ideally suited for AI driven temporal modeling to characterize how normal tissues evolve with time. Each fraction in biologically guided radiotherapy(BGRT) treated non small cell lung cancer (NSCLC) patients records new metabolic, anatomical, and dose information. However, clinical decision making is largely informed by static, population based NTCP models which overlook the dynamic, unique biological trajectories encoded in sequential data. We developed COMPASS (Comprehensive Personalized Assessment System) for safe radiotherapy, functioning as a temporal digital twin architecture utilizing per fraction PET, CT, dosiomics, radiomics, and cumulative biologically equivalent dose (BED) kinetics to model normal tissue biology as a dynamic time series process. A GRU autoencoder was employed to learn organ specific latent trajectories, which were classified via logistic regression to predict eventual CTCAE grade 1 or higher toxicity. Eight NSCLC patients undergoing BGRT contributed to the 99 organ fraction observations covering 24 organ trajectories (spinal cord, heart, and esophagus). Despite the small cohort, intensive temporal phenotyping allowed for comprehensive analysis of individual dose response dynamics. Our findings revealed a viable AI driven early warning window, as increasing risk ratings occurred from several fractions before clinical toxicity. The dense BED driven representation revealed biologically relevant spatial dose texture characteristics that occur before toxicity and are averaged out with traditional volume based dosimetry. COMPASS establishes a proof of concept for AI enabled adaptive radiotherapy, where treatment is guided by a continually updated digital twin that tracks each patients evolving biological response.", "date": "2026-02-15", "categories": []}
{"id": "2602.18521", "title": "AdaptStress: Online Adaptive Learning for Interpretable and Personalized Stress Prediction Using Multivariate and Sparse Physiological Signals", "abstract": "Continuous stress forecasting could potentially contribute to lifestyle interventions. This paper presents a novel, explainable, and individualized approach for stress prediction using physiological data from consumer-grade smartwatches. We develop a time series forecasting model that leverages multivariate features, including heart rate variability, activity patterns, and sleep metrics, to predict stress levels across 16 temporal horizons (History window: 3, 5, 7, 9 days; forecasting window: 1, 3, 5, 7 days). Our evaluation involves 16 participants monitored for 10-15 weeks. We evaluate our approach across 16 participants, comparing against state-of-the-art time series models (Informer, TimesNet, PatchTST) and traditional baselines (CNN, LSTM, CNN-LSTM) across multiple temporal horizons. Our model achieved performance with an MSE of 0.053, MAE of 0.190, and RMSE of 0.226 in optimal settings (5-day input, 1-day prediction). A comparison with the baseline models shows that our model outperforms TimesNet, PatchTST, CNN-LSTM, LSTM, and CNN under all conditions, representing improvements of 36.9%, 25.5%, and 21.5% over the best baseline. According to the explanability analysis, sleep metrics are the most dominant and consistent stress predictors (importance: 1.1, consistency: 0.9-1.0), while activity features exhibit high inter-participant variability (0.1-0.2). Most notably, the model captures individual-specific patterns where identical features can have opposing effects across users, validating its personalization capabilities. These findings establish that consumer wearables, combined with adaptive and interpretable deep learning, can deliver relevant stress assessment adapted to individual physiological responses, providing a foundation for scalable, continuous, explainable mental health monitoring in real-world settings.", "date": "2026-02-19", "categories": []}
{"id": "2602.19668", "title": "Personalized Longitudinal Medical Report Generation via Temporally-Aware Federated Adaptation", "abstract": "Longitudinal medical report generation is clinically important yet remains challenging due to strict privacy constraints and the evolving nature of disease progression. Although federated learning (FL) enables collaborative training without data sharing, existing FL methods largely overlook longitudinal dynamics by assuming stationary client distributions, making them unable to model temporal shifts across visits or patient-specific heterogeneity-ultimately leading to unstable optimization and suboptimal report generation.\n  We introduce Federated Temporal Adaptation (FTA), a federated setting that explicitly accounts for the temporal evolution of client data. Building upon this setting, we propose FedTAR, a framework that integrates demographic-driven personalization with time-aware global aggregation. FedTAR generates lightweight LoRA adapters from demographic embeddings and performs temporal residual aggregation, where updates from different visits are weighted by a meta-learned temporal policy optimized via first-order MAML.\n  Experiments on J-MID (1M exams) and MIMIC-CXR demonstrate consistent improvements in linguistic accuracy, temporal coherence, and cross-site generalization, establishing FedTAR as a robust and privacy-preserving paradigm for federated longitudinal modeling.", "date": "2026-02-23", "categories": []}
{"id": "2602.19987", "title": "Counterfactual Understanding via Retrieval-aware Multimodal Modeling for Time-to-Event Survival Prediction", "abstract": "This paper tackles the problem of time-to-event counterfactual survival prediction, aiming to optimize individualized survival outcomes in the presence of heterogeneity and censored data. We propose CURE, a framework that advances counterfactual survival modeling via comprehensive multimodal embedding and latent subgroup retrieval. CURE integrates clinical, paraclinical, demographic, and multi-omics information, which are aligned and fused through cross-attention mechanisms. Complex multi-omics signals can be adaptively refined using a mixture-of-experts architecture, emphasizing the most informative omics components. Building upon this representation, CURE implicitly retrieves patient-specific latent subgroups that capture both baseline survival dynamics and treatment-dependent variations. Experimental results on METABRIC and TCGA-LUAD datasets demonstrate that proposed CURE model consistently outperforms strong baselines in survival analysis, evaluated using the Time-dependent Concordance Index ($C^{td}$) and Integrated Brier Score (IBS). These findings highlight the potential of CURE to enhance multimodal understanding and serve as a foundation for future treatment recommendation models. All code and related resources are publicly available to facilitate the reproducibility https://github.com/L2R-UET/CURE.", "date": "2026-02-23", "categories": []}
{"id": "2602.20040", "title": "AgenticSum: An Agentic Inference-Time Framework for Faithful Clinical Text Summarization", "abstract": "Large language models (LLMs) offer substantial promise for automating clinical text summarization, yet maintaining factual consistency remains challenging due to the length, noise, and heterogeneity of clinical documentation. We present AgenticSum, an inference-time, agentic framework that separates context selection, generation, verification, and targeted correction to reduce hallucinated content. The framework decomposes summarization into coordinated stages that compress task-relevant context, generate an initial draft, identify weakly supported spans using internal attention grounding signals, and selectively revise flagged content under supervisory control. We evaluate AgenticSum on two public datasets, using reference-based metrics, LLM-as-a-judge assessment, and human evaluation. Across various measures, AgenticSum demonstrates consistent improvements compared to vanilla LLMs and other strong baselines. Our results indicate that structured, agentic design with targeted correction offers an effective inference time solution to improve clinical note summarization using LLMs.", "date": "2026-02-23", "categories": []}
{"id": "2509.06974", "title": "Individualized and Interpretable Sleep Forecasting via a Two-Stage Adaptive Spatial-Temporal Model", "abstract": "Sleep quality impacts well-being. Therefore, healthcare providers and individuals need accessible and reliable forecasting tools for preventive interventions. This paper introduces an interpretable, individualized adaptive spatial-temporal model for predicting sleep quality. We designed a hierarchical architecture, consisting of parallel 1D convolutions with varying kernel sizes and dilated convolution, which extracts multi-resolution temporal patterns-short kernels capture rapid physiological changes, while larger kernels and dilation model slower trends. The extracted features are then refined through channel attention, which learns to emphasize the most predictive variables for each individual, followed by bidirectional LSTM and self-attention that jointly model both local sequential dynamics and global temporal dependencies. Finally, a two-stage adaptation strategy ensures the learned representations transfer effectively to new users. We conducted various experiments with five input window sizes (3, 5, 7, 9, and 11 days) and five prediction window sizes (1, 3, 5, 7, and 9 days). Our model consistently outperformed time series forecasting baseline approaches, including LSTM, Informer, PatchTST, and TimesNet. The best performance was achieved with a three-day input window and a one-day prediction window, yielding an RMSE of 0.216. Furthermore, the model demonstrated good predictive performance even for longer forecasting horizons (e.g., with a 0.257 RMSE for a three-day prediction window), highlighting its practical utility for real-world applications. We also conducted an explainability analysis to examine how different features influence sleep quality. These findings proved that the proposed framework offers a robust, adaptive, and explainable solution for personalized sleep forecasting using sparse data from commercial wearable devices.", "date": "2025-08-28", "categories": []}
{"id": "2602.17888", "title": "Machine Learning Based Prediction of Surgical Outcomes in Chronic Rhinosinusitis from Clinical Data", "abstract": "Artificial intelligence (AI) has increasingly transformed medical prognostics by enabling rapid and accurate analysis across imaging and pathology. However, the investigation of machine learning predictions applied to prospectively collected, standardized data from observational clinical intervention trials remains underexplored, despite its potential to reduce costs and improve patient outcomes. Chronic rhinosinusitis (CRS), a persistent inflammatory disease of the paranasal sinuses lasting more than three months, imposes a substantial burden on quality of life (QoL) and societal cost. Although many patients respond to medical therapy, others with refractory symptoms often pursue surgical intervention. Surgical decision-making in CRS is complex, as it must weigh known procedural risks against uncertain individualized outcomes. In this study, we evaluated supervised machine learning models for predicting surgical benefit in CRS, using the Sino-Nasal Outcome Test-22 (SNOT-22) as the primary patient-reported outcome. Our prospectively collected cohort from an observational intervention trial comprised patients who all underwent surgery; we investigated whether models trained only on preoperative data could identify patients who might not have been recommended surgery prior to the procedure. Across multiple algorithms, including an ensemble approach, our best model achieved approximately 85% classification accuracy, providing accurate and interpretable predictions of surgical candidacy. Moreover, on a held-out set of 30 cases spanning mixed difficulty, our model achieved 80% accuracy, exceeding the average prediction accuracy of expert clinicians (75.6%), demonstrating its potential to augment clinical decision-making and support personalized CRS care.", "date": "2026-02-19", "categories": []}
{"id": "2602.17962", "title": "Improving Generalizability of Hip Fracture Risk Prediction via Domain Adaptation Across Multiple Cohorts", "abstract": "Clinical risk prediction models often fail to be generalized across cohorts because underlying data distributions differ by clinical site, region, demographics, and measurement protocols. This limitation is particularly pronounced in hip fracture risk prediction, where the performance of models trained on one cohort (the source cohort) can degrade substantially when deployed in other cohorts (target cohorts). We used a shared set of clinical and DXA-derived features across three large cohorts - the Study of Osteoporotic Fractures (SOF), the Osteoporotic Fractures in Men Study (MrOS), and the UK Biobank (UKB), to systematically evaluate the performance of three domain adaptation methods - Maximum Mean Discrepancy (MMD), Correlation Alignment (CORAL), and Domain - Adversarial Neural Networks (DANN) and their combinations. For a source cohort with males only and a source cohort with females only, domain-adaptation methods consistently showed improved performance than the no-adaptation baseline (source-only training), and the use of combinations of multiple domain adaptation methods delivered the largest and most stable gains. The method that combines MMD, CORAL, and DANN achieved the highest discrimination with the area under curve (AUC) of 0.88 for a source cohort with males only and 0.95 for a source cohort with females only), demonstrating that integrating multiple domain adaptation methods could produce feature representations that are less sensitive to dataset differences. Unlike existing methods that rely heavily on supervised tuning or assume known outcomes of samples in target cohorts, our outcome-free approaches enable the model selection under realistic deployment conditions and improve generalization of models in hip fracture risk prediction.", "date": "2026-02-20", "categories": []}
{"id": "2409.12709", "title": "SeqRisk: Transformer-augmented latent variable model for robust survival prediction with longitudinal data", "abstract": "In healthcare, risk assessment of patient outcomes has been based on survival analysis for a long time, i.e. modeling time-to-event associations. However, conventional approaches rely on data from a single time-point, making them suboptimal for fully leveraging longitudinal patient history and capturing temporal regularities. Focusing on clinical real-world data and acknowledging its challenges, we utilize latent variable models to effectively handle irregular, noisy, and sparsely observed longitudinal data. We propose SeqRisk, a method that combines variational autoencoder (VAE) or longitudinal VAE (LVAE) with a transformer-based sequence aggregation and Cox proportional hazards module for risk prediction. SeqRisk captures long-range interactions, enhances predictive accuracy and generalizability, as well as provides partial explainability for sample population characteristics in attempts to identify high-risk patients. SeqRisk demonstrated robust performance under conditions of increasing sparsity, consistently surpassing existing approaches.", "date": "2024-09-19", "categories": []}
{"id": "2602.15852", "title": "Building Safe and Deployable Clinical Natural Language Processing under Temporal Leakage Constraints", "abstract": "Clinical natural language processing (NLP) models have shown promise for supporting hospital discharge planning by leveraging narrative clinical documentation. However, note-based models are particularly vulnerable to temporal and lexical leakage, where documentation artifacts encode future clinical decisions and inflate apparent predictive performance. Such behavior poses substantial risks for real-world deployment, where overconfident or temporally invalid predictions can disrupt clinical workflows and compromise patient safety. This study focuses on system-level design choices required to build safe and deployable clinical NLP under temporal leakage constraints. We present a lightweight auditing pipeline that integrates interpretability into the model development process to identify and suppress leakage-prone signals prior to final training. Using next-day discharge prediction after elective spine surgery as a case study, we evaluate how auditing affects predictive behavior, calibration, and safety-relevant trade-offs. Results show that audited models exhibit more conservative and better-calibrated probability estimates, with reduced reliance on discharge-related lexical cues. These findings emphasize that deployment-ready clinical NLP systems should prioritize temporal validity, calibration, and behavioral robustness over optimistic performance.", "date": "2026-01-24", "categories": []}
{"id": "2602.16747", "title": "LiveClin: A Live Clinical Benchmark without Leakage", "abstract": "The reliability of medical LLM evaluation is critically undermined by data contamination and knowledge obsolescence, leading to inflated scores on static benchmarks. To address these challenges, we introduce LiveClin, a live benchmark designed for approximating real-world clinical practice. Built from contemporary, peer-reviewed case reports and updated biannually, LiveClin ensures clinical currency and resists data contamination. Using a verified AI-human workflow involving 239 physicians, we transform authentic patient cases into complex, multimodal evaluation scenarios that span the entire clinical pathway. The benchmark currently comprises 1,407 case reports and 6,605 questions. Our evaluation of 26 models on LiveClin reveals the profound difficulty of these real-world scenarios, with the top-performing model achieving a Case Accuracy of just 35.7%. In benchmarking against human experts, Chief Physicians achieved the highest accuracy, followed closely by Attending Physicians, with both surpassing most models. LiveClin thus provides a continuously evolving, clinically grounded framework to guide the development of medical LLMs towards closing this gap and achieving greater reliability and real-world utility. Our data and code are publicly available at https://github.com/AQ-MedAI/LiveClin.", "date": "2026-02-18", "categories": []}
{"id": "2602.16887", "title": "Construction of a classification model for dementia among Brazilian adults aged 50 and over", "abstract": "To build a dementia classification model for middle-aged and elderly Brazilians, implemented in Python, combining variable selection and multivariable analysis, using low-cost variables with modification potential. Observational study with a predictive modeling approach using a cross-sectional design, aimed at estimating the chances of developing dementia, using data from the Brazilian Longitudinal Study of Aging (ELSI-Brazil), involving 9,412 participants. Dementia was determined based on neuropsychological assessment and informant-based cognitive function. Analyses were performed using Random Forest (RF) and multivariable logistic regression to estimate the risk of dementia in the middle-aged and elderly populations of Brazil. The prevalence of dementia was 9.6%. The highest odds of dementia were observed in illiterate individuals (Odds Ratio (OR) = 7.42), individuals aged 90 years or older (OR = 11.00), low weight (OR = 2.11), low handgrip strength (OR = 2.50), self-reported black skin color (OR = 1.47), physical inactivity (OR = 1.61), self-reported hearing loss (OR = 1.65), and presence of depressive symptoms (OR = 1.72). Higher education (OR=0.44), greater life satisfaction (OR=0.72), and being employed (OR=0.78) were protective factors. The RF model outperformed logistic regression, achieving an area under the ROC curve of 0.776, with a sensitivity of 0.708, a specificity of 0.702, an F1-score of 0.311, a G-means of 0.705, and an accuracy of 0.703. Conclusion: The findings reinforce the multidimensional nature of dementia and the importance of accessible factors for identifying vulnerable individuals. Strengthening public policies focused on promoting brain health can contribute significantly to the efficient allocation of resources in primary care and dementia prevention in Brazil", "date": "2026-02-18", "categories": []}
{"id": "2602.17321", "title": "The Sound of Death: Deep Learning Reveals Vascular Damage from Carotid Ultrasound", "abstract": "Cardiovascular diseases (CVDs) remain the leading cause of mortality worldwide, yet early risk detection is often limited by available diagnostics. Carotid ultrasound, a non-invasive and widely accessible modality, encodes rich structural and hemodynamic information that is largely untapped. Here, we present a machine learning (ML) framework that extracts clinically meaningful representations of vascular damage (VD) from carotid ultrasound videos, using hypertension as a weak proxy label. The model learns robust features that are biologically plausible, interpretable, and strongly associated with established cardiovascular risk factors, comorbidities, and laboratory measures. High VD stratifies individuals for myocardial infarction, cardiac death, and all-cause mortality, matching or outperforming conventional risk models such as SCORE2. Explainable AI analyses reveal that the model relies on vessel morphology and perivascular tissue characteristics, uncovering novel functional and anatomical signatures of vascular damage. This work demonstrates that routine carotid ultrasound contains far more prognostic information than previously recognized. Our approach provides a scalable, non-invasive, and cost-effective tool for population-wide cardiovascular risk assessment, enabling earlier and more personalized prevention strategies without reliance on laboratory tests or complex clinical inputs.", "date": "2026-02-19", "categories": []}
{"id": "2602.17364", "title": "A feature-stable and explainable machine learning framework for trustworthy decision-making under incomplete clinical data", "abstract": "Machine learning models are increasingly applied to biomedical data, yet their adoption in high stakes domains remains limited by poor robustness, limited interpretability, and instability of learned features under realistic data perturbations, such as missingness. In particular, models that achieve high predictive performance may still fail to inspire trust if their key features fluctuate when data completeness changes, undermining reproducibility and downstream decision-making. Here, we present CACTUS (Comprehensive Abstraction and Classification Tool for Uncovering Structures), an explainable machine learning framework explicitly designed to address these challenges in small, heterogeneous, and incomplete clinical datasets. CACTUS integrates feature abstraction, interpretable classification, and systematic feature stability analysis to quantify how consistently informative features are preserved as data quality degrades. Using a real-world haematuria cohort comprising 568 patients evaluated for bladder cancer, we benchmark CACTUS against widely used machine learning approaches, including random forests and gradient boosting methods, under controlled levels of randomly introduced missing data. We demonstrate that CACTUS achieves competitive or superior predictive performance while maintaining markedly higher stability of top-ranked features as missingness increases, including in sex-stratified analyses. Our results show that feature stability provides information complementary to conventional performance metrics and is essential for assessing the trustworthiness of machine learning models applied to biomedical data. By explicitly quantifying robustness to missing data and prioritising interpretable, stable features, CACTUS offers a generalizable framework for trustworthy data-driven decision support.", "date": "2026-02-19", "categories": []}
{"id": "2503.12286", "title": "Integrating Chain-of-Thought and Retrieval Augmented Generation Enhances Rare Disease Diagnosis from Clinical Notes", "abstract": "Background: Several studies show that large language models (LLMs) struggle with phenotype-driven gene prioritization for rare diseases. These studies typically use Human Phenotype Ontology (HPO) terms to prompt foundation models like GPT and LLaMA to predict candidate genes. However, in real-world settings, foundation models are not optimized for domain-specific tasks like clinical diagnosis, yet inputs are unstructured clinical notes rather than standardized terms. How LLMs can be instructed to predict candidate genes or disease diagnosis from unstructured clinical notes remains a major challenge. Methods: We introduce RAG-driven CoT and CoT-driven RAG, two methods that combine Chain-of-Thought (CoT) and Retrieval Augmented Generation (RAG) to analyze clinical notes. A five-question CoT protocol mimics expert reasoning, while RAG retrieves data from sources like HPO and OMIM (Online Mendelian Inheritance in Man). We evaluated these approaches on rare disease datasets, including 5,980 Phenopacket-derived notes, 255 literature-based narratives, and 220 in-house clinical notes from Childrens Hospital of Philadelphia. Results: We found that recent foundations models, including Llama 3.3-70B-Instruct and DeepSeek-R1-Distill-Llama-70B, outperformed earlier versions such as Llama 2 and GPT-3.5. We also showed that RAG-driven CoT and CoT-driven RAG both outperform foundation models in candidate gene prioritization from clinical notes; in particular, both methods with DeepSeek backbone resulted in a top-10 gene accuracy of over 40% on Phenopacket-derived clinical notes. RAG-driven CoT works better for high-quality notes, where early retrieval can anchor the subsequent reasoning steps in domain-specific evidence, while CoT-driven RAG has advantage when processing lengthy and noisy notes.", "date": "2025-03-15", "categories": []}
{"id": "2602.15869", "title": "Towards Fair and Efficient De-identification: Quantifying the Efficiency and Generalizability of De-identification Approaches", "abstract": "Large language models (LLMs) have shown strong performance on clinical de-identification, the task of identifying sensitive identifiers to protect privacy. However, previous work has not examined their generalizability between formats, cultures, and genders. In this work, we systematically evaluate fine-tuned transformer models (BERT, ClinicalBERT, ModernBERT), small LLMs (Llama 1-8B, Qwen 1.5-7B), and large LLMs (Llama-70B, Qwen-72B) at de-identification. We show that smaller models achieve comparable performance while substantially reducing inference cost, making them more practical for deployment. Moreover, we demonstrate that smaller models can be fine-tuned with limited data to outperform larger models in de-identifying identifiers drawn from Mandarin, Hindi, Spanish, French, Bengali, and regional variations of English, in addition to gendered names. To improve robustness in multi-cultural contexts, we introduce and publicly release BERT-MultiCulture-DEID, a set of de-identification models based on BERT, ClinicalBERT, and ModernBERT, fine-tuned on MIMIC with identifiers from multiple language variants. Our findings provide the first comprehensive quantification of the efficiency-generalizability trade-off in de-identification and establish practical pathways for fair and efficient clinical de-identification.\n  Details on accessing the models are available at: https://doi.org/10.5281/zenodo.18342291", "date": "2026-01-27", "categories": []}
{"id": "2602.15159", "title": "Learning Representations from Incomplete EHR Data with Dual-Masked Autoencoding", "abstract": "Learning from electronic health records (EHRs) time series is challenging due to irregular sam- pling, heterogeneous missingness, and the resulting sparsity of observations. Prior self-supervised meth- ods either impute before learning, represent missingness through a dedicated input signal, or optimize solely for imputation, reducing their capacity to efficiently learn representations that support clinical downstream tasks. We propose the Augmented-Intrinsic Dual-Masked Autoencoder (AID-MAE), which learns directly from incomplete time series by applying an intrinsic missing mask to represent naturally missing values and an augmented mask that hides a subset of observed values for reconstruction during training. AID-MAE processes only the unmasked subset of tokens and consistently outperforms strong baselines, including XGBoost and DuETT, across multiple clinical tasks on two datasets. In addition, the learned embeddings naturally stratify patient cohorts in the representation space.", "date": "2026-02-16", "categories": []}
{"id": "2602.15578", "title": "Clinically Inspired Symptom-Guided Depression Detection from Emotion-Aware Speech Representations", "abstract": "Depression manifests through a diverse set of symptoms such as sleep disturbance, loss of interest, and concentration difficulties. However, most existing works treat depression prediction either as a binary label or an overall severity score without explicitly modeling symptom-specific information. This limits their ability to provide symptom-level analysis relevant to clinical screening. To address this, we propose a symptom-specific and clinically inspired framework for depression severity estimation from speech. Our approach uses a symptom-guided cross-attention mechanism that aligns PHQ-8 questionnaire items with emotion-aware speech representations to identify which segments of a participant's speech are more important to each symptom. To account for differences in how symptoms are expressed over time, we introduce a learnable symptom-specific parameter that adaptively controls the sharpness of attention distributions. Our results on EDAIC, a standard clinical-style dataset, demonstrate improved performance outperforming prior works. Further, analyzing the attention distributions showed that higher attention is assigned to utterances containing cues related to multiple depressive symptoms, highlighting the interpretability of our approach. These findings outline the importance of symptom-guided and emotion-aware modeling for speech-based depression screening.", "date": "2026-02-17", "categories": []}
{"id": "2602.15740", "title": "MRC-GAT: A Meta-Relational Copula-Based Graph Attention Network for Interpretable Multimodal Alzheimer's Disease Diagnosis", "abstract": "Alzheimer's disease (AD) is a progressive neurodegenerative condition necessitating early and precise diagnosis to provide prompt clinical management. Given the paramount importance of early diagnosis, recent studies have increasingly focused on computer-aided diagnostic models to enhance precision and reliability. However, most graph-based approaches still rely on fixed structural designs, which restrict their flexibility and limit generalization across heterogeneous patient data. To overcome these limitations, the Meta-Relational Copula-Based Graph Attention Network (MRC-GAT) is proposed as an efficient multimodal model for AD classification tasks. The proposed architecture, copula-based similarity alignment, relational attention, and node fusion are integrated as the core components of episodic meta-learning, such that the multimodal features, including risk factors (RF), Cognitive test scores, and MRI attributes, are first aligned via a copula-based transformation in a common statistical space and then combined by a multi-relational attention mechanism. According to evaluations performed on the TADPOLE and NACC datasets, the MRC-GAT model achieved accuracies of 96.87% and 92.31%, respectively, demonstrating state-of-the-art performance compared to existing diagnostic models. Finally, the proposed model confirms the robustness and applicability of the proposed method by providing interpretability at various stages of disease diagnosis.", "date": "2026-02-17", "categories": []}
{"id": "2207.12381", "title": "LightX3ECG: A Lightweight and eXplainable Deep Learning System for 3-lead Electrocardiogram Classification", "abstract": "Cardiovascular diseases (CVDs) are a group of heart and blood vessel disorders that is one of the most serious dangers to human health, and the number of such patients is still growing. Early and accurate detection plays a key role in successful treatment and intervention. Electrocardiogram (ECG) is the gold standard for identifying a variety of cardiovascular abnormalities. In clinical practices and most of the current research, standard 12-lead ECG is mainly used. However, using a lower number of leads can make ECG more prevalent as it can be conveniently recorded by portable or wearable devices. In this research, we develop a novel deep learning system to accurately identify multiple cardiovascular abnormalities by using only three ECG leads.", "date": "2022-07-25", "categories": []}
{"id": "2506.20430", "title": "An Agentic System for Rare Disease Diagnosis with Traceable Reasoning", "abstract": "Rare diseases affect over 300 million individuals worldwide, yet timely and accurate diagnosis remains an urgent challenge. Patients often endure a prolonged diagnostic odyssey exceeding five years, marked by repeated referrals, misdiagnoses, and unnecessary interventions, leading to delayed treatment and substantial emotional and economic burdens. Here we present DeepRare, a multi-agent system for rare disease differential diagnosis decision support powered by large language models, integrating over 40 specialized tools and up-to-date knowledge sources. DeepRare processes heterogeneous clinical inputs, including free-text descriptions, structured Human Phenotype Ontology terms, and genetic testing results, to generate ranked diagnostic hypotheses with transparent reasoning linked to verifiable medical evidence. Evaluated across nine datasets from literature, case reports and clinical centres across Asia, North America and Europe spanning 14 medical specialties, DeepRare demonstrates exceptional performance on 3,134 diseases. In human-phenotype-ontology-based tasks, it achieves an average Recall@1 of 57.18%, outperforming the next-best method by 23.79%; in multi-modal tests, it reaches 69.1% compared with Exomiser's 55.9% on 168 cases. Expert review achieved 95.4% agreement on its reasoning chains, confirming their validity and traceability. Our work not only advances rare disease diagnosis but also demonstrates how the latest powerful large-language-model-driven agentic systems can reshape current clinical workflows.", "date": "2025-06-25", "categories": []}
{"id": "2602.13846", "title": "Cardiac Output Prediction from Echocardiograms: Self-Supervised Learning with Limited Data", "abstract": "Cardiac Output (CO) is a key parameter in the diagnosis and management of cardiovascular diseases. However, its accurate measurement requires right-heart catheterization, an invasive and time-consuming procedure, motivating the development of reliable non-invasive alternatives using echocardiography. In this work, we propose a self-supervised learning (SSL) pretraining strategy based on SimCLR to improve CO prediction from apical four-chamber echocardiographic videos. The pretraining is performed using the same limited dataset available for the downstream task, demonstrating the potential of SSL even under data scarcity. Our results show that SSL mitigates overfitting and improves representation learning, achieving an average Pearson correlation of 0.41 on the test set and outperforming PanEcho, a model trained on over one million echocardiographic exams. Source code is available at https://github.com/EIDOSLAB/cardiac-output.", "date": "2026-02-14", "categories": []}
{"id": "2602.13979", "title": "Chain-of-Thought Reasoning with Large Language Models for Clinical Alzheimer's Disease Assessment and Diagnosis", "abstract": "Alzheimer's disease (AD) has become a prevalent neurodegenerative disease worldwide. Traditional diagnosis still relies heavily on medical imaging and clinical assessment by physicians, which is often time-consuming and resource-intensive in terms of both human expertise and healthcare resources. In recent years, large language models (LLMs) have been increasingly applied to the medical field using electronic health records (EHRs), yet their application in Alzheimer's disease assessment remains limited, particularly given that AD involves complex multifactorial etiologies that are difficult to observe directly through imaging modalities. In this work, we propose leveraging LLMs to perform Chain-of-Thought (CoT) reasoning on patients' clinical EHRs. Unlike direct fine-tuning of LLMs on EHR data for AD classification, our approach utilizes LLM-generated CoT reasoning paths to provide the model with explicit diagnostic rationale for AD assessment, followed by structured CoT-based predictions. This pipeline not only enhances the model's ability to diagnose intrinsically complex factors but also improves the interpretability of the prediction process across different stages of AD progression. Experimental results demonstrate that the proposed CoT-based diagnostic framework significantly enhances stability and diagnostic performance across multiple CDR grading tasks, achieving up to a 15% improvement in F1 score compared to the zero-shot baseline method.", "date": "2026-02-15", "categories": []}
{"id": "2410.10374", "title": "Class Balancing Diversity Multimodal Ensemble for Alzheimer's Disease Diagnosis and Early Detection", "abstract": "Alzheimer's disease (AD) poses significant global health challenges due to its increasing prevalence and associated societal costs. Early detection and diagnosis of AD are critical for delaying progression and improving patient outcomes. Traditional diagnostic methods and single-modality data often fall short in identifying early-stage AD and distinguishing it from Mild Cognitive Impairment (MCI). This study addresses these challenges by introducing a novel approach: multImodal enseMble via class BALancing diversity for iMbalancEd Data (IMBALMED). IMBALMED integrates multimodal data from the Alzheimer's Disease Neuroimaging Initiative database, including clinical assessments, neuroimaging phenotypes, biospecimen and subject characteristics data. It employs an ensemble of model classifiers, each trained with different class balancing techniques, to overcome class imbalance and enhance model accuracy. We evaluate IMBALMED on two diagnostic tasks (binary and ternary classification) and four binary early detection tasks (at 12, 24, 36, and 48 months), comparing its performance with state-of-the-art algorithms and an unbalanced dataset method. IMBALMED demonstrates superior diagnostic accuracy and predictive performance in both binary and ternary classification tasks, significantly improving early detection of MCI at 48-month time point. The method shows improved classification performance and robustness, offering a promising solution for early detection and management of AD.", "date": "2024-10-14", "categories": []}
{"id": "2502.17503", "title": "Doctor-in-the-Loop: An Explainable, Multi-View Deep Learning Framework for Predicting Pathological Response in Non-Small Cell Lung Cancer", "abstract": "Non-small cell lung cancer (NSCLC) remains a major global health challenge, with high post-surgical recurrence rates underscoring the need for accurate pathological response predictions to guide personalized treatments. Although artificial intelligence models show promise in this domain, their clinical adoption is limited by the lack of medically grounded guidance during training, often resulting in non-explainable intrinsic predictions. To address this, we propose Doctor-in-the-Loop, a novel framework that integrates expert-driven domain knowledge with explainable artificial intelligence techniques, directing the model toward clinically relevant anatomical regions and improving both interpretability and trustworthiness. Our approach employs a gradual multi-view strategy, progressively refining the model's focus from broad contextual features to finer, lesion-specific details. By incorporating domain insights at every stage, we enhance predictive accuracy while ensuring that the model's decision-making process aligns more closely with clinical reasoning. Evaluated on a dataset of NSCLC patients, Doctor-in-the-Loop delivers promising predictive performance and provides transparent, justifiable outputs, representing a significant step toward clinically explainable artificial intelligence in oncology.", "date": "2025-02-21", "categories": []}
{"id": "2506.06977", "title": "Discovering Hierarchy-Grounded Domains with Adaptive Granularity for Clinical Domain Generalization", "abstract": "Domain generalization has become a critical challenge in predictive healthcare, where different patient groups often exhibit shifting data distributions that degrade model performance. Still, regular domain generalization approaches often struggle in clinical settings due to (1) the absence of domain labels and (2) the lack of clinical insight integration. To address these challenges in healthcare, we aim to explore how medical ontologies can be used to discover dynamic yet hierarchy-grounded patient domains, a partitioning strategy that remains under-explored in prior work. Hence, we introduce UdonCare, a hierarchy-pruning method that iteratively divides patients into latent domains and retrieve domain-invariant (label) information from patient data. On two public datasets, UdonCare shows superiority over eight baselines across four representative clinical prediction tasks with substantial domain gaps, highlighting the potential of medical knowledge for enhancing model generalization.", "date": "2025-06-08", "categories": []}
{"id": "2510.17406", "title": "Multi-Window Temporal Analysis for Enhanced Arrhythmia Classification: Leveraging Long-Range Dependencies in Electrocardiogram Signals", "abstract": "Objective. Arrhythmia classification from electrocardiograms (ECGs) suffers from high false positive rates and limited cross-dataset generalization, particularly for atrial fibrillation (AF) detection where specificity ranges from 0.72 to 0.98 using conventional 30-s analysis windows. While most deep learning approaches analyze isolated 30-s ECG windows, many arrhythmias, including AF and atrial flutter, exhibit diagnostic features that emerge over extended time scales. Approach. We introduce S4ECG, a deep learning architecture based on structured state-space models (S4), designed to capture long-range temporal dependencies by jointly analyzing multiple consecutive ECG windows spanning up to 20 min. We evaluate S4ECG on four publicly available databases for multi-class arrhythmia classification and perform systematic cross-dataset evaluations to assess out-of-distribution robustness. Results. Multi-window analysis consistently outperforms single-window approaches across all datasets, improving macro-averaged AUROC by 1.0-11.6 percentage points. For AF, specificity increases from 0.718-0.979 to 0.967-0.998 at a fixed sensitivity threshold, yielding a 3-10-fold reduction in false positive rates. Significance. Compared with convolutional neural network baselines, the S4 architecture shows superior performance, and multi-window training substantially reduces cross-dataset degradation. Optimal diagnostic windows are 10-20 min, beyond which performance plateaus or degrades. These findings demonstrate that structured incorporation of extended temporal context enhances both arrhythmia classification accuracy and cross-dataset robustness. The identified optimal temporal windows provide practical guidance for ECG monitoring system design and may reflect underlying physiological timescales of arrhythmogenic dynamics.", "date": "2025-10-20", "categories": []}
{"id": "2601.00004", "title": "Finetuning Large Language Models for Automated Depression Screening in Nigerian Pidgin English: GENSCORE Pilot Study", "abstract": "Depression is a major contributor to the mental-health burden in Nigeria, yet screening coverage remains limited due to low access to clinicians, stigma, and language barriers. Traditional tools like the Patient Health Questionnaire-9 (PHQ-9) were validated in high-income countries but may be linguistically or culturally inaccessible for low- and middle-income countries and communities such as Nigeria where people communicate in Nigerian Pidgin and more than 520 local languages. This study presents a novel approach to automated depression screening using fine-tuned large language models (LLMs) adapted for conversational Nigerian Pidgin. We collected a dataset of 432 Pidgin-language audio responses from Nigerian young adults aged 18-40 to prompts assessing psychological experiences aligned with PHQ-9 items, performed transcription, rigorous preprocessing and annotation, including semantic labeling, slang and idiom interpretation, and PHQ-9 severity scoring. Three LLMs - Phi-3-mini-4k-instruct, Gemma-3-4B-it, and GPT-4.1 - were fine-tuned on this annotated dataset, and their performance was evaluated quantitatively (accuracy, precision and semantic alignment) and qualitatively (clarity, relevance, and cultural appropriateness). GPT-4.1 achieved the highest quantitative performance, with 94.5% accuracy in PHQ-9 severity scoring prediction, outperforming Gemma-3-4B-it and Phi-3-mini-4k-instruct. Qualitatively, GPT-4.1 also produced the most culturally appropriate, clear, and contextually relevant responses. AI-mediated depression screening for underserved Nigerian communities. This work provides a foundation for deploying conversational mental-health tools in linguistically diverse, resource-constrained environments.", "date": "2025-11-28", "categories": []}
{"id": "2601.07969", "title": "Tuberculosis Screening from Cough Audio: Baseline Models, Clinical Variables, and Uncertainty Quantification", "abstract": "In this paper, we propose a standardized framework for automatic tuberculosis (TB) detection from cough audio and routinely collected clinical data using machine learning. While TB screening from audio has attracted growing interest, progress is difficult to measure because existing studies vary substantially in datasets, cohort definitions, feature representations, model families, validation protocols, and reported metrics. Consequently, reported gains are often not directly comparable, and it remains unclear whether improvements stem from modeling advances or from differences in data and evaluation. We address this gap by establishing a strong, well-documented baseline for TB prediction using cough recordings and accompanying clinical metadata from a recently compiled dataset from several countries. Our pipeline is reproducible end-to-end, covering feature extraction, multimodal fusion, cougher-independent evaluation, and uncertainty quantification, and it reports a consistent suite of clinically relevant metrics to enable fair comparison. We further quantify performance for cough audio-only and fused (audio + clinical metadata) models, and release the full experimental protocol to facilitate benchmarking. This baseline is intended to serve as a common reference point and to reduce methodological variance that currently holds back progress in the field.", "date": "2026-01-12", "categories": []}
{"id": "2602.13047", "title": "Can we trust AI to detect healthy multilingual English speakers among the cognitively impaired cohort in the UK? An investigation using real-world conversational speech", "abstract": "Conversational speech often reveals early signs of cognitive decline, such as dementia and MCI. In the UK, one in four people belongs to an ethnic minority, and dementia prevalence is expected to rise most rapidly among Black and Asian communities. This study examines the trustworthiness of AI models, specifically the presence of bias, in detecting healthy multilingual English speakers among the cognitively impaired cohort, to make these tools clinically beneficial. For experiments, monolingual participants were recruited nationally (UK), and multilingual speakers were enrolled from four community centres in Sheffield and Bradford. In addition to a non-native English accent, multilinguals spoke Somali, Chinese, or South Asian languages, who were further divided into two Yorkshire accents (West and South) to challenge the efficiency of the AI tools thoroughly. Although ASR systems showed no significant bias across groups, classification and regression models using acoustic and linguistic features exhibited bias against multilingual speakers, particularly in memory, fluency, and reading tasks. This bias was more pronounced when models were trained on the publicly available DementiaBank dataset. Moreover, multilinguals were more likely to be misclassified as having cognitive decline. This study is the first of its kind to discover that, despite their strong overall performance, current AI models show bias against multilingual individuals from ethnic minority backgrounds in the UK, and they are also more likely to misclassify speakers with a certain accent (South Yorkshire) as living with a more severe cognitive decline. In this pilot study, we conclude that the existing AI tools are therefore not yet reliable for diagnostic use in these populations, and we aim to address this in future work by developing more generalisable, bias-mitigated models.", "date": "2026-02-13", "categories": []}
{"id": "2507.05517", "title": "Empowering Healthcare Practitioners with Language Models: Structuring Speech Transcripts in Two Real-World Clinical Applications", "abstract": "Large language models (LLMs) such as GPT-4o and o1 have demonstrated strong performance on clinical natural language processing (NLP) tasks across multiple medical benchmarks. Nonetheless, two high-impact NLP tasks - structured tabular reporting from nurse dictations and medical order extraction from doctor-patient consultations - remain underexplored due to data scarcity and sensitivity, despite active industry efforts. Practical solutions to these real-world clinical tasks can significantly reduce the documentation burden on healthcare providers, allowing greater focus on patient care. In this paper, we investigate these two challenging tasks using private and open-source clinical datasets, evaluating the performance of both open- and closed-weight LLMs, and analyzing their respective strengths and limitations. Furthermore, we propose an agentic pipeline for generating realistic, non-sensitive nurse dictations, enabling structured extraction of clinical observations. To support further research in both areas, we release SYNUR and SIMORD, the first open-source datasets for nurse observation extraction and medical order extraction.", "date": "2025-07-07", "categories": []}
{"id": "2602.11237", "title": "AI-Driven Clinical Decision Support System for Enhanced Diabetes Diagnosis and Management", "abstract": "Identifying type 2 diabetes mellitus can be challenging, particularly for primary care physicians. Clinical decision support systems incorporating artificial intelligence (AI-CDSS) can assist medical professionals in diagnosing type 2 diabetes with high accuracy. This study aimed to assess an AI-CDSS specifically developed for the diagnosis of type 2 diabetes by employing a hybrid approach that integrates expert-driven insights with machine learning techniques. The AI-CDSS was developed (training dataset: n = 650) and tested (test dataset: n = 648) using a dataset of 1298 patients with and without type 2 diabetes. To generate predictions, the algorithm utilized key features such as body mass index, plasma fasting glucose, and hemoglobin A1C. Furthermore, a clinical pilot study involving 105 patients was conducted to assess the diagnostic accuracy of the system in comparison to non-endocrinology specialists. The AI-CDSS showed a high degree of accuracy, with 99.8% accuracy in predicting diabetes, 99.3% in predicting prediabetes, 99.2% in identifying at-risk individuals, and 98.8% in predicting no diabetes. The test dataset revealed a 98.8% agreement between endocrinology specialists and the AI-CDSS. Type 2 diabetes was identified in 45% of 105 individuals in the pilot study. Compared with diabetes specialists, the AI-CDSS scored a 98.5% concordance rate, greatly exceeding that of nonendocrinology specialists, who had an 85% agreement rate. These findings indicate that the AI-CDSS has the potential to be a useful tool for accurately identifying type 2 diabetes, especially in situations in which diabetes specialists are not readily available.", "date": "2026-02-11", "categories": []}
{"id": "2602.11360", "title": "Bootstrapping-based Regularisation for Reducing Individual Prediction Instability in Clinical Risk Prediction Models", "abstract": "Clinical prediction models are increasingly used to support patient care, yet many deep learning-based approaches remain unstable, as their predictions can vary substantially when trained on different samples from the same population. Such instability undermines reliability and limits clinical adoption. In this study, we propose a novel bootstrapping-based regularisation framework that embeds the bootstrapping process directly into the training of deep neural networks. This approach constrains prediction variability across resampled datasets, producing a single model with inherent stability properties. We evaluated models constructed using the proposed regularisation approach against conventional and ensemble models using simulated data and three clinical datasets: GUSTO-I, Framingham, and SUPPORT. Across all datasets, our model exhibited improved prediction stability, with lower mean absolute differences (e.g., 0.019 vs. 0.059 in GUSTO-I; 0.057 vs. 0.088 in Framingham) and markedly fewer significantly deviating predictions. Importantly, discriminative performance and feature importance consistency were maintained, with high SHAP correlations between models (e.g., 0.894 for GUSTO-I; 0.965 for Framingham). While ensemble models achieved greater stability, we show that this came at the expense of interpretability, as each constituent model used predictors in different ways. By regularising predictions to align with bootstrapped distributions, our approach allows prediction models to be developed that achieve greater robustness and reproducibility without sacrificing interpretability. This method provides a practical route toward more reliable and clinically trustworthy deep learning models, particularly valuable in data-limited healthcare settings.", "date": "2026-02-11", "categories": []}
{"id": "2602.11520", "title": "Locally Interpretable Individualized Treatment Rules for Black-Box Decision Models", "abstract": "Individualized treatment rules (ITRs) aim to optimize healthcare by tailoring treatment decisions to patient-specific characteristics. Existing methods typically rely on either interpretable but inflexible models or highly flexible black-box approaches that sacrifice interpretability; moreover, most impose a single global decision rule across patients. We introduce the Locally Interpretable Individualized Treatment Rule (LI-ITR) method, which combines flexible machine learning models to accurately learn complex treatment outcomes with locally interpretable approximations to construct subject-specific treatment rules. LI-ITR employs variational autoencoders to generate realistic local synthetic samples and learns individualized decision rules through a mixture of interpretable experts. Simulation studies show that LI-ITR accurately recovers true subject-specific local coefficients and optimal treatment strategies. An application to precision side-effect management in breast cancer illustrates the necessity of flexible predictive modeling and highlights the practical utility of LI-ITR in estimating optimal treatment rules while providing transparent, clinically interpretable explanations.", "date": "2026-02-12", "categories": []}
{"id": "2602.11668", "title": "Explainable Machine-Learning based Detection of Knee Injuries in Runners", "abstract": "Running is a widely practiced activity but shows a high incidence of knee injuries, especially Patellofemoral Pain Syndrome (PFPS) and Iliotibial Band Syndrome (ITBS). Identifying gait patterns linked to these injuries can improve clinical decision-making, which requires precise systems capable of capturing and analyzing temporal kinematic data.\n  This study uses optical motion capture systems to enhance detection of injury-related running patterns. We analyze a public dataset of 839 treadmill recordings from healthy and injured runners to evaluate how effectively these systems capture dynamic parameters relevant to injury classification. The focus is on the stance phase, using joint and segment angle time series and discrete point values.\n  Three classification tasks are addressed: healthy vs. injured, healthy vs. PFPS, and healthy vs. ITBS. We examine different feature spaces, from traditional point-based metrics to full stance-phase time series and hybrid representations. Multiple models are tested, including classical algorithms (K-Nearest Neighbors, Gaussian Processes, Decision Trees) and deep learning architectures (CNNs, LSTMs).\n  Performance is evaluated with accuracy, precision, recall, and F1-score. Explainability tools such as Shapley values, saliency maps, and Grad-CAM are used to interpret model behavior. Results show that combining time series with point values substantially improves detection. Deep learning models outperform classical ones, with CNNs achieving the highest accuracy: 77.9% for PFPS, 73.8% for ITBS, and 71.43% for the combined injury class.\n  These findings highlight the potential of motion capture systems coupled with advanced machine learning to identify knee injury-related running patterns.", "date": "2026-02-12", "categories": []}
{"id": "2602.09793", "title": "Fully-automated sleep staging: multicenter validation of a generalizable deep neural network for Parkinson's disease and isolated REM sleep behavior disorder", "abstract": "Isolated REM sleep behavior disorder (iRBD) is a key prodromal marker of Parkinson's disease (PD), and video-polysomnography (vPSG) remains the diagnostic gold standard. However, manual sleep staging is particularly challenging in neurodegenerative diseases due to EEG abnormalities and fragmented sleep, making PSG assessments a bottleneck for deploying new RBD screening technologies at scale. We adapted U-Sleep, a deep neural network, for generalizable sleep staging in PD and iRBD. A pretrained U-Sleep model, based on a large, multisite non-neurodegenerative dataset (PUB; 19,236 PSGs across 12 sites), was fine-tuned on research datasets from two centers (Lundbeck Foundation Parkinson's Disease Research Center (PACE) and the Cologne-Bonn Cohort (CBC); 112 PD, 138 iRBD, 89 age-matched controls. The resulting model was evaluated on an independent dataset from the Danish Center for Sleep Medicine (DCSM; 81 PD, 36 iRBD, 87 sleep-clinic controls). A subset of PSGs with low agreement between the human rater and the model (Cohen's $\\kappa$ < 0.6) was re-scored by a second blinded human rater to identify sources of disagreement. Finally, we applied confidence-based thresholds to optimize REM sleep staging. The pretrained model achieved mean $\\kappa$ = 0.81 in PUB, but $\\kappa$ = 0.66 when applied directly to PACE/CBC. By fine-tuning the model, we developed a generalized model with $\\kappa$ = 0.74 on PACE/CBC (p < 0.001 vs. the pretrained model). In DCSM, mean and median $\\kappa$ increased from 0.60 to 0.64 (p < 0.001) and 0.64 to 0.69 (p < 0.001), respectively. In the interrater study, PSGs with low agreement between the model and the initial scorer showed similarly low agreement between human scorers. Applying a confidence threshold increased the proportion of correctly identified REM sleep epochs from 85% to 95.5%, while preserving sufficient (> 5 min) REM sleep for 95% of subjects.", "date": "2026-02-10", "categories": []}
{"id": "2602.10119", "title": "Large Language Models Predict Functional Outcomes after Acute Ischemic Stroke", "abstract": "Accurate prediction of functional outcomes after acute ischemic stroke can inform clinical decision-making and resource allocation. Prior work on modified Rankin Scale (mRS) prediction has relied primarily on structured variables (e.g., age, NIHSS) and conventional machine learning. The ability of large language models (LLMs) to infer future mRS scores directly from routine admission notes remains largely unexplored. We evaluated encoder (BERT, NYUTron) and generative (Llama-3.1-8B, MedGemma-4B) LLMs, in both frozen and fine-tuned settings, for discharge and 90-day mRS prediction using a large, real-world stroke registry. The discharge outcome dataset included 9,485 History and Physical notes and the 90-day outcome dataset included 1,898 notes from the NYU Langone Get With The Guidelines-Stroke registry (2016-2025). Data were temporally split with the most recent 12 months held out for testing. Performance was assessed using exact (7-class) mRS accuracy and binary functional outcome (mRS 0-2 vs. 3-6) accuracy and compared against established structured-data baselines incorporating NIHSS and age. Fine-tuned Llama achieved the highest performance, with 90-day exact mRS accuracy of 33.9% [95% CI, 27.9-39.9%] and binary accuracy of 76.3% [95% CI, 70.7-81.9%]. Discharge performance reached 42.0% [95% CI, 39.0-45.0%] exact accuracy and 75.0% [95% CI, 72.4-77.6%] binary accuracy. For 90-day prediction, Llama performed comparably to structured-data baselines. Fine-tuned LLMs can predict post-stroke functional outcomes from admission notes alone, achieving performance comparable to models requiring structured variable abstraction. Our findings support the development of text-based prognostic tools that integrate seamlessly into clinical workflows without manual data extraction.", "date": "2026-01-18", "categories": []}
{"id": "2602.10303", "title": "ICODEN: Ordinary Differential Equation Neural Networks for Interval-Censored Data", "abstract": "Predicting time-to-event outcomes when event times are interval censored is challenging because the exact event time is unobserved. Many existing survival analysis approaches for interval-censored data rely on strong model assumptions or cannot handle high-dimensional predictors. We develop ICODEN, an ordinary differential equation-based neural network for interval-censored data that models the hazard function through deep neural networks and obtains the cumulative hazard by solving an ordinary differential equation. ICODEN does not require the proportional hazards assumption or a prespecified parametric form for the hazard function, thereby permitting flexible survival modeling. Across simulation settings with proportional or non-proportional hazards and both linear and nonlinear covariate effects, ICODEN consistently achieves satisfactory predictive accuracy and remains stable as the number of predictors increases. Applications to data from multiple phases of the Alzheimer's Disease Neuroimaging Initiative (ADNI) and to two Age-Related Eye Disease Studies (AREDS and AREDS2) for age-related macular degeneration (AMD) demonstrate ICODEN's robust prediction performance. In both applications, predicting time-to-AD or time-to-late AMD, ICODEN effectively uses hundreds to more than 1,000 SNPs and supports data-driven subgroup identification with differential progression risk profiles. These results establish ICODEN as a practical assumption-lean tool for prediction with interval-censored survival data in high-dimensional biomedical settings.", "date": "2026-02-10", "categories": []}
{"id": "2602.10315", "title": "Uncertainty-Aware Ordinal Deep Learning for cross-Dataset Diabetic Retinopathy Grading", "abstract": "Diabetes mellitus is a chronic metabolic disorder characterized by persistent hyperglycemia due to insufficient insulin production or impaired insulin utilization. One of its most severe complications is diabetic retinopathy (DR), a progressive retinal disease caused by microvascular damage, leading to hemorrhages, exudates, and potential vision loss. Early and reliable detection of DR is therefore critical for preventing irreversible blindness.\n  In this work, we propose an uncertainty-aware deep learning framework for automated DR severity grading that explicitly models the ordinal nature of disease progression. Our approach combines a convolutional backbone with lesion-query attention pooling and an evidential Dirichlet-based ordinal regression head, enabling both accurate severity prediction and principled estimation of predictive uncertainty. The model is trained using an ordinal evidential loss with annealed regularization to encourage calibrated confidence under domain shift.\n  We evaluate the proposed method on a multi-domain training setup combining APTOS, Messidor-2, and a subset of EyePACS fundus datasets. Experimental results demonstrate strong cross-dataset generalization, achieving competitive classification accuracy and high quadratic weighted kappa on held-out test sets, while providing meaningful uncertainty estimates for low-confidence cases. These results suggest that ordinal evidential learning is a promising direction for robust and clinically reliable diabetic retinopathy grading.", "date": "2026-02-10", "categories": []}
{"id": "2602.10381", "title": "Deep learning outperforms traditional machine learning methods in predicting childhood malnutrition: evidence from survey data", "abstract": "Childhood malnutrition remains a major public health concern in Nepal and other low-resource settings, while conventional case-finding approaches are labor-intensive and frequently unavailable in remote areas. This study provides the first comprehensive assessment of machine learning and deep learning methodologies for identifying malnutrition among children under five years of age in Nepal. We systematically compared 16 algorithms spanning deep learning, gradient boosting, and traditional machine learning families, using data from the Nepal Multiple Indicator Cluster Survey (MICS) 2019. A composite malnutrition indicator was constructed by integrating stunting, wasting, and underweight status, and model performance was evaluated using ten metrics, with emphasis on F1-score and recall to account for substantial class imbalance and the high cost of failing to detect malnourished children. Among all models, TabNet demonstrated the best performance, likely attributable to its attention-based architecture, and outperformed both support vector machine and AdaBoost classifiers. A consensus feature importance analysis identified maternal education, household wealth index, and child age as the primary predictors of malnutrition, followed by geographic characteristics, vaccination status, and meal frequency. Collectively, these results demonstrate a scalable, survey-based screening framework for identifying children at elevated risk of malnutrition and for guiding targeted nutritional interventions. The proposed approach supports Nepal's progress toward the Sustainable Development Goals and offers a transferable methodological template for similar low-resource settings globally.", "date": "2026-02-11", "categories": []}
{"id": "2602.10544", "title": "Bridging the Compression-Precision Paradox: A Hybrid Architecture for Clinical EEG Report Generation with Guaranteed Measurement Accuracy", "abstract": "Automated EEG monitoring requires clinician-level precision for seizure detection and reporting. Clinical EEG recordings exceed LLM context windows, requiring extreme compression (400:1+ ratios) that destroys fine-grained temporal precision. A 0.5 Hz error distinguishes absence epilepsy from Lennox-Gastaut syndrome. LLMs lack inherent time-series comprehension and rely on statistical associations from compressed representations. This dual limitation causes systems to hallucinate clinically incorrect measurement values.\n  We separate measurement extraction from text generation. Our hybrid architecture computes exact clinical values via signal processing before compression, employs a cross-modal bridge for EEG-to-language translation, and uses parameter-efficient fine-tuning with constrained decoding around frozen slots. Multirate sampling maintains long-range context while preserving event-level precision. Evaluation on TUH and CHB-MIT datasets achieves 60% fewer false alarms, 50% faster detection, and sub-clinical measurement precision. This is the first system guaranteeing clinical measurement accuracy in automated EEG reports.", "date": "2026-02-11", "categories": []}
{"id": "2602.10553", "title": "Contrastive Learning for Multi Label ECG Classification with Jaccard Score Based Sigmoid Loss", "abstract": "Recent advances in large language models (LLMs) have enabled the development of multimodal medical AI. While models such as MedGemini achieve high accuracy on VQA tasks like USMLE MM, their performance on ECG based tasks remains limited, and some models, such as MedGemma, do not support ECG data at all. Interpreting ECGs is inherently challenging, and diagnostic accuracy can vary depending on the interpreter's experience. Although echocardiography provides rich diagnostic information, it requires specialized equipment and personnel, limiting its availability. In this study, we focus on constructing a robust ECG encoder for multimodal pretraining using real world hospital data. We employ SigLIP, a CLIP based model with a sigmoid based loss function enabling multi label prediction, and introduce a modified loss function tailored to the multi label nature of ECG data. Experiments demonstrate that incorporating medical knowledge in the language model and applying the modified loss significantly improve multi label ECG classification. To further enhance performance, we increase the embedding dimensionality and apply random cropping to mitigate data drift. Finally, per label analysis reveals which ECG findings are easier or harder to predict. Our study provides a foundational framework for developing medical models that utilize ECG data.", "date": "2026-02-11", "categories": []}
{"id": "2602.11028", "title": "Linguistic Indicators of Early Cognitive Decline in the DementiaBank Pitt Corpus: A Statistical and Machine Learning Study", "abstract": "Background: Subtle changes in spontaneous language production are among the earliest indicators of cognitive decline. Identifying linguistically interpretable markers of dementia can support transparent and clinically grounded screening approaches.\n  Methods: This study analyzes spontaneous speech transcripts from the DementiaBank Pitt Corpus using three linguistic representations: raw cleaned text, a part-of-speech (POS)-enhanced representation combining lexical and grammatical information, and a POS-only syntactic representation. Logistic regression and random forest models were evaluated under two protocols: transcript-level train-test splits and subject-level five-fold cross-validation to prevent speaker overlap. Model interpretability was examined using global feature importance, and statistical validation was conducted using Mann-Whitney U tests with Cliff's delta effect sizes.\n  Results: Across representations, models achieved stable performance, with syntactic and grammatical features retaining strong discriminative power even in the absence of lexical content. Subject-level evaluation yielded more conservative but consistent results, particularly for POS-enhanced and POS-only representations. Statistical analysis revealed significant group differences in functional word usage, lexical diversity, sentence structure, and discourse coherence, aligning closely with machine learning feature importance findings.\n  Conclusion: The results demonstrate that abstract linguistic features capture robust markers of early cognitive decline under clinically realistic evaluation. By combining interpretable machine learning with non-parametric statistical validation, this study supports the use of linguistically grounded features for transparent and reliable language-based cognitive screening.", "date": "2026-02-11", "categories": []}
{"id": "2412.07818", "title": "A Real-Time DDS-Based Chest X-Ray Decision Support System for Resource-Constrained Clinics", "abstract": "Internet of Things (IoT)-based healthcare systems offer significant potential for improving healthcare delivery in humanitarian and resource-constrained environments, providing essential services to underserved populations in remote areas. However, limited network infrastructure in such regions makes reliable communication challenging for traditional IoT systems. This paper presents a real-time chest X-ray decision support system designed for hospitals in remote locations. The proposed system integrates a fine-tuned ResNet50 deep learning model for disease classification with Fast DDS real-time middleware to ensure reliable and low-latency communication between healthcare practitioners and the inference system. Experimental results show that the model achieves an accuracy of 88.61%, precision of 88.76%, and recall of 88.49%. The system attains an average throughput of 3.2 KB/s and an average latency of 65 ms, demonstrating its suitability for deployment in bandwidth-constrained environments. These results highlight the effectiveness of DDS-based middleware in enabling real-time medical decision support for remote healthcare applications.", "date": "2024-12-10", "categories": []}
{"id": "2601.20906", "title": "TwinWeaver: An LLM-Based Foundation Model Framework for Pan-Cancer Digital Twins", "abstract": "Precision oncology requires forecasting clinical events and trajectories, yet modeling sparse, multi-modal clinical time series remains a critical challenge. We introduce TwinWeaver, an open-source framework that serializes longitudinal patient histories into text, enabling unified event prediction as well as forecasting with large language models, and use it to build Genie Digital Twin (GDT) on 93,054 patients across 20 cancer types. In benchmarks, GDT significantly reduces forecasting error, achieving a median Mean Absolute Scaled Error (MASE) of 0.87 compared to 0.97 for the strongest time-series baseline (p<0.001). Furthermore, GDT improves risk stratification, achieving an average concordance index (C-index) of 0.703 across survival, progression, and therapy switching tasks, surpassing the best baseline of 0.662. GDT also generalizes to out-of-distribution clinical trials, matching trained baselines at zero-shot and surpassing them with fine-tuning, achieving a median MASE of 0.75-0.88 and outperforming the strongest baseline in event prediction with an average C-index of 0.672 versus 0.648. Finally, TwinWeaver enables an interpretable clinical reasoning extension, providing a scalable and transparent foundation for longitudinal clinical modeling.", "date": "2026-01-28", "categories": []}
{"id": "2602.09079", "title": "Patient foundation model for risk stratification in low-risk overweight patients", "abstract": "Accurate risk stratification in patients with overweight or obesity is critical for guiding preventive care and allocating high-cost therapies such as GLP-1 receptor agonists. We present PatientTPP, a neural temporal point process (TPP) model trained on over 500,000 real-world clinical trajectories to learn patient representations from sequences of diagnoses, labs, and medications. We extend existing TPP modeling approaches to include static and numeric features and incorporate clinical knowledge for event encoding. PatientTPP representations support downstream prediction tasks, including classification of obesity-associated outcomes in low-risk individuals, even for events not explicitly modeled during training. In health economic evaluation, PatientTPP outperformed body mass index in stratifying patients by future cardiovascular-related healthcare costs, identifying higher-risk patients more efficiently. By modeling both the type and timing of clinical events, PatientTPP offers an interpretable, general-purpose foundation for patient risk modeling with direct applications to obesity-related care and cost targeting.", "date": "2026-02-09", "categories": []}
{"id": "2602.09328", "title": "In-Hospital Stroke Prediction from PPG-Derived Hemodynamic Features", "abstract": "The absence of pre-hospital physiological data in standard clinical datasets fundamentally constrains the early prediction of stroke, as patients typically present only after stroke has occurred, leaving the predictive value of continuous monitoring signals such as photoplethysmography (PPG) unvalidated. In this work, we overcome this limitation by focusing on a rare but clinically critical cohort - patients who suffered stroke during hospitalization while already under continuous monitoring - thereby enabling the first large-scale analysis of pre-stroke PPG waveforms aligned to verified onset times. Using MIMIC-III and MC-MED, we develop an LLM-assisted data mining pipeline to extract precise in-hospital stroke onset timestamps from unstructured clinical notes, followed by physician validation, identifying 176 patients (MIMIC) and 158 patients (MC-MED) with high-quality synchronized pre-onset PPG data, respectively. We then extract hemodynamic features from PPG and employ a ResNet-1D model to predict impending stroke across multiple early-warning horizons. The model achieves F1-scores of 0.7956, 0.8759, and 0.9406 at 4, 5, and 6 hours prior to onset on MIMIC-III, and, without re-tuning, reaches 0.9256, 0.9595, and 0.9888 on MC-MED for the same horizons. These results provide the first empirical evidence from real-world clinical data that PPG contains predictive signatures of stroke several hours before onset, demonstrating that passively acquired physiological signals can support reliable early warning, supporting a shift from post-event stroke recognition to proactive, physiology-based surveillance that may materially improve patient outcomes in routine clinical care.", "date": "2026-02-10", "categories": []}
{"id": "2602.09469", "title": "NOWJ @BioCreative IX ToxHabits: An Ensemble Deep Learning Approach for Detecting Substance Use and Contextual Information in Clinical Texts", "abstract": "Extracting drug use information from unstructured Electronic Health Records remains a major challenge in clinical Natural Language Processing. While Large Language Models demonstrate advancements, their use in clinical NLP is limited by concerns over trust, control, and efficiency. To address this, we present NOWJ submission to the ToxHabits Shared Task at BioCreative IX. This task targets the detection of toxic substance use and contextual attributes in Spanish clinical texts, a domain-specific, low-resource setting. We propose a multi-output ensemble system tackling both Subtask 1 - ToxNER and Subtask 2 - ToxUse. Our system integrates BETO with a CRF layer for sequence labeling, employs diverse training strategies, and uses sentence filtering to boost precision. Our top run achieved 0.94 F1 and 0.97 precision for Trigger Detection, and 0.91 F1 for Argument Detection.", "date": "2026-02-10", "categories": []}
{"id": "2509.19671", "title": "Revisiting Performance Claims for Chest X-Ray Models Using Clinical Context", "abstract": "Public datasets of Chest X-Rays (CXRs) have long been a popular benchmark for developing machine learning (ML) computer vision models in healthcare. However, the reported strong average-case performance of these models do not necessarily reflect their actual utility when used in heterogeneous clinical settings, potentially masking weaker performance in medically significant scenarios. In this work we use clinical context to provide a more holistic evaluation of current \"state-of-the-art\" (SOTA) models for CXR diagnosis. In particular, we use discharge summaries, recorded prior to each CXR, to derive a \"pre-CXR\" probability of each CXR label, as a proxy for existing contextual knowledge available to clinicians when interpreting CXRs. We use this measure to probe model performance along two dimensions: First, using a stratified analysis, we show that models tend to have lower performance (as measured by AUROC and other metrics) among individuals with higher pre-CXR probability. Second, by controlling for pre-CXR probability via matching and re-weighting, we demonstrate that performance degrades when the correlation is broken between prior context and the current CXR label, suggesting that model performance may depend in part on inference of pre-CXR clinical context.", "date": "2025-09-24", "categories": []}
{"id": "2601.05814", "title": "A Dual Pipeline Machine Learning Framework for Automated Multi Class Sleep Disorder Screening Using Hybrid Resampling and Ensemble Learning", "abstract": "Accurate classification of sleep disorders, particularly insomnia and sleep apnea, is important for reducing long term health risks and improving patient quality of life. However, clinical sleep studies are resource intensive and are difficult to scale for population level screening. This paper presents a Dual Pipeline Machine Learning Framework for multi class sleep disorder screening using the Sleep Health and Lifestyle dataset. The framework consists of two parallel processing streams: a statistical pipeline that targets linear separability using Mutual Information and Linear Discriminant Analysis, and a wrapper based pipeline that applies Boruta feature selection with an autoencoder for non linear representation learning. To address class imbalance, we use the hybrid SMOTETomek resampling strategy. In experiments, Extra Trees and K Nearest Neighbors achieved an accuracy of 98.67%, outperforming recent baselines on the same dataset. Statistical testing using the Wilcoxon Signed Rank Test indicates that the improvement over baseline configurations is significant, and inference latency remains below 400 milliseconds. These results suggest that the proposed dual pipeline design supports accurate and efficient automated screening for non invasive sleep disorder risk stratification.", "date": "2026-01-09", "categories": []}
{"id": "2602.07933", "title": "Attention-Based Deep Learning for Early Parkinson's Disease Detection with Tabular Biomedical Data", "abstract": "Early and accurate detection of Parkinson's disease (PD) remains a critical challenge in medical diagnostics due to the subtlety of early-stage symptoms and the complex, non-linear relationships inherent in biomedical data. Traditional machine learning (ML) models, though widely applied to PD detection, often rely on extensive feature engineering and struggle to capture complex feature interactions. This study investigates the effectiveness of attention-based deep learning models for early PD detection using tabular biomedical data. We present a comparative evaluation of four classification models: Multi-Layer Perceptron (MLP), Gradient Boosting, TabNet, and SAINT, using a benchmark dataset from the UCI Machine Learning Repository consisting of biomedical voice measurements from PD patients and healthy controls.\n  Experimental results show that SAINT consistently outperformed all baseline models across multiple evaluation metrics, achieving a weighted precision of 0.98, weighted recall of 0.97, weighted F1-score of 0.97, a Matthews Correlation Coefficient (MCC) of 0.9990, and the highest Area Under the ROC Curve (AUC-ROC). TabNet and MLP demonstrated competitive performance, while Gradient Boosting yielded the lowest overall scores. The superior performance of SAINT is attributed to its dual attention mechanism, which effectively models feature interactions within and across samples.\n  These findings demonstrate the diagnostic potential of attention-based deep learning architectures for early Parkinson's disease detection and highlight the importance of dynamic feature representation in clinical prediction tasks.", "date": "2026-02-08", "categories": []}
{"id": "2602.08171", "title": "A Causal Machine Learning Framework for Treatment Personalization in Clinical Trials: Application to Ulcerative Colitis", "abstract": "Randomized controlled trials estimate average treatment effects, but treatment response heterogeneity motivates personalized approaches. A critical question is whether statistically detectable heterogeneity translates into improved treatment decisions -- these are distinct questions that can yield contradictory answers. We present a modular causal machine learning framework that evaluates each question separately: permutation importance identifies which features predict heterogeneity, best linear predictor (BLP) testing assesses statistical significance, and doubly robust policy evaluation measures whether acting on the heterogeneity improves patient outcomes. We apply this framework to patient-level data from the UNIFI maintenance trial of ustekinumab in ulcerative colitis, comparing placebo, standard-dose ustekinumab every 12 weeks, and dose-intensified ustekinumab every 8 weeks, using cross-fitted X-learner models with baseline demographics, medication history, week-8 clinical scores, laboratory biomarkers, and video-derived endoscopic features. BLP testing identified strong associations between endoscopic features and treatment effect heterogeneity for ustekinumab versus placebo, yet doubly robust policy evaluation showed no improvement in expected remission from incorporating endoscopic features, and out-of-fold multi-arm evaluation showed worse performance. Diagnostic comparison of prognostic contribution against policy value revealed that endoscopic scores behaved as disease severity markers -- improving outcome prediction in untreated patients but adding noise to treatment selection -- while clinical variables (fecal calprotectin, age, CRP) captured the decision-relevant variation. These results demonstrate that causal machine learning applications to clinical trials should include policy-level evaluation alongside heterogeneity testing.", "date": "2026-02-09", "categories": []}
{"id": "2405.00734", "title": "EEG-MACS: Manifold Attention and Confidence Stratification for EEG-based Cross-Center Brain Disease Diagnosis under Unreliable Annotations", "abstract": "Cross-center data heterogeneity and annotation unreliability significantly challenge the intelligent diagnosis of diseases using brain signals. A notable example is the EEG-based diagnosis of neurodegenerative diseases, which features subtler abnormal neural dynamics typically observed in small-group settings. To advance this area, in this work, we introduce a transferable framework employing Manifold Attention and Confidence Stratification (MACS) to diagnose neurodegenerative disorders based on EEG signals sourced from four centers with unreliable annotations. The MACS framework's effectiveness stems from these features: 1) The Augmentor generates various EEG-represented brain variants to enrich the data space; 2) The Switcher enhances the feature space for trusted samples and reduces overfitting on incorrectly labeled samples; 3) The Encoder uses the Riemannian manifold and Euclidean metrics to capture spatiotemporal variations and dynamic synchronization in EEG; 4) The Projector, equipped with dual heads, monitors consistency across multiple brain variants and ensures diagnostic accuracy; 5) The Stratifier adaptively stratifies learned samples by confidence levels throughout the training process; 6) Forward and backpropagation in MACS are constrained by confidence stratification to stabilize the learning system amid unreliable annotations. Our subject-independent experiments, conducted on both neurocognitive and movement disorders using cross-center corpora, have demonstrated superior performance compared to existing related algorithms. This work not only improves EEG-based diagnostics for cross-center and small-setting brain diseases but also offers insights into extending MACS techniques to other data analyses, tackling data heterogeneity and annotation unreliability in multimedia and multimodal content understanding.", "date": "2024-04-29", "categories": []}
{"id": "2411.04285", "title": "Robust Real-Time Mortality Prediction in the Intensive Care Unit using Temporal Difference Learning", "abstract": "The task of predicting long-term patient outcomes using supervised machine learning is a challenging one, in part because of the high variance of each patient's trajectory, which can result in the model over-fitting to the training data. Temporal difference (TD) learning, a common reinforcement learning technique, may reduce variance by generalising learning to the pattern of state transitions rather than terminal outcomes. However, in healthcare this method requires several strong assumptions about patient states, and there appears to be limited literature evaluating the performance of TD learning against traditional supervised learning methods for long-term health outcome prediction tasks. In this study, we define a framework for applying TD learning to real-time irregularly sampled time series data using a Semi-Markov Reward Process. We evaluate the model framework in predicting intensive care mortality and show that TD learning under this framework can result in improved model robustness compared to standard supervised learning methods. and that this robustness is maintained even when validated on external datasets. This approach may offer a more reliable method when learning to predict patient outcomes using high-variance irregular time series data.", "date": "2024-11-06", "categories": []}
{"id": "2510.20714", "title": "An interpretable data-driven approach to optimizing clinical fall risk assessment", "abstract": "In this study we aim to better align fall risk prediction from the Johns Hopkins Fall Risk Assessment Tool (JHFRAT) with additional clinically meaningful measures via a data-driven modelling approach. We conducted a retrospective analysis of 54,209 inpatient admissions from three Johns Hopkins Health System hospitals between March 2022 and October 2023. A total of 20,208 admissions were included as high fall risk encounters, and 13,941 were included as low fall risk encounters. To incorporate clinical knowledge and maintain interpretability, we employed constrained score optimization (CSO) models on JHFRAT assessment data and additional electronic health record (EHR) variables. The model demonstrated significant improvements in predictive performance over the current JHFRAT (CSO AUC-ROC=0.91, JHFRAT AUC-ROC=0.86). The constrained score optimization models performed similarly with and without the EHR variables. Although the benchmark black-box model (XGBoost), improves upon the performance metrics of the knowledge-based constrained logistic regression (AUC-ROC=0.94), the CSO demonstrates more robustness to variations in risk labelling. This evidence-based approach provides a robust foundation for health systems to systematically enhance inpatient fall prevention protocols and patient safety using data-driven optimization techniques, contributing to improved risk assessment and resource allocation in healthcare settings.", "date": "2025-10-23", "categories": []}
{"id": "2602.06611", "title": "Adaptive-CaRe: Adaptive Causal Regularization for Robust Outcome Prediction", "abstract": "Accurate prediction of outcomes is crucial for clinical decision-making and personalized patient care. Supervised machine learning algorithms, which are commonly used for outcome prediction in the medical domain, optimize for predictive accuracy, which can result in models latching onto spurious correlations instead of robust predictors. Causal structure learning methods on the other hand have the potential to provide robust predictors for the target, but can be too conservative because of algorithmic and data assumptions, resulting in loss of diagnostic precision. Therefore, we propose a novel model-agnostic regularization strategy, Adaptive-CaRe, for generalized outcome prediction in the medical domain. Adaptive-CaRe strikes a balance between both predictive value and causal robustness by incorporating a penalty that is proportional to the difference between the estimated statistical contribution and estimated causal contribution of the input features for model predictions. Our experiments on synthetic data establish the efficacy of the proposed Adaptive-CaRe regularizer in finding robust predictors for the target while maintaining competitive predictive accuracy. With experiments on a standard causal benchmark, we provide a blueprint for navigating the trade-off between predictive accuracy and causal robustness by tweaking the regularization strength, $\\lambda$. Validation using real-world dataset confirms that the results translate to practical, real-domain settings. Therefore, Adaptive-CaRe provides a simple yet effective solution to the long-standing trade-off between predictive accuracy and causal robustness in the medical domain. Future work would involve studying alternate causal structure learning frameworks and complex classification models to provide deeper insights at a larger scale.", "date": "2026-02-06", "categories": []}
{"id": "2404.03208", "title": "HiMAL: A Multimodal Hierarchical Multi-task Auxiliary Learning framework for predicting and explaining Alzheimer disease progression", "abstract": "Objective: We aimed to develop and validate a novel multimodal framework HiMAL (Hierarchical, Multi-task Auxiliary Learning) framework, for predicting cognitive composite functions as auxiliary tasks that estimate the longitudinal risk of transition from Mild Cognitive Impairment (MCI) to Alzheimer Disease (AD).\n  Methods: HiMAL utilized multimodal longitudinal visit data including imaging features, cognitive assessment scores, and clinical variables from MCI patients in the Alzheimer Disease Neuroimaging Initiative (ADNI) dataset, to predict at each visit if an MCI patient will progress to AD within the next 6 months. Performance of HiMAL was compared with state-of-the-art single-task and multi-task baselines using area under the receiver operator curve (AUROC) and precision recall curve (AUPRC) metrics. An ablation study was performed to assess the impact of each input modality on model performance. Additionally, longitudinal explanations regarding risk of disease progression were provided to interpret the predicted cognitive decline.\n  Results: Out of 634 MCI patients (mean [IQR] age : 72.8 [67-78], 60% men), 209 (32%) progressed to AD. HiMAL showed better prediction performance compared to all single-modality singe-task baselines (AUROC = 0.923 [0.915-0.937]; AUPRC= 0.623 [0.605-0.644]; all p<0.05). Ablation analysis highlighted that imaging and cognition scores with maximum contribution towards prediction of disease progression.\n  Discussion: Clinically informative model explanations anticipate cognitive decline 6 months in advance, aiding clinicians in future disease progression assessment. HiMAL relies on routinely collected EHR variables for proximal (6 months) prediction of AD onset, indicating its translational potential for point-of-care monitoring and managing of high-risk patients.", "date": "2024-04-04", "categories": []}
{"id": "2506.05701", "title": "Statistically Valid Post-Deployment Monitoring Should Be Standard for AI-Based Digital Health", "abstract": "This position paper argues that post-deployment monitoring in clinical AI is underdeveloped and proposes statistically valid and label-efficient testing frameworks as a principled foundation for ensuring reliability and safety in real-world deployment. A recent review found that only 9% of FDA-registered AI-based healthcare tools include a post-deployment surveillance plan. Existing monitoring approaches are often manual, sporadic, and reactive, making them ill-suited for the dynamic environments in which clinical models operate. We contend that post-deployment monitoring should be grounded in label-efficient and statistically valid testing frameworks, offering a principled alternative to current practices. We use the term \"statistically valid\" to refer to methods that provide explicit guarantees on error rates (e.g., Type I/II error), enable formal inference under pre-defined assumptions, and support reproducibility--features that align with regulatory requirements. Specifically, we propose that the detection of changes in the data and model performance degradation should be framed as distinct statistical hypothesis testing problems. Grounding monitoring in statistical rigor ensures a reproducible and scientifically sound basis for maintaining the reliability of clinical AI systems. Importantly, it also opens new research directions for the technical community--spanning theory, methods, and tools for statistically principled detection, attribution, and mitigation of post-deployment model failures in real-world settings.", "date": "2025-06-06", "categories": []}
{"id": "2601.20916", "title": "Noninvasive Intracranial Pressure Estimation Using Subspace System Identification and Bespoke Machine Learning Algorithms: A Learning-to-Rank Approach", "abstract": "Accurate noninvasive estimation of intracranial pressure (ICP) remains a major challenge in critical care. We developed a bespoke machine learning algorithm that integrates system identification and ranking-constrained optimization to estimate mean ICP from noninvasive signals. A machine learning framework was proposed to obtain accurate mean ICP values using arbitrary noninvasive signals. The subspace system identification algorithm is employed to identify cerebral hemodynamics models for ICP simulation using arterial blood pressure (ABP), cerebral blood velocity (CBv), and R-wave to R-wave interval (R-R interval) signals in a comprehensive database. A mapping function to describe the relationship between the features of noninvasive signals and the estimation errors is learned using innovative ranking constraints through convex optimization. Patients across multiple clinical settings were randomly split into testing and training datasets for performance evaluation of the mapping function. The results indicate that about 31.88% of testing entries achieved estimation errors within 2 mmHg and 34.07% of testing entries between 2 mmHg and 6 mmHg from the nonlinear mapping with constraints. Our results demonstrate the feasibility of the proposed noninvasive ICP estimation approach. Further validation and technical refinement are required before clinical deployment, but this work lays the foundation for safe and broadly accessible ICP monitoring in patients with acute brain injury and related conditions.", "date": "2026-01-28", "categories": []}
{"id": "2507.13551", "title": "Reading Between the Lines: Combining Pause Dynamics and Semantic Coherence for Automated Assessment of Thought Disorder", "abstract": "Formal thought disorder (FTD), a hallmark of schizophrenia spectrum disorders, manifests as incoherent speech and poses challenges for clinical assessment. Traditional clinical rating scales, though validated, are resource-intensive and lack scalability. Automated speech recognition (ASR) allows for objective quantification of linguistic and temporal features of speech, offering scalable alternatives. Furthermore, ASR-derived utterance timestamps provide access to pause dynamics, which are thought to reflect the cognitive processes underlying speech production. Yet, their added value beyond semantic measures remains insufficiently explored. In this study, we evaluated a scalable multimodal framework that integrates pause features with semantic coherence metrics across three datasets: naturalistic self-recorded diaries (AVH), structured picture descriptions (TOPSY), and dream narratives (PsyCL). Pause-related features were evaluated alongside established coherence measures using support vector regression to predict clinical FTD scores. Models using pause features alone robustly predict manually rated FTD severity consistently across datasets. Integrating pause features with semantic coherence metrics enhanced predictive performance compared to coherence-only models, with late fusion yielding the most robust and consistent gains in all three datasets. On average across datasets, Spearman correlation increased from \\r{ho} = 0.413 for semantic-only models to \\r{ho} = 0.455 with late fusion. The performance gains from semantic and pause features integration held consistently across all contexts, though the nature of the most informative pause patterns was dataset-dependent. These findings suggest that both pause dynamics and semantic coherence reflect complementary aspects of thought disorganization.", "date": "2025-07-17", "categories": []}
{"id": "2602.03305", "title": "medR: Reward Engineering for Clinical Offline Reinforcement Learning via Tri-Drive Potential Functions", "abstract": "Reinforcement Learning (RL) offers a powerful framework for optimizing dynamic treatment regimes (DTRs). However, clinical RL is fundamentally bottlenecked by reward engineering: the challenge of defining signals that safely and effectively guide policy learning in complex, sparse offline environments. Existing approaches often rely on manual heuristics that fail to generalize across diverse pathologies. To address this, we propose an automated pipeline leveraging Large Language Models (LLMs) for offline reward design and verification. We formulate the reward function using potential functions consisted of three core components: survival, confidence, and competence. We further introduce quantitative metrics to rigorously evaluate and select the optimal reward structure prior to deployment. By integrating LLM-driven domain knowledge, our framework automates the design of reward functions for specific diseases while significantly enhancing the performance of the resulting policies.", "date": "2026-02-03", "categories": []}
{"id": "2510.24473", "title": "Methodology for Comparing Machine Learning Algorithms for Survival Analysis", "abstract": "This study presents a comparative methodological analysis of six machine learning models for survival analysis (MLSA). Using data from nearly 45,000 colorectal cancer patients in the Hospital-Based Cancer Registries of S\\~ao Paulo, we evaluated Random Survival Forest (RSF), Gradient Boosting for Survival Analysis (GBSA), Survival SVM (SSVM), XGBoost-Cox (XGB-Cox), XGBoost-AFT (XGB-AFT), and LightGBM (LGBM), capable of predicting survival considering censored data. Hyperparameter optimization was performed with different samplers, and model performance was assessed using the Concordance Index (C-Index), C-Index IPCW, time-dependent AUC, and Integrated Brier Score (IBS). Survival curves produced by the models were compared with predictions from classification algorithms, and predictor interpretation was conducted using SHAP and permutation importance. XGB-AFT achieved the best performance (C-Index = 0.7618; IPCW = 0.7532), followed by GBSA and RSF. The results highlight the potential and applicability of MLSA to improve survival prediction and support decision making.", "date": "2025-10-28", "categories": []}
{"id": "2602.02517", "title": "What Drives Length of Stay After Elective Spine Surgery? Insights from a Decade of Predictive Modeling", "abstract": "Objective: Predicting length of stay after elective spine surgery is essential for optimizing patient outcomes and hospital resource use. This systematic review synthesizes computational methods used to predict length of stay in this patient population, highlighting model performance and key predictors. Methods: Following PRISMA guidelines, we systematically searched PubMed, Google Scholar, and ACM Digital Library for studies published between December 1st, 2015, and December 1st, 2024. Eligible studies applied statistical or machine learning models to predict length of stay for elective spine surgery patients. Three reviewers independently screened studies and extracted data. Results: Out of 1,263 screened studies, 29 studies met inclusion criteria. Length of stay was predicted as a continuous, binary, or percentile-based outcome. Models included logistic regression, random forest, boosting algorithms, and neural networks. Machine learning models consistently outperformed traditional statistical models, with AUCs ranging from 0.94 to 0.99. K-Nearest Neighbors and Naive Bayes achieved top performance in some studies. Common predictors included age, comorbidities (notably hypertension and diabetes), BMI, type and duration of surgery, and number of spinal levels. However, external validation and reporting practices varied widely across studies. Discussion: There is growing interest in artificial intelligence and machine learning in length of stay prediction, but lack of standardization and external validation limits clinical utility. Future studies should prioritize standardized outcome definitions and transparent reporting needed to advance real-world deployment. Conclusion: Machine learning models offer strong potential for length of stay prediction after elective spine surgery, highlighting their potential for improving discharge planning and hospital resource management.", "date": "2026-01-24", "categories": []}
{"id": "2602.02731", "title": "Predicting first-episode homelessness among US Veterans using longitudinal EHR data: time-varying models and social risk factors", "abstract": "Homelessness among US veterans remains a critical public health challenge, yet risk prediction offers a pathway for proactive intervention. In this retrospective prognostic study, we analyzed electronic health record (EHR) data from 4,276,403 Veterans Affairs patients during a 2016 observation period to predict first-episode homelessness occurring 3-12 months later in 2017 (prevalence: 0.32-1.19%). We constructed static and time-varying EHR representations, utilizing clinician-informed logic to model the persistence of clinical conditions and social risks over time. We then compared the performance of classical machine learning, transformer-based masked language models, and fine-tuned large language models (LLMs). We demonstrate that incorporating social and behavioral factors into longitudinal models improved precision-recall area under the curve (PR-AUC) by 15-30%. In the top 1% risk tier, models yielded positive predictive values ranging from 3.93-4.72% at 3 months, 7.39-8.30% at 6 months, 9.84-11.41% at 9 months, and 11.65-13.80% at 12 months across model architectures. Large language models underperformed encoder-based models on discrimination but showed smaller performance disparities across racial groups. These results demonstrate that longitudinal, socially informed EHR modeling concentrates homelessness risk into actionable strata, enabling targeted and data-informed prevention strategies for at-risk veterans.", "date": "2026-02-02", "categories": []}
{"id": "2602.02917", "title": "Weighted Temporal Decay Loss for Learning Wearable PPG Data with Sparse Clinical Labels", "abstract": "Advances in wearable computing and AI have increased interest in leveraging PPG for health monitoring over the past decade. One of the biggest challenges in developing health algorithms based on such biosignals is the sparsity of clinical labels, which makes biosignals temporally distant from lab draws less reliable for supervision. To address this problem, we introduce a simple training strategy that learns a biomarker-specific decay of sample weight over the time gap between a segment and its ground truth label and uses this weight in the loss with a regularizer to prevent trivial solutions. On smartwatch PPG from 450 participants across 10 biomarkers, the approach improves over baselines. In the subject-wise setting, the proposed approach averages 0.715 AUPRC, compared to 0.674 for a fine-tuned self-supervised baseline and 0.626 for a feature-based Random Forest. A comparison of four decay families shows that a simple linear decay function is most robust on average. Beyond accuracy, the learned decay rates summarize how quickly each biomarker's PPG evidence becomes stale, providing an interpretable view of temporal sensitivity.", "date": "2026-02-02", "categories": []}
{"id": "2602.02930", "title": "Rare Event Early Detection: A Dataset of Sepsis Onset for Critically Ill Trauma Patients", "abstract": "Sepsis is a major public health concern due to its high morbidity, mortality, and cost. Its clinical outcome can be substantially improved through early detection and timely intervention. By leveraging publicly available datasets, machine learning (ML) has driven advances in both research and clinical practice. However, existing public datasets consider ICU patients (Intensive Care Unit) as a uniform group and neglect the potential challenges presented by critically ill trauma patients in whom injury-related inflammation and organ dysfunction can overlap with the clinical features of sepsis. We propose that a targeted identification of post-traumatic sepsis is necessary in order to develop methods for early detection. Therefore, we introduce a publicly available standardized post-trauma sepsis onset dataset extracted, relabeled using standardized post-trauma clinical facts, and validated from MIMIC-III. Furthermore, we frame early detection of post-trauma sepsis onset according to clinical workflow in ICUs in a daily basis resulting in a new rare event detection problem. We then establish a general benchmark through comprehensive experiments, which shows the necessity of further advancements using this new dataset. The data code is available at https://github.com/ML4UWHealth/SepsisOnset_TraumaCohort.git.", "date": "2026-02-03", "categories": []}
{"id": "2602.03215", "title": "Latent Neural-ODE for Model-Informed Precision Dosing: Overcoming Structural Assumptions in Pharmacokinetics", "abstract": "Accurate estimation of tacrolimus exposure, quantified by the area under the concentration-time curve (AUC), is essential for precision dosing after renal transplantation. Current practice relies on population pharmacokinetic (PopPK) models based on nonlinear mixed-effects (NLME) methods. However, these models depend on rigid, pre-specified assumptions and may struggle to capture complex, patient-specific dynamics, leading to model misspecification.\n  In this study, we introduce a novel data-driven alternative based on Latent Ordinary Differential Equations (Latent ODEs) for tacrolimus AUC prediction. This deep learning approach learns individualized pharmacokinetic dynamics directly from sparse clinical data, enabling greater flexibility in modeling complex biological behavior. The model was evaluated through extensive simulations across multiple scenarios and benchmarked against two standard approaches: NLME-based estimation and the iterative two-stage Bayesian (it2B) method. We further performed a rigorous clinical validation using a development dataset (n = 178) and a completely independent external dataset (n = 75).\n  In simulation, the Latent ODE model demonstrated superior robustness, maintaining high accuracy even when underlying biological mechanisms deviated from standard assumptions. Regarding experiments on clinical datasets, in internal validation, it achieved significantly higher precision with a mean RMSPE of 7.99% compared with 9.24% for it2B (p < 0.001). On the external cohort, it achieved an RMSPE of 10.82%, comparable to the two standard estimators (11.48% and 11.54%).\n  These results establish the Latent ODE as a powerful and reliable tool for AUC prediction. Its flexible architecture provides a promising foundation for next-generation, multi-modal models in personalized medicine.", "date": "2026-02-03", "categories": []}
{"id": "2410.12985", "title": "Leveraging LLMs for Translating and Classifying Mental Health Data", "abstract": "Large language models (LLMs) are increasingly used in medical fields. In mental health support, the early identification of linguistic markers associated with mental health conditions can provide valuable support to mental health professionals, and reduce long waiting times for patients. Despite the benefits of LLMs for mental health support, there is limited research on their application in mental health systems for languages other than English. Our study addresses this gap by focusing on the detection of depression severity in Greek through user-generated posts which are automatically translated from English. Our results show that GPT3.5-turbo is not very successful in identifying the severity of depression in English, and it has a varying performance in Greek as well. Our study underscores the necessity for further research, especially in languages with less resources. Also, careful implementation is necessary to ensure that LLMs are used effectively in mental health platforms, and human supervision remains crucial to avoid misdiagnosis.", "date": "2024-10-16", "categories": []}
{"id": "2502.01678", "title": "LEAD: An EEG Foundation Model for Alzheimer's Disease Detection", "abstract": "Electroencephalography (EEG) provides a non-invasive, highly accessible, and cost-effective approach for detecting Alzheimer's disease (AD). However, existing methods, whether based on handcrafted feature engineering or standard deep learning, face three major challenges: 1) the lack of large-scale EEG-based AD datasets for robust representation learning; 2) limited generalizability across subjects; and 3) difficulty in adapting to highly heterogeneous data. To address these challenges, we curate the world's largest EEG-AD corpus to date, comprising 2,238 subjects. Leveraging this unique resource, we propose LEAD, the first large-scale foundation model for EEG-based AD detection. Specifically, we design a gated temporal-spatial Transformer that can adapt to EEG recordings with arbitrary lengths, channel configurations, and sampling rates. In addition, we introduce a subject-regularized training strategy to enhance subject-level feature learning. We further employ medical contrastive learning for pre-training on 13 datasets, including 4 AD datasets and 9 non-AD neurological disorder datasets, and fine-tune/test the model on the other 5 AD datasets. LEAD achieves the best average ranking across all 20 evaluations on 5 downstream datasets, substantially outperforming existing approaches, including state-of-the-art (SOTA) EEG foundation models. These results strongly demonstrate the effectiveness and practical potential of the proposed method for real-world EEG-based AD detection. Source code: https://github.com/DL4mHealth/LEAD", "date": "2025-02-02", "categories": []}
{"id": "2508.02741", "title": "DeepGB-TB: A Risk-Balanced Cross-Attention Gradient-Boosted Convolutional Network for Rapid, Interpretable Tuberculosis Screening", "abstract": "Large-scale tuberculosis (TB) screening is limited by the high cost and operational complexity of traditional diagnostics, creating a need for artificial-intelligence solutions. We propose DeepGB-TB, a non-invasive system that instantly assigns TB risk scores using only cough audio and basic demographic data. The model couples a lightweight one-dimensional convolutional neural network for audio processing with a gradient-boosted decision tree for tabular features. Its principal innovation is a Cross-Modal Bidirectional Cross-Attention module (CM-BCA) that iteratively exchanges salient cues between modalities, emulating the way clinicians integrate symptoms and risk factors. To meet the clinical priority of minimizing missed cases, we design a Tuberculosis Risk-Balanced Loss (TRBL) that places stronger penalties on false-negative predictions, thereby reducing high-risk misclassifications. DeepGB-TB is evaluated on a diverse dataset of 1,105 patients collected across seven countries, achieving an AUROC of 0.903 and an F1-score of 0.851, representing a new state of the art. Its computational efficiency enables real-time, offline inference directly on common mobile devices, making it ideal for low-resource settings. Importantly, the system produces clinically validated explanations that promote trust and adoption by frontline health workers. By coupling AI innovation with public-health requirements for speed, affordability, and reliability, DeepGB-TB offers a tool for advancing global TB control.", "date": "2025-08-02", "categories": []}
{"id": "2508.17649", "title": "Longitudinal Progression Prediction of Alzheimer's Disease with Tabular Foundation Model", "abstract": "Alzheimer's disease is a progressive neurodegenerative disorder that remains challenging to predict due to its multifactorial etiology and the complexity of multimodal clinical data. Accurate forecasting of clinically relevant biomarkers, including diagnostic and quantitative measures, is essential for effective monitoring of disease progression. This work introduces L2C-TabPFN, a method that integrates a longitudinal-to-cross-sectional (L2C) transformation with a pre-trained Tabular Foundation Model (TabPFN) to predict Alzheimer's disease outcomes using the TADPOLE dataset. L2C-TabPFN converts sequential patient records into fixed-length feature vectors, enabling robust prediction of diagnosis, cognitive scores, and ventricular volume. Experimental results demonstrate that, while L2C-TabPFN achieves competitive performance on diagnostic and cognitive outcomes, it provides state-of-the-art results in ventricular volume prediction. This key imaging biomarker reflects neurodegeneration and progression in Alzheimer's disease. These findings highlight the potential of tabular foundational models for advancing longitudinal prediction of clinically relevant imaging markers in Alzheimer's disease.", "date": "2025-08-25", "categories": []}
{"id": "2602.01005", "title": "Predicting Anemia Among Under-Five Children in Nepal Using Machine Learning and Deep Learning", "abstract": "Childhood anemia remains a major public health challenge in Nepal and is associated with impaired growth, cognition, and increased morbidity. Using World Health Organization hemoglobin thresholds, we defined anemia status for children aged 6-59 months and formulated a binary classification task by grouping all anemia severities as \\emph{anemic} versus \\emph{not anemic}. We analyzed Nepal Demographic and Health Survey (NDHS 2022) microdata comprising 1,855 children and initially considered 48 candidate features spanning demographic, socioeconomic, maternal, and child health characteristics. To obtain a stable and substantiated feature set, we applied four features selection techniques (Chi-square, mutual information, point-biserial correlation, and Boruta) and prioritized features supported by multi-method consensus. Five features: child age, recent fever, household size, maternal anemia, and parasite deworming were consistently selected by all methods, while amenorrhea, ethnicity indicators, and provinces were frequently retained. We then compared eight traditional machine learning classifiers (LR, KNN, DT, RF, XGBoost, SVM, NB, LDA) with two deep learning models (DNN and TabNet) using standard evaluation metrics, emphasizing F1-score and recall due to class imbalance. Among all models, logistic regression attained the best recall (0.701) and the highest F1-score (0.649), while DNN achieved the highest accuracy (0.709), and SVM yielded the strongest discrimination with the highest AUC (0.736). Overall, the results indicate that both machine learning and deep learning models can provide competitive anemia prediction and the interpretable features such as child age, infection proxy, maternal anemia, and deworming history are central for risk stratification and public health screening in Nepal.", "date": "2026-02-01", "categories": []}
{"id": "2602.01924", "title": "Bayesian Integration of Nonlinear Incomplete Clinical Data", "abstract": "Multimodal clinical data are characterized by high dimensionality, heterogeneous representations, and structured missingness, posing significant challenges for predictive modeling, data integration, and interpretability. We propose BIONIC (Bayesian Integration of Nonlinear Incomplete Clinical data), a unified probabilistic framework that integrates heterogeneous multimodal data under missingness through a joint generative-discriminative latent architecture. BIONIC uses pretrained embeddings for complex modalities such as medical images and clinical text, while incorporating structured clinical variables directly within a Bayesian multimodal formulation. The proposed framework enables robust learning in partially observed and semi-supervised settings by explicitly modeling modality-level and variable-level missingness, as well as missing labels. We evaluate BIONIC on three multimodal clinical and biomedical datasets, demonstrating strong and consistent discriminative performance compared to representative multimodal baselines, particularly under incomplete data scenarios. Beyond predictive accuracy, BIONIC provides intrinsic interpretability through its latent structure, enabling population-level analysis of modality relevance and supporting clinically meaningful insight.", "date": "2026-02-02", "categories": []}
{"id": "2602.02179", "title": "SurvKAN: A Fully Parametric Survival Model Based on Kolmogorov-Arnold Networks", "abstract": "Accurate prediction of time-to-event outcomes is critical for clinical decision-making, treatment planning, and resource allocation in modern healthcare. While classical survival models such as Cox remain widely adopted in standard practice, they rely on restrictive assumptions, including linear covariate relationships and proportional hazards over time, that often fail to capture real-world clinical dynamics. Recent deep learning approaches like DeepSurv and DeepHit offer improved expressivity but sacrifice interpretability, limiting clinical adoption where trust and transparency are paramount. Hybrid models incorporating Kolmogorov-Arnold Networks (KANs), such as CoxKAN, have begun to address this trade-off but remain constrained by the semi-parametric Cox framework. In this work we introduce SurvKAN, a fully parametric, time-continuous survival model based on KAN architectures that eliminates the proportional hazards constraint. SurvKAN treats time as an explicit input to a KAN that directly predicts the log-hazard function, enabling end-to-end training on the full survival likelihood. Our architecture preserves interpretability through learnable univariate functions that indicate how individual features influence risk over time. Extensive experiments on standard survival benchmarks demonstrate that SurvKAN achieves competitive or superior performance compared to classical and state-of-the-art baselines across concordance and calibration metrics. Additionally, interpretability analyses reveal clinically meaningful patterns that align with medical domain knowledge.", "date": "2026-02-02", "categories": []}
{"id": "2601.22820", "title": "User-Adaptive Meta-Learning for Cold-Start Medication Recommendation with Uncertainty Filtering", "abstract": "Large-scale Electronic Health Record (EHR) databases have become indispensable in supporting clinical decision-making through data-driven treatment recommendations. However, existing medication recommender methods often struggle with a user (i.e., patient) cold-start problem, where recommendations for new patients are usually unreliable due to the lack of sufficient prescription history for patient profiling. While prior studies have utilized medical knowledge graphs to connect medication concepts through pharmacological or chemical relationships, these methods primarily focus on mitigating the item cold-start issue and fall short in providing personalized recommendations that adapt to individual patient characteristics. Meta-learning has shown promise in handling new users with sparse interactions in recommender systems. However, its application to EHRs remains underexplored due to the unique sequential structure of EHR data. To tackle these challenges, we propose MetaDrug, a multi-level, uncertainty-aware meta-learning framework designed to address the patient cold-start problem in medication recommendation. MetaDrug proposes a novel two-level meta-adaptation mechanism, including self-adaptation, which adapts the model to new patients using their own medical events as support sets to capture temporal dependencies; and peer-adaptation, which adapts the model using similar visits from peer patients to enrich new patient representations. Meanwhile, to further improve meta-adaptation outcomes, we introduce an uncertainty quantification module that ranks the support visits and filters out the unrelated information for adaptation consistency. We evaluate our approach on the MIMIC-III and Acute Kidney Injury (AKI) datasets. Experimental results on both datasets demonstrate that MetaDrug consistently outperforms state-of-the-art medication recommendation methods on cold-start patients.", "date": "2026-01-30", "categories": []}
{"id": "2601.22964", "title": "EvoClinician: A Self-Evolving Agent for Multi-Turn Medical Diagnosis via Test-Time Evolutionary Learning", "abstract": "Prevailing medical AI operates on an unrealistic ''one-shot'' model, diagnosing from a complete patient file. However, real-world diagnosis is an iterative inquiry where Clinicians sequentially ask questions and order tests to strategically gather information while managing cost and time. To address this, we first propose Med-Inquire, a new benchmark designed to evaluate an agent's ability to perform multi-turn diagnosis. Built upon a dataset of real-world clinical cases, Med-Inquire simulates the diagnostic process by hiding a complete patient file behind specialized Patient and Examination agents. They force the agent to proactively ask questions and order tests to gather information piece by piece. To tackle the challenges posed by Med-Inquire, we then introduce EvoClinician, a self-evolving agent that learns efficient diagnostic strategies at test time. Its core is a ''Diagnose-Grade-Evolve'' loop: an Actor agent attempts a diagnosis; a Process Grader agent performs credit assignment by evaluating each action for both clinical yield and resource efficiency; finally, an Evolver agent uses this feedback to update the Actor's strategy by evolving its prompt and memory. Our experiments show EvoClinician outperforms continual learning baselines and other self-evolving agents like memory agents. The code is available at https://github.com/yf-he/EvoClinician", "date": "2026-01-30", "categories": []}
{"id": "2404.00032", "title": "Testing of Deep Learning Model in Real World Clinical Setting: A Case Study in Obstetric Ultrasound", "abstract": "Despite the rapid development of AI models in medical image analysis, their validation in real-world clinical settings remains limited. To address this, we introduce a generic framework designed for deploying image-based AI models in such settings. Using this framework, we deployed a trained model for fetal ultrasound standard plane detection, and evaluated it in real-time sessions with both novice and expert users. Feedback from these sessions revealed that while the model offers potential benefits to medical practitioners, the need for navigational guidance was identified as a key area for improvement. These findings underscore the importance of early deployment of AI models in real-world settings, leading to insights that can guide the refinement of the model and system based on actual user feedback.", "date": "2024-03-22", "categories": []}
{"id": "2404.13318", "title": "Federated Learning for Heterogeneous Electronic Health Record Systems with Cost Effective Participant Selection", "abstract": "The increasing volume of electronic health records (EHRs) presents the opportunity to improve the accuracy and robustness of models in clinical prediction tasks. Unlike traditional centralized approaches, federated learning enables training on data from multiple institutions while preserving patient privacy and complying with regulatory constraints. In practice, healthcare institutions (i.e., hosts) often need to build predictive models tailored to their specific needs (e.g., creatinine-level prediction, N-day readmission prediction) using federated learning. When building a federated learning model for a single healthcare institution, two key challenges arise: (1) ensuring compatibility across heterogeneous EHR systems, and (2) managing federated learning costs within budget constraints. Specifically, heterogeneity in EHR systems across institutions hinders compatible modeling, while the computational costs of federated learning can exceed practical budget limits for healthcare institutions. To address these challenges, we propose EHRFL, a federated learning framework designed for building a cost-effective, host-specific predictive model using patient EHR data. EHRFL consists of two components: (1) text-based EHR modeling, which facilitates cross-institution compatibility without costly data standardization, and (2) a participant selection strategy based on averaged patient embedding similarity to reduce the number of participants without degrading performance. Our participant selection strategy sharing averaged patient embeddings is differentially private, ensuring patient privacy. Experiments on multiple open-source EHR datasets demonstrate the effectiveness of both components. With our framework, healthcare institutions can build institution-specific predictive models under budgetary constraints with reduced costs and time.", "date": "2024-04-20", "categories": []}
{"id": "2508.19325", "title": "PRISM: A Framework Harnessing Unsupervised Visual Representations and Textual Prompts for Explainable MACE Survival Prediction from Cardiac Cine MRI", "abstract": "Accurate prediction of major adverse cardiac events (MACE) remains a central challenge in cardiovascular prognosis. We present PRISM (Prompt-guided Representation Integration for Survival Modeling), a self-supervised framework that integrates visual representations from non-contrast cardiac cine magnetic resonance imaging with structured electronic health records (EHRs) for survival analysis. PRISM extracts temporally synchronized imaging features through motion-aware multi-view distillation and modulates them using medically informed textual prompts to enable fine-grained risk prediction. Across four independent clinical cohorts, PRISM consistently surpasses classical survival prediction models and state-of-the-art (SOTA) deep learning baselines under internal and external validation. Further clinical findings demonstrate that the combined imaging and EHR representations derived from PRISM provide valuable insights into cardiac risk across diverse cohorts. Three distinct imaging signatures associated with elevated MACE risk are uncovered, including lateral wall dyssynchrony, inferior wall hypersensitivity, and anterior elevated focus during diastole. Prompt-guided attribution further identifies hypertension, diabetes, and smoking as dominant contributors among clinical and physiological EHR factors.", "date": "2025-08-26", "categories": []}
{"id": "2509.12991", "title": "Bridging Performance Gaps for ECG Foundation Models: A Post-Training Strategy", "abstract": "ECG foundation models are increasingly popular due to their adaptability across various tasks. However, their clinical applicability is often limited by performance gaps compared to task-specific models, even after pre-training on large ECG datasets and fine-tuning on target data. This limitation is likely due to the lack of an effective post-training strategy. In this paper, we propose a simple yet effective post-training approach to enhance ECG foundation models. We evaluate it on a publicly available Transformer-based foundation model. Experiments across multiple ECG tasks show that our method consistently outperforms baseline fine-tuning. On the PTB-XL benchmarks, it improves macro AUROC by 0.7%-8.9% and macro AUPRC by 23.3%-77.9%, also outperforming several recent state-of-the-art approaches, including task-specific and advanced architectures. Further analyses demonstrate improved training dynamics and data efficiency, with only 30% of the training data outperforming the baseline trained on the full dataset. Ablation studies highlight the importance of stochastic depth and preview linear probing. These findings underscore the potential of post-training strategies to improve ECG foundation models, and we hope this work will contribute to the continued development of foundation models in the ECG domain.", "date": "2025-09-16", "categories": []}
{"id": "2601.20874", "title": "Analyzing the Temporal Factors for Anxiety and Depression Symptoms with the Rashomon Perspective", "abstract": "This paper introduces a new modeling perspective in the public mental health domain to provide a robust interpretation of the relations between anxiety and depression, and the demographic and temporal factors. This perspective particularly leverages the Rashomon Effect, where multiple models exhibit similar predictive performance but rely on diverse internal structures. Instead of considering these multiple models, choosing a single best model risks masking alternative narratives embedded in the data. To address this, we employed this perspective in the interpretation of a large-scale psychological dataset, specifically focusing on the Patient Health Questionnaire-4. We use a random forest model combined with partial dependence profiles to rigorously assess the robustness and stability of predictive relationships across the resulting Rashomon set, which consists of multiple models that exhibit similar predictive performance. Our findings confirm that demographic variables \\texttt{age}, \\texttt{sex}, and \\texttt{education} lead to consistent structural shifts in anxiety and depression risk. Crucially, we identify significant temporal effects: risk probability demonstrates clear diurnal and circaseptan fluctuations, peaking during early morning hours. This work demonstrates the necessity of moving beyond the best model to analyze the entire Rashomon set. Our results highlight that the observed variability, particularly due to circadian and circaseptan rhythms, must be meticulously considered for robust interpretation in psychological screening. We advocate for a multiplicity-aware approach to enhance the stability and generalizability of ML-based conclusions in mental health research.", "date": "2026-01-18", "categories": []}
{"id": "2601.21022", "title": "AI-based Prediction of Biochemical Recurrence from Biopsy and Prostatectomy Samples", "abstract": "Biochemical recurrence (BCR) after radical prostatectomy (RP) is a surrogate marker for aggressive prostate cancer with adverse outcomes, yet current prognostic tools remain imprecise. We trained an AI-based model on diagnostic prostate biopsy slides from the STHLM3 cohort (n = 676) to predict patient-specific risk of BCR, using foundation models and attention-based multiple instance learning. Generalizability was assessed across three external RP cohorts: LEOPARD (n = 508), CHIMERA (n = 95), and TCGA-PRAD (n = 379). The image-based approach achieved 5-year time-dependent AUCs of 0.64, 0.70, and 0.70, respectively. Integrating clinical variables added complementary prognostic value and enabled statistically significant risk stratification. Compared with guideline-based CAPRA-S, AI incrementally improved postoperative prognostication. These findings suggest biopsy-trained histopathology AI can generalize across specimen types to support preoperative and postoperative decision making, but the added value of AI-based multimodal approaches over simpler predictive models should be critically scrutinized in further studies.", "date": "2026-01-28", "categories": []}
{"id": "2601.21076", "title": "Multi-modal Imputation for Alzheimer's Disease Classification", "abstract": "Deep learning has been successful in predicting neurodegenerative disorders, such as Alzheimer's disease, from magnetic resonance imaging (MRI). Combining multiple imaging modalities, such as T1-weighted (T1) and diffusion-weighted imaging (DWI) scans, can increase diagnostic performance. However, complete multimodal datasets are not always available. We use a conditional denoising diffusion probabilistic model to impute missing DWI scans from T1 scans. We perform extensive experiments to evaluate whether such imputation improves the accuracy of uni-modal and bi-modal deep learning models for 3-way Alzheimer's disease classification-cognitively normal, mild cognitive impairment, and Alzheimer's disease. We observe improvements in several metrics, particularly those sensitive to minority classes, for several imputation configurations.", "date": "2026-01-28", "categories": []}
{"id": "2601.21950", "title": "Embracing Aleatoric Uncertainty in Medical Multimodal Learning with Missing Modalities", "abstract": "Medical multimodal learning faces significant challenges with missing modalities prevalent in clinical practice. Existing approaches assume equal contribution of modality and random missing patterns, neglecting inherent uncertainty in medical data acquisition. In this regard, we propose the Aleatoric Uncertainty Modeling (AUM) that explicitly quantifies unimodal aleatoric uncertainty to address missing modalities. Specifically, AUM models each unimodal representation as a multivariate Gaussian distribution to capture aleatoric uncertainty and enable principled modality reliability quantification. To adaptively aggregate captured information, we develop a dynamic message-passing mechanism within a bipartite patient-modality graph using uncertainty-aware aggregation mechanism. Through this process, missing modalities are naturally accommodated, while more reliable information from available modalities is dynamically emphasized to guide representation generation. Our AUM framework achieves an improvement of 2.26% AUC-ROC on MIMIC-IV mortality prediction and 2.17% gain on eICU, outperforming existing state-of-the-art approaches.", "date": "2026-01-29", "categories": []}
{"id": "2601.22128", "title": "The Patient is not a Moving Document: A World Model Training Paradigm for Longitudinal EHR", "abstract": "Large language models (LLMs) trained with next-word-prediction have achieved success as clinical foundation models. Representations from these language backbones yield strong linear probe performance across biomedical tasks, suggesting that patient semantics emerge from next-token prediction at scale. However, this paradigm treats patients as a document to be summarized rather than a dynamical system to be simulated; a patient's trajectory emerges from their state evolving under interventions and time, requiring models that simulate dynamics rather than predict tokens. To address this, we introduce SMB-Structure, a world model for structured EHR that grounds a joint-embedding prediction architecture (JEPA) with next-token prediction (SFT). SFT grounds our model to reconstruct future patient states in token space, while JEPA predicts those futures in latent space from the initial patient representation alone, forcing trajectory dynamics to be encoded before the next state is observed. We validate across two large-scale cohorts: Memorial Sloan Kettering (23,319 oncology patients; 323,000+ patient-years) and INSPECT (19,402 pulmonary embolism patients). Using a linear probe evaluated at multiple points along the disease trajectory, we demonstrate that our training paradigm learns embeddings that capture disease dynamics not recoverable by autoregressive baselines, enabling SMB-Structure to achieve competitive performance on complex tasks characterized by high patient heterogeneity. Model weights are available at https://huggingface.co/standardmodelbio/SMB-v1-1.7B-Structure.", "date": "2026-01-29", "categories": []}
{"id": "2508.03461", "title": "Evaluating the Predictive Value of Preoperative MRI for Erectile Dysfunction Following Radical Prostatectomy", "abstract": "Accurate preoperative prediction of erectile dysfunction (ED) is important for counseling patients undergoing radical prostatectomy. While clinical features are established predictors, the added value of preoperative MRI remains underexplored. We investigate whether MRI provides additional predictive value for ED at 12 months post-surgery, evaluating four modeling strategies: (1) a clinical-only baseline, representing current state-of-the-art; (2) classical models using handcrafted anatomical features derived from MRI; (3) deep learning models trained directly on MRI slices; and (4) multimodal fusion of imaging and clinical inputs. Imaging-based models (maximum AUC 0.569) slightly outperformed handcrafted anatomical approaches (AUC 0.554) but fell short of the clinical baseline (AUC 0.663). Fusion models offered marginal gains (AUC 0.586) but did not exceed clinical-only performance. SHAP analysis confirmed that clinical features contributed most to predictive performance. Saliency maps from the best-performing imaging model suggested a predominant focus on anatomically plausible regions, such as the prostate and neurovascular bundles. While MRI-based models did not improve predictive performance over clinical features, our findings suggest that they try to capture patterns in relevant anatomical structures and may complement clinical predictors in future multimodal approaches.", "date": "2025-08-05", "categories": []}
{"id": "2601.05194", "title": "An interpretable data-driven approach to optimizing clinical fall risk assessment", "abstract": "In this study, we aim to better align fall risk prediction from the Johns Hopkins Fall Risk Assessment Tool (JHFRAT) with additional clinically meaningful measures via a data-driven modelling approach. We conducted a retrospective cohort analysis of 54,209 inpatient admissions from three Johns Hopkins Health System hospitals between March 2022 and October 2023. A total of 20,208 admissions were included as high fall risk encounters, and 13,941 were included as low fall risk encounters. To incorporate clinical knowledge and maintain interpretability, we employed constrained score optimization (CSO) models to reweight the JHFRAT scoring weights, while preserving its additive structure and clinical thresholds. Recalibration refers to adjusting item weights so that the resulting score can order encounters more consistently by the study's risk labels, and without changing the tool's form factor or deployment workflow. The model demonstrated significant improvements in predictive performance over the current JHFRAT (CSO AUC-ROC=0.91, JHFRAT AUC-ROC=0.86). This performance improvement translates to protecting an additional 35 high-risk patients per week across the Johns Hopkins Health System. The constrained score optimization models performed similarly with and without the EHR variables. Although the benchmark black-box model (XGBoost), improves upon the performance metrics of the knowledge-based constrained logistic regression (AUC-ROC=0.94), the CSO demonstrates more robustness to variations in risk labeling. This evidence-based approach provides a robust foundation for health systems to systematically enhance inpatient fall prevention protocols and patient safety using data-driven optimization techniques, contributing to improved risk assessment and resource allocation in healthcare settings.", "date": "2026-01-08", "categories": []}
{"id": "2601.19939", "title": "oculomix: Hierarchical Sampling for Retinal-Based Systemic Disease Prediction", "abstract": "Oculomics - the concept of predicting systemic diseases, such as cardiovascular disease and dementia, through retinal imaging - has advanced rapidly due to the data efficiency of transformer-based foundation models like RETFound. Image-level mixed sample data augmentations, such as CutMix and MixUp, are frequently used for training transformers, yet these techniques perturb patient-specific attributes, such as medical comorbidity and clinical factors, since they only account for images and labels. To address this limitation, we propose a hierarchical sampling strategy, Oculomix, for mixed sample augmentations. Our method is based on two clinical priors. First (exam level), images acquired from the same patient at the same time point share the same attributes. Second (patient level), images acquired from the same patient at different time points have a soft temporal trend, as morbidity generally increases over time. Guided by these priors, our method constrains the mixing space to the patient and exam levels to better preserve patient-specific characteristics and leverages their hierarchical relationships. The proposed method is validated using ViT models on a five-year prediction of major adverse cardiovascular events (MACE) in a large ethnically diverse population (Alzeye). We show that Oculomix consistently outperforms image-level CutMix and MixUp by up to 3% in AUROC, demonstrating the necessity and value of the proposed method in oculomics.", "date": "2026-01-16", "categories": []}
{"id": "2601.20046", "title": "Externally Validated Longitudinal GRU Model for Visit-Level 180-Day Mortality Risk in Metastatic Castration-Resistant Prostate Cancer", "abstract": "Metastatic castration-resistant prostate cancer (mCRPC) is a highly aggressive disease with poor prognosis and heterogeneous treatment response. In this work, we developed and externally validated a visit-level 180-day mortality risk model using longitudinal data from two Phase III cohorts (n=526 and n=640). Only visits with observable 180-day outcomes were labeled; right-censored cases were excluded from analysis. We compared five candidate architectures: Long Short-Term Memory, Gated Recurrent Unit (GRU), Cox Proportional Hazards, Random Survival Forest (RSF), and Logistic Regression. For each dataset, we selected the smallest risk-threshold that achieved an 85% sensitivity floor. The GRU and RSF models showed high discrimination capabilities initially (C-index: 87% for both). In external validation, the GRU obtained a higher calibration (slope: 0.93; intercept: 0.07) and achieved an PR-AUC of 0.87. Clinical impact analysis showed a median time-in-warning of 151.0 days for true positives (59.0 days for false positives) and 18.3 alerts per 100 patient-visits. Given late-stage frailty or cachexia and hemodynamic instability, permutation importance ranked BMI and systolic blood pressure as the strongest associations. These results suggest that longitudinal routine clinical markers can estimate short-horizon mortality risk in mCRPC and support proactive care planning over a multi-month window.", "date": "2026-01-27", "categories": []}
{"id": "2512.06652", "title": "Adaptive Test-Time Training for Predicting Need for Invasive Mechanical Ventilation in Multi-Center Cohorts", "abstract": "Accurate prediction of the need for invasive mechanical ventilation (IMV) in intensive care units (ICUs) patients is crucial for timely interventions and resource allocation. However, variability in patient populations, clinical practices, and electronic health record (EHR) systems across institutions introduces domain shifts that degrade the generalization performance of predictive models during deployment. Test-Time Training (TTT) has emerged as a promising approach to mitigate such shifts by adapting models dynamically during inference without requiring labeled target-domain data. In this work, we introduce Adaptive Test-Time Training (AdaTTT), an enhanced TTT framework tailored for EHR-based IMV prediction in ICU settings. We begin by deriving information-theoretic bounds on the test-time prediction error and demonstrate that it is constrained by the uncertainty between the main and auxiliary tasks. To enhance their alignment, we introduce a self-supervised learning framework with pretext tasks: reconstruction and masked feature modeling optimized through a dynamic masking strategy that emphasizes features critical to the main task. Additionally, to improve robustness against domain shifts, we incorporate prototype learning and employ Partial Optimal Transport (POT) for flexible, partial feature alignment while maintaining clinically meaningful patient representations. Experiments across multi-center ICU cohorts demonstrate competitive classification performance on different test-time adaptation benchmarks.", "date": "2025-12-07", "categories": []}
{"id": "2601.18830", "title": "How Much Temporal Modeling is Enough? A Systematic Study of Hybrid CNN-RNN Architectures for Multi-Label ECG Classification", "abstract": "Accurate multi-label classification of electrocardiogram (ECG) signals remains challenging due to the coexistence of multiple cardiac conditions, pronounced class imbalance, and long-range temporal dependencies in multi-lead recordings. Although recent studies increasingly rely on deep and stacked recurrent architectures, the necessity and clinical justification of such architectural complexity have not been rigorously examined. In this work, we perform a systematic comparative evaluation of convolutional neural networks (CNNs) combined with multiple recurrent configurations, including LSTM, GRU, Bidirectional LSTM (BiLSTM), and their stacked variants, for multi-label ECG classification on the PTB-XL dataset comprising 23 diagnostic categories. The CNN component serves as a morphology-driven baseline, while recurrent layers are progressively integrated to assess their contribution to temporal modeling and generalization performance. Experimental results indicate that a CNN integrated with a single BiLSTM layer achieves the most favorable trade-off between predictive performance and model complexity. This configuration attains superior Hamming loss (0.0338), macro-AUPRC (0.4715), micro-F1 score (0.6979), and subset accuracy (0.5723) compared with deeper recurrent combinations. Although stacked recurrent models occasionally improve recall for specific rare classes, our results provide empirical evidence that increasing recurrent depth yields diminishing returns and may degrade generalization due to reduced precision and overfitting. These findings suggest that architectural alignment with the intrinsic temporal structure of ECG signals, rather than increased recurrent depth, is a key determinant of robust performance and clinically relevant deployment.", "date": "2026-01-25", "categories": []}
{"id": "2601.19286", "title": "ReToP: Learning to Rewrite Electronic Health Records for Clinical Prediction", "abstract": "Electronic Health Records (EHRs) provide crucial information for clinical decision-making. However, their high-dimensionality, heterogeneity, and sparsity make clinical prediction challenging. Large Language Models (LLMs) allowed progress towards addressing this challenge by leveraging parametric medical knowledge to enhance EHR data for clinical prediction tasks. Despite the significant achievements made so far, most of the existing approaches are fundamentally task-agnostic in the sense that they deploy LLMs as EHR encoders or EHR completion modules without fully integrating signals from the prediction tasks. This naturally hinders task performance accuracy. In this work, we propose Rewrite-To-Predict (ReToP), an LLM-based framework that addresses this limitation through an end-to-end training of an EHR rewriter and a clinical predictor. To cope with the lack of EHR rewrite training data, we generate synthetic pseudo-labels using clinical-driven feature selection strategies to create diverse patient rewrites for fine-tuning the EHR rewriter. ReToP aligns the rewriter with prediction objectives using a novel Classifier Supervised Contribution (CSC) score that enables the EHR rewriter to generate clinically relevant rewrites that directly enhance prediction. Our ReToP framework surpasses strong baseline models across three clinical tasks on MIMIC-IV. Moreover, the analysis of ReToP shows its generalizability to unseen datasets and tasks with minimal fine-tuning while preserving faithful rewrites and emphasizing task-relevant predictive features.", "date": "2026-01-27", "categories": []}
{"id": "2305.15629", "title": "Patient Outcome Predictions Improve Operations at a Large Hospital Network", "abstract": "Problem definition: Access to accurate predictions of patients' outcomes can enhance medical staff's decision-making, which ultimately benefits all stakeholders in the hospitals. A large hospital network in the US has been collaborating with academics and consultants to predict short-term and long-term outcomes for all inpatients across their seven hospitals. Methodology/results: We develop machine learning models that predict the probabilities of next 24-hr/48-hr discharge and intensive care unit transfers, end-of-stay mortality and discharge dispositions. All models achieve high out-of-sample AUC (75.7%-92.5%) and are well calibrated. In addition, combining 48-hr discharge predictions with doctors' predictions simultaneously enables more patient discharges (10%-28.7%) and fewer 7-day/30-day readmissions ($p$-value $<0.001$). We implement an automated pipeline that extracts data and updates predictions every morning, as well as user-friendly software and a color-coded alert system to communicate these patient-level predictions (alongside explanations) to clinical teams. Managerial implications: Since we have been gradually deploying the tool, and training medical staff, over 200 doctors, nurses, and case managers across seven hospitals use it in their daily patient review process. We observe a significant reduction in the average length of stay (0.67 days per patient) following its adoption and anticipate substantial financial benefits (between \\$55 and \\$72 million annually) for the healthcare system.", "date": "2023-05-25", "categories": []}
{"id": "2506.10769", "title": "Mind the Gap: Benchmarking LLM Uncertainty and Calibration with Specialty-Aware Clinical QA and Reasoning-Based Behavioural Features", "abstract": "Reliable uncertainty quantification (UQ) is essential when employing large language models (LLMs) in high-risk domains such as clinical question answering (QA). In this work, we evaluate uncertainty estimation methods for clinical QA focusing, for the first time, on eleven clinical specialties and six question types, and across ten open-source LLMs (general-purpose, biomedical, and reasoning models), alongside representative proprietary models. We analyze score-based UQ methods, present a case study introducing a novel lightweight method based on behavioral features derived from reasoning-oriented models, and examine conformal prediction as a complementary set-based approach. Our findings reveal that uncertainty reliability is not a monolithic property, but one that depends on clinical specialty and question type due to shifts in calibration and discrimination. Our results highlight the need to select or ensemble models based on their distinct, complementary strengths and clinical use.", "date": "2025-06-12", "categories": []}
{"id": "2508.11669", "title": "Collaborative Learning-Enhanced Lightweight Models for Predicting Arterial Blood Pressure Waveform in a Large-scale Perioperative Dataset", "abstract": "Noninvasive arterial blood pressure (ABP) monitoring is essential for patient management in critical care and perioperative settings, providing continuous assessment of cardiovascular hemodynamics with minimal risks. Numerous deep learning models have developed to reconstruct ABP waveform from noninvasively acquired physiological signals such as electrocardiogram and photoplethysmogram. However, limited research has addressed the issue of model performance and computational load for deployment on embedded systems. The study introduces a lightweight sInvResUNet, along with a collaborative learning scheme named KDCL_sInvResUNet. With only 0.89 million parameters and a computational load of 0.02 GFLOPS, real-time ABP estimation was successfully achieved on embedded devices with an inference time of just 8.49 milliseconds for a 10-second output. We performed subject-independent validation in a large-scale and heterogeneous perioperative dataset containing 1,257,141 data segments from 2,154 patients, with a wide BP range (41-257 mmHg for SBP, and 31-234 mmHg for DBP). The proposed KDCL_sInvResUNet achieved lightly better performance compared to large models, with a mean absolute error of 10.06 mmHg and mean Pearson correlation of 0.88 in tracking ABP changes. Despite these promising results, all deep learning models showed significant performance variations across different demographic and cardiovascular conditions, highlighting their limited ability to generalize across such a broad and diverse population. This study lays a foundation work for real-time, unobtrusive ABP monitoring in real-world perioperative settings, providing baseline for future advancements in this area.", "date": "2025-08-07", "categories": []}
{"id": "2509.25804", "title": "CardioForest: An Explainable Ensemble Learning Model for Automatic Wide QRS Complex Tachycardia Diagnosis from ECG", "abstract": "This study aims to develop and evaluate an ensemble machine learning-based framework for the automatic detection of Wide QRS Complex Tachycardia (WCT) from ECG signals, emphasizing diagnostic accuracy and interpretability using Explainable AI. The proposed system integrates ensemble learning techniques, i.e., an optimized Random Forest known as CardioForest, and models like XGBoost and LightGBM. The models were trained and tested on ECG data from the publicly available MIMIC-IV dataset. The testing was carried out with the assistance of accuracy, balanced accuracy, precision, recall, F1 score, ROC-AUC, and error rate (RMSE, MAE) measures. In addition, SHAP (SHapley Additive exPlanations) was used to ascertain model explainability and clinical relevance. The CardioForest model performed best on all metrics, achieving a test accuracy of 95.19%, a balanced accuracy of 88.76%, a precision of 95.26%, a recall of 78.42%, and an ROC-AUC of 0.8886. SHAP analysis confirmed the model's ability to rank the most relevant ECG features, such as QRS duration, in accordance with clinical intuitions, thereby fostering trust and usability in clinical practice. The findings recognize CardioForest as an extremely dependable and interpretable WCT detection model. Being able to offer accurate predictions and transparency through explainability makes it a valuable tool to help cardiologists make timely and well-informed diagnoses, especially for high-stakes and emergency scenarios.", "date": "2025-09-30", "categories": []}
{"id": "2509.26136", "title": "CliniBench: A Clinical Outcome Prediction Benchmark for Generative and Encoder-Based Language Models", "abstract": "With their growing capabilities, generative large language models (LLMs) are being increasingly investigated for complex medical tasks. However, their effectiveness in real-world clinical applications remains underexplored. To address this, we present CliniBench, the first benchmark that enables comparability of well-studied encoder-based classifiers and generative LLMs for discharge diagnosis prediction from admission notes in MIMIC-IV dataset. Our extensive study compares 12 generative LLMs and 3 encoder-based classifiers and demonstrates that encoder-based classifiers consistently outperform generative models in diagnosis prediction. We assess several retrieval augmentation strategies for in-context learning from similar patients and find that they provide notable performance improvements for generative LLMs.", "date": "2025-09-30", "categories": []}
{"id": "2511.11293", "title": "Toward Scalable Early Cancer Detection: Evaluating EHR-Based Predictive Models Against Traditional Screening Criteria", "abstract": "Current cancer screening guidelines cover only a few cancer types and rely on narrowly defined criteria such as age or a single risk factor like smoking history, to identify high-risk individuals. Predictive models using electronic health records (EHRs), which capture large-scale longitudinal patient-level health information, may provide a more effective tool for identifying high-risk groups by detecting subtle prediagnostic signals of cancer. Recent advances in large language and foundation models have further expanded this potential, yet evidence remains limited on how useful EHR-based models are compared with traditional risk factors currently used in screening guidelines. We systematically evaluated the clinical utility of EHR-based predictive models against traditional risk factors, including gene mutations and family history of cancer, for identifying high-risk individuals across eight major cancers (breast, lung, colorectal, prostate, ovarian, liver, pancreatic, and stomach), using data from the All of Us Research Program, which integrates EHR, genomic, and survey data from over 865,000 participants. Even with a baseline modeling approach, EHR-based models achieved a 3- to 6-fold higher enrichment of true cancer cases among individuals identified as high risk compared with traditional risk factors alone, whether used as a standalone or complementary tool. The EHR foundation model, a state-of-the-art approach trained on comprehensive patient trajectories, further improved predictive performance across 26 cancer types, demonstrating the clinical potential of EHR-based predictive modeling to support more precise and scalable early detection strategies.", "date": "2025-11-14", "categories": []}
{"id": "2511.14682", "title": "Machine Learning Models for Predicting Smoking-Related Health Decline and Disease Risk", "abstract": "Smoking continues to be a major preventable cause of death worldwide, affecting millions through damage to the heart, metabolism, liver, and kidneys. However, current medical screening methods often miss the early warning signs of smoking-related health problems, leading to late-stage diagnoses when treatment options become limited. This study presents a systematic comparative evaluation of machine learning approaches for smoking-related health risk assessment, emphasizing clinical interpretability and practical deployment over algorithmic innovation. We analyzed health screening data from 55,691 individuals, examining various health indicators, including body measurements, blood tests, and demographic information. We tested three advanced prediction algorithms - Random Forest, XGBoost, and LightGBM - to determine which could most accurately identify people at high risk. This study employed a cross-sectional design to classify current smoking status based on health screening biomarkers, not to predict future disease development. Our Random Forest model performed best, achieving an Area Under the Curve (AUC) of 0.926, meaning it could reliably distinguish between high-risk and lower-risk individuals. Using SHAP (SHapley Additive exPlanations) analysis to understand what the model was detecting, we found that key health markers played crucial roles in prediction: blood pressure levels, triglyceride concentrations, liver enzyme readings, and kidney function indicators (serum creatinine) were the strongest signals of declining health in smokers.", "date": "2025-11-18", "categories": []}
{"id": "2601.15530", "title": "Machine learning-enhanced non-amnestic Alzheimer's disease diagnosis from MRI and clinical features", "abstract": "Alzheimer's disease (AD), defined as an abnormal buildup of amyloid plaques and tau tangles in the brain can be diagnosed with high accuracy based on protein biomarkers via PET or CSF analysis. However, due to the invasive nature of biomarker collection, most AD diagnoses are made in memory clinics using cognitive tests and evaluation of hippocampal atrophy based on MRI. While clinical assessment and hippocampal volume show high diagnostic accuracy for amnestic or typical AD (tAD), a substantial subgroup of AD patients with atypical presentation (atAD) are routinely misdiagnosed. To improve diagnosis of atAD patients, we propose a machine learning approach to distinguish between atAD and non-AD cognitive impairment using clinical testing battery and MRI data collected as standard-of-care. We develop and evaluate our approach using 1410 subjects across four groups (273 tAD, 184 atAD, 235 non-AD, and 685 cognitively normal) collected from one private data set and two public data sets from the National Alzheimer's Coordinating Center (NACC) and the Alzheimer's Disease Neuroimaging Initiative (ADNI). We perform multiple atAD vs. non-AD classification experiments using clinical features and hippocampal volume as well as a comprehensive set of MRI features from across the brain. The best performance is achieved by incorporating additional important MRI features, which outperforms using hippocampal volume alone. Furthermore, we use the Boruta statistical approach to identify and visualize significant brain regions distinguishing between diagnostic groups. Our ML approach improves the percentage of correctly diagnosed atAD cases (the recall) from 52% to 69% for NACC and from 34% to 77% for ADNI, while achieving high precision. The proposed approach has important implications for improving diagnostic accuracy for non-amnestic atAD in clinical settings using only clinical testing battery and MRI.", "date": "2026-01-21", "categories": []}
{"id": "2601.17080", "title": "PC-MCL: Patient-Consistent Multi-Cycle Learning with multi-label bias correction for respiratory sound classification", "abstract": "Automated respiratory sound classification supports the diagnosis of pulmonary diseases. However, many deep models still rely on cycle-level analysis and suffer from patient-specific overfitting. We propose PC-MCL (Patient-Consistent Multi-Cycle Learning) to address these limitations by utilizing three key components: multi-cycle concatenation, a 3-label formulation, and a patient-matching auxiliary task. Our work resolves a multi-label distributional bias in respiratory sound classification, a critical issue inherent to applying multi-cycle concatenation with the conventional 2-label formulation (crackle, wheeze). This bias manifests as a systematic loss of normal signal information when normal and abnormal cycles are combined. Our proposed 3-label formulation (normal, crackle, wheeze) corrects this by preserving information from all constituent cycles in mixed samples. Furthermore, the patient-matching auxiliary task acts as a multi-task regularizer, encouraging the model to learn more robust features and improving generalization. On the ICBHI 2017 benchmark, PC-MCL achieves an ICBHI Score of 65.37%, outperforming existing baselines. Ablation studies confirm that all three components are essential, working synergistically to improve the detection of abnormal respiratory events.", "date": "2026-01-23", "categories": []}
{"id": "2601.17183", "title": "Federated Proximal Optimization for Privacy-Preserving Heart Disease Prediction: A Controlled Simulation Study on Non-IID Clinical Data", "abstract": "Healthcare institutions have access to valuable patient data that could be of great help in the development of improved diagnostic models, but privacy regulations like HIPAA and GDPR prevent hospitals from directly sharing data with one another. Federated Learning offers a way out to this problem by facilitating collaborative model training without having the raw patient data centralized. However, clinical datasets intrinsically have non-IID (non-independent and identically distributed) features brought about by demographic disparity and diversity in disease prevalence and institutional practices. This paper presents a comprehensive simulation research of Federated Proximal Optimization (FedProx) for Heart Disease prediction based on UCI Heart Disease dataset. We generate realistic non-IID data partitions by simulating four heterogeneous hospital clients from the Cleveland Clinic dataset (303 patients), by inducing statistical heterogeneity by demographic-based stratification. Our experimental results show that FedProx with proximal parameter mu=0.05 achieves 85.00% accuracy, which is better than both centralized learning (83.33%) and isolated local models (78.45% average) without revealing patient privacy. Through generous sheer ablation studies with statistical validation on 50 independent runs we demonstrate that proximal regularization is effective in curbing client drift in heterogeneous environments. This proof-of-concept research offers algorithmic insights and practical deployment guidelines for real-world federated healthcare systems, and thus, our results are directly transferable to hospital IT-administrators, implementing privacy-preserving collaborative learning.", "date": "2026-01-23", "categories": []}
{"id": "2601.17310", "title": "High-Fidelity Longitudinal Patient Simulation Using Real-World Data", "abstract": "Simulation is a powerful tool for exploring uncertainty. Its potential in clinical medicine is transformative and includes personalized treatment planning and virtual clinical trials. However, simulating patient trajectories is challenging because of complex biological and sociocultural influences. Here, we show that real-world clinical records can be leveraged to empirically model patient timelines. We developed a generative simulator model that takes a patient's history as input and synthesizes fine-grained, realistic future trajectories. The model was pretrained on more than 200 million clinical records. It produced high-fidelity future timelines, closely matching event occurrence rates, laboratory test results, and temporal dynamics in real patient future data. It also accurately estimated future event probabilities, with observed-to-expected ratios consistently near 1.0 across diverse outcomes and time horizons. Our results reveal the untapped value of real-world data in electronic health records and introduce a scalable framework for in silico modeling of clinical care.", "date": "2026-01-24", "categories": []}
{"id": "2601.18102", "title": "CHiRPE: A Step Towards Real-World Clinical NLP with Clinician-Oriented Model Explanations", "abstract": "The medical adoption of NLP tools requires interpretability by end users, yet traditional explainable AI (XAI) methods are misaligned with clinical reasoning and lack clinician input. We introduce CHiRPE (Clinical High-Risk Prediction with Explainability), an NLP pipeline that takes transcribed semi-structured clinical interviews to: (i) predict psychosis risk; and (ii) generate novel SHAP explanation formats co-developed with clinicians. Trained on 944 semi-structured interview transcripts across 24 international clinics of the AMP-SCZ study, the CHiRPE pipeline integrates symptom-domain mapping, LLM summarisation, and BERT classification. CHiRPE achieved over 90% accuracy across three BERT variants and outperformed baseline models. Explanation formats were evaluated by 28 clinical experts who indicated a strong preference for our novel concept-guided explanations, especially hybrid graph-and-text summary formats. CHiRPE demonstrates that clinically-guided model development produces both accurate and interpretable results. Our next step is focused on real-world testing across our 24 international sites.", "date": "2026-01-26", "categories": []}
{"id": "2601.18132", "title": "RareAlert: Aligning heterogeneous large language model reasoning for early rare disease risk screening", "abstract": "Missed and delayed diagnosis remains a major challenge in rare disease care. At the initial clinical encounters, physicians assess rare disease risk using only limited information under high uncertainty. When high-risk patients are not recognised at this stage, targeted diagnostic testing is often not initiated, resulting in missed diagnosis. Existing primary care triage processes are structurally insufficient to reliably identify patients with rare diseases at initial clinical presentation and universal screening is needed to reduce diagnostic delay. Here we present RareAlert, an early screening system which predict patient-level rare disease risk from routinely available primary-visit information. RareAlert integrates reasoning generated by ten LLMs, calibrates and weights these signals using machine learning, and distils the aligned reasoning into a single locally deployable model. To develop and evaluate RareAlert, we curated RareBench, a real-world dataset of 158,666 cases covering 33 Orphanet disease categories and more than 7,000 rare conditions, including both rare and non-rare presentations. The results showed that rare disease identification can be reconceptualised as a universal uncertainty resolution process applied to the general patient population. On an independent test set, RareAlert, a Qwen3-4B based model trained with calibrated reasoning signals, achieved an AUC of 0.917, outperforming the best machine learning ensemble and all evaluated LLMs, including GPT-5, DeepSeek-R1, Claude-3.7-Sonnet, o3-mini, Gemini-2.5-Pro, and Qwen3-235B. These findings demonstrate the diversity in LLM medical reasoning and the effectiveness of aligning such reasoning in highly uncertain clinical tasks. By incorporating calibrated reasoning into a single model, RareAlert enables accurate, privacy-preserving, and scalable rare disease risk screening suitable for large-scale local deployment.", "date": "2026-01-26", "categories": []}
{"id": "2511.12409", "title": "Interpretable Fine-Gray Deep Survival Model for Competing Risks: Predicting Post-Discharge Foot Complications for Diabetic Patients in Ontario", "abstract": "Model interpretability is crucial for establishing AI safety and clinician trust in medical applications for example, in survival modelling with competing risks. Recent deep learning models have attained very good predictive performance but their limited transparency, being black-box models, hinders their integration into clinical practice. To address this gap, we propose an intrinsically interpretable survival model called CRISPNAM-FG. Leveraging the structure of Neural Additive Models (NAMs) with separate projection vectors for each risk, our approach predicts the Cumulative Incidence Function using the Fine-Gray formulation, achieving high predictive power with intrinsically transparent and auditable predictions. We validated the model on several benchmark datasets and applied our model to predict future foot complications in diabetic patients across 29 Ontario hospitals (2016-2023). Our method achieves competitive performance compared to other deep survival models while providing transparency through shape functions and feature importance plots.", "date": "2025-11-16", "categories": []}
{"id": "2511.17655", "title": "Intelligent Systems in Neuroimaging: Pioneering AI Techniques for Brain Tumor Detection", "abstract": "This study deliberates on the application of advanced AI techniques for brain tumor classification through MRI, wherein the training includes the present best deep learning models to enhance diagnosis accuracy and the potential of usability in clinical practice. By combining custom convolutional models with pre-trained neural network architectures, our approach exposes the utmost performance in the classification of four classes: glioma, meningioma, pituitary tumors, and no-tumor cases. Assessing the models on a large dataset of over 7,000 MRI images focused on detection accuracy, computational efficiency, and generalization to unseen data. The results indicate that the Xception architecture surpasses all other were tested, obtaining a testing accuracy of 98.71% with the least validation loss. While presenting this case with findings that demonstrate AI as a probable scorer in brain tumor diagnosis, we demonstrate further motivation by reducing computational complexity toward real-world clinical deployment. These aspirations offer an abundant future for progress in automated neuroimaging diagnostics.", "date": "2025-11-20", "categories": []}
{"id": "2601.01176", "title": "CardioMOD-Net: A Modal Decomposition-Neural Network Framework for Diagnosis and Prognosis of HFpEF from Echocardiography Cine Loops", "abstract": "Introduction: Heart failure with preserved ejection fraction (HFpEF) arises from diverse comorbidities and progresses through prolonged subclinical stages, making early diagnosis and prognosis difficult. Current echocardiography-based Artificial Intelligence (AI) models focus primarily on binary HFpEF detection in humans and do not provide comorbidity-specific phenotyping or temporal estimates of disease progression towards decompensation. We aimed to develop a unified AI framework, CardioMOD-Net, to perform multiclass diagnosis and continuous prediction of HFpEF onset directly from standard echocardiography cine loops in preclinical models.\n  Methods: Mouse echocardiography videos from four groups were used: control (CTL), hyperglycaemic (HG), obesity (OB), and systemic arterial hypertension (SAH). Two-dimensional parasternal long-axis cine loops were decomposed using Higher Order Dynamic Mode Decomposition (HODMD) to extract temporal features for downstream analysis. A shared latent representation supported Vision Transformers, one for a classifier for diagnosis and another for a regression module for predicting the age at HFpEF onset.\n  Results: Overall diagnostic accuracy across the four groups was 65%, with all classes exceeding 50% accuracy. Misclassifications primarily reflected early-stage overlap between OB or SAH and CTL. The prognostic module achieved a root-mean-square error of 21.72 weeks for time-to-HFpEF prediction, with OB and SAH showing the most accurate estimates. Predicted HFpEF onset closely matched true distributions in all groups.\n  Discussion: This unified framework demonstrates that multiclass phenotyping and continuous HFpEF onset prediction can be obtained from a single cine loop, even under small-data conditions. The approach offers a foundation for integrating diagnostic and prognostic modelling in preclinical HFpEF research.", "date": "2026-01-03", "categories": []}
{"id": "2601.16324", "title": "Student Mental Health Screening via Fitbit Data Collected During the COVID-19 Pandemic", "abstract": "College students experience many stressors, resulting in high levels of anxiety and depression. Wearable technology provides unobtrusive sensor data that can be used for the early detection of mental illness. However, current research is limited concerning the variety of psychological instruments administered, physiological modalities, and time series parameters. In this research, we collect the Student Mental and Environmental Health (StudentMEH) Fitbit dataset from students at our institution during the pandemic. We provide a comprehensive assessment of the ability of predictive machine learning models to screen for depression, anxiety, and stress using different Fitbit modalities. Our findings indicate potential in physiological modalities such as heart rate and sleep to screen for mental illness with the F1 scores as high as 0.79 for anxiety, the former modality reaching 0.77 for stress screening, and the latter modality achieving 0.78 for depression. This research highlights the potential of wearable devices to support continuous mental health monitoring, the importance of identifying best data aggregation levels and appropriate modalities for screening for different mental ailments.", "date": "2026-01-22", "categories": []}
{"id": "2401.14193", "title": "Clinical Melanoma Diagnosis with Artificial Intelligence: Insights from a Prospective Multicenter Study", "abstract": "Early detection of melanoma, a potentially lethal type of skin cancer with high prevalence worldwide, improves patient prognosis. In retrospective studies, artificial intelligence (AI) has proven to be helpful for enhancing melanoma detection. However, there are few prospective studies confirming these promising results. Existing studies are limited by low sample sizes, too homogenous datasets, or lack of inclusion of rare melanoma subtypes, preventing a fair and thorough evaluation of AI and its generalizability, a crucial aspect for its application in the clinical setting. Therefore, we assessed 'All Data are Ext' (ADAE), an established open-source ensemble algorithm for detecting melanomas, by comparing its diagnostic accuracy to that of dermatologists on a prospectively collected, external, heterogeneous test set comprising eight distinct hospitals, four different camera setups, rare melanoma subtypes, and special anatomical sites. We advanced the algorithm with real test-time augmentation (R-TTA, i.e. providing real photographs of lesions taken from multiple angles and averaging the predictions), and evaluated its generalization capabilities. Overall, the AI showed higher balanced accuracy than dermatologists (0.798, 95% confidence interval (CI) 0.779-0.814 vs. 0.781, 95% CI 0.760-0.802; p<0.001), obtaining a higher sensitivity (0.921, 95% CI 0.900- 0.942 vs. 0.734, 95% CI 0.701-0.770; p<0.001) at the cost of a lower specificity (0.673, 95% CI 0.641-0.702 vs. 0.828, 95% CI 0.804-0.852; p<0.001). As the algorithm exhibited a significant performance advantage on our heterogeneous dataset exclusively comprising melanoma-suspicious lesions, AI may offer the potential to support dermatologists particularly in diagnosing challenging cases.", "date": "2024-01-25", "categories": []}
{"id": "2508.01693", "title": "SURE-Med: Systematic Uncertainty Reduction for Enhanced Reliability in Medical Report Generation", "abstract": "Automated medical report generation (MRG) holds great promise for reducing the heavy workload of radiologists. However, its clinical deployment is hindered by three major sources of uncertainty. First, visual uncertainty, caused by noisy or incorrect view annotations, compromises feature extraction. Second, label distribution uncertainty, stemming from long-tailed disease prevalence, biases models against rare but clinically critical conditions. Third, contextual uncertainty, introduced by unverified historical reports, often leads to factual hallucinations. These challenges collectively limit the reliability and clinical trustworthiness of MRG systems. To address these issues, we propose SURE-Med, a unified framework that systematically reduces uncertainty across three critical dimensions: visual, distributional, and contextual. To mitigate visual uncertainty, a Frontal-Aware View Repair Resampling module corrects view annotation errors and adaptively selects informative features from supplementary views. To tackle label distribution uncertainty, we introduce a Token Sensitive Learning objective that enhances the modeling of critical diagnostic sentences while reweighting underrepresented diagnostic terms, thereby improving sensitivity to infrequent conditions. To reduce contextual uncertainty, our Contextual Evidence Filter validates and selectively incorporates prior information that aligns with the current image, effectively suppressing hallucinations. Extensive experiments on the MIMIC-CXR and IU-Xray benchmarks demonstrate that SURE-Med achieves state-of-the-art performance. By holistically reducing uncertainty across multiple input modalities, SURE-Med sets a new benchmark for reliability in medical report generation and offers a robust step toward trustworthy clinical decision support.", "date": "2025-08-03", "categories": []}
{"id": "2601.13710", "title": "Who Benefits From Sinus Surgery? Comparing Generative AI and Supervised Machine Learning for Predicting Surgical Outcomes in Chronic Rhinosinusitis", "abstract": "Artificial intelligence has reshaped medical imaging, yet the use of AI on clinical data for prospective decision support remains limited. We study pre-operative prediction of clinically meaningful improvement in chronic rhinosinusitis (CRS), defining success as a more than 8.9-point reduction in SNOT-22 at 6 months (MCID). In a prospectively collected cohort where all patients underwent surgery, we ask whether models using only pre-operative clinical data could have identified those who would have poor outcomes, i.e. those who should have avoided surgery. We benchmark supervised ML (logistic regression, tree ensembles, and an in-house MLP) against generative AI (ChatGPT, Claude, Gemini, Perplexity), giving each the same structured inputs and constraining outputs to binary recommendations with confidence. Our best ML model (MLP) achieves 85 % accuracy with superior calibration and decision-curve net benefit. GenAI models underperform on discrimination and calibration across zero-shot setting. Notably, GenAI justifications align with clinician heuristics and the MLP's feature importance, repeatedly highlighting baseline SNOT-22, CT/endoscopy severity, polyp phenotype, and physchology/pain comorbidities. We provide a reproducible tabular-to-GenAI evaluation protocol and subgroup analyses. Findings support an ML-first, GenAI- augmented workflow: deploy calibrated ML for primary triage of surgical candidacy, with GenAI as an explainer to enhance transparency and shared decision-making.", "date": "2026-01-20", "categories": []}
{"id": "2601.15306", "title": "Uncovering Latent Bias in LLM-Based Emergency Department Triage Through Proxy Variables", "abstract": "Recent advances in large language models (LLMs) have enabled their integration into clinical decision-making; however, hidden biases against patients across racial, social, economic, and clinical backgrounds persist. In this study, we investigate bias in LLM-based medical AI systems applied to emergency department (ED) triage. We employ 32 patient-level proxy variables, each represented by paired positive and negative qualifiers, and evaluate their effects using both public (MIMIC-IV-ED Demo, MIMIC-IV Demo) and restricted-access credentialed (MIMIC-IV-ED and MIMIC-IV) datasets as appropriate~\\cite{mimiciv_ed_demo,mimiciv_ed,mimiciv}. Our results reveal discriminatory behavior mediated through proxy variables in ED triage scenarios, as well as a systematic tendency for LLMs to modify perceived patient severity when specific tokens appear in the input context, regardless of whether they are framed positively or negatively. These findings indicate that AI systems is still imperfectly trained on noisy, sometimes non-causal signals that do not reliably reflect true patient acuity. Consequently, more needs to be done to ensure the safe and responsible deployment of AI technologies in clinical settings.", "date": "2026-01-13", "categories": []}
{"id": "2601.15546", "title": "Beyond validation loss: Clinically-tailored optimization metrics improve a model's clinical performance", "abstract": "A key task in ML is to optimize models at various stages, e.g. by choosing hyperparameters or picking a stopping point. A traditional ML approach is to use validation loss, i.e. to apply the training loss function on a validation set to guide these optimizations. However, ML for healthcare has a distinct goal from traditional ML: Models must perform well relative to specific clinical requirements, vs. relative to the loss function used for training. These clinical requirements can be captured more precisely by tailored metrics. Since many optimization tasks do not require the driving metric to be differentiable, they allow a wider range of options, including the use of metrics tailored to be clinically-relevant. In this paper we describe two controlled experiments which show how the use of clinically-tailored metrics provide superior model optimization compared to validation loss, in the sense of better performance on the clinical task. The use of clinically-relevant metrics for optimization entails some extra effort, to define the metrics and to code them into the pipeline. But it can yield models that better meet the central goal of ML for healthcare: strong performance in the clinic.", "date": "2026-01-22", "categories": []}
{"id": "2601.15798", "title": "VitalDiagnosis: AI-Driven Ecosystem for 24/7 Vital Monitoring and Chronic Disease Management", "abstract": "Chronic diseases have become the leading cause of death worldwide, a challenge intensified by strained medical resources and an aging population. Individually, patients often struggle to interpret early signs of deterioration or maintain adherence to care plans. In this paper, we introduce VitalDiagnosis, an LLM-driven ecosystem designed to shift chronic disease management from passive monitoring to proactive, interactive engagement. By integrating continuous data from wearable devices with the reasoning capabilities of LLMs, the system addresses both acute health anomalies and routine adherence. It analyzes triggers through context-aware inquiries, produces provisional insights within a collaborative patient-clinician workflow, and offers personalized guidance. This approach aims to promote a more proactive and cooperative care paradigm, with the potential to enhance patient self-management and reduce avoidable clinical workload.", "date": "2026-01-22", "categories": []}
{"id": "2501.09595", "title": "IFRA: a machine learning-based Instrumented Fall Risk Assessment Scale derived from Instrumented Timed Up and Go test in stroke patients", "abstract": "Background/Objectives: Falls represent a major health concern for stroke survivors, necessitating effective risk assessment tools. This study proposes the Instrumented Fall Risk Assessment (IFRA) scale, a novel screening tool derived from Instrumented Timed Up and Go (ITUG) test data, designed to capture mobility measures often missed by traditional scales. Methods: We employed a two-step machine learning approach to develop the IFRA scale: first, identifying predictive mobility features from ITUG data and, second, creating a stratification strategy to classify patients into low-, medium-, or high-fall-risk categories. This study included 142 participants, who were divided into training (including synthetic cases), validation, and testing sets (comprising 22 non-fallers and 10 fallers). IFRA's performance was compared against traditional clinical scales (e.g., standard TUG and Mini-BESTest) using Fisher's Exact test. Results: Machine learning analysis identified specific features as key predictors, namely vertical and medio-lateral acceleration, and angular velocity during walking and sit-to-walk transitions. IFRA demonstrated a statistically significant association with fall status (Fisher's Exact test p = 0.004) and was the only scale to assign more than half of the actual fallers to the high-risk category, outperforming the comparative clinical scales in this dataset. Conclusions: This proof-of-concept study demonstrates IFRA's potential as an automated, complementary approach for fall risk stratification in post-stroke patients. While IFRA shows promising discriminative capability, particularly for identifying high-risk individuals, these preliminary findings require validation in larger cohorts before clinical implementation.", "date": "2025-01-16", "categories": []}
{"id": "2601.11860", "title": "Adversarial Drift-Aware Predictive Transfer: Toward Durable Clinical AI", "abstract": "Clinical AI systems frequently suffer performance decay post-deployment due to temporal data shifts, such as evolving populations, diagnostic coding updates (e.g., ICD-9 to ICD-10), and systemic shocks like the COVID-19 pandemic. Addressing this ``aging'' effect via frequent retraining is often impractical due to computational costs and privacy constraints. To overcome these hurdles, we introduce Adversarial Drift-Aware Predictive Transfer (ADAPT), a novel framework designed to confer durability against temporal drift with minimal retraining. ADAPT innovatively constructs an uncertainty set of plausible future models by combining historical source models and limited current data. By optimizing worst-case performance over this set, it balances current accuracy with robustness against degradation due to future drifts. Crucially, ADAPT requires only summary-level model estimators from historical periods, preserving data privacy and ensuring operational simplicity. Validated on longitudinal suicide risk prediction using electronic health records from Mass General Brigham (2005--2021) and Duke University Health Systems, ADAPT demonstrated superior stability across coding transitions and pandemic-induced shifts. By minimizing annual performance decay without labeling or retraining future data, ADAPT offers a scalable pathway for sustaining reliable AI in high-stakes healthcare environments.", "date": "2026-01-17", "categories": []}
{"id": "2601.14609", "title": "Communication-Efficient Federated Risk Difference Estimation for Time-to-Event Clinical Outcomes", "abstract": "Privacy-preserving model co-training in medical research is often hindered by server-dependent architectures incompatible with protected hospital data systems and by the predominant focus on relative effect measures (hazard ratios) which lack clinical interpretability for absolute survival risk assessment. We propose FedRD, a communication-efficient framework for federated risk difference estimation in distributed survival data. Unlike typical federated learning frameworks (e.g., FedAvg) that require persistent server connections and extensive iterative communication, FedRD is server-independent with minimal communication: one round of summary statistics exchange for the stratified model and three rounds for the unstratified model. Crucially, FedRD provides valid confidence intervals and hypothesis testing--capabilities absent in FedAvg-based frameworks. We provide theoretical guarantees by establishing the asymptotic properties of FedRD and prove that FedRD (unstratified) is asymptotically equivalent to pooled individual-level analysis. Simulation studies and real-world clinical applications across different countries demonstrate that FedRD outperforms local and federated baselines in both estimation accuracy and prediction performance, providing an architecturally feasible solution for absolute risk assessment in privacy-restricted, multi-site clinical studies.", "date": "2026-01-21", "categories": []}
{"id": "2601.14917", "title": "Tailoring Adverse Event Prediction in Type 1 Diabetes with Patient-Specific Deep Learning Models", "abstract": "Effective management of Type 1 Diabetes requires continuous glucose monitoring and precise insulin adjustments to prevent hyperglycemia and hypoglycemia. With the growing adoption of wearable glucose monitors and mobile health applications, accurate blood glucose prediction is essential for enhancing automated insulin delivery and decision-support systems. This paper presents a deep learning-based approach for personalized blood glucose prediction, leveraging patient-specific data to improve prediction accuracy and responsiveness in real-world scenarios. Unlike traditional generalized models, our method accounts for individual variability, enabling more effective subject-specific predictions. We compare Leave-One-Subject-Out Cross-Validation with a fine-tuning strategy to evaluate their ability to model patient-specific dynamics. Results show that personalized models significantly improve the prediction of adverse events, enabling more precise and timely interventions in real-world scenarios. To assess the impact of patient-specific data, we conduct experiments comparing a multimodal, patient-specific approach against traditional CGM-only methods. Additionally, we perform an ablation study to investigate model performance with progressively smaller training sets, identifying the minimum data required for effective personalization-an essential consideration for real-world applications where extensive data collection is often challenging. Our findings underscore the potential of adaptive, personalized glucose prediction models for advancing next-generation diabetes management, particularly in wearable and mobile health platforms, enhancing consumer-oriented diabetes care solutions.", "date": "2026-01-21", "categories": []}
{"id": "2312.10259", "title": "ComplicaCode: Enhancing Disease Complication Detection in Electronic Health Records through ICD Path Generation", "abstract": "The target of Electronic Health Record (EHR) coding is to find the diagnostic codes according to the EHRs. In previous research, researchers have preferred to do multi-classification on the EHR coding task; most of them encode the EHR first and then process it to get the probability of each code based on the EHR representation. However, the question of complicating diseases is neglected among all these methods. In this paper, we propose a novel EHR coding framework, which is the first attempt at detecting complicating diseases, called ComplicaCode. This method refers to the idea of adversarial learning; a Path Generator and a Path Discriminator are designed to more efficiently finish the task of EHR coding. We propose a copy module to detect complicating diseases; by the proposed copy module and the adversarial learning strategy, we identify complicating diseases efficiently. Extensive experiments show that our method achieves a 57.30\\% ratio of complicating diseases in predictions, and achieves the state-of-the-art performance among cnn-based baselines, it also surpasses transformer methods in the complication detection task, demonstrating the effectiveness of our proposed model. According to the ablation study, the proposed copy mechanism plays a crucial role in detecting complicating diseases.", "date": "2023-12-15", "categories": []}
{"id": "2505.17344", "title": "A Multi-Head Attention Soft Random Forest for Interpretable Patient No-Show Prediction", "abstract": "Unattended scheduled appointments, defined as patient no-shows, adversely affect both healthcare providers and patients' health, disrupting the continuity of care, operational efficiency, and the efficient allocation of medical resources. Accurate predictive modeling is needed to reduce the impact of no-shows. Although machine learning methods, such as logistic regression, random forest models, and decision trees, are widely used in predicting patient no-shows, they often rely on hard decision splits and static feature importance, limiting their adaptability to specific or complex patient behaviors. To address this limitation, we propose a new hybrid Multi-Head Attention Soft Random Forest (MHASRF) model that integrates attention mechanisms into a random forest model using probabilistic soft splitting instead of hard splitting. The MHASRF model assigns attention weights differently across the trees, enabling attention on specific patient behaviors. The model exhibited 93.72% accuracy, 94.77% specificity, 90.23% precision, 89.38% recall, a 91.54% F1 score and AUC 97.87%, demonstrated high and balance performance across metrics, outperforming decision tree, random forest, logistic regression, and naive bayes models overall. Furthermore, MHASRF was able to identify key predictors of patient no-shows using two levels of feature importance (tree level and attention mechanism level), offering deeper insights into patient no-show predictors. The proposed model is a robust, adaptable, and interpretable method for predicting patient no-shows that will help healthcare providers in optimizing resources.", "date": "2025-05-22", "categories": []}
{"id": "2510.09895", "title": "Chain-of-Influence: Tracing Interdependencies Across Time and Features in Clinical Predictive Modelings", "abstract": "Modeling clinical time-series data is hampered by the challenge of capturing latent, time-varying dependencies among features. State-of-the-art approaches often rely on black-box mechanisms or simple aggregation, failing to explicitly model how the influence of one clinical variable propagates through others over time. We propose $\\textbf{Chain-of-Influence (CoI)}$, an interpretable deep learning framework that constructs an explicit, time-unfolded graph of feature interactions. CoI enables the tracing of influence pathways, providing a granular audit trail that shows how any feature at any time contributes to the final prediction, both directly and through its influence on other variables. We evaluate CoI on mortality and disease progression tasks using the MIMIC-IV dataset and a chronic kidney disease cohort. Our framework achieves state-of-the-art predictive performance (AUROC of 0.960 on CKD progression and 0.950 on ICU mortality), with deletion-based sensitivity analyses confirming that CoI's learned attributions faithfully reflect its decision process. Through case studies, we demonstrate that CoI uncovers clinically meaningful, patient-specific patterns of disease progression, offering enhanced transparency into the temporal and cross-feature dependencies that inform clinical decision-making.", "date": "2025-10-10", "categories": []}
{"id": "2601.11559", "title": "MIMIC-RD: Can LLMs differentially diagnose rare diseases in real-world clinical settings?", "abstract": "Despite rare diseases affecting 1 in 10 Americans, their differential diagnosis remains challenging. Due to their impressive recall abilities, large language models (LLMs) have been recently explored for differential diagnosis. Existing approaches to evaluating LLM-based rare disease diagnosis suffer from two critical limitations: they rely on idealized clinical case studies that fail to capture real-world clinical complexity, or they use ICD codes as disease labels, which significantly undercounts rare diseases since many lack direct mappings to comprehensive rare disease databases like Orphanet. To address these limitations, we explore MIMIC-RD, a rare disease differential diagnosis benchmark constructed by directly mapping clinical text entities to Orphanet. Our methodology involved an initial LLM-based mining process followed by validation from four medical annotators to confirm identified entities were genuine rare diseases. We evaluated various models on our dataset of 145 patients and found that current state-of-the-art LLMs perform poorly on rare disease differential diagnosis, highlighting the substantial gap between existing capabilities and clinical needs. From our findings, we outline several future steps towards improving differential diagnosis of rare diseases.", "date": "2025-12-18", "categories": []}
{"id": "2601.11615", "title": "A Review on Machine Learning Approaches for the Prediction of Glucose Levels and Hypogylcemia", "abstract": "Type 1 Diabetes (T1D) is an autoimmune disease leading to insulin insufficiency. Thus, patients require lifelong insulin therapy, which has a side effect of hypoglycemia. Hypoglycemia is a critical state of decreased blood glucose levels (BGL) below 70 mg/dL and is associated with increased risk of mortality. Machine learning (ML) models can improve diabetes management by predicting hypoglycemia and providing optimal prevention methods. ML models are classified into regression and classification based, that forecast glucose levels and identify events based on defined labels, respectively. This review investigates state-of-the-art models trained on data of continuous glucose monitoring (CGM) devices from patients with T1D. We compare the models' performance across short-term (15 to 120 min) and long term (3 to more than 24 hours) prediction horizons (PHs). Particularly, we explore: 1) How much in advance can glucose values or a hypoglycemic event be accurately predicted? 2) Which models have the best performance? 3) Which factors impact the performance? and 4) Does personalization increase performance? The results show that 1) a PH of up to 1 hour provides the best results. 2) Conventional ML methods yield the best results for classification and DL for regression. A single model cannot adequately classify across multiple PHs. 3) The model performance is influenced by multivariate datasets and the input sequence length (ISL). 4) Personal data enhances performance but due to limited data quality population-based models are preferred.", "date": "2026-01-09", "categories": []}
{"id": "2601.12068", "title": "Bridging the Gap in Bangla Healthcare: Machine Learning Based Disease Prediction Using a Symptoms-Disease Dataset", "abstract": "Increased access to reliable health information is essential for non-English-speaking populations, yet resources in Bangla for disease prediction remain limited. This study addresses this gap by developing a comprehensive Bangla symptoms-disease dataset containing 758 unique symptom-disease relationships spanning 85 diseases. To ensure transparency and reproducibility, we also make our dataset publicly available. The dataset enables the prediction of diseases based on Bangla symptom inputs, supporting healthcare accessibility for Bengali-speaking populations. Using this dataset, we evaluated multiple machine learning models to predict diseases based on symptoms provided in Bangla and analyzed their performance on our dataset. Both soft and hard voting ensemble approaches combining top-performing models achieved 98\\% accuracy, demonstrating superior robustness and generalization. Our work establishes a foundational resource for disease prediction in Bangla, paving the way for future advancements in localized health informatics and diagnostic tools. This contribution aims to enhance equitable access to health information for Bangla-speaking communities, particularly for early disease detection and healthcare interventions.", "date": "2026-01-17", "categories": []}
{"id": "2601.12227", "title": "Learning Longitudinal Health Representations from EHR and Wearable Data", "abstract": "Foundation models trained on electronic health records show strong performance on many clinical prediction tasks but are limited by sparse and irregular documentation. Wearable devices provide dense continuous physiological signals but lack semantic grounding. Existing methods usually model these data sources separately or combine them through late fusion. We propose a multimodal foundation model that jointly represents electronic health records and wearable data as a continuous time latent process. The model uses modality specific encoders and a shared temporal backbone pretrained with self supervised and cross modal objectives. This design produces representations that are temporally coherent and clinically grounded. Across forecasting physiological and risk modeling tasks the model outperforms strong electronic health record only and wearable only baselines especially at long horizons and under missing data. These results show that joint electronic health record and wearable pretraining yields more faithful representations of longitudinal health.", "date": "2026-01-18", "categories": []}
{"id": "2601.12405", "title": "Explainable Machine Learning for Pediatric Dental Risk Stratification Using Socio-Demographic Determinants", "abstract": "Background: Pediatric dental disease remains one of the most prevalent and inequitable chronic health conditions worldwide. Although strong epidemiological evidence links oral health outcomes to socio-economic and demographic determinants, most artificial intelligence (AI) applications in dentistry rely on image-based diagnosis and black-box prediction models, limiting transparency and ethical applicability in pediatric populations.\n  Objective: This study aimed to develop and evaluate an explainable machine learning framework for pediatric dental risk stratification that prioritizes interpretability, calibration, and ethical deployment over maximal predictive accuracy.\n  Methods: A supervised machine learning model was trained using population-level pediatric data including age, income-to-poverty ratio, race/ethnicity, gender, and medical history. Model performance was assessed using receiver operating characteristic (ROC) analysis and calibration curves. Explainability was achieved using SHapley Additive exPlanations (SHAP) to provide global and individual-level interpretation of predictions.\n  Results: The model achieved modest discrimination (AUC = 0.61) with conservative calibration, underestimating risk at higher probability levels. SHAP analysis identified age and income-to-poverty ratio as the strongest contributors to predicted risk, followed by race/ethnicity and gender.\n  Conclusion: Explainable machine learning enables transparent, prevention-oriented pediatric dental risk stratification and supports population screening and equitable resource allocation rather than diagnostic decision-making.", "date": "2026-01-18", "categories": []}
{"id": "2601.13094", "title": "Patient-Conditioned Adaptive Offsets for Reliable Diagnosis across Subgroups", "abstract": "AI models for medical diagnosis often exhibit uneven performance across patient populations due to heterogeneity in disease prevalence, imaging appearance, and clinical risk profiles. Existing algorithmic fairness approaches typically seek to reduce such disparities by suppressing sensitive attributes. However, in medical settings these attributes often carry essential diagnostic information, and removing them can degrade accuracy and reliability, particularly in high-stakes applications. In contrast, clinical decision making explicitly incorporates patient context when interpreting diagnostic evidence, suggesting a different design direction for subgroup-aware models. In this paper, we introduce HyperAdapt, a patient-conditioned adaptation framework that improves subgroup reliability while maintaining a shared diagnostic model. Clinically relevant attributes such as age and sex are encoded into a compact embedding and used to condition a hypernetwork-style module, which generates small residual modulation parameters for selected layers of a shared backbone. This design preserves the general medical knowledge learned by the backbone while enabling targeted adjustments that reflect patient-specific variability. To ensure efficiency and robustness, adaptations are constrained through low-rank and bottlenecked parameterizations, limiting both model complexity and computational overhead. Experiments across multiple public medical imaging benchmarks demonstrate that the proposed approach consistently improves subgroup-level performance without sacrificing overall accuracy. On the PAD-UFES-20 dataset, our method outperforms the strongest competing baseline by 4.1% in recall and 4.4% in F1 score, with larger gains observed for underrepresented patient populations.", "date": "2026-01-19", "categories": []}
{"id": "2601.13178", "title": "Medical Triage as Pairwise Ranking: A Benchmark for Urgency in Patient Portal Messages", "abstract": "Medical triage is the task of allocating medical resources and prioritizing patients based on medical need. This paper introduces the first large-scale public dataset for studying medical triage in the context of asynchronous outpatient portal messages. Our novel task formulation views patient message triage as a pairwise inference problem, where we train LLMs to choose `\"which message is more medically urgent\" in a head-to-head tournament-style re-sort of a physician's inbox. Our novel benchmark PMR-Bench contains 1569 unique messages and 2,000+ high-quality test pairs for pairwise medical urgency assessment alongside a scalable training data generation pipeline. PMR-Bench includes samples that contain both unstructured patient-written messages alongside real electronic health record (EHR) data, emulating a real-world medical triage scenario.\n  We develop a novel automated data annotation strategy to provide LLMs with in-domain guidance on this task. The resulting data is used to train two model classes, UrgentReward and UrgentSFT, leveraging Bradley-Terry and next token prediction objective, respectively to perform pairwise urgency classification. We find that UrgentSFT achieves top performance on PMR-Bench, with UrgentReward showing distinct advantages in low-resource settings. For example, UrgentSFT-8B and UrgentReward-8B provide a 15- and 16-point boost, respectively, on inbox sorting metrics over off-the-shelf 8B models. Paper resources can be found at https://tinyurl.com/Patient-Message-Triage", "date": "2026-01-19", "categories": []}
{"id": "2601.13388", "title": "Structured Insight from Unstructured Data: Large Language Models for SDOH-Driven Diabetes Risk Prediction", "abstract": "Social determinants of health (SDOH) play a critical role in Type 2 Diabetes (T2D) management but are often absent from electronic health records and risk prediction models. Most individual-level SDOH data is collected through structured screening tools, which lack the flexibility to capture the complexity of patient experiences and unique needs of a clinic's population. This study explores the use of large language models (LLMs) to extract structured SDOH information from unstructured patient life stories and evaluate the predictive value of both the extracted features and the narratives themselves for assessing diabetes control. We collected unstructured interviews from 65 T2D patients aged 65 and older, focused on their lived experiences, social context, and diabetes management. These narratives were analyzed using LLMs with retrieval-augmented generation to produce concise, actionable qualitative summaries for clinical interpretation and structured quantitative SDOH ratings for risk prediction modeling. The structured SDOH ratings were used independently and in combination with traditional laboratory biomarkers as inputs to linear and tree-based machine learning models (Ridge, Lasso, Random Forest, and XGBoost) to demonstrate how unstructured narrative data can be applied in conventional risk prediction workflows. Finally, we evaluated several LLMs on their ability to predict a patient's level of diabetes control (low, medium, high) directly from interview text with A1C values redacted. LLMs achieved 60% accuracy in predicting diabetes control levels from interview text. This work demonstrates how LLMs can translate unstructured SDOH-related data into structured insights, offering a scalable approach to augment clinical risk models and decision-making.", "date": "2026-01-19", "categories": []}
{"id": "2601.13748", "title": "EEG-Titans: Long-Horizon Seizure Forecasting via Dual-Branch Attention and Neural Memory", "abstract": "Accurate epileptic seizure prediction from electroencephalography (EEG) remains challenging because pre-ictal dynamics may span long time horizons while clinically relevant signatures can be subtle and transient. Many deep learning models face a persistent trade-off between capturing local spatiotemporal patterns and maintaining informative long-range context when operating on ultralong sequences. We propose EEG-Titans, a dualbranch architecture that incorporates a modern neural memory mechanism for long-context modeling. The model combines sliding-window attention to capture short-term anomalies with a recurrent memory pathway that summarizes slower, progressive trends over time. On the CHB-MIT scalp EEG dataset, evaluated under a chronological holdout protocol, EEG-Titans achieves 99.46% average segment-level sensitivity across 18 subjects. We further analyze safety-first operating points on artifact-prone recordings and show that a hierarchical context strategy extending the receptive field for high-noise subjects can markedly reduce false alarms (down to 0.00 FPR/h in an extreme outlier) without sacrificing sensitivity. These results indicate that memory-augmented long-context modeling can provide robust seizure forecasting under clinically constrained evaluation", "date": "2026-01-20", "categories": []}
{"id": "2601.14154", "title": "LLM Augmented Intervenable Multimodal Adaptor for Post-operative Complication Prediction in Lung Cancer Surgery", "abstract": "Postoperative complications remain a critical concern in clinical practice, adversely affecting patient outcomes and contributing to rising healthcare costs. We present MIRACLE, a deep learning architecture for prediction of risk of postoperative complications in lung cancer surgery by integrating preoperative clinical and radiological data. MIRACLE employs a hyperspherical embedding space fusion of heterogeneous inputs, enabling the extraction of robust, discriminative features from both structured clinical records and high-dimensional radiological images. To enhance transparency of prediction and clinical utility, we incorporate an interventional deep learning module in MIRACLE, that not only refines predictions but also provides interpretable and actionable insights, allowing domain experts to interactively adjust recommendations based on clinical expertise. We validate our approach on POC-L, a real-world dataset comprising 3,094 lung cancer patients who underwent surgery at Roswell Park Comprehensive Cancer Center. Our results demonstrate that MIRACLE outperforms various traditional machine learning models and contemporary large language models (LLM) variants alone, for personalized and explainable postoperative risk management.", "date": "2026-01-20", "categories": []}
{"id": "2601.10748", "title": "AnyECG: Evolved ECG Foundation Model for Holistic Health Profiling", "abstract": "Background: Artificial intelligence enabled electrocardiography (AI-ECG) has demonstrated the ability to detect diverse pathologies, but most existing models focus on single disease identification, neglecting comorbidities and future risk prediction. Although ECGFounder expanded cardiac disease coverage, a holistic health profiling model remains needed.\n  Methods: We constructed a large multicenter dataset comprising 13.3 million ECGs from 2.98 million patients. Using transfer learning, ECGFounder was fine-tuned to develop AnyECG, a foundation model for holistic health profiling. Performance was evaluated using external validation cohorts and a 10-year longitudinal cohort for current diagnosis, future risk prediction, and comorbidity identification.\n  Results: AnyECG demonstrated systemic predictive capability across 1172 conditions, achieving an AUROC greater than 0.7 for 306 diseases. The model revealed novel disease associations, robust comorbidity patterns, and future disease risks. Representative examples included high diagnostic performance for hyperparathyroidism (AUROC 0.941), type 2 diabetes (0.803), Crohn disease (0.817), lymphoid leukemia (0.856), and chronic obstructive pulmonary disease (0.773).\n  Conclusion: The AnyECG foundation model provides substantial evidence that AI-ECG can serve as a systemic tool for concurrent disease detection and long-term risk prediction.", "date": "2026-01-12", "categories": []}
{"id": "2601.11433", "title": "Inter-patient ECG Arrhythmia Classification with LGNs and LUTNs", "abstract": "Deep Differentiable Logic Gate Networks (LGNs) and Lookup Table Networks (LUTNs) are demonstrated to be suitable for the automatic classification of electrocardiograms (ECGs) using the inter-patient paradigm. The methods are benchmarked using the MIT-BIH arrhythmia data set, achieving up to 94.28% accuracy and a $j\\kappa$ index of 0.683 on a four-class classification problem. Our models use between 2.89k and 6.17k FLOPs, including preprocessing and readout, which is three to six orders of magnitude less compared to SOTA methods. A novel preprocessing method is utilized that attains superior performance compared to existing methods for both the mixed-patient and inter-patient paradigms. In addition, a novel method for training the Lookup Tables (LUTs) in LUTNs is devised that uses the Boolean equation of a multiplexer (MUX). Additionally, rate coding was utilized for the first time in these LGNs and LUTNs, enhancing the performance of LGNs. Furthermore, it is the first time that LGNs and LUTNs have been benchmarked on the MIT-BIH arrhythmia dataset using the inter-patient paradigm. Using an Artix 7 FPGA, between 2000 and 2990 LUTs were needed, and between 5 to 7 mW (i.e. 50 pJ to 70 pJ per inference) was estimated for running these models. The performance in terms of both accuracy and $j\\kappa$-index is significantly higher compared to previous LGN results. These positive results suggest that one can utilize LGNs and LUTNs for the detection of arrhythmias at extremely low power and high speeds in heart implants or wearable devices, even for patients not included in the training set.", "date": "2026-01-16", "categories": []}
{"id": "2405.19631", "title": "Leveraging Open-Source Large Language Models for encoding Social Determinants of Health using an Intelligent Router", "abstract": "Social Determinants of Health (SDOH), also known as Health-Related Social Needs (HSRN), play a significant role in patient health outcomes. The Centers for Disease Control and Prevention (CDC) introduced a subset of ICD-10 codes called Z-codes to recognize and measure SDOH. However, Z-codes are infrequently coded in a patient's Electronic Health Record (EHR), and instead, in many cases, need to be inferred from clinical notes. Previous research has shown that large language models (LLMs) show promise on extracting unstructured data from EHRs, but it can be difficult to identify a single model that performs best on varied coding tasks. Further, clinical notes contain protected health information posing a challenge for the use of closed-source language models from commercial vendors. The identification of open-source LLMs that can be run within health organizations and exhibit high performance on SDOH tasks is an important issue to solve. Here, we introduce an intelligent routing system for SDOH coding that uses a language model router to direct medical record data to open-source LLMs that demonstrate optimal performance on specific SDOH codes. This intelligent routing system exhibits state of the art performance of 96.4% accuracy averaged across 13 codes, including homelessness and food insecurity, outperforming closed models such as GPT-4o. We leveraged a publicly-available, deidentified dataset of medical record notes to run the router, but we also introduce a synthetic data generation and validation paradigm to increase the scale of training data without needing privacy-protected medical records. Together, we demonstrate an architecture for intelligent routing of inputs to task-optimal language models to achieve high performance across a set of medical coding sub-tasks.", "date": "2024-05-30", "categories": []}
{"id": "2412.17228", "title": "MatchMiner-AI: An Open-Source Solution for Cancer Clinical Trial Matching", "abstract": "Background Clinical trials are essential to advancing cancer treatments, yet fewer than 10% of adults with cancer enroll in trials, and many studies fail to meet accrual targets. Artificial intelligence (AI) could improve identification of appropriate trials for patients, but sharing AI models trained on protected health information remains difficult due to privacy restrictions.\n  Methods We developed MatchMiner-AI, an open-source platform for clinical trial search and ranking trained entirely on synthetic electronic health record (EHR) data. The system extracts core clinical criteria from longitudinal EHR text and embeds patient summaries and trial \"spaces\" (target populations) in a shared vector space for rapid retrieval. It then applies custom text classifiers to assess whether each patient-trial pairing is a clinically reasonable consideration. The pipeline was evaluated on real clinical data.\n  Results Across retrospective evaluations on real EHR data, the fine-tuned pipeline outperformed baseline text-embedding approaches. For trial-enrolled patients, 90% of the top 20 recommended trials were relevant matches (compared to 17% for the baseline model). Similar improvements were noted for patients who received standard-of-care treatments (88% of the top 20 matches were relevant, compared to 14% for baseline). Text classification modules demonstrated strong discrimination (AUROC 0.94-0.98) for evaluating candidate patient-trial space pair eligibility; incorporating these components consistently increased mean average precision to ~ 0.90 across patient- and trial-centric use cases. Synthetic training data, model weights, inference tools, and demonstration frontends are publicly available.\n  Conclusions MatchMiner-AI demonstrates an openly accessible, privacy-preserving approach to distilling a clinical trial matching AI pipeline from LLM-generated synthetic EHR data.", "date": "2024-12-23", "categories": []}
{"id": "2506.14375", "title": "Advancing Safe Mechanical Ventilation Using Offline RL With Hybrid Actions and Clinically Aligned Rewards", "abstract": "Invasive mechanical ventilation (MV) is a life-sustaining therapy commonly used in the intensive care unit (ICU) for patients with severe and acute conditions. These patients frequently rely on MV for breathing. Given the high risk of death in such cases, optimal MV settings can reduce mortality, minimize ventilator-induced lung injury, shorten ICU stays, and ease the strain on healthcare resources. However, optimizing MV settings remains a complex and error-prone process due to patient-specific variability. While Offline Reinforcement Learning (RL) shows promise for optimizing MV settings, current methods struggle with the hybrid (continuous and discrete) nature of MV settings. Discretizing continuous settings leads to exponential growth in the action space, which limits the number of optimizable settings. Converting the predictions back to continuous can cause a distribution shift, compromising safety and performance. To address this challenge, in the IntelliLung project, we are developing an AI-based approach where we constrain the action space and employ factored action critics. This approach allows us to scale to six optimizable settings compared to 2-3 in previous studies. We adapt SOTA offline RL algorithms to operate directly on hybrid action spaces, avoiding the pitfalls of discretization. We also introduce a clinically grounded reward function based on ventilator-free days and physiological targets. Using multiobjective optimization for reward selection, we show that this leads to a more equitable consideration of all clinically relevant objectives. Notably, we develop a system in close collaboration with healthcare professionals that is aligned with real-world clinical objectives and designed with future deployment in mind.", "date": "2025-06-17", "categories": []}
{"id": "2601.09709", "title": "Social Determinants of Health Prediction for ICD-9 Code with Reasoning Models", "abstract": "Social Determinants of Health correlate with patient outcomes but are rarely captured in structured data. Recent attention has been given to automatically extracting these markers from clinical text to supplement diagnostic systems with knowledge of patients' social circumstances. Large language models demonstrate strong performance in identifying Social Determinants of Health labels from sentences. However, prediction in large admissions or longitudinal notes is challenging given long distance dependencies. In this paper, we explore hospital admission multi-label Social Determinants of Health ICD-9 code classification on the MIMIC-III dataset using reasoning models and traditional large language models. We exploit existing ICD-9 codes for prediction on admissions, which achieved an 89% F1. Our contributions include our findings, missing SDoH codes in 139 admissions, and code to reproduce the results.", "date": "2025-12-14", "categories": []}
{"id": "2601.10020", "title": "EHRNavigator: A Multi-Agent System for Patient-Level Clinical Question Answering over Heterogeneous Electronic Health Records", "abstract": "Clinical decision-making increasingly relies on timely and context-aware access to patient information within Electronic Health Records (EHRs), yet most existing natural language question-answering (QA) systems are evaluated solely on benchmark datasets, limiting their practical relevance. To overcome this limitation, we introduce EHRNavigator, a multi-agent framework that harnesses AI agents to perform patient-level question answering across heterogeneous and multimodal EHR data. We assessed its performance using both public benchmark and institutional datasets under realistic hospital conditions characterized by diverse schemas, temporal reasoning demands, and multimodal evidence integration. Through quantitative evaluation and clinician-validated chart review, EHRNavigator demonstrated strong generalization, achieving 86% accuracy on real-world cases while maintaining clinically acceptable response times. Overall, these findings confirm that EHRNavigator effectively bridges the gap between benchmark evaluation and clinical deployment, offering a robust, adaptive, and efficient solution for real-world EHR question answering.", "date": "2026-01-15", "categories": []}
{"id": "2601.10070", "title": "Comparative Evaluation of Deep Learning-Based and WHO-Informed Approaches for Sperm Morphology Assessment", "abstract": "Assessment of sperm morphological quality remains a critical yet subjective component of male fertility evaluation, often limited by inter-observer variability and resource constraints. This study presents a comparative biomedical artificial intelligence framework evaluating an image-based deep learning model (HuSHeM) alongside a clinically grounded baseline derived from World Health Organization criteria augmented with the Systemic Inflammation Response Index (WHO(+SIRI)).\n  The HuSHeM model was trained on high-resolution sperm morphology images and evaluated using an independent clinical cohort. Model performance was assessed using discrimination, calibration, and clinical utility analyses. The HuSHeM model demonstrated higher discriminative performance, as reflected by an increased area under the receiver operating characteristic curve with relatively narrow confidence intervals compared to WHO(+SIRI). Precision-recall analysis further indicated improved performance under class imbalance, with higher precision-recall area values across evaluated thresholds. Calibration analysis indicated closer agreement between predicted probabilities and observed outcomes for HuSHeM, while decision curve analysis suggested greater net clinical benefit across clinically relevant threshold probabilities.\n  These findings suggest that image-based deep learning may offer improved predictive reliability and clinical utility compared with traditional rule-based and inflammation-augmented criteria. The proposed framework supports objective and reproducible assessment of sperm morphology and may serve as a decision-support tool within fertility screening and referral workflows. The proposed models are intended as decision-support or referral tools and are not designed to replace clinical judgment or laboratory assessment.", "date": "2026-01-15", "categories": []}
{"id": "2601.10092", "title": "LeMoF: Level-guided Multimodal Fusion for Heterogeneous Clinical Data", "abstract": "Multimodal clinical prediction is widely used to integrate heterogeneous data such as Electronic Health Records (EHR) and biosignals. However, existing methods tend to rely on static modality integration schemes and simple fusion strategies. As a result, they fail to fully exploit modality-specific representations. In this paper, we propose Level-guided Modal Fusion (LeMoF), a novel framework that selectively integrates level-guided representations within each modality. Each level refers to a representation extracted from a different layer of the encoder. LeMoF explicitly separates and learns global modality-level predictions from level-specific discriminative representations. This design enables LeMoF to achieve a balanced performance between prediction stability and discriminative capability even in heterogeneous clinical environments. Experiments on length of stay prediction using Intensive Care Unit (ICU) data demonstrate that LeMoF consistently outperforms existing state-of-the-art multimodal fusion techniques across various encoder configurations. We also confirmed that level-wise integration is a key factor in achieving robust predictive performance across various clinical conditions.", "date": "2026-01-15", "categories": []}
{"id": "2404.16198", "title": "Towards Efficient Patient Recruitment for Clinical Trials: Application of a Prompt-Based Learning Model", "abstract": "Objective: Clinical trials are essential for advancing pharmaceutical interventions, but they face a bottleneck in selecting eligible participants. Although leveraging electronic health records (EHR) for recruitment has gained popularity, the complex nature of unstructured medical texts presents challenges in efficiently identifying participants. Natural Language Processing (NLP) techniques have emerged as a solution with a recent focus on transformer models. In this study, we aimed to evaluate the performance of a prompt-based large language model for the cohort selection task from unstructured medical notes collected in the EHR. Methods: To process the medical records, we selected the most related sentences of the records to the eligibility criteria needed for the trial. The SNOMED CT concepts related to each eligibility criterion were collected. Medical records were also annotated with MedCAT based on the SNOMED CT ontology. Annotated sentences including concepts matched with the criteria-relevant terms were extracted. A prompt-based large language model (Generative Pre-trained Transformer (GPT) in this study) was then used with the extracted sentences as the training set. To assess its effectiveness, we evaluated the model's performance using the dataset from the 2018 n2c2 challenge, which aimed to classify medical records of 311 patients based on 13 eligibility criteria through NLP techniques. Results: Our proposed model showed the overall micro and macro F measures of 0.9061 and 0.8060 which were among the highest scores achieved by the experiments performed with this dataset. Conclusion: The application of a prompt-based large language model in this study to classify patients based on eligibility criteria received promising scores. Besides, we proposed a method of extractive summarization with the aid of SNOMED CT ontology that can be also applied to other medical texts.", "date": "2024-04-24", "categories": []}
{"id": "2512.17340", "title": "Penalized Fair Regression for Multiple Groups in Chronic Kidney Disease", "abstract": "Fair regression methods have the potential to mitigate societal bias concerns in health care, but there has been little work on penalized fair regression when multiple groups experience such bias. We propose a general regression framework that addresses this gap with unfairness penalties for multiple groups. Our approach is demonstrated for binary outcomes with true positive rate disparity penalties. It can be efficiently implemented through reduction to a cost-sensitive classification problem. We additionally introduce novel score functions for automatically selecting penalty weights. Our penalized fair regression methods are empirically studied in simulations, where they achieve a fairness-accuracy frontier beyond that of existing comparison methods. Finally, we apply these methods to a national multi-site primary care study of chronic kidney disease to develop a fair classifier for end-stage renal disease. There we find substantial improvements in fairness for multiple race and ethnicity groups who experience societal bias in the health care system without any appreciable loss in overall fit.", "date": "2025-12-19", "categories": []}
{"id": "2601.08988", "title": "ART: Action-based Reasoning Task Benchmarking for Medical AI Agents", "abstract": "Reliable clinical decision support requires medical AI agents capable of safe, multi-step reasoning over structured electronic health records (EHRs). While large language models (LLMs) show promise in healthcare, existing benchmarks inadequately assess performance on action-based tasks involving threshold evaluation, temporal aggregation, and conditional logic. We introduce ART, an Action-based Reasoning clinical Task benchmark for medical AI agents, which mines real-world EHR data to create challenging tasks targeting known reasoning weaknesses. Through analysis of existing benchmarks, we identify three dominant error categories: retrieval failures, aggregation errors, and conditional logic misjudgments. Our four-stage pipeline -- scenario identification, task generation, quality audit, and evaluation -- produces diverse, clinically validated tasks grounded in real patient data. Evaluating GPT-4o-mini and Claude 3.5 Sonnet on 600 tasks shows near-perfect retrieval after prompt refinement, but substantial gaps in aggregation (28--64%) and threshold reasoning (32--38%). By exposing failure modes in action-oriented EHR reasoning, ART advances toward more reliable clinical agents, an essential step for AI systems that reduce cognitive load and administrative burden, supporting workforce capacity in high-demand care settings", "date": "2026-01-13", "categories": []}
{"id": "2505.10556", "title": "An AI-driven framework for the prediction of personalised health response to air pollution", "abstract": "Air pollution is a growing global health threat, exacerbated by climate change and linked to cardiovascular and respiratory diseases. While personal sensing devices enable real-time physiological monitoring, their integration with environmental data for individualised health prediction remains underdeveloped. Here, we present a modular, cloud-based framework that predicts personalised physiological responses to pollution by combining wearable-derived data with real-time environmental exposures. At its core is an Adversarial Autoencoder (AAE), initially trained on high-resolution pollution-health data from the INHALE study and fine-tuned using smartwatch data via transfer learning to capture individual-specific patterns. Consistent with changes in pollution levels commonly observed in the real-world, simulated pollution spikes (+100%) revealed modest but measurable increases in vital signs (e.g., +2.5% heart rate, +3.5% breathing rate). To assess clinical relevance, we analysed U-BIOPRED data and found that individuals with such subclinical vital sign elevations had higher asthma burden scores or elevated Fractional Exhaled Nitric Oxide (FeNO), supporting the physiological validity of these AI-predicted responses. This integrative approach demonstrates the feasibility of anticipatory, personalised health modelling in response to environmental challenges, offering a scalable and secure infrastructure for AI-driven environmental health monitoring.", "date": "2025-05-15", "categories": []}
{"id": "2509.09923", "title": "Engineering Spatial and Molecular Features from Cellular Niches to Inform Predictions of Inflammatory Bowel Disease", "abstract": "Differentiating between the two main subtypes of Inflammatory Bowel Disease (IBD): Crohns disease (CD) and ulcerative colitis (UC) is a persistent clinical challenge due to overlapping presentations. This study introduces a novel computational framework that employs spatial transcriptomics (ST) to create an explainable machine learning model for IBD classification. We analyzed ST data from the colonic mucosa of healthy controls (HC), UC, and CD patients. Using Non-negative Matrix Factorization (NMF), we first identified four recurring cellular niches, representing distinct functional microenvironments within the tissue. From these niches, we systematically engineered 44 features capturing three key aspects of tissue pathology: niche composition, neighborhood enrichment, and niche-gene signals. A multilayer perceptron (MLP) classifier trained on these features achieved an accuracy of $0.774 \\pm 0.161$ for the more challenging three-class problem (HC, UC, and CD) and $0.916 \\pm 0.118$ in the two-class problem of distinguishing IBD from healthy tissue. Crucially, model explainability analysis revealed that disruptions in the spatial organization of niches were the strongest predictors of general inflammation, while the classification between UC and CD relied on specific niche-gene expression signatures. This work provides a robust, proof-of-concept pipeline that transforms descriptive spatial data into an accurate and explainable predictive tool, offering not only a potential new diagnostic paradigm but also deeper insights into the distinct biological mechanisms that drive IBD subtypes.", "date": "2025-09-12", "categories": []}
{"id": "2601.07866", "title": "Bridging the Trust Gap: Clinician-Validated Hybrid Explainable AI for Maternal Health Risk Assessment in Bangladesh", "abstract": "While machine learning shows promise for maternal health risk prediction, clinical adoption in resource-constrained settings faces a critical barrier: lack of explainability and trust. This study presents a hybrid explainable AI (XAI) framework combining ante-hoc fuzzy logic with post-hoc SHAP explanations, validated through systematic clinician feedback. We developed a fuzzy-XGBoost model on 1,014 maternal health records, achieving 88.67% accuracy (ROC-AUC: 0.9703). A validation study with 14 healthcare professionals in Bangladesh revealed strong preference for hybrid explanations (71.4% across three clinical cases) with 54.8% expressing trust for clinical use. SHAP analysis identified healthcare access as the primary predictor, with the engineered fuzzy risk score ranking third, validating clinical knowledge integration (r=0.298). Clinicians valued integrated clinical parameters but identified critical gaps: obstetric history, gestational age, and connectivity barriers. This work demonstrates that combining interpretable fuzzy rules with feature importance explanations enhances both utility and trust, providing practical insights for XAI deployment in maternal healthcare.", "date": "2026-01-10", "categories": []}
{"id": "2601.08240", "title": "Temporal-Enhanced Interpretable Multi-Modal Prognosis and Risk Stratification Framework for Diabetic Retinopathy (TIMM-ProRS)", "abstract": "Diabetic retinopathy (DR), affecting millions globally with projections indicating a significant rise, poses a severe blindness risk and strains healthcare systems. Diagnostic complexity arises from visual symptom overlap with conditions like age-related macular degeneration and hypertensive retinopathy, exacerbated by high misdiagnosis rates in underserved regions. This study introduces TIMM-ProRS, a novel deep learning framework integrating Vision Transformer (ViT), Convolutional Neural Network (CNN), and Graph Neural Network (GNN) with multi-modal fusion. TIMM-ProRS uniquely leverages both retinal images and temporal biomarkers (HbA1c, retinal thickness) to capture multi-modal and temporal dynamics. Evaluated comprehensively across diverse datasets including APTOS 2019 (trained), Messidor-2, RFMiD, EyePACS, and Messidor-1 (validated), the model achieves 97.8\\% accuracy and an F1-score of 0.96, demonstrating state-of-the-art performance and outperforming existing methods like RSG-Net and DeepDR. This approach enables early, precise, and interpretable diagnosis, supporting scalable telemedical management and enhancing global eye health sustainability.", "date": "2026-01-13", "categories": []}
{"id": "2601.08455", "title": "Developing Predictive and Robust Radiomics Models for Chemotherapy Response in High-Grade Serous Ovarian Carcinoma", "abstract": "Objectives: High-grade serous ovarian carcinoma (HGSOC) is typically diagnosed at an advanced stage with extensive peritoneal metastases, making treatment challenging. Neoadjuvant chemotherapy (NACT) is often used to reduce tumor burden before surgery, but about 40% of patients show limited response. Radiomics, combined with machine learning (ML), offers a promising non-invasive method for predicting NACT response by analyzing computed tomography (CT) imaging data. This study aimed to improve response prediction in HGSOC patients undergoing NACT by integration different feature selection methods. Materials and methods: A framework for selecting robust radiomics features was introduced by employing an automated randomisation algorithm to mimic inter-observer variability, ensuring a balance between feature robustness and prediction accuracy. Four response metrics were used: chemotherapy response score (CRS), RECIST, volume reduction (VolR), and diameter reduction (DiaR). Lesions in different anatomical sites were studied. Pre- and post-NACT CT scans were used for feature extraction and model training on one cohort, and an independent cohort was used for external testing. Results: The best prediction performance was achieved using all lesions combined for VolR prediction, with an AUC of 0.83. Omental lesions provided the best results for CRS prediction (AUC 0.77), while pelvic lesions performed best for DiaR (AUC 0.76). Conclusion: The integration of robustness into the feature selection processes ensures the development of reliable models and thus facilitates the implementation of the radiomics models in clinical applications for HGSOC patients. Future work should explore further applications of radiomics in ovarian cancer, particularly in real-time clinical settings.", "date": "2026-01-13", "categories": []}
{"id": "2601.08503", "title": "Temporal Fusion Nexus: A task-agnostic multi-modal embedding model for clinical narratives and irregular time series in post-kidney transplant care", "abstract": "We introduce Temporal Fusion Nexus (TFN), a multi-modal and task-agnostic embedding model to integrate irregular time series and unstructured clinical narratives. We analysed TFN in post-kidney transplant (KTx) care, with a retrospective cohort of 3382 patients, on three key outcomes: graft loss, graft rejection, and mortality. Compared to state-of-the-art model in post KTx care, TFN achieved higher performance for graft loss (AUC 0.96 vs. 0.94) and graft rejection (AUC 0.84 vs. 0.74). In mortality prediction, TFN yielded an AUC of 0.86. TFN outperformed unimodal baselines (approx 10% AUC improvement over time series only baseline, approx 5% AUC improvement over time series with static patient data). Integrating clinical text improved performance across all tasks. Disentanglement metrics confirmed robust and interpretable latent factors in the embedding space, and SHAP-based attributions confirmed alignment with clinical reasoning. TFN has potential application in clinical tasks beyond KTx, where heterogeneous data sources, irregular longitudinal data, and rich narrative documentation are available.", "date": "2026-01-13", "categories": []}
{"id": "2601.08604", "title": "Interpretability and Individuality in Knee MRI: Patient-Specific Radiomic Fingerprint with Reconstructed Healthy Personas", "abstract": "For automated assessment of knee MRI scans, both accuracy and interpretability are essential for clinical use and adoption. Traditional radiomics rely on predefined features chosen at the population level; while more interpretable, they are often too restrictive to capture patient-specific variability and can underperform end-to-end deep learning (DL). To address this, we propose two complementary strategies that bring individuality and interpretability: radiomic fingerprints and healthy personas. First, a radiomic fingerprint is a dynamically constructed, patient-specific feature set derived from MRI. Instead of applying a uniform population-level signature, our model predicts feature relevance from a pool of candidate features and selects only those most predictive for each patient, while maintaining feature-level interpretability. This fingerprint can be viewed as a latent-variable model of feature usage, where an image-conditioned predictor estimates usage probabilities and a transparent logistic regression with global coefficients performs classification. Second, a healthy persona synthesises a pathology-free baseline for each patient using a diffusion model trained to reconstruct healthy knee MRIs. Comparing features extracted from pathological images against their personas highlights deviations from normal anatomy, enabling intuitive, case-specific explanations of disease manifestations. We systematically compare fingerprints, personas, and their combination across three clinical tasks. Experimental results show that both approaches yield performance comparable to or surpassing state-of-the-art DL models, while supporting interpretability at multiple levels. Case studies further illustrate how these perspectives facilitate human-explainable biomarker discovery and pathology localisation.", "date": "2026-01-13", "categories": []}
{"id": "2505.17893", "title": "Consensus in the Parliament of AI: Harmonized Multi-Region CT-Radiomics and Foundation-Model Signatures for Multicentre NSCLC Risk Stratification", "abstract": "Purpose: This study evaluates the impact of harmonization and multi-region feature integration on survival prediction in non-small cell lung cancer (NSCLC) patients. We assess the prognostic utility of handcrafted radiomics and pretrained deep features from thoracic CT images, integrating them with clinical data using a multicentre dataset.\n  Methods: Survival models were built using handcrafted radiomic and deep features from lung, tumor, mediastinal nodes, coronary arteries, and coronary artery calcium (CAC) scores from 876 patients across five centres. CT features were harmonized using ComBat, reconstruction kernel normalization (RKN), and RKN-ComBat. Models were constructed at the region of interest (ROI) level and through ensemble strategies. Regularized Cox models estimated overall survival, with performance assessed via the concordance index (C-index), 5-year time-dependent area under the curve (t-AUC), and hazard ratios. SHAP values interpreted feature contributions, while consensus analysis categorized predicted survival probabilities at fixed time points.\n  Results: TNM staging showed prognostic value (C-index = 0.67; hazard ratio = 2.70; t-AUC = 0.85). The clinical and tumor texture radiomics model with ComBat yielded high performance (C-index = 0.76; t-AUC = 0.88). FM deep features from 50 voxel cubes also showed predictive value (C-index = 0.76; t-AUC = 0.89). An ensemble model combining tumor, lung, mediastinal node, CAC, and FM features achieved a C-index of 0.71 and t-AUC of 0.79. Consensus analysis identified a high-confidence patient subset, resulting in a model with a 5-year t-AUC of 0.92, sensitivity of 96.8%, and specificity of 70.0%.\n  Conclusion: Harmonization and multi-region feature integration enhance survival prediction in NSCLC patients using CT imaging, supporting individualized risk stratification in multicentre settings.", "date": "2025-05-23", "categories": []}
{"id": "2511.09559", "title": "Enhancing Rare Codes via Probability-Biased Directed Graph Attention for Long-Tail ICD Coding", "abstract": "Automated international classification of diseases (ICD) coding aims to assign multiple disease codes to clinical documents and plays a critical role in healthcare informatics. However, its performance is hindered by the extreme long-tail distribution of the ICD ontology, where a few common codes dominate while thousands of rare codes have very few examples. To address this issue, we propose a Probability-Biased Directed Graph Attention model (ProBias) that partitions codes into common and rare sets and allows information to flow only from common to rare codes. Edge weights are determined by conditional co-occurrence probabilities, which guide the attention mechanism to enrich rare-code representations with clinically related signals. To provide higher-quality semantic representations as model inputs, we further employ large language models to generate enriched textual descriptions for ICD codes, offering external clinical context that complements statistical co-occurrence signals. Applied to automated ICD coding, our approach significantly improves the representation and prediction of rare codes, achieving state-of-the-art performance on three benchmark datasets. In particular, we observe substantial gains in macro-averaged F1 score, a key metric for long-tail classification.", "date": "2025-10-31", "categories": []}
{"id": "2511.14317", "title": "Intervention Efficiency and Perturbation Validation Framework: Capacity-Aware and Robust Clinical Model Selection under the Rashomon Effect", "abstract": "In clinical machine learning, the coexistence of multiple models with comparable performance (a manifestation of the Rashomon Effect) poses fundamental challenges for trustworthy deployment and evaluation. Small, imbalanced, and noisy datasets, coupled with high-dimensional and weakly identified clinical features, amplify this multiplicity and make conventional validation schemes unreliable. As a result, selecting among equally performing models becomes uncertain, particularly when resource constraints and operational priorities are not considered by conventional metrics like F1 score. To address these issues, we propose two complementary tools for robust model assessment and selection: Intervention Efficiency (IE) and the Perturbation Validation Framework (PVF). IE is a capacity-aware metric that quantifies how efficiently a model identifies actionable true positives when only limited interventions are feasible, thereby linking predictive performance with clinical utility. PVF introduces a structured approach to assess the stability of models under data perturbations, identifying models whose performance remains most invariant across noisy or shifted validation sets. Empirical results on synthetic and real-world healthcare datasets show that using these tools facilitates the selection of models that generalize more robustly and align with capacity constraints, offering a new direction for tackling the Rashomon Effect in clinical settings.", "date": "2025-11-18", "categories": []}
{"id": "2511.19577", "title": "From Wearables to Warnings: Predicting Pain Spikes in Patients with Opioid Use Disorder", "abstract": "Chronic pain (CP) and opioid use disorder (OUD) are common and interrelated chronic medical conditions. Currently, there is a paucity of evidence-based integrated treatments for CP and OUD among individuals receiving medication for opioid use disorder (MOUD). Wearable devices have the potential to monitor complex patient information and inform treatment development for persons with OUD and CP, including pain variability (e.g., exacerbations of pain or pain spikes) and clinical correlates (e.g., perceived stress). However, the application of large language models (LLMs) with wearable data for understanding pain spikes, remains unexplored. Consequently, the aim of this pilot study was to examine the clinical correlates of pain spikes using a range of AI approaches. We found that machine learning models achieved relatively high accuracy (>0.7) in predicting pain spikes, while LLMs were limited in providing insights on pain spikes. Real-time monitoring through wearable devices, combined with advanced AI models, could facilitate early detection of pain spikes and support personalized interventions that may help mitigate the risk of opioid relapse, improve adherence to MOUD, and enhance the integration of CP and OUD care. Given overall limited LLM performance, these findings highlight the need to develop LLMs which can provide actionable insights in the OUD/CP context.", "date": "2025-11-24", "categories": []}
{"id": "2601.06149", "title": "A Foundation Model Approach for Fetal Stress Prediction During Labor From cardiotocography (CTG) recordings", "abstract": "Intrapartum cardiotocography (CTG) is widely used for fetal monitoring during labor, yet its interpretation suffers from high inter-observer variability and limited predictive accuracy. Deep learning approaches have been constrained by the scarcity of CTG recordings with clinical outcome labels. We present the first application of self-supervised pre-training to intrapartum CTG analysis, leveraging 2,444 hours of unlabeled recordings for masked pre-training followed by fine-tuning on the 552-recording CTU-UHB benchmark. Using a PatchTST transformer architecture with a channel-asymmetric masking scheme designed for fetal heart rate reconstruction, we achieve an area under the receiver operating characteristic curve of 0.83 on the full test set and 0.853 on uncomplicated vaginal deliveries, exceeding previously reported results on this benchmark (0.68-0.75). Error analysis reveals that false-positive alerts typically correspond to CTG patterns judged concerning on retrospective clinical review, suggesting clinically meaningful predictions even when umbilical pH is normal. We release standardized dataset splits and model weights to enable reproducible benchmarking. Our results demonstrate that self-supervised pre-training can address data scarcity in fetal monitoring, offering a path toward reliable decision support in the labor room.", "date": "2026-01-05", "categories": []}
{"id": "2601.06159", "title": "Can we Improve Prediction of Psychotherapy Outcomes Through Pretraining With Simulated Data?", "abstract": "In the context of personalized medicine, machine learning algorithms are growing in popularity. These algorithms require substantial information, which can be acquired effectively through the usage of previously gathered data. Open data and the utilization of synthetization techniques have been proposed to address this. In this paper, we propose and evaluate alternative approach that uses additional simulated data based on summary statistics published in the literature. The simulated data are used to pretrain random forests, which are afterwards fine-tuned on a real dataset. We compare the predictive performance of the new approach to random forests trained only on the real data. A Monte Carlo Cross Validation (MCCV) framework with 100 iterations was employed to investigate significance and stability of the results. Since a first study yielded inconclusive results, a second study with improved methodology (i.e., systematic information extraction and different prediction outcome) was conducted. In Study 1, some pretrained random forests descriptively outperformed the standard random forest. However, this improvement was not significant (t(99) = 0.89, p = 0.19). Contrary to expectations, in Study 2 the random forest trained only with the real data outperformed the pretrained random forests. We conclude with a discussion of challenges, such as the scarcity of informative publications, and recommendations for future research.", "date": "2026-01-06", "categories": []}
{"id": "2601.06645", "title": "A Multimodal Deep Learning Framework for Predicting ICU Deterioration: Integrating ECG Waveforms with Clinical Data and Clinician Benchmarking", "abstract": "Artificial intelligence holds strong potential to support clinical decision making in intensive care units where timely and accurate risk assessment is critical. However, many existing models focus on isolated outcomes or limited data types, while clinicians integrate longitudinal history, real time physiology, and heterogeneous clinical information. To address this gap, we developed MDS ICU, a unified multimodal machine learning framework that fuses routinely collected data including demographics, biometrics, vital signs, laboratory values, ECG waveforms, surgical procedures, and medical device usage to provide continuous predictive support during ICU stays. Using 63001 samples from 27062 patients in MIMIC IV, we trained a deep learning architecture that combines structured state space S4 encoders for ECG waveforms with multilayer perceptron RealMLP encoders for tabular data to jointly predict 33 clinically relevant outcomes spanning mortality, organ dysfunction, medication needs, and acute deterioration. The model achieved strong discrimination with AUROCs of 0.90 for 24 hour mortality, 0.92 for sedative administration, 0.97 for invasive mechanical ventilation, and 0.93 for coagulation dysfunction. Calibration analysis showed close agreement between predicted and observed risks, with consistent gains from ECG waveform integration. Comparisons with clinicians and large language models showed that model predictions alone outperformed both, and that providing model outputs as decision support further improved their performance. These results demonstrate that multimodal AI can deliver clinically meaningful risk stratification across diverse ICU outcomes while augmenting rather than replacing clinical expertise, establishing a scalable foundation for precision critical care decision support.", "date": "2026-01-10", "categories": []}
{"id": "2601.07124", "title": "Towards Automated Diagnosis of Inherited Arrhythmias: Combined Arrhythmia Classification Using Lead-Aware Spatial Attention Networks", "abstract": "Arrhythmogenic right ventricular cardiomyopathy (ARVC) and long QT syndrome (LQTS) are inherited arrhythmia syndromes associated with sudden cardiac death. Deep learning shows promise for ECG interpretation, but multi-class inherited arrhythmia classification with clinically grounded interpretability remains underdeveloped. Our objective was to develop and validate a lead-aware deep learning framework for multi-class (ARVC vs LQTS vs control) and binary inherited arrhythmia classification, and to determine optimal strategies for integrating ECG foundation models within arrhythmia screening tools. We assembled a 13-center Canadian cohort (645 patients; 1,344 ECGs). We evaluated four ECG foundation models using three transfer learning approaches: linear probing, fine-tuning, and combined strategies. We developed lead-aware spatial attention networks (LASAN) and assessed integration strategies combining LASAN with foundation models. Performance was compared against the established foundation model baselines. Lead-group masking quantified disease-specific lead dependence. Fine-tuning outperformed linear probing and combined strategies across all foundation models (mean macro-AUROC 0.904 vs 0.825). The best lead-aware integrations achieved near-ceiling performance (HuBERT-ECG hybrid: macro-AUROC 0.990; ARVC vs control AUROC 0.999; LQTS vs control AUROC 0.994). Lead masking demonstrated physiologic plausibility: V1-V3 were most critical for ARVC detection (4.54% AUROC reduction), while lateral leads were preferentially important for LQTS (2.60% drop). Lead-aware architectures achieved state-of-the-art performance for inherited arrhythmia classification, outperforming all existing published models on both binary and multi-class tasks while demonstrating clinically aligned lead dependence. These findings support potential utility for automated ECG screening pending validation.", "date": "2026-01-12", "categories": []}
{"id": "2601.07385", "title": "Computing patient similarity based on unstructured clinical notes", "abstract": "Clinical notes hold rich yet unstructured details about diagnoses, treatments, and outcomes that are vital to precision medicine but hard to exploit at scale. We introduce a method that represents each patient as a matrix built from aggregated embeddings of all their notes, enabling robust patient similarity computation based on their latent low-rank representations. Using clinical notes of 4,267 Czech breast-cancer patients and expert similarity labels from Masaryk Memorial Cancer Institute, we evaluate several matrix-based similarity measures and analyze their strengths and limitations across different similarity facets, such as clinical history, treatment, and adverse events. The results demonstrate the usefulness of the presented method for downstream tasks, such as personalized therapy recommendations or toxicity warnings.", "date": "2026-01-12", "categories": []}
{"id": "2601.07496", "title": "Graph Inference Towards ICD Coding", "abstract": "Automated ICD coding involves assigning standardized diagnostic codes to clinical narratives. The vast label space and extreme class imbalance continue to challenge precise prediction. To address these issues, LabGraph is introduced -- a unified framework that reformulates ICD coding as a graph generation task. By combining adversarial domain adaptation, graph-based reinforcement learning, and perturbation regularization, LabGraph effectively enhances model robustness and generalization. In addition, a label graph discriminator dynamically evaluates each generated code, providing adaptive reward feedback during training. Experiments on benchmark datasets demonstrate that LabGraph consistently outperforms previous approaches on micro-F1, micro-AUC, and P@K.", "date": "2026-01-12", "categories": []}
{"id": "2601.07585", "title": "Robust Multicentre Detection and Classification of Colorectal Liver Metastases on CT: Application of Foundation Models", "abstract": "Colorectal liver metastases (CRLM) are a major cause of cancer-related mortality, and reliable detection on CT remains challenging in multi-centre settings. We developed a foundation model-based AI pipeline for patient-level classification and lesion-level detection of CRLM on contrast-enhanced CT, integrating uncertainty quantification and explainability. CT data from the EuCanImage consortium (n=2437) and an external TCIA cohort (n=197) were used. Among several pretrained models, UMedPT achieved the best performance and was fine-tuned with an MLP head for classification and an FCOS-based head for lesion detection. The classification model achieved an AUC of 0.90 and a sensitivity of 0.82 on the combined test set, with a sensitivity of 0.85 on the external cohort. Excluding the most uncertain 20 percent of cases improved AUC to 0.91 and balanced accuracy to 0.86. Decision curve analysis showed clinical benefit for threshold probabilities between 0.30 and 0.40. The detection model identified 69.1 percent of lesions overall, increasing from 30 percent to 98 percent across lesion size quartiles. Grad-CAM highlighted lesion-corresponding regions in high-confidence cases. These results demonstrate that foundation model-based pipelines can support robust and interpretable CRLM detection and classification across heterogeneous CT data.", "date": "2026-01-12", "categories": []}
{"id": "2601.07685", "title": "Predictive Analytics for Dementia: Machine Learning on Healthcare Data", "abstract": "Dementia is a complex syndrome impacting cognitive and emotional functions, with Alzheimer's disease being the most common form. This study focuses on enhancing dementia prediction using machine learning (ML) techniques on patient health data. Supervised learning algorithms are applied in this study, including K-Nearest Neighbors (KNN), Quadratic Discriminant Analysis (QDA), Linear Discriminant Analysis (LDA), and Gaussian Process Classifiers. To address class imbalance and improve model performance, techniques such as Synthetic Minority Over-sampling Technique (SMOTE) and Term Frequency-Inverse Document Frequency (TF-IDF) vectorization were employed. Among the models, LDA achieved the highest testing accuracy of 98%. This study highlights the importance of model interpretability and the correlation of dementia with features such as the presence of the APOE-epsilon4 allele and chronic conditions like diabetes. This research advocates for future ML innovations, particularly in integrating explainable AI approaches, to further improve predictive capabilities in dementia care.", "date": "2026-01-12", "categories": []}
{"id": "2601.07778", "title": "DT-ICU: Towards Explainable Digital Twins for ICU Patient Monitoring via Multi-Modal and Multi-Task Iterative Inference", "abstract": "We introduce DT-ICU, a multimodal digital twin framework for continuous risk estimation in intensive care. DT-ICU integrates variable-length clinical time series with static patient information in a unified multitask architecture, enabling predictions to be updated as new observations accumulate over the ICU stay. We evaluate DT-ICU on the large, publicly available MIMIC-IV dataset, where it consistently outperforms established baseline models under different evaluation settings. Our test-length analysis shows that meaningful discrimination is achieved shortly after admission, while longer observation windows further improve the ranking of high-risk patients in highly imbalanced cohorts. To examine how the model leverages heterogeneous data sources, we perform systematic modality ablations, revealing that the model learnt a reasonable structured reliance on interventions, physiological response observations, and contextual information. These analyses provide interpretable insights into how multimodal signals are combined and how trade-offs between sensitivity and precision emerge. Together, these results demonstrate that DT-ICU delivers accurate, temporally robust, and interpretable predictions, supporting its potential as a practical digital twin framework for continuous patient monitoring in critical care. The source code and trained model weights for DT-ICU are publicly available at https://github.com/GUO-W/DT-ICU-release.", "date": "2026-01-12", "categories": []}
{"id": "2507.22533", "title": "CliCARE: Grounding Large Language Models in Clinical Guidelines for Decision Support over Longitudinal Cancer Electronic Health Records", "abstract": "Large Language Models (LLMs) hold significant promise for improving clinical decision support and reducing physician burnout by synthesizing complex, longitudinal cancer Electronic Health Records (EHRs). However, their implementation in this critical field faces three primary challenges: the inability to effectively process the extensive length and fragmented nature of patient records for accurate temporal analysis; a heightened risk of clinical hallucination, as conventional grounding techniques such as Retrieval-Augmented Generation (RAG) do not adequately incorporate process-oriented clinical guidelines; and unreliable evaluation metrics that hinder the validation of AI systems in oncology. To address these issues, we propose CliCARE, a framework for Grounding Large Language Models in Clinical Guidelines for Decision Support over Longitudinal Cancer Electronic Health Records. The framework operates by transforming unstructured, longitudinal EHRs into patient-specific Temporal Knowledge Graphs (TKGs) to capture long-range dependencies, and then grounding the decision support process by aligning these real-world patient trajectories with a normative guideline knowledge graph. This approach provides oncologists with evidence-grounded decision support by generating a high-fidelity clinical summary and an actionable recommendation. We validated our framework using large-scale, longitudinal data from a private Chinese cancer dataset and the public English MIMIC-IV dataset. In these settings, CliCARE significantly outperforms baselines, including leading long-context LLMs and Knowledge Graph-enhanced RAG methods. The clinical validity of our results is supported by a robust evaluation protocol, which demonstrates a high correlation with assessments made by oncologists.", "date": "2025-07-30", "categories": []}
{"id": "2601.05353", "title": "GlyRAG: Context-Aware Retrieval-Augmented Framework for Blood Glucose Forecasting", "abstract": "Accurate forecasting of blood glucose from CGM is essential for preventing dysglycemic events, thus enabling proactive diabetes management. However, current forecasting models treat blood glucose readings captured using CGMs as a numerical sequence, either ignoring context or relying on additional sensors/modalities that are difficult to collect and deploy at scale. Recently, LLMs have shown promise for time-series forecasting tasks, yet their role as agentic context extractors in diabetes care remains largely unexplored. To address these limitations, we propose GlyRAG, a context-aware, retrieval-augmented forecasting framework that derives semantic understanding of blood glucose dynamics directly from CGM traces without requiring additional sensor modalities. GlyRAG employs an LLM as a contextualization agent to generate clinical summaries. These summaries are embedded by a language model and fused with patch-based glucose representations in a multimodal transformer architecture with a cross translation loss aligining textual and physiological embeddings. A retrieval module then identifies similar historical episodes in the learned embedding space and uses cross-attention to integrate these case-based analogues prior to making a forecasting inference. Extensive evaluations on two T1D cohorts show that GlyRAG consistently outperforms state-of-the art methods, achieving up to 39% lower RMSE and a further 1.7% reduction in RMSE over the baseline. Clinical evaluation shows that GlyRAG places 85% predictions in safe zones and achieves 51% improvement in predicting dysglycemic events across both cohorts. These results indicate that LLM-based contextualization and retrieval over CGM traces can enhance the accuracy and clinical reliability of long-horizon glucose forecasting without the need for extra sensors, thus supporting future agentic decision-support tools for diabetes management.", "date": "2026-01-08", "categories": []}
{"id": "2410.23084", "title": "AI-assisted prostate cancer detection and localisation on biparametric MR by classifying radiologist-positives", "abstract": "Prostate cancer diagnosis through MR imaging have currently relied on radiologists' interpretation, whilst modern AI-based methods have been developed to detect clinically significant cancers independent of radiologists. In this study, we propose to develop deep learning models that improve the overall cancer diagnostic accuracy, by classifying radiologist-identified patients or lesions (i.e. radiologist-positives), as opposed to the existing models that are trained to discriminate over all patients. We develop a single voxel-level classification model, with a simple percentage threshold to determine positive cases, at levels of lesions, Barzell-zones and patients. Based on the presented experiments from two clinical data sets, consisting of histopathology-labelled MR images from more than 800 and 500 patients in the respective UCLA and UCL PROMIS studies, we show that the proposed strategy can improve the diagnostic accuracy, by augmenting the radiologist reading of the MR imaging. Among varying definition of clinical significance, the proposed strategy, for example, achieved a specificity of 44.1% (with AI assistance) from 36.3% (by radiologists alone), at a controlled sensitivity of 80.0% on the publicly available UCLA data set. This provides measurable clinical values in a range of applications such as reducing unnecessary biopsies, lowering cost in cancer screening and quantifying risk in therapies.", "date": "2024-10-30", "categories": []}
{"id": "2506.06099", "title": "DermaCon-IN: A Multi-concept Annotated Dermatological Image Dataset of Indian Skin Disorders for Clinical AI Research", "abstract": "Artificial intelligence is poised to augment dermatological care by enabling scalable image-based diagnostics. Yet, the development of robust and equitable models remains hindered by datasets that fail to capture the clinical and demographic complexity of real-world practice. This complexity stems from region-specific disease distributions, wide variation in skin tones, and the underrepresentation of outpatient scenarios from non-Western populations. We introduce DermaCon-IN, a prospectively curated dermatology dataset comprising 5,450 clinical images from 3,002 patients across outpatient clinics in South India. Each image is annotated by board-certified dermatologists with 245 distinct diagnoses, structured under a hierarchical, aetiology-based taxonomy adapted from Rook's classification. The dataset captures a wide spectrum of dermatologic conditions and tonal variation commonly seen in Indian outpatient care. We benchmark a range of architectures, including convolutional models (ResNet, DenseNet, EfficientNet), transformer-based models (ViT, MaxViT, Swin), and Concept Bottleneck Models to establish baseline performance and explore how anatomical and concept-level cues may be integrated. These results are intended to guide future efforts toward interpretable and clinically realistic models. DermaCon-IN provides a scalable and representative foundation for advancing dermatology AI.", "date": "2025-06-06", "categories": []}
{"id": "2512.00949", "title": "Multi-Modal AI for Remote Patient Monitoring in Cancer Care", "abstract": "For patients undergoing systemic cancer therapy, the time between clinic visits is full of uncertainties and risks of unmonitored side effects. To bridge this gap in care, we developed and prospectively trialed a multi-modal AI framework for remote patient monitoring (RPM). This system integrates multi-modal data from the HALO-X platform, such as demographics, wearable sensors, daily surveys, and clinical events. Our observational trial is one of the largest of its kind and has collected over 2.1 million data points (6,080 patient-days) of monitoring from 84 patients. We developed and adapted a multi-modal AI model to handle the asynchronous and incomplete nature of real-world RPM data, forecasting a continuous risk of future adverse events. The model achieved an accuracy of 83.9% (AUROC=0.70). Notably, the model identified previous treatments, wellness check-ins, and daily maximum heart rate as key predictive features. A case study demonstrated the model's ability to provide early warnings by outputting escalating risk profiles prior to the event. This work establishes the feasibility of multi-modal AI RPM for cancer care and offers a path toward more proactive patient support.(Accepted at Europe NeurIPS 2025 Multimodal Representation Learning for Healthcare Workshop. Best Paper Poster Award.)", "date": "2025-11-30", "categories": []}
{"id": "2601.04299", "title": "Transformer-Based Multi-Modal Temporal Embeddings for Explainable Metabolic Phenotyping in Type 1 Diabetes", "abstract": "Type 1 diabetes (T1D) is a highly metabolically heterogeneous disease that cannot be adequately characterized by conventional biomarkers such as glycated hemoglobin (HbA1c). This study proposes an explainable deep learning framework that integrates continuous glucose monitoring (CGM) data with laboratory profiles to learn multimodal temporal embeddings of individual metabolic status. Temporal dependencies across modalities are modeled using a transformer encoder, while latent metabolic phenotypes are identified via Gaussian mixture modeling. Model interpretability is achieved through transformer attention visualization and SHAP-based feature attribution. Five latent metabolic phenotypes, ranging from metabolic stability to elevated cardiometabolic risk, were identified among 577 individuals with T1D. These phenotypes exhibit distinct biochemical profiles, including differences in glycemic control, lipid metabolism, renal markers, and thyrotropin (TSH) levels. Attention analysis highlights glucose variability as a dominant temporal factor, while SHAP analysis identifies HbA1c, triglycerides, cholesterol, creatinine, and TSH as key contributors to phenotype differentiation. Phenotype membership shows statistically significant, albeit modest, associations with hypertension, myocardial infarction, and heart failure. Overall, this explainable multimodal temporal embedding framework reveals physiologically coherent metabolic subgroups in T1D and supports risk stratification beyond single biomarkers.", "date": "2026-01-07", "categories": []}
{"id": "2601.04449", "title": "Explainable Admission-Level Predictive Modeling for Prolonged Hospital Stay in Elderly Populations: Challenges in Low- and Middle-Income Countries", "abstract": "Prolonged length of stay (pLoS) is a significant factor associated with the risk of adverse in-hospital events. We develop and explain a predictive model for pLos using admission-level patient and hospital administrative data. The approach includes a feature selection method by selecting non-correlated features with the highest information value. The method uses features weights of evidence to select a representative within cliques from graph theory. The prognosis study analyzed the records from 120,354 hospital admissions at the Hospital Alma Mater de Antioquia between January 2017 and March 2022. After a cleaning process the dataset was split into training (67%), test (22%), and validation (11%) cohorts. A logistic regression model was trained to predict the pLoS in two classes: less than or greater than 7 days. The performance of the model was evaluated using accuracy, precision, sensitivity, specificity, and AUC-ROC metrics. The feature selection method returns nine interpretable variables, enhancing the models' transparency. In the validation cohort, the pLoS model achieved a specificity of 0.83 (95% CI, 0.82-0.84), sensitivity of 0.64 (95% CI, 0.62-0.65), accuracy of 0.76 (95% CI, 0.76-0.77), precision of 0.67 (95% CI, 0.66-0.69), and AUC-ROC of 0.82 (95% CI, 0.81-0.83). The model exhibits strong predictive performance and offers insights into the factors that influence prolonged hospital stays. This makes it a valuable tool for hospital management and for developing future intervention studies aimed at reducing pLoS.", "date": "2026-01-07", "categories": []}
{"id": "2601.05148", "title": "Atlas 2 -- Foundation models for clinical deployment", "abstract": "Pathology foundation models substantially advanced the possibilities in computational pathology -- yet tradeoffs in terms of performance, robustness, and computational requirements remained, which limited their clinical deployment. In this report, we present Atlas 2, Atlas 2-B, and Atlas 2-S, three pathology vision foundation models which bridge these shortcomings by showing state-of-the-art performance in prediction performance, robustness, and resource efficiency in a comprehensive evaluation across eighty public benchmarks. Our models were trained on the largest pathology foundation model dataset to date comprising 5.5 million histopathology whole slide images, collected from three medical institutions Charit\\'e - Universt\\\"atsmedizin Berlin, LMU Munich, and Mayo Clinic.", "date": "2026-01-08", "categories": []}
{"id": "2601.03475", "title": "CPGPrompt: Translating Clinical Guidelines into LLM-Executable Decision Support", "abstract": "Clinical practice guidelines (CPGs) provide evidence-based recommendations for patient care; however, integrating them into Artificial Intelligence (AI) remains challenging. Previous approaches, such as rule-based systems, face significant limitations, including poor interpretability, inconsistent adherence to guidelines, and narrow domain applicability. To address this, we develop and validate CPGPrompt, an auto-prompting system that converts narrative clinical guidelines into large language models (LLMs).\n  Our framework translates CPGs into structured decision trees and utilizes an LLM to dynamically navigate them for patient case evaluation. Synthetic vignettes were generated across three domains (headache, lower back pain, and prostate cancer) and distributed into four categories to test different decision scenarios. System performance was assessed on both binary specialty-referral decisions and fine-grained pathway-classification tasks.\n  The binary specialty referral classification achieved consistently strong performance across all domains (F1: 0.85-1.00), with high recall (1.00 $\\pm$ 0.00). In contrast, multi-class pathway assignment showed reduced performance, with domain-specific variations: headache (F1: 0.47), lower back pain (F1: 0.72), and prostate cancer (F1: 0.77). Domain-specific performance differences reflected the structure of each guideline. The headache guideline highlighted challenges with negation handling. The lower back pain guideline required temporal reasoning. In contrast, prostate cancer pathways benefited from quantifiable laboratory tests, resulting in more reliable decision-making.", "date": "2026-01-07", "categories": []}
{"id": "2601.03977", "title": "Stage-specific cancer survival prediction enriched by explainable machine learning", "abstract": "Despite the fact that cancer survivability rates vary greatly between stages, traditional survival prediction models have frequently been trained and assessed using examples from all combined phases of the disease. This method may result in an overestimation of performance and ignore the stage-specific variations. Using the SEER dataset, we created and verified explainable machine learning (ML) models to predict stage-specific cancer survivability in colorectal, stomach, and liver cancers. ML-based cancer survival analysis has been a long-standing topic in the literature; however, studies involving the explainability and transparency of ML survivability models are limited. Our use of explainability techniques, including SHapley Additive exPlanations (SHAP) and Local Interpretable Model-agnostic Explanations (LIME), enabled us to illustrate significant feature-cancer stage interactions that would have remained hidden in traditional black-box models. We identified how certain demographic and clinical variables influenced survival differently across cancer stages and types. These insights provide not only transparency but also clinical relevance, supporting personalized treatment planning. By focusing on stage-specific models, this study provides new insights into the most important factors at each stage of cancer, offering transparency and potential clinical relevance to support personalized treatment planning.", "date": "2026-01-07", "categories": []}
{"id": "2312.16713", "title": "CSAI: Conditional Self-Attention Imputation for Healthcare Time-series", "abstract": "We introduce the Conditional Self-Attention Imputation (CSAI) model, a novel recurrent neural network architecture designed to address the challenges of complex missing data patterns in multivariate time series derived from hospital electronic health records (EHRs). CSAI extends state-of-the-art neural network-based imputation by introducing key modifications specific to EHR data: a) attention-based hidden state initialisation to capture both long- and short-range temporal dependencies prevalent in EHRs, b) domain-informed temporal decay to mimic clinical data recording patterns, and c) a non-uniform masking strategy that models non-random missingness by calibrating weights according to both temporal and cross-sectional data characteristics. Comprehensive evaluation across four EHR benchmark datasets demonstrates CSAI's effectiveness compared to state-of-the-art architectures in data restoration and downstream tasks. CSAI is integrated into PyPOTS, an open-source Python toolbox designed for machine learning tasks on partially observed time series. This work significantly advances the state of neural network imputation applied to EHRs by more closely aligning algorithmic imputation with clinical realities.", "date": "2023-12-27", "categories": []}
{"id": "2508.16742", "title": "Learning the Language of Histopathology Images reveals Prognostic Subgroups in Invasive Lung Adenocarcinoma Patients", "abstract": "Recurrence remains a major clinical challenge in surgically resected invasive lung adenocarcinoma, where existing grading and staging systems fail to capture the cellular complexity that underlies tumor aggressiveness. We present PathRosetta, a novel AI model that conceptualizes histopathology as a language, where cells serve as words, spatial neighborhoods form syntactic structures, and tissue architecture composes sentences. By learning this language of histopathology, PathRosetta predicts five-year recurrence directly from hematoxylin-and-eosin (H&E) slides, treating them as documents representing the state of the disease. In a multi-cohort dataset of 289 patients (600 slides), PathRosetta achieved an area under the curve (AUC) of 0.78 +- 0.04 on the internal cohort, significantly outperforming IASLC grading (AUC:0.71), AJCC staging (AUC:0.64), and other state-of-the-art AI models (AUC:0.62-0.67). It yielded a hazard ratio of 9.54 and a concordance index of 0.70, generalized robustly to external TCGA (AUC:0.75) and CPTAC (AUC:0.76) cohorts, and performed consistently across demographic and clinical subgroups. Beyond whole-slide prediction, PathRosetta uncovered prognostic subgroups within individual cell types, revealing that even within benign epithelial, stromal, or other cells, distinct morpho-spatial phenotypes correspond to divergent outcomes. Moreover, because the model explicitly understands what it is looking at, including cell types, cellular neighborhoods, and higher-order tissue morphology, it is inherently interpretable and can articulate the rationale behind its predictions. These findings establish that representing histopathology as a language enables interpretable and generalizable prognostication from routine histology.", "date": "2025-08-22", "categories": []}
{"id": "2512.18829", "title": "HARBOR: Holistic Adaptive Risk assessment model for BehaviORal healthcare", "abstract": "Behavioral healthcare risk assessment remains a challenging problem due to the highly multimodal nature of patient data and the temporal dynamics of mood and affective disorders. While large language models (LLMs) have demonstrated strong reasoning capabilities, their effectiveness in structured clinical risk scoring remains unclear. In this work, we introduce HARBOR, a behavioral health aware language model designed to predict a discrete mood and risk score, termed the Harbor Risk Score (HRS), on an integer scale from -3 (severe depression) to +3 (mania). We also release PEARL, a longitudinal behavioral healthcare dataset spanning four years of monthly observations from three patients, containing physiological, behavioral, and self reported mental health signals. We benchmark traditional machine learning models, proprietary LLMs, and HARBOR across multiple evaluation settings and ablations. Our results show that HARBOR outperforms classical baselines and off the shelf LLMs, achieving 69 percent accuracy compared to 54 percent for logistic regression and 29 percent for the strongest proprietary LLM baseline.", "date": "2025-12-21", "categories": []}
{"id": "2601.01008", "title": "An Explainable Agentic AI Framework for Uncertainty-Aware and Abstention-Enabled Acute Ischemic Stroke Imaging Decisions", "abstract": "Artificial intelligence models have shown strong potential in acute ischemic stroke imaging, particularly for lesion detection and segmentation using computed tomography and magnetic resonance imaging. However, most existing approaches operate as black box predictors, producing deterministic outputs without explicit uncertainty awareness or structured mechanisms to abstain under ambiguous conditions. This limitation raises serious safety and trust concerns in high risk emergency radiology settings. In this paper, we propose an explainable agentic AI framework for uncertainty aware and abstention enabled decision support in acute ischemic stroke imaging. The framework follows a modular agentic pipeline in which a perception agent performs lesion aware image analysis, an uncertainty estimation agent computes slice level predictive reliability, and a decision agent determines whether to issue a prediction or abstain based on predefined uncertainty thresholds. Unlike prior stroke imaging systems that primarily focus on improving segmentation or classification accuracy, the proposed framework explicitly prioritizes clinical safety, transparency, and clinician aligned decision behavior. Qualitative and case based analyses across representative stroke imaging scenarios demonstrate that uncertainty driven abstention naturally emerges in diagnostically ambiguous regions and low information slices. The framework further integrates visual explanation mechanisms to support both predictive and abstention decisions, addressing a key limitation of existing uncertainty aware medical imaging systems. Rather than introducing a new performance benchmark, this work presents agentic control, uncertainty awareness, and selective abstention as essential design principles for developing safe and trustworthy medical imaging AI systems.", "date": "2026-01-03", "categories": []}
{"id": "2601.01223", "title": "Adaptive Conformal Prediction via Bayesian Uncertainty Weighting for Hierarchical Healthcare Data", "abstract": "Clinical decision-making demands uncertainty quantification that provides both distribution-free coverage guarantees and risk-adaptive precision, requirements that existing methods fail to jointly satisfy. We present a hybrid Bayesian-conformal framework that addresses this fundamental limitation in healthcare predictions. Our approach integrates Bayesian hierarchical random forests with group-aware conformal calibration, using posterior uncertainties to weight conformity scores while maintaining rigorous coverage validity. Evaluated on 61,538 admissions across 3,793 U.S. hospitals and 4 regions, our method achieves target coverage (94.3% vs 95% target) with adaptive precision: 21% narrower intervals for low-uncertainty cases while appropriately widening for high-risk predictions. Critically, we demonstrate that well-calibrated Bayesian uncertainties alone severely under-cover (14.1%), highlighting the necessity of our hybrid approach. This framework enables risk-stratified clinical protocols, efficient resource planning for high-confidence predictions, and conservative allocation with enhanced oversight for uncertain cases, providing uncertainty-aware decision support across diverse healthcare settings.", "date": "2026-01-03", "categories": []}
{"id": "2601.02106", "title": "Prototype-Based Learning for Healthcare: A Demonstration of Interpretable AI", "abstract": "Despite recent advances in machine learning and explainable AI, a gap remains in personalized preventive healthcare: predictions, interventions, and recommendations should be both understandable and verifiable for all stakeholders in the healthcare sector. We present a demonstration of how prototype-based learning can address these needs. Our proposed framework, ProtoPal, features both front- and back-end modes; it achieves superior quantitative performance while also providing an intuitive presentation of interventions and their simulated outcomes.", "date": "2026-01-05", "categories": []}
{"id": "2601.00081", "title": "Cuffless, calibration-free hemodynamic monitoring with physics-informed machine learning models", "abstract": "Wearable technologies have the potential to transform ambulatory and at-home hemodynamic monitoring by providing continuous assessments of cardiovascular health metrics and guiding clinical management. However, existing cuffless wearable devices for blood pressure (BP) monitoring often rely on methods lacking theoretical foundations, such as pulse wave analysis or pulse arrival time, making them vulnerable to physiological and experimental confounders that undermine their accuracy and clinical utility. Here, we developed a smartwatch device with real-time electrical bioimpedance (BioZ) sensing for cuffless hemodynamic monitoring. We elucidate the biophysical relationship between BioZ and BP via a multiscale analytical and computational modeling framework, and identify physiological, anatomical, and experimental parameters that influence the pulsatile BioZ signal at the wrist. A signal-tagged physics-informed neural network incorporating fluid dynamics principles enables calibration-free estimation of BP and radial and axial blood velocity. We successfully tested our approach with healthy individuals at rest and after physical activity including physical and autonomic challenges, and with patients with hypertension and cardiovascular disease in outpatient and intensive care settings. Our findings demonstrate the feasibility of BioZ technology for cuffless BP and blood velocity monitoring, addressing critical limitations of existing cuffless technologies.", "date": "2025-12-31", "categories": []}
{"id": "2601.00355", "title": "The Impact of Lesion Focus on the Performance of AI-Based Melanoma Classification", "abstract": "Melanoma is the most lethal subtype of skin cancer, and early and accurate detection of this disease can greatly improve patients' outcomes. Although machine learning models, especially convolutional neural networks (CNNs), have shown great potential in automating melanoma classification, their diagnostic reliability still suffers due to inconsistent focus on lesion areas. In this study, we analyze the relationship between lesion attention and diagnostic performance, involving masked images, bounding box detection, and transfer learning. We used multiple explainability and sensitivity analysis approaches to investigate how well models aligned their attention with lesion areas and how this alignment correlated with precision, recall, and F1-score. Results showed that models with a higher focus on lesion areas achieved better diagnostic performance, suggesting the potential of interpretable AI in medical diagnostics. This study provides a foundation for developing more accurate and trustworthy melanoma classification models in the future.", "date": "2026-01-01", "categories": []}
{"id": "2512.24253", "title": "Early Prediction of Sepsis using Heart Rate Signals and Genetic Optimized LSTM Algorithm", "abstract": "Sepsis, characterized by a dysregulated immune response to infection, results in significant mortality, morbidity, and healthcare costs. The timely prediction of sepsis progression is crucial for reducing adverse outcomes through early intervention. Despite the development of numerous models for Intensive Care Unit (ICU) patients, there remains a notable gap in approaches for the early detection of sepsis in non-ward settings. This research introduces and evaluates four novel machine learning algorithms designed for predicting the onset of sepsis on wearable devices by analyzing heart rate data. The architecture of these models was refined through a genetic algorithm, optimizing for performance, computational complexity, and memory requirements. Performance metrics were subsequently extracted for each model to evaluate their feasibility for implementation on wearable devices capable of accurate heart rate monitoring. The models were initially tailored for a prediction window of one hour, later extended to four hours through transfer learning. The encouraging outcomes of this study suggest the potential for wearable technology to facilitate early sepsis detection outside ICU and ward environments.", "date": "2025-12-30", "categories": []}
{"id": "2512.24564", "title": "CPR: Causal Physiological Representation Learning for Robust ECG Analysis under Distribution Shifts", "abstract": "Deep learning models for Electrocardiogram (ECG) diagnosis have achieved remarkable accuracy but exhibit fragility against adversarial perturbations, particularly Smooth Adversarial Perturbations (SAP) that mimic biological morphology. Existing defenses face a critical dilemma: Adversarial Training (AT) provides robustness but incurs a prohibitive computational burden, while certified methods like Randomized Smoothing (RS) introduce significant inference latency, rendering them impractical for real-time clinical monitoring. We posit that this vulnerability stems from the models' reliance on non-robust spurious correlations rather than invariant pathological features. To address this, we propose Causal Physiological Representation Learning (CPR). Unlike standard denoising approaches that operate without semantic constraints, CPR incorporates a Physiological Structural Prior within a causal disentanglement framework. By modeling ECG generation via a Structural Causal Model (SCM), CPR enforces a structural intervention that strictly separates invariant pathological morphology (P-QRS-T complex) from non-causal artifacts. Empirical results on PTB-XL demonstrate that CPR significantly outperforms standard clinical preprocessing methods. Specifically, under SAP attacks, CPR achieves an F1 score of 0.632, surpassing Median Smoothing (0.541 F1) by 9.1%. Crucially, CPR matches the certified robustness of Randomized Smoothing while maintaining single-pass inference efficiency, offering a superior trade-off between robustness, efficiency, and clinical interpretability.", "date": "2025-12-31", "categories": []}
{"id": "2504.10340", "title": "Forecasting Clinical Risk from Textual Time Series: Structuring Narratives for Temporal AI in Healthcare", "abstract": "Clinical case reports encode temporal patient trajectories that are often underexploited by traditional machine learning methods relying on structured data. In this work, we introduce the forecasting problem from textual time series, where timestamped clinical findings -- extracted via an LLM-assisted annotation pipeline -- serve as the primary input for prediction. We systematically evaluate a diverse suite of models, including fine-tuned decoder-based large language models and encoder-based transformers, on tasks of event occurrence prediction, temporal ordering, and survival analysis. Our experiments reveal that encoder-based models consistently achieve higher F1 scores and superior temporal concordance for short- and long-horizon event forecasting, while fine-tuned masking approaches enhance ranking performance. In contrast, instruction-tuned decoder models demonstrate a relative advantage in survival analysis, especially in early prognosis settings. Our sensitivity analyses further demonstrate the importance of time ordering, which requires clinical time series construction, as compared to text ordering, the format of the text inputs that LLMs are classically trained on. This highlights the additional benefit that can be ascertained from time-ordered corpora, with implications for temporal tasks in the era of widespread LLM use.", "date": "2025-04-14", "categories": []}
{"id": "2508.01956", "title": "Scaling Clinician-Grade Feature Generation from Clinical Notes with Multi-Agent Language Models", "abstract": "Developing accurate clinical prediction models is often bottlenecked by the difficulty of deriving meaningful structured features from unstructured EHR notes, a process that traditionally requires manual, unscalable clinical abstraction. In this study, we first established a rigorous patient-level Clinician Feature Generation (CFG) protocol, in which domain experts manually reviewed notes to define and extract nuanced features for a cohort of 147 patients with prostate cancer. As a high-fidelity ground truth, this labor-intensive process provided the blueprint for SNOW (Scalable Note-to-Outcome Workflow), a transparent multi-agent large language model (LLM) system designed to autonomously mimic the iterative reasoning and validation workflow of clinical experts. On 5-year cancer recurrence prediction, SNOW (AUC-ROC 0.767) achieved performance comparable to manual CFG (0.762) and outperformed structured baselines, clinician-guided LLM extraction, and six representational feature generation (RFG) approaches. Once configured, SNOW produced the full patient-level feature table in 12 hours with 5 hours of clinician oversight, reducing human expert effort by approximately 48-fold versus manual CFG. To test scalability where manual CFG is infeasible, we deployed SNOW on an external heart failure with preserved ejection fraction (HFpEF) cohort from MIMIC-IV (n=2,084); without task-specific tuning, SNOW generated prognostic features that outperformed baseline and RFG methods for 30-day (SNOW: 0.851) and 1-year (SNOW: 0.763) mortality prediction. These results demonstrate that a modular LLM agent-based system can scale expert-level feature generation from clinical notes, while enabling interpretable use of unstructured EHR text in outcome prediction and preserving generalizability across a variety of settings and conditions.", "date": "2025-08-03", "categories": []}
{"id": "2511.18595", "title": "Timepoint-Specific Benchmarking of Deep Learning Models for Glioblastoma Follow-Up MRI", "abstract": "Differentiating true tumor progression (TP) from treatment-related pseudoprogression (PsP) in glioblastoma remains challenging, especially at early follow-up. We present the first stage-specific, cross-sectional benchmarking of deep learning models for follow-up MRI using the Burdenko GBM Progression cohort (n = 180). We analyze different post-RT scans independently to test whether architecture performance depends on time-point. Eleven representative DL families (CNNs, LSTMs, hybrids, transformers, and selective state-space models) were trained under a unified, QC-driven pipeline with patient-level cross-validation. Across both stages, accuracies were comparable (~0.70-0.74), but discrimination improved at the second follow-up, with F1 and AUC increasing for several models, indicating richer separability later in the care pathway. A Mamba+CNN hybrid consistently offered the best accuracy-efficiency trade-off, while transformer variants delivered competitive AUCs at substantially higher computational cost and lightweight CNNs were efficient but less reliable. Performance also showed sensitivity to batch size, underscoring the need for standardized training protocols. Notably, absolute discrimination remained modest overall, reflecting the intrinsic difficulty of TP vs. PsP and the dataset's size imbalance. These results establish a stage-aware benchmark and motivate future work incorporating longitudinal modeling, multi-sequence MRI, and larger multi-center cohorts.", "date": "2025-11-23", "categories": []}
{"id": "2512.22242", "title": "Fairness Evaluation of Risk Estimation Models for Lung Cancer Screening", "abstract": "Lung cancer is the leading cause of cancer-related mortality in adults worldwide. Screening high-risk individuals with annual low-dose CT (LDCT) can support earlier detection and reduce deaths, but widespread implementation may strain the already limited radiology workforce. AI models have shown potential in estimating lung cancer risk from LDCT scans. However, high-risk populations for lung cancer are diverse, and these models' performance across demographic groups remains an open question. In this study, we drew on the considerations on confounding factors and ethically significant biases outlined in the JustEFAB framework to evaluate potential performance disparities and fairness in two deep learning risk estimation models for lung cancer screening: the Sybil lung cancer risk model and the Venkadesh21 nodule risk estimator. We also examined disparities in the PanCan2b logistic regression model recommended in the British Thoracic Society nodule management guideline. Both deep learning models were trained on data from the US-based National Lung Screening Trial (NLST), and assessed on a held-out NLST validation set. We evaluated AUROC, sensitivity, and specificity across demographic subgroups, and explored potential confounding from clinical risk factors. We observed a statistically significant AUROC difference in Sybil's performance between women (0.88, 95% CI: 0.86, 0.90) and men (0.81, 95% CI: 0.78, 0.84, p < .001). At 90% specificity, Venkadesh21 showed lower sensitivity for Black (0.39, 95% CI: 0.23, 0.59) than White participants (0.69, 95% CI: 0.65, 0.73). These differences were not explained by available clinical confounders and thus may be classified as unfair biases according to JustEFAB. Our findings highlight the importance of improving and monitoring model performance across underrepresented subgroups, and further research on algorithmic fairness, in lung cancer screening.", "date": "2025-12-23", "categories": []}
{"id": "2512.22259", "title": "Cardiac mortality prediction in patients undergoing PCI based on real and synthetic data", "abstract": "Patient status, angiographic and procedural characteristics encode crucial signals for predicting long-term outcomes after percutaneous coronary intervention (PCI). The aim of the study was to develop a predictive model for assessing the risk of cardiac death based on the real and synthetic data of patients undergoing PCI and to identify the factors that have the greatest impact on mortality. We analyzed 2,044 patients, who underwent a PCI for bifurcation lesions. The primary outcome was cardiac death at 3-year follow-up. Several machine learning models were applied to predict three-year mortality after PCI. To address class imbalance and improve the representation of the minority class, an additional 500 synthetic samples were generated and added to the training set. To evaluate the contribution of individual features to model performance, we applied permutation feature importance. An additional experiment was conducted to evaluate how the model's predictions would change after removing non-informative features from the training and test datasets. Without oversampling, all models achieve high overall accuracy (0.92-0.93), yet they almost completely ignore the minority class. Across models, augmentation consistently increases minority-class recall with minimal loss of AUROC, improves probability quality, and yields more clinically reasonable risk estimates on the constructed severe profiles. According to feature importance analysis, four features emerged as the most influential: Age, Ejection Fraction, Peripheral Artery Disease, and Cerebrovascular Disease. These results show that straightforward augmentation with realistic and extreme cases can expose, quantify, and reduce brittleness in imbalanced clinical prediction using only tabular records, and motivate routine reporting of probability quality and stress tests alongside headline metrics.", "date": "2025-12-24", "categories": []}
{"id": "2512.22262", "title": "INSIGHT: Spatially resolved survival modelling from routine histology crosslinked with molecular profiling reveals prognostic epithelial-immune axes in stage II/III colorectal cancer", "abstract": "Routine histology contains rich prognostic information in stage II/III colorectal cancer, much of which is embedded in complex spatial tissue organisation. We present INSIGHT, a graph neural network that predicts survival directly from routine histology images. Trained and cross-validated on TCGA (n=342) and SURGEN (n=336), INSIGHT produces patient-level spatially resolved risk scores. Large independent validation showed superior prognostic performance compared with pTNM staging (C-index 0.68-0.69 vs 0.44-0.58). INSIGHT spatial risk maps recapitulated canonical prognostic histopathology and identified nuclear solidity and circularity as quantitative risk correlates. Integrating spatial risk with data-driven spatial transcriptomic signatures, spatial proteomics, bulk RNA-seq, and single-cell references revealed an epithelium-immune risk manifold capturing epithelial dedifferentiation and fetal programs, myeloid-driven stromal states including $\\mathrm{SPP1}^{+}$ macrophages and $\\mathrm{LAMP3}^{+}$ dendritic cells, and adaptive immune dysfunction. This analysis exposed patient-specific epithelial heterogeneity, stratification within MSI-High tumours, and high-risk routes of CDX2/HNF4A loss and CEACAM5/6-associated proliferative programs, highlighting coordinated therapeutic vulnerabilities.", "date": "2025-12-24", "categories": []}
{"id": "2512.22758", "title": "A Micro-Macro Machine Learning Framework for Predicting Childhood Obesity Risk Using NHANES and Environmental Determinants", "abstract": "Childhood obesity remains a major public health challenge in the United States, strongly influenced by a combination of individual-level, household-level, and environmental-level risk factors. Traditional epidemiological studies typically analyze these levels independently, limiting insights into how structural environmental conditions interact with individual-level characteristics to influence health outcomes. In this study, we introduce a micro-macro machine learning framework that integrates (1) individual-level anthropometric and socioeconomic data from NHANES and (2) macro-level structural environment features, including food access, air quality, and socioeconomic vulnerability extracted from USDA and EPA datasets. Four machine learning models Logistic Regression, Random Forest, XGBoost, and LightGBM were trained to predict obesity using NHANES microdata. XGBoost achieved the strongest performance. A composite environmental vulnerability index (EnvScore) was constructed using normalized indicators from USDA and EPA at the state level. Multi-level comparison revealed strong geographic similarity between states with high environmental burden and the nationally predicted micro-level obesity risk distribution. This demonstrates the feasibility of integrating multi-scale datasets to identify environment-driven disparities in obesity risk. This work contributes a scalable, data-driven, multi-level modeling pipeline suitable for public health informatics, demonstrating strong potential for expansion into causal modeling, intervention planning, and real-time analytics.", "date": "2025-12-28", "categories": []}
{"id": "2506.15019", "title": "Stable CDE Autoencoders with Acuity Regularization for Offline Reinforcement Learning in Sepsis Treatment", "abstract": "Effective reinforcement learning (RL) for sepsis treatment depends on learning stable, clinically meaningful state representations from irregular ICU time series. While previous works have explored representation learning for this task, the critical challenge of training instability in sequential representations and its detrimental impact on policy performance has been overlooked. This work demonstrates that Controlled Differential Equations (CDE) state representation can achieve strong RL policies when two key factors are met: (1) ensuring training stability through early stopping or stabilization methods, and (2) enforcing acuity-aware representations by correlation regularization with clinical scores (SOFA, SAPS-II, OASIS). Experiments on the MIMIC-III sepsis cohort reveal that stable CDE autoencoder produces representations strongly correlated with acuity scores and enables RL policies with superior performance (WIS return $> 0.9$). In contrast, unstable CDE representation leads to degraded representations and policy failure (WIS return $\\sim$ 0). Visualizations of the latent space show that stable CDEs not only separate survivor and non-survivor trajectories but also reveal clear acuity score gradients, whereas unstable training fails to capture either pattern. These findings highlight practical guidelines for using CDEs to encode irregular medical time series in clinical RL, emphasizing the need for training stability in sequential representation learning.", "date": "2025-06-17", "categories": []}
{"id": "2511.00013", "title": "Using machine learning methods to predict cognitive age from psychophysiological tests", "abstract": "This study introduces a novel method for predicting cognitive age using psychophysiological tests. To determine cognitive age, subjects were asked to complete a series of psychological tests measuring various cognitive functions, including reaction time and cognitive conflict, short-term memory, verbal functions, and color and spatial perception. Based on the tests completed, the average completion time, proportion of correct answers, average absolute delta of the color campimetry test, number of guessed words in the M\\\"unsterberg matrix, and other parameters were calculated for each subject. The obtained characteristics of the subjects were preprocessed and used to train a machine learning algorithm implementing a regression task for predicting a person's cognitive age. These findings contribute to the field of remote screening using mobile devices for human health for diagnosing and monitoring cognitive aging.", "date": "2025-10-20", "categories": []}
{"id": "2502.06842", "title": "Agentic AI for Scaling Diagnosis and Care in Neurodegenerative Disease", "abstract": "United States healthcare systems are struggling to meet the growing demand for neurological care, particularly in Alzheimer's disease and related dementias (ADRD). Generative AI built on language models (LLMs) now enables agentic AI systems that can enhance clinician capabilities to approach specialist-level assessment and decision-making in ADRD care at scale. This article presents a comprehensive six-phase roadmap for responsible design and integration of such systems into ADRD care: (1) high-quality standardized data collection across modalities; (2) decision support; (3) clinical integration enhancing workflows; (4) rigorous validation and monitoring protocols; (5) continuous learning through clinical feedback; and (6) robust ethics and risk management frameworks. This human centered approach optimizes clinicians' capabilities in comprehensive data collection, interpretation of complex clinical information, and timely application of relevant medical knowledge while prioritizing patient safety, healthcare equity, and transparency. Though focused on ADRD, these principles offer broad applicability across medical specialties facing similar systemic challenges.", "date": "2025-02-06", "categories": []}
{"id": "2512.20669", "title": "Improving Cardiac Risk Prediction Using Data Generation Techniques", "abstract": "Cardiac rehabilitation constitutes a structured clinical process involving multiple interdependent phases, individualized medical decisions, and the coordinated participation of diverse healthcare professionals. This sequential and adaptive nature enables the program to be modeled as a business process, thereby facilitating its analysis. Nevertheless, studies in this context face significant limitations inherent to real-world medical databases: data are often scarce due to both economic costs and the time required for collection; many existing records are not suitable for specific analytical purposes; and, finally, there is a high prevalence of missing values, as not all patients undergo the same diagnostic tests. To address these limitations, this work proposes an architecture based on a Conditional Variational Autoencoder (CVAE) for the synthesis of realistic clinical records that are coherent with real-world observations. The primary objective is to increase the size and diversity of the available datasets in order to enhance the performance of cardiac risk prediction models and to reduce the need for potentially hazardous diagnostic procedures, such as exercise stress testing. The results demonstrate that the proposed architecture is capable of generating coherent and realistic synthetic data, whose use improves the accuracy of the various classifiers employed for cardiac risk detection, outperforming state-of-the-art deep learning approaches for synthetic data generation.", "date": "2025-12-19", "categories": []}
{"id": "2512.20948", "title": "Foundation Model-based Evaluation of Neuropsychiatric Disorders: A Lifespan-Inclusive, Multi-Modal, and Multi-Lingual Study", "abstract": "Neuropsychiatric disorders, such as Alzheimer's disease (AD), depression, and autism spectrum disorder (ASD), are characterized by linguistic and acoustic abnormalities, offering potential biomarkers for early detection. Despite the promise of multi-modal approaches, challenges like multi-lingual generalization and the absence of a unified evaluation framework persist. To address these gaps, we propose FEND (Foundation model-based Evaluation of Neuropsychiatric Disorders), a comprehensive multi-modal framework integrating speech and text modalities for detecting AD, depression, and ASD across the lifespan. Leveraging 13 multi-lingual datasets spanning English, Chinese, Greek, French, and Dutch, we systematically evaluate multi-modal fusion performance. Our results show that multi-modal fusion excels in AD and depression detection but underperforms in ASD due to dataset heterogeneity. We also identify modality imbalance as a prevalent issue, where multi-modal fusion fails to surpass the best mono-modal models. Cross-corpus experiments reveal robust performance in task- and language-consistent scenarios but noticeable degradation in multi-lingual and task-heterogeneous settings. By providing extensive benchmarks and a detailed analysis of performance-influencing factors, FEND advances the field of automated, lifespan-inclusive, and multi-lingual neuropsychiatric disorder assessment. We encourage researchers to adopt the FEND framework for fair comparisons and reproducible research.", "date": "2025-12-24", "categories": []}
{"id": "2512.21127", "title": "A Real-World Evaluation of LLM Medication Safety Reviews in NHS Primary Care", "abstract": "Large language models (LLMs) often match or exceed clinician-level performance on medical benchmarks, yet very few are evaluated on real clinical data or examined beyond headline metrics. We present, to our knowledge, the first evaluation of an LLM-based medication safety review system on real NHS primary care data, with detailed characterisation of key failure behaviours across varying levels of clinical complexity. In a retrospective study using a population-scale EHR spanning 2,125,549 adults in NHS Cheshire and Merseyside, we strategically sampled patients to capture a broad range of clinical complexity and medication safety risk, yielding 277 patients after data-quality exclusions. An expert clinician reviewed these patients and graded system-identified issues and proposed interventions. Our primary LLM system showed strong performance in recognising when a clinical issue is present (sensitivity 100\\% [95\\% CI 98.2--100], specificity 83.1\\% [95\\% CI 72.7--90.1]), yet correctly identified all issues and interventions in only 46.9\\% [95\\% CI 41.1--52.8] of patients. Failure analysis reveals that, in this setting, the dominant failure mechanism is contextual reasoning rather than missing medication knowledge, with five primary patterns: overconfidence in uncertainty, applying standard guidelines without adjusting for patient context, misunderstanding how healthcare is delivered in practice, factual errors, and process blindness. These patterns persisted across patient complexity and demographic strata, and across a range of state-of-the-art models and configurations. We provide 45 detailed vignettes that comprehensively cover all identified failure cases. This work highlights shortcomings that must be addressed before LLM-based clinical AI can be safely deployed. It also begs larger-scale, prospective evaluations and deeper study of LLM behaviours in clinical contexts.", "date": "2025-12-24", "categories": []}
{"id": "2512.21170", "title": "A Unified Framework for EEG Seizure Detection Using Universum-Integrated Generalized Eigenvalues Proximal Support Vector Machine", "abstract": "The paper presents novel Universum-enhanced classifiers: the Universum Generalized Eigenvalue Proximal Support Vector Machine (U-GEPSVM) and the Improved U-GEPSVM (IU-GEPSVM) for EEG signal classification. Using the computational efficiency of generalized eigenvalue decomposition and the generalization benefits of Universum learning, the proposed models address critical challenges in EEG analysis: non-stationarity, low signal-to-noise ratio, and limited labeled data. U-GEPSVM extends the GEPSVM framework by incorporating Universum constraints through a ratio-based objective function, while IU-GEPSVM enhances stability through a weighted difference-based formulation that provides independent control over class separation and Universum alignment. The models are evaluated on the Bonn University EEG dataset across two binary classification tasks: (O vs S)-healthy (eyes closed) vs seizure, and (Z vs S)-healthy (eyes open) vs seizure. IU-GEPSVM achieves peak accuracies of 85% (O vs S) and 80% (Z vs S), with mean accuracies of 81.29% and 77.57% respectively, outperforming baseline methods.", "date": "2025-12-24", "categories": []}
{"id": "2508.13256", "title": "CardAIc-Agents: A Multimodal Framework with Hierarchical Adaptation for Cardiac Care Support", "abstract": "Cardiovascular diseases (CVDs) remain the foremost cause of mortality worldwide, a burden worsened by a severe deficit of healthcare workers. Artificial intelligence (AI) agents have shown potential to alleviate this gap through automated detection and proactive screening, yet their clinical application remains limited by: 1) rigid sequential workflows, whereas clinical care often requires adaptive reasoning that select specific tests and, based on their results, guides personalised next steps; 2) reliance solely on intrinsic model capabilities to perform role assignment without domain-specific tool support; 3) general and static knowledge bases without continuous learning capability; and 4) fixed unimodal or bimodal inputs and lack of on-demand visual outputs when clinicians require visual clarification. In response, a multimodal framework, CardAIc-Agents, was proposed to augment models with external tools and adaptively support diverse cardiac tasks. First, a CardiacRAG agent generated task-aware plans from updatable cardiac knowledge, while the Chief agent integrated tools to autonomously execute these plans and deliver decisions. Second, to enable adaptive and case-specific customization, a stepwise update strategy was developed to dynamically refine plans based on preceding execution results, once the task was assessed as complex. Third, a multidisciplinary discussion team was proposed which was automatically invoked to interpret challenging cases, thereby supporting further adaptation. In addition, visual review panels were provided to assist validation when clinicians raised concerns. Experiments across three datasets showed the efficiency of CardAIc-Agents compared to mainstream Vision-Language Models (VLMs) and state-of-the-art agentic systems.", "date": "2025-08-18", "categories": []}
{"id": "2512.19716", "title": "Development and external validation of a multimodal artificial intelligence mortality prediction model of critically ill patients using multicenter data", "abstract": "Early prediction of in-hospital mortality in critically ill patients can aid clinicians in optimizing treatment. The objective was to develop a multimodal deep learning model, using structured and unstructured clinical data, to predict in-hospital mortality risk among critically ill patients after their initial 24 hour intensive care unit (ICU) admission. We used data from MIMIC-III, MIMIC-IV, eICU, and HiRID. A multimodal model was developed on the MIMIC datasets, featuring time series components occurring within the first 24 hours of ICU admission and predicting risk of subsequent inpatient mortality. Inputs included time-invariant variables, time-variant variables, clinical notes, and chest X-ray images. External validation occurred in a temporally separated MIMIC population, HiRID, and eICU datasets. A total of 203,434 ICU admissions from more than 200 hospitals between 2001 to 2022 were included, in which mortality rate ranged from 5.2% to 7.9% across the four datasets. The model integrating structured data points had AUROC, AUPRC, and Brier scores of 0.92, 0.53, and 0.19, respectively. We externally validated the model on eight different institutions within the eICU dataset, demonstrating AUROCs ranging from 0.84-0.92. When including only patients with available clinical notes and imaging data, inclusion of notes and imaging into the model, the AUROC, AUPRC, and Brier score improved from 0.87 to 0.89, 0.43 to 0.48, and 0.37 to 0.17, respectively. Our findings highlight the importance of incorporating multiple sources of patient information for mortality prediction and the importance of external validation.", "date": "2025-12-15", "categories": []}
{"id": "2512.20319", "title": "Deep Learning Classification of EEG Responses to Multi-Dimensional Transcranial Electrical Stimulation", "abstract": "A major shortcoming of medical practice is the lack of an objective measure of conscious level. Impairment of consciousness is common, e.g. following brain injury and seizures, which can also interfere with sensory processing and volitional responses. This is also an important pitfall in neurophysiological methods that infer awareness via command following, e.g. using functional MRI or electroencephalography (EEG).\n  Transcranial electrical stimulation (TES) can be employed to non-invasively stimulate the brain, bypassing sensory inputs, and has already showed promising results in providing reliable indicators of brain state. However, current non-invasive solutions have been limited to magnetic stimulation, which is not easily translatable to clinical settings. Our long-term vision is to develop an objective measure of brain state that can be used at the bedside, without requiring patients to understand commands or initiate motor responses.\n  In this study, we demonstrated the feasibility of a framework using Deep Learning algorithms to classify EEG brain responses evoked by a defined multi-dimensional pattern of TES. We collected EEG-TES data from 11 participants and found that delivering transcranial direct current stimulation (tDCS) to posterior cortical areas targeting the angular gyrus elicited an exceptionally reliable brain response. For this paradigm, our best Convolutional Neural Network model reached a 92% classification F1-score on Holdout data from participants never seen during training, significantly surpassing human-level performance at 60-70% accuracy.\n  These findings establish a framework for robust consciousness measurement for clinical use. In this spirit, we documented and open-sourced our datasets and codebase in full, to be used freely by the neuroscience and AI research communities, who may replicate our results with free tools like GitHub, Kaggle, and Colab.", "date": "2025-12-23", "categories": []}
{"id": "2512.20344", "title": "A DeepSeek-Powered AI System for Automated Chest Radiograph Interpretation in Clinical Practice", "abstract": "A global shortage of radiologists has been exacerbated by the significant volume of chest X-ray workloads, particularly in primary care. Although multimodal large language models show promise, existing evaluations predominantly rely on automated metrics or retrospective analyses, lacking rigorous prospective clinical validation. Janus-Pro-CXR (1B), a chest X-ray interpretation system based on DeepSeek Janus-Pro model, was developed and rigorously validated through a multicenter prospective trial (NCT07117266). Our system outperforms state-of-the-art X-ray report generation models in automated report generation, surpassing even larger-scale models including ChatGPT 4o (200B parameters), while demonstrating reliable detection of six clinically critical radiographic findings. Retrospective evaluation confirms significantly higher report accuracy than Janus-Pro and ChatGPT 4o. In prospective clinical deployment, AI assistance significantly improved report quality scores, reduced interpretation time by 18.3% (P < 0.001), and was preferred by a majority of experts in 54.3% of cases. Through lightweight architecture and domain-specific optimization, Janus-Pro-CXR improves diagnostic reliability and workflow efficiency, particularly in resource-constrained settings. The model architecture and implementation framework will be open-sourced to facilitate the clinical translation of AI-assisted radiology solutions.", "date": "2025-12-23", "categories": []}
{"id": "2512.20520", "title": "Benchmarking LLMs for Predictive Applications in the Intensive Care Units", "abstract": "With the advent of LLMs, various tasks across the natural language processing domain have been transformed. However, their application in predictive tasks remains less researched. This study compares large language models, including GatorTron-Base (trained on clinical data), Llama 8B, and Mistral 7B, against models like BioBERT, DocBERT, BioClinicalBERT, Word2Vec, and Doc2Vec, setting benchmarks for predicting Shock in critically ill patients. Timely prediction of shock can enable early interventions, thus improving patient outcomes. Text data from 17,294 ICU stays of patients in the MIMIC III database were scored for length of stay > 24 hours and shock index (SI) > 0.7 to yield 355 and 87 patients with normal and abnormal SI-index, respectively. Both focal and cross-entropy losses were used during finetuning to address class imbalances. Our findings indicate that while GatorTron Base achieved the highest weighted recall of 80.5%, the overall performance metrics were comparable between SLMs and LLMs. This suggests that LLMs are not inherently superior to SLMs in predicting future clinical events despite their strong performance on text-based tasks. To achieve meaningful clinical outcomes, future efforts in training LLMs should prioritize developing models capable of predicting clinical trajectories rather than focusing on simpler tasks such as named entity recognition or phenotyping.", "date": "2025-12-23", "categories": []}
{"id": "2411.17570", "title": "Learning Explainable Treatment Policies with Clinician-Informed Representations: A Practical Approach", "abstract": "Digital health interventions (DHIs) and remote patient monitoring (RPM) have shown great potential in improving chronic disease management through personalized care. However, barriers like limited efficacy and workload concerns hinder adoption of existing DHIs; while limited sample sizes and lack of interpretability limit the effectiveness and adoption of purely black-box algorithmic DHIs. In this paper, we address these challenges by developing a pipeline for learning explainable treatment policies for RPM-enabled DHIs. We apply our approach in the real-world setting of RPM using a DHI to improve glycemic control of youth with type 1 diabetes. Our main contribution is to reveal the importance of clinical domain knowledge in developing state and action representations for effective, efficient, and interpretable targeting policies. We observe that policies learned from clinician-informed representations are significantly more efficacious and efficient than policies learned from black-box representations. This work emphasizes the importance of collaboration between ML researchers and clinicians for developing effective DHIs in the real world.", "date": "2024-11-26", "categories": []}
{"id": "2512.07651", "title": "Liver Fibrosis Quantification and Analysis: The LiQA Dataset and Baseline Method", "abstract": "Liver fibrosis represents a significant global health burden, necessitating accurate staging for effective clinical management. This report introduces the LiQA (Liver Fibrosis Quantification and Analysis) dataset, established as part of the CARE 2024 challenge. Comprising $440$ patients with multi-phase, multi-center MRI scans, the dataset is curated to benchmark algorithms for Liver Segmentation (LiSeg) and Liver Fibrosis Staging (LiFS) under complex real-world conditions, including domain shifts, missing modalities, and spatial misalignment. We further describe the challenge's top-performing methodology, which integrates a semi-supervised learning framework with external data for robust segmentation, and utilizes a multi-view consensus approach with Class Activation Map (CAM)-based regularization for staging. Evaluation of this baseline demonstrates that leveraging multi-source data and anatomical constraints significantly enhances model robustness in clinical settings.", "date": "2025-12-08", "categories": []}
{"id": "2512.17934", "title": "Comparative Evaluation of Explainable Machine Learning Versus Linear Regression for Predicting County-Level Lung Cancer Mortality Rate in the United States", "abstract": "Lung cancer (LC) is a leading cause of cancer-related mortality in the United States. Accurate prediction of LC mortality rates is crucial for guiding targeted interventions and addressing health disparities. Although traditional regression-based models have been commonly used, explainable machine learning models may offer enhanced predictive accuracy and deeper insights into the factors influencing LC mortality. This study applied three models: random forest (RF), gradient boosting regression (GBR), and linear regression (LR) to predict county-level LC mortality rates across the United States. Model performance was evaluated using R-squared and root mean squared error (RMSE). Shapley Additive Explanations (SHAP) values were used to determine variable importance and their directional impact. Geographic disparities in LC mortality were analyzed through Getis-Ord (Gi*) hotspot analysis. The RF model outperformed both GBR and LR, achieving an R2 value of 41.9% and an RMSE of 12.8. SHAP analysis identified smoking rate as the most important predictor, followed by median home value and the percentage of the Hispanic ethnic population. Spatial analysis revealed significant clusters of elevated LC mortality in the mid-eastern counties of the United States. The RF model demonstrated superior predictive performance for LC mortality rates, emphasizing the critical roles of smoking prevalence, housing values, and the percentage of Hispanic ethnic population. These findings offer valuable actionable insights for designing targeted interventions, promoting screening, and addressing health disparities in regions most affected by LC in the United States.", "date": "2025-12-10", "categories": []}
{"id": "2512.18450", "title": "Agent-Based Output Drift Detection for Breast Cancer Response Prediction in a Multisite Clinical Decision Support System", "abstract": "Modern clinical decision support systems can concurrently serve multiple, independent medical imaging institutions, but their predictive performance may degrade across sites due to variations in patient populations, imaging hardware, and acquisition protocols. Continuous surveillance of predictive model outputs offers a safe and reliable approach for identifying such distributional shifts without ground truth labels. However, most existing methods rely on centralized monitoring of aggregated predictions, overlooking site-specific drift dynamics. We propose an agent-based framework for detecting drift and assessing its severity in multisite clinical AI systems. To evaluate its effectiveness, we simulate a multi-center environment for output-based drift detection, assigning each site a drift monitoring agent that performs batch-wise comparisons of model outputs against a reference distribution. We analyse several multi-center monitoring schemes, that differ in how the reference is obtained (site-specific, global, production-only and adaptive), alongside a centralized baseline. Results on real-world breast cancer imaging data using a pathological complete response prediction model shows that all multi-center schemes outperform centralized monitoring, with F1-score improvements up to 10.3% in drift detection. In the absence of site-specific references, the adaptive scheme performs best, with F1-scores of 74.3% for drift detection and 83.7% for drift severity classification. These findings suggest that adaptive, site-aware agent-based drift monitoring can enhance reliability of multisite clinical decision support systems.", "date": "2025-12-20", "categories": []}
{"id": "2512.18528", "title": "WoundNet-Ensemble: A Novel IoMT System Integrating Self-Supervised Deep Learning and Multi-Model Fusion for Automated, High-Accuracy Wound Classification and Healing Progression Monitoring", "abstract": "Chronic wounds, including diabetic foot ulcers which affect up to one-third of people with diabetes, impose a substantial clinical and economic burden, with U.S. healthcare costs exceeding 25 billion dollars annually. Current wound assessment remains predominantly subjective, leading to inconsistent classification and delayed interventions. We present WoundNet-Ensemble, an Internet of Medical Things system leveraging a novel ensemble of three complementary deep learning architectures: ResNet-50, the self-supervised Vision Transformer DINOv2, and Swin Transformer, for automated classification of six clinically distinct wound types. Our system achieves 99.90 percent ensemble accuracy on a comprehensive dataset of 5,175 wound images spanning diabetic foot ulcers, pressure ulcers, venous ulcers, thermal burns, pilonidal sinus wounds, and fungating malignant tumors. The weighted fusion strategy demonstrates a 3.7 percent improvement over previous state-of-the-art methods. Furthermore, we implement a longitudinal wound healing tracker that computes healing rates, severity scores, and generates clinical alerts. This work demonstrates a robust, accurate, and clinically deployable tool for modernizing wound care through artificial intelligence, addressing critical needs in telemedicine and remote patient monitoring. The implementation and trained models will be made publicly available to support reproducibility.", "date": "2025-12-20", "categories": []}
{"id": "2406.06650", "title": "Assessing the risk of recurrence in early-stage breast cancer through H&E stained whole slide images", "abstract": "Accurate prediction of the likelihood of recurrence is important in the selection of postoperative treatment for patients with early-stage breast cancer. In this study, we investigated whether deep learning algorithms can predict patients' risk of recurrence by analyzing the pathology images of their cancer histology.We analyzed 125 hematoxylin and eosin-stained whole slide images (WSIs) from 125 patients across two institutions (National Cancer Center and Korea University Medical Center Guro Hospital) to predict breast cancer recurrence risk using deep learning. Sensitivity reached 0.857, 0.746, and 0.529 for low, intermediate, and high-risk categories, respectively, with specificity of 0.816, 0.803, and 0.972, and a Pearson correlation of 0.61 with histological grade. Class activation maps highlighted features like tubule formation and mitotic rate, suggesting a cost-effective approach to risk stratification, pending broader validation. These findings suggest that deep learning models trained exclusively on hematoxylin and eosin stained whole slide images can approximate genomic assay results, offering a cost-effective and scalable tool for breast cancer recurrence risk assessment. However, further validation using larger and more balanced datasets is needed to confirm the clinical applicability of our approach.", "date": "2024-06-10", "categories": []}
{"id": "2507.08874", "title": "An Automated Classifier of Harmful Brain Activities for Clinical Usage Based on a Vision-Inspired Pre-trained Framework", "abstract": "Timely identification of harmful brain activities via electroencephalography (EEG) is critical for brain disease diagnosis and treatment, which remains limited application due to inter-rater variability, resource constraints, and poor generalizability of existing artificial intelligence (AI) models. In this study, a convolutional neural network model, VIPEEGNet, was developed and validated using EEGs recorded from Massachusetts General Hospital/Harvard Medical School. The VIPEEGNet was developed and validated using two independent datasets, collected between 2006 and 2020. The development cohort included EEG recordings from 1950 patients, with 106,800 EEG segments annotated by at least one experts (ranging from 1 to 28). The online testing cohort consisted of EEG segments from a subset of an additional 1,532 patients, each annotated by at least 10 experts. For the development cohort (n=1950), the VIPEEGNet achieved high accuracy, with an AUROC for binary classification of seizure, LPD, GPD, LRDA, GRDA, and \"other\" categories at 0.972 (95% CI, 0.957-0.988), 0.962 (95% CI, 0.954-0.970), 0.972 (95% CI, 0.960-0.984), 0.938 (95% CI, 0.917-0.959), 0.949 (95% CI, 0.941-0.957), and 0.930 (95% CI, 0.926-0.935). For multi classification, the sensitivity of VIPEEGNET for the six categories ranges from 36.8% to 88.2% and the precision ranges from 55.6% to 80.4%, and performance similar to human experts. Notably, the external validation showed Kullback-Leibler Divergence (KLD)of 0.223 and 0.273, ranking top 2 among the existing 2,767 competing algorithms, while we only used 2.8% of the parameters of the first-ranked algorithm.", "date": "2025-07-10", "categories": []}
{"id": "2512.17559", "title": "Towards Explainable Conversational AI for Early Diagnosis with Large Language Models", "abstract": "Healthcare systems around the world are grappling with issues like inefficient diagnostics, rising costs, and limited access to specialists. These problems often lead to delays in treatment and poor health outcomes. Most current AI and deep learning diagnostic systems are not very interactive or transparent, making them less effective in real-world, patient-centered environments. This research introduces a diagnostic chatbot powered by a Large Language Model (LLM), using GPT-4o, Retrieval-Augmented Generation, and explainable AI techniques. The chatbot engages patients in a dynamic conversation, helping to extract and normalize symptoms while prioritizing potential diagnoses through similarity matching and adaptive questioning. With Chain-of-Thought prompting, the system also offers more transparent reasoning behind its diagnoses. When tested against traditional machine learning models like Naive Bayes, Logistic Regression, SVM, Random Forest, and KNN, the LLM-based system delivered impressive results, achieving an accuracy of 90% and Top-3 accuracy of 100%. These findings offer a promising outlook for more transparent, interactive, and clinically relevant AI in healthcare.", "date": "2025-12-19", "categories": []}
{"id": "2403.13843", "title": "Machine Learning and Transformers for Thyroid Carcinoma Diagnosis: A Review", "abstract": "The growing interest in developing smart diagnostic systems to help medical experts process extensive data for treating incurable diseases has been notable. In particular, the challenge of identifying thyroid cancer (TC) has seen progress with the use of machine learning (ML) and big data analysis, incorporating Transformers to evaluate TC prognosis and determine the risk of malignancy in individuals. This review article presents a summary of various studies on AI-based approaches, especially those employing Transformers, for diagnosing TC. It introduces a new categorization system for these methods based on artificial intelligence (AI) algorithms, the goals of the framework, and the computing environments used. Additionally, it scrutinizes and contrasts the available TC datasets by their features. The paper highlights the importance of AI instruments in aiding the diagnosis and treatment of TC through supervised, unsupervised, or mixed approaches, with a special focus on the ongoing importance of Transformers and large language models (LLMs) in medical diagnostics and disease management. It further discusses the progress made and the continuing obstacles in this area. Lastly, it explores future directions and focuses within this research field.", "date": "2024-03-17", "categories": []}
{"id": "2509.05037", "title": "ModalSurv: Investigating opportunities and limitations of multimodal deep survival learning in prostate and bladder cancer", "abstract": "Accurate survival prediction is essential for personalised cancer treatment. We propose ModalSurv, a multimodal deep survival framework integrating clinical, MRI, histopathology, and RNA-sequencing data via modality-specific projections and cross-attention fusion. On the CHIMERA Grand Challenge datasets, ModalSurv achieved a C-index of 0.7402 (1st) for prostate and 0.5740 (5th) for bladder cancer. Notably, clinical features alone outperformed multimodal models on external tests, highlighting challenges of limited multimodal alignment and potential overfitting. Local validation showed multimodal gains but limited generalisation. ModalSurv provides a systematic evaluation of multimodal survival modelling, underscoring both its promise and current limitations for scalable, generalisable cancer prognosis.", "date": "2025-09-05", "categories": []}
{"id": "2512.07741", "title": "A multimodal Bayesian Network for symptom-level depression and anxiety prediction from voice and speech data", "abstract": "During psychiatric assessment, clinicians observe not only what patients report, but important nonverbal signs such as tone, speech rate, fluency, responsiveness, and body language. Weighing and integrating these different information sources is a challenging task and a good candidate for support by intelligence-driven tools - however this is yet to be realized in the clinic. Here, we argue that several important barriers to adoption can be addressed using Bayesian network modelling. To demonstrate this, we evaluate a model for depression and anxiety symptom prediction from voice and speech features in large-scale datasets (30,135 unique speakers). Alongside performance for conditions and symptoms (for depression, anxiety ROC-AUC=0.842,0.831 ECE=0.018,0.015; core individual symptom ROC-AUC>0.74), we assess demographic fairness and investigate integration across and redundancy between different input modality types. Clinical usefulness metrics and acceptability to mental health service users are explored. When provided with sufficiently rich and large-scale multimodal data streams and specified to represent common mental conditions at the symptom rather than disorder level, such models are a principled approach for building robust assessment support tools: providing clinically-relevant outputs in a transparent and explainable format that is directly amenable to expert clinical supervision.", "date": "2025-12-08", "categories": []}
{"id": "2512.16876", "title": "Training Together, Diagnosing Better: Federated Learning for Collagen VI-Related Dystrophies", "abstract": "The application of Machine Learning (ML) to the diagnosis of rare diseases, such as collagen VI-related dystrophies (COL6-RD), is fundamentally limited by the scarcity and fragmentation of available data. Attempts to expand sampling across hospitals, institutions, or countries with differing regulations face severe privacy, regulatory, and logistical obstacles that are often difficult to overcome. The Federated Learning (FL) provides a promising solution by enabling collaborative model training across decentralized datasets while keeping patient data local and private. Here, we report a novel global FL initiative using the Sherpa.ai FL platform, which leverages FL across distributed datasets in two international organizations for the diagnosis of COL6-RD, using collagen VI immunofluorescence microscopy images from patient-derived fibroblast cultures. Our solution resulted in an ML model capable of classifying collagen VI patient images into the three primary pathogenic mechanism groups associated with COL6-RD: exon skipping, glycine substitution, and pseudoexon insertion. This new approach achieved an F1-score of 0.82, outperforming single-organization models (0.57-0.75). These results demonstrate that FL substantially improves diagnostic utility and generalizability compared to isolated institutional models. Beyond enabling more accurate diagnosis, we anticipate that this approach will support the interpretation of variants of uncertain significance and guide the prioritization of sequencing strategies to identify novel pathogenic variants.", "date": "2025-12-18", "categories": []}
{"id": "2411.17645", "title": "Explainable AI for Classifying UTI Risk Groups Using a Real-World Linked EHR and Pathology Lab Dataset", "abstract": "The use of machine learning and AI on electronic health records (EHRs) holds substantial potential for clinical insight. However, this approach faces challenges due to data heterogeneity, sparsity, temporal misalignment, and limited labeled outcomes. In this context, we leverage a linked EHR dataset of approximately one million de-identified individuals from Bristol, North Somerset, and South Gloucestershire, UK, to characterize urinary tract infections (UTIs). We implemented a data pre-processing and curation pipeline that transforms the raw EHR data into a structured format suitable for developing predictive models focused on data fairness, accountability and transparency. Given the limited availability and biases of ground truth UTI outcomes, we introduce a UTI risk estimation framework informed by clinical expertise to estimate UTI risk across individual patient timelines. Pairwise XGBoost models are trained using this framework to differentiate UTI risk categories with explainable AI techniques applied to identify key predictors and support interpretability. Our findings reveal differences in clinical and demographic predictors across risk groups. While this study highlights the potential of AI-driven insights to support UTI clinical decision-making, further investigation of patient sub-strata and extensive validation are needed to ensure robustness and applicability in clinical practice.", "date": "2024-11-26", "categories": []}
{"id": "2504.05627", "title": "Maternal and Fetal Health Status Assessment by Using Machine Learning on Optical 3D Body Scans", "abstract": "Monitoring maternal and fetal health during pregnancy is crucial for preventing adverse outcomes. While tests such as ultrasound scans offer high accuracy, they can be costly and inconvenient. Telehealth and more accessible body shape information provide pregnant women with a convenient way to monitor their health. This study explores the potential of 3D body scan data, captured during the 18-24 gestational weeks, to predict adverse pregnancy outcomes and estimate clinical parameters. We developed a novel algorithm with two parallel streams which are used for extract body shape features: one for supervised learning to extract sequential abdominal circumference information, and another for unsupervised learning to extract global shape descriptors, alongside a branch for demographic data.\n  Our results indicate that 3D body shape can assist in predicting preterm labor, gestational diabetes mellitus (GDM), gestational hypertension (GH), and in estimating fetal weight. Compared to other machine learning models, our algorithm achieved the best performance, with prediction accuracies exceeding 88% and fetal weight estimation accuracy of 76.74% within a 10% error margin, outperforming conventional anthropometric methods by 22.22%.", "date": "2025-04-08", "categories": []}
{"id": "2506.02976", "title": "Deep Learning for Retinal Degeneration Assessment: A Comprehensive Analysis of the MARIO Challenge", "abstract": "The MARIO challenge, held at MICCAI 2024, focused on advancing the automated detection and monitoring of age-related macular degeneration (AMD) through the analysis of optical coherence tomography (OCT) images. Designed to evaluate algorithmic performance in detecting neovascular activity changes within AMD, the challenge incorporated unique multi-modal datasets. The primary dataset, sourced from Brest, France, was used by participating teams to train and test their models. The final ranking was determined based on performance on this dataset. An auxiliary dataset from Algeria was used post-challenge to evaluate population and device shifts from submitted solutions. Two tasks were involved in the MARIO challenge. The first one was the classification of evolution between two consecutive 2D OCT B-scans. The second one was the prediction of future AMD evolution over three months for patients undergoing anti-vascular endothelial growth factor (VEGF) therapy. Thirty-five teams participated, with the top 12 finalists presenting their methods. This paper outlines the challenge's structure, tasks, data characteristics, and winning methodologies, setting a benchmark for AMD monitoring using OCT, infrared imaging, and clinical data (such as the number of visits, age, gender, etc.). The results of this challenge indicate that artificial intelligence (AI) performs as well as a physician in measuring AMD progression (Task 1) but is not yet able of predicting future evolution (Task 2).", "date": "2025-06-03", "categories": []}
{"id": "2512.14712", "title": "SepsisSuite: Beyond Risk Stratification -- A Comparative Analysis of Deep Fusion vs. Expert Stacking for Prescriptive Sepsis AI", "abstract": "Sepsis accounts for nearly 20% of global ICU admissions, yet conventional prediction models often fail to effectively integrate heterogeneous data streams, remaining either siloed by modality or reliant on brittle early fusion. In this work, we present a rigorous architectural comparison between End-to-End Deep Fusion and Context-Aware Stacking for sepsis tasks. We initially hypothesized that a novel Quad-Modal Hierarchical Gated Attention Network -- termed SepsisFusionFormer -- would resolve complex cross-modal interactions between vitals, text, and imaging. However, experiments on MIMIC-IV revealed that SepsisFusionFormer suffered from \"attention starvation\" in the small antibiotic cohort ($N \\approx 2,100$), resulting in overfitting (AUC 0.66). This counterintuitive result informed the design of SepsisLateFusion, a \"leaner\" Context-Aware Mixture-of-Experts (MoE) architecture. By treating modalities as orthogonal experts -- the \"Historian\" (Static), the \"Monitor\" (Temporal), and the \"Reader\" (NLP) -- and dynamically gating them via a CatBoost meta-learner, we achieved State-of-the-Art (SOTA) performance: 0.915 AUC for prediction 4 hours prior to clinical onset. By calibrating the decision threshold for clinical safety, we reduced missed cases by 48% relative to the default operating point, thus opening a true preventative window for timely intervention over reactive alerts. Furthermore, for the novel prescriptive task of multi-class antibiotic selection, we demonstrate that a Quad-Modal Ensemble achieved the highest performance (0.72 AUC). These models are integrated into SepsisSuite, a deployment-ready Python framework for clinical decision support. SepsisSuite is available for free at: https://github.com/RyanCartularo/SepsisSuite-Info", "date": "2025-12-08", "categories": []}
{"id": "2309.13135", "title": "Global Deep Forecasting with Patient-Specific Pharmacokinetics", "abstract": "Forecasting healthcare time series data is vital for early detection of adverse outcomes and patient monitoring. However, it can be challenging in practice due to variable medication administration and unique pharmacokinetic (PK) properties of each patient. To address these challenges, we propose a novel hybrid global-local architecture and a PK encoder that informs deep learning models of patient-specific treatment effects. We showcase the efficacy of our approach in achieving significant accuracy gains in a blood glucose forecasting task using both realistically simulated and real-world data. Our PK encoder surpasses baselines by up to 16.4% on simulated data and 4.9% on real-world data for individual patients during critical events of severely high and low glucose levels. Furthermore, our proposed hybrid global-local architecture outperforms patient-specific PK models by 15.8%, on average.", "date": "2023-09-22", "categories": []}
{"id": "2408.13926", "title": "FedGlu: A personalized federated learning-based glucose forecasting algorithm for improved performance in glycemic excursion regions", "abstract": "Continuous glucose monitoring (CGM) devices provide real-time glucose monitoring and timely alerts for glycemic excursions, improving glycemic control among patients with diabetes. However, identifying rare events like hypoglycemia and hyperglycemia remain challenging due to their infrequency. Moreover, limited access to sensitive patient data hampers the development of robust machine learning models. Our objective is to accurately predict glycemic excursions while addressing data privacy concerns. To tackle excursion prediction, we propose a novel Hypo-Hyper (HH) loss function, which significantly improves performance in the glycemic excursion regions. The HH loss function demonstrates a 46% improvement over mean-squared error (MSE) loss across 125 patients. To address privacy concerns, we propose FedGlu, a machine learning model trained in a federated learning (FL) framework. FL allows collaborative learning without sharing sensitive data by training models locally and sharing only model parameters across other patients. FedGlu achieves a 35% superior glycemic excursion detection rate compared to local models. This improvement translates to enhanced performance in predicting both, hypoglycemia and hyperglycemia, for 105 out of 125 patients. These results underscore the effectiveness of the proposed HH loss function in augmenting the predictive capabilities of glucose predictions. Moreover, implementing models within a federated learning framework not only ensures better predictive capabilities but also safeguards sensitive data concurrently.", "date": "2024-08-25", "categories": []}
{"id": "2506.07584", "title": "MIRA: Medical Time Series Foundation Model for Real-World Health Data", "abstract": "A unified foundation model for medical time series -- pretrained on open access and ethics board-approved medical corpora -- offers the potential to reduce annotation burdens, minimize model customization, and enable robust transfer across clinical institutions, modalities, and tasks, particularly in data-scarce or privacy-constrained environments. However, existing generalist time series foundation models struggle to handle medical time series data due to their inherent challenges, including irregular intervals, heterogeneous sampling rates, and frequent missing values. To address these challenges, we introduce MIRA, a unified foundation model specifically designed for medical time series forecasting. MIRA incorporates a Continuous-Time Rotary Positional Encoding that enables fine-grained modeling of variable time intervals, a frequency-specific mixture-of-experts layer that routes computation across latent frequency regimes to further promote temporal specialization, and a Continuous Dynamics Extrapolation Block based on Neural ODE that models the continuous trajectory of latent states, enabling accurate forecasting at arbitrary target timestamps. Pretrained on a large-scale and diverse medical corpus comprising over 454 billion time points collect from publicly available datasets, MIRA achieves reductions in forecasting errors by an average of 10% and 7% in out-of-distribution and in-distribution scenarios, respectively, when compared to other zero-shot and fine-tuned baselines. We also introduce a comprehensive benchmark spanning multiple downstream clinical tasks, establishing a foundation for future research in medical time series modeling.", "date": "2025-06-09", "categories": []}
{"id": "2509.18638", "title": "Learning neuroimaging models from health system-scale data", "abstract": "Neuroimaging is a ubiquitous tool for evaluating patients with neurological diseases. The global demand for magnetic resonance imaging (MRI) studies has risen steadily, placing significant strain on health systems, prolonging turnaround times, and intensifying physician burnout. These challenges disproportionately impact patients in low-resource and rural settings. Here, we utilized a large academic health system as a data engine to develop Prima, the first vision language model (VLM) serving as an AI foundation for neuroimaging that supports real-world, clinical MRI studies as input. Trained on over 220,000 MRI studies, Prima uses a hierarchical vision architecture that provides general and transferable MRI features. Prima was tested in a 1-year health system-wide study that included 30K MRI studies. Across 52 radiologic diagnoses from the major neurologic disorders, including neoplastic, inflammatory, infectious, and developmental lesions, Prima achieved a mean diagnostic area under the ROC curve of 92.0, outperforming other state-of-the-art general and medical AI models. Prima offers explainable differential diagnoses, worklist priority for radiologists, and clinical referral recommendations across diverse patient demographics and MRI systems. Prima demonstrates algorithmic fairness across sensitive groups and can help mitigate health system biases, such as prolonged turnaround times for low-resource populations. These findings highlight the transformative potential of health system-scale VLMs and Prima's role in advancing AI-driven healthcare.", "date": "2025-09-23", "categories": []}
{"id": "2512.14563", "title": "Residual GRU+MHSA: A Lightweight Hybrid Recurrent Attention Model for Cardiovascular Disease Detection", "abstract": "Cardiovascular disease (CVD) remains the leading cause of mortality worldwide, underscoring the need for reliable and efficient predictive tools that support early intervention. Traditional diagnostic approaches rely on handcrafted features and clinician expertise, while machine learning methods improve reproducibility but often struggle to generalize across noisy and heterogeneous clinical data. In this work, we propose Residual GRU with Multi-Head Self-Attention, a compact deep learning architecture designed for tabular clinical records. The model integrates residual bidirectional gated recurrent units for sequential modeling of feature columns, a channel reweighting block, and multi-head self-attention pooling with a learnable classification token to capture global context. We evaluate the model on the UCI Heart Disease dataset using 5-fold stratified cross-validation and compare it against classical methods such as Logistic Regression, Random Forest, and Support Vector Machines, as well as modern deep learning baselines including DeepMLP, convolutional networks, recurrent networks, and Transformers. The proposed model achieves an accuracy of 0.861, macro-F1 of 0.860, ROC-AUC of 0.908, and PR-AUC of 0.904, outperforming all baselines. Ablation studies confirm the individual contributions of residual recurrence, channel gating, and attention pooling. t-SNE visualizations further indicate that the learned embeddings exhibit clearer separation between disease and non-disease classes compared to raw features. These results demonstrate that lightweight hybrid recurrent and attention-based architectures provide a strong balance between accuracy and efficiency for clinical risk prediction, supporting deployment in resource-constrained healthcare settings.", "date": "2025-12-16", "categories": []}
{"id": "2512.14683", "title": "Early Warning Index for Patient Deteriorations in Hospitals", "abstract": "Hospitals lack automated systems to harness the growing volume of heterogeneous clinical and operational data to effectively forecast critical events. Early identification of patients at risk for deterioration is essential not only for patient care quality monitoring but also for physician care management. However, translating varied data streams into accurate and interpretable risk assessments poses significant challenges due to inconsistent data formats. We develop a multimodal machine learning framework, the Early Warning Index (EWI), to predict the aggregate risk of ICU admission, emergency response team dispatch, and mortality. Key to EWI's design is a human-in-the-loop process: clinicians help determine alert thresholds and interpret model outputs, which are enhanced by explainable outputs using Shapley Additive exPlanations (SHAP) to highlight clinical and operational factors (e.g., scheduled surgeries, ward census) driving each patient's risk. We deploy EWI in a hospital dashboard that stratifies patients into three risk tiers. Using a dataset of 18,633 unique patients at a large U.S. hospital, our approach automatically extracts features from both structured and unstructured electronic health record (EHR) data and achieves C-statistics of 0.796. It is currently used as a triage tool for proactively managing at-risk patients. The proposed approach saves physicians valuable time by automatically sorting patients of varying risk levels, allowing them to concentrate on patient care rather than sifting through complex EHR data. By further pinpointing specific risk drivers, the proposed model provides data-informed adjustments to caregiver scheduling and allocation of critical resources. As a result, clinicians and administrators can avert downstream complications, including costly procedures or high readmission rates and improve overall patient flow.", "date": "2025-12-16", "categories": []}
{"id": "2410.00015", "title": "A Multitask VAE for Time Series Preprocessing and Prediction of Blood Glucose Level", "abstract": "Data preprocessing is a critical part of time series data analysis. Data from connected medical devices often have missing or abnormal values during acquisition. Handling such situations requires additional assumptions and domain knowledge. This can be time-consuming, and can introduce a significant bias affecting predictive model accuracy and thus, medical interpretation. To overcome this issue, we propose a new deep learning model to mitigate the preprocessing assumptions. The model architecture relies on a variational auto-encoder (VAE) to produce a preprocessing latent space, and a recurrent VAE to preserve the temporal dynamics of the data. We demonstrate the effectiveness of such an architecture on telemonitoring data to forecast glucose-level of diabetic patients. Our results show an improvement in terms of accuracy with respect of existing state-of-the-art methods and architectures.", "date": "2024-09-14", "categories": []}
{"id": "2512.12795", "title": "TRACER: Transfer Learning based Real-time Adaptation for Clinical Evolving Risk", "abstract": "Clinical decision support tools built on electronic health records often experience performance drift due to temporal population shifts, particularly when changes in the clinical environment initially affect only a subset of patients, resulting in a transition to mixed populations. Such case-mix changes commonly arise following system-level operational updates or the emergence of new diseases, such as COVID-19. We propose TRACER (Transfer Learning-based Real-time Adaptation for Clinical Evolving Risk), a framework that identifies encounter-level transition membership and adapts predictive models using transfer learning without full retraining. In simulation studies, TRACER outperformed static models trained on historical or contemporary data. In a real-world application predicting hospital admission following emergency department visits across the COVID-19 transition, TRACER improved both discrimination and calibration. TRACER provides a scalable approach for maintaining robust predictive performance under evolving and heterogeneous clinical conditions.", "date": "2025-12-14", "categories": []}
{"id": "2512.13078", "title": "Heart Disease Prediction using Case Based Reasoning (CBR)", "abstract": "This study provides an overview of heart disease prediction using an intelligent system. Predicting disease accurately is crucial in the medical field, but traditional methods relying solely on a doctor's experience often lack precision. To address this limitation, intelligent systems are applied as an alternative to traditional approaches. While various intelligent system methods exist, this study focuses on three: Fuzzy Logic, Neural Networks, and Case-Based Reasoning (CBR). A comparison of these techniques in terms of accuracy was conducted, and ultimately, Case-Based Reasoning (CBR) was selected for heart disease prediction. In the prediction phase, the heart disease dataset underwent data pre-processing to clean the data and data splitting to separate it into training and testing sets. The chosen intelligent system was then employed to predict heart disease outcomes based on the processed data. The experiment concluded with Case-Based Reasoning (CBR) achieving a notable accuracy rate of 97.95% in predicting heart disease. The findings also revealed that the probability of heart disease was 57.76% for males and 42.24% for females. Further analysis from related studies suggests that factors such as smoking and alcohol consumption are significant contributors to heart disease, particularly among males.", "date": "2025-12-15", "categories": []}
{"id": "2512.13440", "title": "IMILIA: interpretable multiple instance learning for inflammation prediction in IBD from H&E whole slide images", "abstract": "As the therapeutic target for Inflammatory Bowel Disease (IBD) shifts toward histologic remission, the accurate assessment of microscopic inflammation has become increasingly central for evaluating disease activity and response to treatment. In this work, we introduce IMILIA (Interpretable Multiple Instance Learning for Inflammation Analysis), an end-to-end framework designed for the prediction of inflammation presence in IBD digitized slides stained with hematoxylin and eosin (H&E), followed by the automated computation of markers characterizing tissue regions driving the predictions. IMILIA is composed of an inflammation prediction module, consisting of a Multiple Instance Learning (MIL) model, and an interpretability module, divided in two blocks: HistoPLUS, for cell instance detection, segmentation and classification; and EpiSeg, for epithelium segmentation. IMILIA achieves a cross-validation ROC-AUC of 0.83 on the discovery cohort, and a ROC-AUC of 0.99 and 0.84 on two external validation cohorts. The interpretability module yields biologically consistent insights: tiles with higher predicted scores show increased densities of immune cells (lymphocytes, plasmocytes, neutrophils and eosinophils), whereas lower-scored tiles predominantly contain normal epithelial cells. Notably, these patterns were consistent across all datasets. Code and models to partially replicate the results on the public IBDColEpi dataset can be found at https://github.com/owkin/imilia.", "date": "2025-12-15", "categories": []}
{"id": "2512.13685", "title": "Beyond surface form: A pipeline for semantic analysis in Alzheimer's Disease detection from spontaneous speech", "abstract": "Alzheimer's Disease (AD) is a progressive neurodegenerative condition that adversely affects cognitive abilities. Language-related changes can be automatically identified through the analysis of outputs from linguistic assessment tasks, such as picture description. Language models show promise as a basis for screening tools for AD, but their limited interpretability poses a challenge in distinguishing true linguistic markers of cognitive decline from surface-level textual patterns. To address this issue, we examine how surface form variation affects classification performance, with the goal of assessing the ability of language models to represent underlying semantic indicators. We introduce a novel approach where texts surface forms are transformed by altering syntax and vocabulary while preserving semantic content. The transformations significantly modify the structure and lexical content, as indicated by low BLEU and chrF scores, yet retain the underlying semantics, as reflected in high semantic similarity scores, isolating the effect of semantic information, and finding models perform similarly to if they were using the original text, with only small deviations in macro-F1. We also investigate whether language from picture descriptions retains enough detail to reconstruct the original image using generative models. We found that image-based transformations add substantial noise reducing classification accuracy. Our methodology provides a novel way of looking at what features influence model predictions, and allows the removal of possible spurious correlations. We find that just using semantic information, language model based classifiers can still detect AD. This work shows that difficult to detect semantic impairment can be identified, addressing an overlooked feature of linguistic deterioration, and opening new pathways for early detection systems.", "date": "2025-12-15", "categories": []}
{"id": "2512.10973", "title": "TECM*: A Data-Driven Assessment to Reinforcement Learning Methods and Application to Heparin Treatment Strategy for Surgical Sepsis", "abstract": "Objective: Sepsis is a life-threatening condition caused by severe infection leading to acute organ dysfunction. This study proposes a data-driven metric and a continuous reward function to optimize personalized heparin therapy in surgical sepsis patients. Methods: Data from the MIMIC-IV v1.0 and eICU v2.0 databases were used for model development and evaluation. The training cohort consisted of abdominal surgery patients receiving unfractionated heparin (UFH) after postoperative sepsis onset. We introduce a new RL-based framework: converting the discrete SOFA score to a continuous cxSOFA for more nuanced state and reward functions; Second, defining \"good\" or \"bad\" strategies based on cxSOFA by a stepwise manner; Third, proposing a Treatment Effect Comparison Matrix (TECM), analogous to a confusion matrix for classification tasks, to evaluate the treatment strategies. We applied different RL algorithms, Q-Learning, DQN, DDQN, BCQ and CQL to optimize the treatment and comprehensively evaluated the framework. Results: Among the AI-derived strategies, the cxSOFA-CQL model achieved the best performance, reducing mortality from 1.83% to 0.74% with the average hospital stay from 11.11 to 9.42 days. TECM demonstrated consistent outcomes across models, highlighting robustness. Conclusion: The proposed RL framework enables interpretable and robust optimization of heparin therapy in surgical sepsis. Continuous cxSOFA scoring and TECM-based evaluation provide nuanced treatment assessment, showing promise for improving clinical outcomes and decision-support reliability.", "date": "2025-12-02", "categories": []}
{"id": "2512.10995", "title": "Boosted Random Forests for Predicting Treatment Failure of Chemotherapy Regimens", "abstract": "Cancer patients may undergo lengthy and painful chemotherapy treatments, comprising several successive regimens or plans. Treatment inefficacy and other adverse events can lead to discontinuation (or failure) of these plans, or prematurely changing them, which results in a significant amount of physical, financial, and emotional toxicity to the patients and their families. In this work, we build treatment failure models based on the Real World Evidence (RWE) gathered from patients' profiles available in our oncology EMR/EHR system. We also describe our feature engineering pipeline, experimental methods, and valuable insights obtained about treatment failures from trained models. We report our findings on five primary cancer types with the most frequent treatment failures (or discontinuations) to build unique and novel feature vectors from the clinical notes, diagnoses, and medications that are available in our oncology EMR. After following a novel three axes - performance, complexity and explainability - design exploration framework, boosted random forests are selected because they provide a baseline accuracy of 80% and an F1 score of 75%, with reduced model complexity, thus making them more interpretable to and usable by oncologists.", "date": "2025-12-10", "categories": []}
{"id": "2512.11095", "title": "Investigating ECG Diagnosis with Ambiguous Labels using Partial Label Learning", "abstract": "Label ambiguity is an inherent problem in real-world electrocardiogram (ECG) diagnosis, arising from overlapping conditions and diagnostic disagreement. However, current ECG models are trained under the assumption of clean and non-ambiguous annotations, which limits both the development and the meaningful evaluation of models under real-world conditions. Although Partial Label Learning (PLL) frameworks are designed to learn from ambiguous labels, their effectiveness in medical time-series domains, ECG in particular, remains largely unexplored. In this work, we present the first systematic study of PLL methods for ECG diagnosis. We adapt nine PLL algorithms to multi-label ECG diagnosis and evaluate them using a diverse set of clinically motivated ambiguity generation strategies, capturing both unstructured (e.g., random) and structured ambiguities (e.g., cardiologist-derived similarities, treatment relationships, and diagnostic taxonomies). Our experiments on the PTB-XL and Chapman datasets demonstrate that PLL methods vary substantially in their robustness to different types and degrees of ambiguity. Through extensive analysis, we identify key limitations of current PLL approaches in clinical settings and outline future directions for developing robust and clinically aligned ambiguity-aware learning frameworks for ECG diagnosis.", "date": "2025-12-11", "categories": []}
{"id": "2512.10056", "title": "Mitigating Exposure Bias in Risk-Aware Time Series Forecasting with Soft Tokens", "abstract": "Autoregressive forecasting is central to predictive control in diabetes and hemodynamic management, where different operating zones carry different clinical risks. Standard models trained with teacher forcing suffer from exposure bias, yielding unstable multi-step forecasts for closed-loop use. We introduce Soft-Token Trajectory Forecasting (SoTra), which propagates continuous probability distributions (``soft tokens'') to mitigate exposure bias and learn calibrated, uncertainty-aware trajectories. A risk-aware decoding module then minimizes expected clinical harm. In glucose forecasting, SoTra reduces average zone-based risk by 18\\%; in blood-pressure forecasting, it lowers effective clinical risk by approximately 15\\%. These improvements support its use in safety-critical predictive control.", "date": "2025-12-10", "categories": []}
{"id": "2512.10308", "title": "An Interpretable AI Tool for SAVR vs TAVR in Low to Intermediate Risk Patients with Severe Aortic Stenosis", "abstract": "Background. Treatment selection for low to intermediate risk patients with severe aortic stenosis between surgical (SAVR) and transcatheter (TAVR) aortic valve replacement remains variable in clinical practice, driven by patient heterogeneity and institutional preferences. While existing models predict postprocedural risk, there is a lack of interpretable, individualized treatment recommendations that directly optimize long-term outcomes.\n  Methods. We introduce an interpretable prescriptive framework that integrates prognostic matching, counterfactual outcome modeling, and an Optimal Policy Tree (OPT) to recommend the treatment minimizing expected 5-year mortality. Using data from Hartford Hospital and St. Vincent's Hospital, we emulate randomization via prognostic matching and sample weighting and estimate counterfactual mortality under both SAVR and TAVR. The policy model, trained on these counterfactual predictions, partitions patients into clinically coherent subgroups and prescribes the treatment associated with lower estimated risk.\n  Findings. If the OPT prescriptions are applied, counterfactual evaluation showed an estimated reduction in 5-year mortality of 20.3\\% in Hartford and 13.8\\% in St. Vincent's relative to real-life prescriptions, showing promising generalizability to unseen data from a different institution. The learned decision boundaries aligned with real-world outcomes and clinical observations.\n  Interpretation. Our interpretable prescriptive framework is, to the best of our knowledge, the first to provide transparent, data-driven recommendations for TAVR versus SAVR that improve estimated long-term outcomes both in an internal and external cohort, while remaining clinically grounded and contributing toward a more systematic and evidence-based approach to precision medicine in structural heart disease.", "date": "2025-12-11", "categories": []}
{"id": "2303.09085", "title": "Preoperative Prognosis Assessment of Lumbar Spinal Surgery for Low Back Pain and Sciatica Patients based on Multimodalities and Multimodal Learning", "abstract": "Low back pain (LBP) and sciatica may require surgical therapy when they are symptomatic of severe pain. However, there is no effective measures to evaluate the surgical outcomes in advance. This work combined elements of Eastern medicine and machine learning, and developed a preoperative assessment tool to predict the prognosis of lumbar spinal surgery in LBP and sciatica patients. Standard operative assessments, traditional Chinese medicine body constitution assessments, planned surgical approach, and vowel pronunciation recordings were collected and stored in different modalities. Our work provides insights into leveraging modality combinations, multimodals, and fusion strategies. The interpretability of models and correlations between modalities were also inspected. Based on the recruited 105 patients, we found that combining standard operative assessments, body constitution assessments, and planned surgical approach achieved the best performance in 0.81 accuracy. Our approach is effective and can be widely applied in general practice due to simplicity and effective.", "date": "2023-03-16", "categories": []}
{"id": "2406.00532", "title": "Breast Cancer Diagnosis: A Comprehensive Exploration of Explainable Artificial Intelligence (XAI) Techniques", "abstract": "Breast cancer (BC) stands as one of the most common malignancies affecting women worldwide, necessitating advancements in diagnostic methodologies for better clinical outcomes. This article provides a comprehensive exploration of the application of Explainable Artificial Intelligence (XAI) techniques in the detection and diagnosis of breast cancer. As Artificial Intelligence (AI) technologies continue to permeate the healthcare sector, particularly in oncology, the need for transparent and interpretable models becomes imperative to enhance clinical decision-making and patient care. This review discusses the integration of various XAI approaches, such as SHAP, LIME, Grad-CAM, and others, with machine learning and deep learning models utilized in breast cancer detection and classification. By investigating the modalities of breast cancer datasets, including mammograms, ultrasounds and their processing with AI, the paper highlights how XAI can lead to more accurate diagnoses and personalized treatment plans. It also examines the challenges in implementing these techniques and the importance of developing standardized metrics for evaluating XAI's effectiveness in clinical settings. Through detailed analysis and discussion, this article aims to highlight the potential of XAI in bridging the gap between complex AI models and practical healthcare applications, thereby fostering trust and understanding among medical professionals and improving patient outcomes.", "date": "2024-06-01", "categories": []}
{"id": "2512.08934", "title": "Motion2Meaning: A Clinician-Centered Framework for Contestable LLM in Parkinson's Disease Gait Interpretation", "abstract": "AI-assisted gait analysis holds promise for improving Parkinson's Disease (PD) care, but current clinical dashboards lack transparency and offer no meaningful way for clinicians to interrogate or contest AI decisions. To address this issue, we present Motion2Meaning, a clinician-centered framework that advances Contestable AI through a tightly integrated interface designed for interpretability, oversight, and procedural recourse. Our approach leverages vertical Ground Reaction Force (vGRF) time-series data from wearable sensors as an objective biomarker of PD motor states. The system comprises three key components: a Gait Data Visualization Interface (GDVI), a one-dimensional Convolutional Neural Network (1D-CNN) that predicts Hoehn & Yahr severity stages, and a Contestable Interpretation Interface (CII) that combines our novel Cross-Modal Explanation Discrepancy (XMED) safeguard with a contestable Large Language Model (LLM). Our 1D-CNN achieves 89.0% F1-score on the public PhysioNet gait dataset. XMED successfully identifies model unreliability by detecting a five-fold increase in explanation discrepancies in incorrect predictions (7.45%) compared to correct ones (1.56%), while our LLM-powered interface enables clinicians to validate correct predictions and successfully contest a portion of the model's errors. A human-centered evaluation of this contestable interface reveals a crucial trade-off between the LLM's factual grounding and its readability and responsiveness to clinical feedback. This work demonstrates the feasibility of combining wearable sensor analysis with Explainable AI (XAI) and contestable LLMs to create a transparent, auditable system for PD gait interpretation that maintains clinical oversight while leveraging advanced AI capabilities. Our implementation is publicly available at: https://github.com/hungdothanh/motion2meaning.", "date": "2025-10-21", "categories": []}
{"id": "2512.08954", "title": "An Electrocardiogram Multi-task Benchmark with Comprehensive Evaluations and Insightful Findings", "abstract": "In the process of patient diagnosis, non-invasive measurements are widely used due to their low risks and quick results. Electrocardiogram (ECG), as a non-invasive method to collect heart activities, is used to diagnose cardiac conditions. Analyzing the ECG typically requires domain expertise, which is a roadblock to applying artificial intelligence (AI) for healthcare. Through advances in self-supervised learning and foundation models, AI systems can now acquire and leverage domain knowledge without relying solely on human expertise. However, there is a lack of comprehensive analyses over the foundation models' performance on ECG. This study aims to answer the research question: \"Are Foundation Models Useful for ECG Analysis?\" To address it, we evaluate language/general time-series/ECG foundation models in comparison with time-series deep learning models. The experimental results show that general time-series/ECG foundation models achieve a top performance rate of 80%, indicating their effectiveness in ECG analysis. In-depth analyses and insights are provided along with comprehensive experimental results. This study highlights the limitations and potential of foundation models in advancing physiological waveform analysis. The data and code for this benchmark are publicly available at https://github.com/yuhaoxu99/ECGMultitasks-Benchmark.", "date": "2025-11-28", "categories": []}
{"id": "2512.08959", "title": "EEG-Bench: A Benchmark for EEG Foundation Models in Clinical Applications", "abstract": "We introduce a unified benchmarking framework focused on evaluating EEG-based foundation models in clinical applications. The benchmark spans 11 well-defined diagnostic tasks across 14 publicly available EEG datasets, including epilepsy, schizophrenia, Parkinson's disease, OCD, and mild traumatic brain injury. It features minimal preprocessing, standardized evaluation protocols, and enables side-by-side comparisons of classical baselines and modern foundation models. Our results show that while foundation models achieve strong performance in certain settings, simpler models often remain competitive, particularly under clinical distribution shifts. To facilitate reproducibility and adoption, we release all prepared data and code in an accessible and extensible format.", "date": "2025-11-28", "categories": []}
{"id": "2512.08992", "title": "Enhanced Chest Disease Classification Using an Improved CheXNet Framework with EfficientNetV2-M and Optimization-Driven Learning", "abstract": "The interpretation of Chest X-ray is an important diagnostic issue in clinical practice and especially in the resource-limited setting where the shortage of radiologists plays a role in delayed diagnosis and poor patient outcomes. Although the original CheXNet architecture has shown potential in automated analysis of chest radiographs, DenseNet-121 backbone is computationally inefficient and poorly single-label classifier. To eliminate such shortcomings, we suggest a better classification framework of chest disease that relies on EfficientNetV2-M and incorporates superior training approaches such as Automatic Mixed Precision training, AdamW, Cosine Annealing learning rate scheduling, and Exponential Moving Average regularization. We prepared a dataset of 18,080 chest X-ray images of three source materials of high authority and representing five key clinically significant disease categories which included Cardiomegaly, COVID-19, Normal, Pneumonia, and Tuberculosis. To achieve statistical reliability and reproducibility, nine independent experimental runs were run. The suggested architecture showed significant gains with mean test accuracy of 96.45 percent compared to 95.30 percent at baseline (p less than 0.001) and macro-averaged F1-score increased to 91.08 percent (p less than 0.001). Critical infectious diseases showed near-perfect classification performance with COVID-19 detection having 99.95 percent accuracy and Tuberculosis detection having 99.97 percent accuracy. Although 6.8 times more parameters are included, the training time was reduced by 11.4 percent and performance stability was increased by 22.7 percent. This framework presents itself as a decision-support tool that can be used to respond to a pandemic, screen tuberculosis, and assess thoracic disease regularly in various healthcare facilities.", "date": "2025-12-08", "categories": []}
{"id": "2512.09198", "title": "Towards Optimal Valve Prescription for Transcatheter Aortic Valve Replacement (TAVR) Surgery: A Machine Learning Approach", "abstract": "Transcatheter Aortic Valve Replacement (TAVR) has emerged as a minimally invasive treatment option for patients with severe aortic stenosis, a life-threatening cardiovascular condition. Multiple transcatheter heart valves (THV) have been approved for use in TAVR, but current guidelines regarding valve type prescription remain an active topic of debate. We propose a data-driven clinical support tool to identify the optimal valve type with the objective of minimizing the risk of permanent pacemaker implantation (PPI), a predominant postoperative complication. We synthesize a novel dataset that combines U.S. and Greek patient populations and integrates three distinct data sources (patient demographics, computed tomography scans, echocardiograms) while harmonizing differences in each country's record system. We introduce a leaf-level analysis to leverage population heterogeneity and avoid benchmarking against uncertain counterfactual risk estimates. The final prescriptive model shows a reduction in PPI rates of 26% and 16% compared with the current standard of care in our internal U.S. population and external Greek validation cohort, respectively. To the best of our knowledge, this work represents the first unified, personalized prescription strategy for THV selection in TAVR.", "date": "2025-12-09", "categories": []}
{"id": "2408.03463", "title": "Identifying treatment response subgroups in observational time-to-event data", "abstract": "Identifying patient subgroups with different treatment responses is an important task to inform medical recommendations, guidelines, and the design of future clinical trials. Existing approaches for treatment effect estimation primarily rely on Randomised Controlled Trials (RCTs), which tend to feature more homogeneous patient groups, making them less relevant for uncovering subgroups in the population encountered in real-world clinical practice. Subgroup analyses established for RCTs suffer from significant statistical biases when applied to observational studies, which benefit from larger and more representative populations. Our work introduces a novel, outcome-guided, subgroup analysis strategy for identifying subgroups of treatment response in both RCTs and observational studies alike. It hence positions itself in-between individualised and average treatment effect estimation to uncover patient subgroups with distinct treatment responses, critical for actionable insights that may influence treatment guidelines. In experiments, our approach significantly outperforms the current state-of-the-art method for subgroup analysis in both randomised and observational treatment regimes.", "date": "2024-08-06", "categories": []}
{"id": "2512.02023", "title": "An Improved Ensemble-Based Machine Learning Model with Feature Optimization for Early Diabetes Prediction", "abstract": "Diabetes is a serious worldwide health issue, and successful intervention depends on early detection. However, overlapping risk factors and data asymmetry make prediction difficult. To use extensive health survey data to create a machine learning framework for diabetes classification that is both accurate and comprehensible, to produce results that will aid in clinical decision-making. Using the BRFSS dataset, we assessed a number of supervised learning techniques. SMOTE and Tomek Links were used to correct class imbalance. To improve prediction performance, both individual models and ensemble techniques such as stacking were investigated. The 2015 BRFSS dataset, which includes roughly 253,680 records with 22 numerical features, is used in this study. Strong ROC-AUC performance of approximately 0.96 was attained by the individual models Random Forest, XGBoost, CatBoost, and LightGBM.The stacking ensemble with XGBoost and KNN yielded the best overall results with 94.82\\% accuracy, ROC-AUC of 0.989, and PR-AUC of 0.991, indicating a favourable balance between recall and precision. In our study, we proposed and developed a React Native-based application with a Python Flask backend to support early diabetes prediction, providing users with an accessible and efficient health monitoring tool.", "date": "2025-11-15", "categories": []}
{"id": "2512.04207", "title": "Orchestrator Multi-Agent Clinical Decision Support System for Secondary Headache Diagnosis in Primary Care", "abstract": "Unlike most primary headaches, secondary headaches need specialized care and can have devastating consequences if not treated promptly. Clinical guidelines highlight several 'red flag' features, such as thunderclap onset, meningismus, papilledema, focal neurologic deficits, signs of temporal arteritis, systemic illness, and the 'worst headache of their life' presentation. Despite these guidelines, determining which patients require urgent evaluation remains challenging in primary care settings. Clinicians often work with limited time, incomplete information, and diverse symptom presentations, which can lead to under-recognition and inappropriate care. We present a large language model (LLM)-based multi-agent clinical decision support system built on an orchestrator-specialist architecture, designed to perform explicit and interpretable secondary headache diagnosis from free-text clinical vignettes. The multi-agent system decomposes diagnosis into seven domain-specialized agents, each producing a structured and evidence-grounded rationale, while a central orchestrator performs task decomposition and coordinates agent routing. We evaluated the multi-agent system using 90 expert-validated secondary headache cases and compared its performance with a single-LLM baseline across two prompting strategies: question-based prompting (QPrompt) and clinical practice guideline-based prompting (GPrompt). We tested five open-source LLMs (Qwen-30B, GPT-OSS-20B, Qwen-14B, Qwen-8B, and Llama-3.1-8B), and found that the orchestrated multi-agent system with GPrompt consistently achieved the highest F1 scores, with larger gains in smaller models. These findings demonstrate that structured multi-agent reasoning improves accuracy beyond prompt engineering alone and offers a transparent, clinically aligned approach for explainable decision support in secondary headache diagnosis.", "date": "2025-12-03", "categories": []}
{"id": "2512.07856", "title": "Medical Test-free Disease Detection Based on Big Data", "abstract": "Accurate disease detection is of paramount importance for effective medical treatment and patient care. However, the process of disease detection is often associated with extensive medical testing and considerable costs, making it impractical to perform all possible medical tests on a patient to diagnose or predict hundreds or thousands of diseases. In this work, we propose Collaborative Learning for Disease Detection (CLDD), a novel graph-based deep learning model that formulates disease detection as a collaborative learning task by exploiting associations among diseases and similarities among patients adaptively. CLDD integrates patient-disease interactions and demographic features from electronic health records to detect hundreds or thousands of diseases for every patient, with little to no reliance on the corresponding medical tests. Extensive experiments on a processed version of the MIMIC-IV dataset comprising 61,191 patients and 2,000 diseases demonstrate that CLDD consistently outperforms representative baselines across multiple metrics, achieving a 6.33\\% improvement in recall and 7.63\\% improvement in precision. Furthermore, case studies on individual patients illustrate that CLDD can successfully recover masked diseases within its top-ranked predictions, demonstrating both interpretability and reliability in disease prediction. By reducing diagnostic costs and improving accessibility, CLDD holds promise for large-scale disease screening and social health security.", "date": "2025-11-26", "categories": []}
{"id": "2512.08257", "title": "Geometric-Stochastic Multimodal Deep Learning for Predictive Modeling of SUDEP and Stroke Vulnerability", "abstract": "Sudden Unexpected Death in Epilepsy (SUDEP) and acute ischemic stroke are life-threatening conditions involving complex interactions across cortical, brainstem, and autonomic systems. We present a unified geometric-stochastic multimodal deep learning framework that integrates EEG, ECG, respiration, SpO2, EMG, and fMRI signals to model SUDEP and stroke vulnerability. The approach combines Riemannian manifold embeddings, Lie-group invariant feature representations, fractional stochastic dynamics, Hamiltonian energy-flow modeling, and cross-modal attention mechanisms. Stroke propagation is modeled using fractional epidemic diffusion over structural brain graphs. Experiments on the MULTI-CLARID dataset demonstrate improved predictive accuracy and interpretable biomarkers derived from manifold curvature, fractional memory indices, attention entropy, and diffusion centrality. The proposed framework provides a mathematically principled foundation for early detection, risk stratification, and interpretable multimodal modeling in neural-autonomic disorders.", "date": "2025-12-09", "categories": []}
{"id": "2512.08572", "title": "From Cells to Survival: Hierarchical Analysis of Cell Inter-Relations in Multiplex Microscopy for Lung Cancer Prognosis", "abstract": "The tumor microenvironment (TME) has emerged as a promising source of prognostic biomarkers. To fully leverage its potential, analysis methods must capture complex interactions between different cell types. We propose HiGINE -- a hierarchical graph-based approach to predict patient survival (short vs. long) from TME characterization in multiplex immunofluorescence (mIF) images and enhance risk stratification in lung cancer. Our model encodes both local and global inter-relations in cell neighborhoods, incorporating information about cell types and morphology. Multimodal fusion, aggregating cancer stage with mIF-derived features, further boosts performance. We validate HiGINE on two public datasets, demonstrating improved risk stratification, robustness, and generalizability.", "date": "2025-12-09", "categories": []}
{"id": "2512.08659", "title": "An Agentic AI System for Multi-Framework Communication Coding", "abstract": "Clinical communication is central to patient outcomes, yet large-scale human annotation of patient-provider conversation remains labor-intensive, inconsistent, and difficult to scale. Existing approaches based on large language models typically rely on single-task models that lack adaptability, interpretability, and reliability, especially when applied across various communication frameworks and clinical domains. In this study, we developed a Multi-framework Structured Agentic AI system for Clinical Communication (MOSAIC), built on a LangGraph-based architecture that orchestrates four core agents, including a Plan Agent for codebook selection and workflow planning, an Update Agent for maintaining up-to-date retrieval databases, a set of Annotation Agents that applies codebook-guided retrieval-augmented generation (RAG) with dynamic few-shot prompting, and a Verification Agent that provides consistency checks and feedback. To evaluate performance, we compared MOSAIC outputs against gold-standard annotations created by trained human coders. We developed and evaluated MOSAIC using 26 gold standard annotated transcripts for training and 50 transcripts for testing, spanning rheumatology and OB/GYN domains. On the test set, MOSAIC achieved an overall F1 score of 0.928. Performance was highest in the Rheumatology subset (F1 = 0.962) and strongest for Patient Behavior (e.g., patients asking questions, expressing preferences, or showing assertiveness). Ablations revealed that MOSAIC outperforms baseline benchmarking.", "date": "2025-12-09", "categories": []}
{"id": "2409.15753", "title": "Development and Validation of Heparin Dosing Policies Using an Offline Reinforcement Learning Algorithm", "abstract": "Appropriate medication dosages in the intensive care unit (ICU) are critical for patient survival. Heparin, used to treat thrombosis and inhibit blood clotting in the ICU, requires careful administration due to its complexity and sensitivity to various factors, including patient clinical characteristics, underlying medical conditions, and potential drug interactions. Incorrect dosing can lead to severe complications such as strokes or excessive bleeding. To address these challenges, this study proposes a reinforcement learning (RL)-based personalized optimal heparin dosing policy that guides dosing decisions reliably within the therapeutic range based on individual patient conditions. A batch-constrained policy was implemented to minimize out-of-distribution errors in an offline RL environment and effectively integrate RL with existing clinician policies. The policy's effectiveness was evaluated using weighted importance sampling, an off-policy evaluation method, and the relationship between state representations and Q-values was explored using t-SNE. Both quantitative and qualitative analyses were conducted using the Medical Information Mart for Intensive Care III (MIMIC-III) database, demonstrating the efficacy of the proposed RL-based medication policy. Leveraging advanced machine learning techniques and extensive clinical data, this research enhances heparin administration practices and establishes a precedent for the development of sophisticated decision-support tools in medicine.", "date": "2024-09-24", "categories": []}
{"id": "2509.11606", "title": "Scaling to Multimodal and Multichannel Heart Sound Classification with Synthetic and Augmented Biosignals", "abstract": "Cardiovascular diseases (CVDs) are the leading cause of death worldwide, accounting for approximately 17.9 million deaths each year. Early detection is critical, creating a demand for accurate and inexpensive pre-screening methods. Deep learning has recently been applied to classify abnormal heart sounds indicative of CVDs using synchronised phonocardiogram (PCG) and electrocardiogram (ECG) signals, as well as multichannel PCG (mPCG). However, state-of-the-art architectures remain underutilised due to the limited availability of synchronised and multichannel datasets. Augmented datasets and pre-trained models provide a pathway to overcome these limitations, enabling transformer-based architectures to be trained effectively. This work combines traditional signal processing with denoising diffusion models, WaveGrad and DiffWave, to create an augmented dataset to fine-tune a Wav2Vec 2.0-based classifier on multimodal and multichannel heart sound datasets. The approach achieves state-of-the-art performance. On the Computing in Cardiology (CinC) 2016 dataset of single channel PCG, accuracy, unweighted average recall (UAR), sensitivity, specificity and Matthew's correlation coefficient (MCC) reach 92.48%, 93.05%, 93.63%, 92.48%, 94.93% and 0.8283, respectively. Using the synchronised PCG and ECG signals of the training-a dataset from CinC, 93.14%, 92.21%, 94.35%, 90.10%, 95.12% and 0.8380 are achieved for accuracy, UAR, sensitivity, specificity and MCC, respectively. Using a wearable vest dataset consisting of mPCG data, the model achieves 77.13% accuracy, 74.25% UAR, 86.47% sensitivity, 62.04% specificity, and 0.5082 MCC. These results demonstrate the effectiveness of transformer-based models for CVD detection when supported by augmented datasets, highlighting their potential to advance multimodal and multichannel heart sound classification.", "date": "2025-09-15", "categories": []}
{"id": "2512.03471", "title": "SweetDeep: A Wearable AI Solution for Real-Time Non-Invasive Diabetes Screening", "abstract": "The global rise in type 2 diabetes underscores the need for scalable and cost-effective screening methods. Current diagnosis requires biochemical assays, which are invasive and costly. Advances in consumer wearables have enabled early explorations of machine learning-based disease detection, but prior studies were limited to controlled settings. We present SweetDeep, a compact neural network trained on physiological and demographic data from 285 (diabetic and non-diabetic) participants in the EU and MENA regions, collected using Samsung Galaxy Watch 7 devices in free-living conditions over six days. Each participant contributed multiple 2-minute sensor recordings per day, totaling approximately 20 recordings per individual. Despite comprising fewer than 3,000 parameters, SweetDeep achieves 82.5% patient-level accuracy (82.1% macro-F1, 79.7% sensitivity, 84.6% specificity) under three-fold cross-validation, with an expected calibration error of 5.5%. Allowing the model to abstain on less than 10% of low-confidence patient predictions yields an accuracy of 84.5% on the remaining patients. These findings demonstrate that combining engineered features with lightweight architectures can support accurate, rapid, and generalizable detection of type 2 diabetes in real-world wearable settings.", "date": "2025-12-03", "categories": []}
{"id": "2512.06134", "title": "Physics-Informed Neural Koopman Machine for Interpretable Longitudinal Personalized Alzheimer's Disease Forecasting", "abstract": "Early forecasting of individual cognitive decline in Alzheimer's disease (AD) is central to disease evaluation and management. Despite advances, it is as of yet challenging for existing methodological frameworks to integrate multimodal data for longitudinal personalized forecasting while maintaining interpretability. To address this gap, we present the Neural Koopman Machine (NKM), a new machine learning architecture inspired by dynamical systems and attention mechanisms, designed to forecast multiple cognitive scores simultaneously using multimodal genetic, neuroimaging, proteomic, and demographic data. NKM integrates analytical ($\\alpha$) and biological ($\\beta$) knowledge to guide feature grouping and control the hierarchical attention mechanisms to extract relevant patterns. By implementing Fusion Group-Aware Hierarchical Attention within the Koopman operator framework, NKM transforms complex nonlinear trajectories into interpretable linear representations. To demonstrate NKM's efficacy, we applied it to study the Alzheimer's Disease Neuroimaging Initiative (ADNI) dataset. Our results suggest that NKM consistently outperforms both traditional machine learning methods and deep learning models in forecasting trajectories of cognitive decline. Specifically, NKM (1) forecasts changes of multiple cognitive scores simultaneously, (2) quantifies differential biomarker contributions to predicting distinctive cognitive scores, and (3) identifies brain regions most predictive of cognitive deterioration. Together, NKM advances personalized, interpretable forecasting of future cognitive decline in AD using past multimodal data through an explainable, explicit system and reveals potential multimodal biological underpinnings of AD progression.", "date": "2025-12-05", "categories": []}
{"id": "2512.06511", "title": "Diagnosis-based mortality prediction for intensive care unit patients via transfer learning", "abstract": "In the intensive care unit, the underlying causes of critical illness vary substantially across diagnoses, yet prediction models accounting for diagnostic heterogeneity have not been systematically studied. To address the gap, we evaluate transfer learning approaches for diagnosis-specific mortality prediction and apply both GLM- and XGBoost-based models to the eICU Collaborative Research Database. Our results demonstrate that transfer learning consistently outperforms models trained only on diagnosis-specific data and those using a well-known ICU severity-of-illness score, i.e., APACHE IVa, alone, while also achieving better calibration than models trained on the pooled data. Our findings also suggest that the Youden cutoff is a more appropriate decision threshold than the conventional 0.5 for binary outcomes, and that transfer learning maintains consistently high predictive performance across various cutoff criteria.", "date": "2025-12-06", "categories": []}
{"id": "2512.05216", "title": "Coefficient of Variation Masking: A Volatility-Aware Strategy for EHR Foundation Models", "abstract": "Masked autoencoders (MAEs) are increasingly applied to electronic health records (EHR) for learning general-purpose representations that support diverse clinical tasks. However, existing approaches typically rely on uniform random masking, implicitly assuming all features are equally predictable. In reality, laboratory tests exhibit substantial heterogeneity in volatility: some biomarkers (e.g., sodium) remain stable, while others (e.g., lactate) fluctuate considerably and are more difficult to model. Clinically, volatile biomarkers often signal acute pathophysiology and require more sophisticated modeling to capture their complex temporal patterns. We propose a volatility-aware pretraining strategy, Coefficient of Variation Masking (CV-Masking), that adaptively adjusts masking probabilities according to the intrinsic variability of each feature. Combined with a value-only masking objective aligned with clinical workflows, CV-Masking yields systematic improvements over random and variance-based strategies. Experiments on a large panel of laboratory tests show that CV-Masking enhances reconstruction, improves downstream predictive performance, and accelerates convergence, producing more robust and clinically meaningful EHR representations.", "date": "2025-12-04", "categories": []}
{"id": "2512.05217", "title": "Rethinking Tokenization for Clinical Time Series: When Less is More", "abstract": "Tokenization strategies shape how models process electronic health records, yet fair comparisons of their effectiveness remain limited. We present a systematic evaluation of tokenization approaches for clinical time series modeling using transformer-based architectures, revealing task-dependent and sometimes counterintuitive findings about temporal and value feature importance. Through controlled ablations across four clinical prediction tasks on MIMIC-IV, we demonstrate that explicit time encodings provide no consistent statistically significant benefit for the evaluated downstream tasks. Value features show task-dependent importance, affecting mortality prediction but not readmission, suggesting code sequences alone can carry sufficient predictive signal. We further show that frozen pretrained code encoders dramatically outperform their trainable counterparts while requiring dramatically fewer parameters. Larger clinical encoders provide consistent improvements across tasks, benefiting from frozen embeddings that eliminate computational overhead. Our controlled evaluation enables fairer tokenization comparisons and demonstrates that simpler, parameter-efficient approaches can, in many cases, achieve strong performance, though the optimal tokenization strategy remains task-dependent.", "date": "2025-12-04", "categories": []}
{"id": "2512.05256", "title": "Enhancing Clinical Note Generation with ICD-10, Clinical Ontology Knowledge Graphs, and Chain-of-Thought Prompting Using GPT-4", "abstract": "In the past decade a surge in the amount of electronic health record (EHR) data in the United States, attributed to a favorable policy environment created by the Health Information Technology for Economic and Clinical Health (HITECH) Act of 2009 and the 21st Century Cures Act of 2016. Clinical notes for patients' assessments, diagnoses, and treatments are captured in these EHRs in free-form text by physicians, who spend a considerable amount of time entering and editing them. Manually writing clinical notes takes a considerable amount of a doctor's valuable time, increasing the patient's waiting time and possibly delaying diagnoses. Large language models (LLMs) possess the ability to generate news articles that closely resemble human-written ones. We investigate the usage of Chain-of-Thought (CoT) prompt engineering to improve the LLM's response in clinical note generation. In our prompts, we use as input International Classification of Diseases (ICD) codes and basic patient information. We investigate a strategy that combines the traditional CoT with semantic search results to improve the quality of generated clinical notes. Additionally, we infuse a knowledge graph (KG) built from clinical ontology to further enrich the domain-specific knowledge of generated clinical notes. We test our prompting technique on six clinical cases from the CodiEsp test dataset using GPT-4 and our results show that it outperformed the clinical notes generated by standard one-shot prompts.", "date": "2025-12-04", "categories": []}
{"id": "2512.05416", "title": "Sepsis Prediction Using Graph Convolutional Networks over Patient-Feature-Value Triplets", "abstract": "In the intensive care setting, sepsis continues to be a major contributor to patient illness and death; however, its timely detection is hindered by the complex, sparse, and heterogeneous nature of electronic health record (EHR) data. We propose Triplet-GCN, a single-branch graph convolutional model that represents each encounter as patient-feature-value triplets, constructs a bipartite EHR graph, and learns patient embeddings via a Graph Convolutional Network (GCN) followed by a lightweight multilayer perceptron (MLP). The pipeline applies type-specific preprocessing -- median imputation and standardization for numeric variables, effect coding for binary features, and mode imputation with low-dimensional embeddings for rare categorical attributes -- and initializes patient nodes with summary statistics, while retaining measurement values on edges to preserve \"who measured what and by how much\". In a retrospective, multi-center Chinese cohort (N = 648; 70/30 train-test split) drawn from three tertiary hospitals, Triplet-GCN consistently outperforms strong tabular baselines (KNN, SVM, XGBoost, Random Forest) across discrimination and balanced error metrics, yielding a more favorable sensitivity-specificity trade-off and improved overall utility for early warning. These findings indicate that encoding EHR as triplets and propagating information over a patient-feature graph produce more informative patient representations than feature-independent models, offering a simple, end-to-end blueprint for deployable sepsis risk stratification.", "date": "2025-12-05", "categories": []}
{"id": "2512.04354", "title": "SmartAlert: Implementing Machine Learning-Driven Clinical Decision Support for Inpatient Lab Utilization Reduction", "abstract": "Repetitive laboratory testing unlikely to yield clinically useful information is a common practice that burdens patients and increases healthcare costs. Education and feedback interventions have limited success, while general test ordering restrictions and electronic alerts impede appropriate clinical care. We introduce and evaluate SmartAlert, a machine learning (ML)-driven clinical decision support (CDS) system integrated into the electronic health record that predicts stable laboratory results to reduce unnecessary repeat testing. This case study describes the implementation process, challenges, and lessons learned from deploying SmartAlert targeting complete blood count (CBC) utilization in a randomized controlled pilot across 9270 admissions in eight acute care units across two hospitals between August 15, 2024, and March 15, 2025. Results show significant decrease in number of CBC results within 52 hours of SmartAlert display (1.54 vs 1.82, p <0.01) without adverse effect on secondary safety outcomes, representing a 15% relative reduction in repetitive testing. Implementation lessons learned include interpretation of probabilistic model predictions in clinical contexts, stakeholder engagement to define acceptable model behavior, governance processes for deploying a complex model in a clinical environment, user interface design considerations, alignment with clinical operational priorities, and the value of qualitative feedback from end users. In conclusion, a machine learning-driven CDS system backed by a deliberate implementation and governance process can provide precision guidance on inpatient laboratory testing to safely reduce unnecessary repetitive testing.", "date": "2025-12-04", "categories": []}
{"id": "2512.03339", "title": "ProtoEFNet: Dynamic Prototype Learning for Inherently Interpretable Ejection Fraction Estimation in Echocardiography", "abstract": "Ejection fraction (EF) is a crucial metric for assessing cardiac function and diagnosing conditions such as heart failure. Traditionally, EF estimation requires manual tracing and domain expertise, making the process time-consuming and subject to interobserver variability. Most current deep learning methods for EF prediction are black-box models with limited transparency, which reduces clinical trust. Some post-hoc explainability methods have been proposed to interpret the decision-making process after the prediction is made. However, these explanations do not guide the model's internal reasoning and therefore offer limited reliability in clinical applications. To address this, we introduce ProtoEFNet, a novel video-based prototype learning model for continuous EF regression. The model learns dynamic spatiotemporal prototypes that capture clinically meaningful cardiac motion patterns. Additionally, the proposed Prototype Angular Separation (PAS) loss enforces discriminative representations across the continuous EF spectrum. Our experiments on the EchonetDynamic dataset show that ProtoEFNet can achieve accuracy on par with its non-interpretable counterpart while providing clinically relevant insight. The ablation study shows that the proposed loss boosts performance with a 2% increase in F1 score from 77.67$\\pm$2.68 to 79.64$\\pm$2.10. Our source code is available at: https://github.com/DeepRCL/ProtoEF", "date": "2025-12-03", "categories": []}
{"id": "2505.19785", "title": "medDreamer: Model-Based Reinforcement Learning with Latent Imagination on Complex EHRs for Clinical Decision Support", "abstract": "Timely and personalized treatment decisions are essential across a wide range of healthcare settings where patient responses can vary significantly and evolve over time. Clinical data used to support these treatment decisions are often irregularly sampled, where missing data frequencies may implicitly convey information about the patient's condition. Existing Reinforcement Learning (RL) based clinical decision support systems often ignore the missing patterns and distort them with coarse discretization and simple imputation. They are also predominantly model-free and largely depend on retrospective data, which could lead to insufficient exploration and bias by historical behaviors. To address these limitations, we propose medDreamer, a novel model-based reinforcement learning framework for personalized treatment recommendation. medDreamer contains a world model with an Adaptive Feature Integration module that simulates latent patient states from irregular data and a two-phase policy trained on a hybrid of real and imagined trajectories. This enables learning optimal policies that go beyond the sub-optimality of historical clinical decisions, while remaining close to real clinical data. We evaluate medDreamer on both sepsis and mechanical ventilation treatment tasks using two large-scale Electronic Health Records (EHRs) datasets. Comprehensive evaluations show that medDreamer significantly outperforms model-free and model-based baselines in both clinical outcomes and off-policy metrics.", "date": "2025-05-26", "categories": []}
{"id": "2512.02088", "title": "Comparing Baseline and Day-1 Diffusion MRI Using Multimodal Deep Embeddings for Stroke Outcome Prediction", "abstract": "This study compares baseline (J0) and 24-hour (J1) diffusion magnetic resonance imaging (MRI) for predicting three-month functional outcomes after acute ischemic stroke (AIS). Seventy-four AIS patients with paired apparent diffusion coefficient (ADC) scans and clinical data were analyzed. Three-dimensional ResNet-50 embeddings were fused with structured clinical variables, reduced via principal component analysis (<=12 components), and classified using linear support vector machines with eight-fold stratified group cross-validation. J1 multimodal models achieved the highest predictive performance (AUC = 0.923 +/- 0.085), outperforming J0-based configurations (AUC <= 0.86). Incorporating lesion-volume features further improved model stability and interpretability. These findings demonstrate that early post-treatment diffusion MRI provides superior prognostic value to pre-treatment imaging and that combining MRI, clinical, and lesion-volume features produces a robust and interpretable framework for predicting three-month functional outcomes in AIS patients.", "date": "2025-12-01", "categories": []}
{"id": "2512.02180", "title": "CLEF: Clinically-Guided Contrastive Learning for Electrocardiogram Foundation Models", "abstract": "The electrocardiogram (ECG) is a key diagnostic tool in cardiovascular health. Single-lead ECG recording is integrated into both clinical-grade and consumer wearables. While self-supervised pretraining of foundation models on unlabeled ECGs improves diagnostic performance, existing approaches do not incorporate domain knowledge from clinical metadata. We introduce a novel contrastive learning approach that utilizes an established clinical risk score to adaptively weight negative pairs: clinically-guided contrastive learning. It aligns the similarities of ECG embeddings with clinically meaningful differences between subjects, with an explicit mechanism to handle missing metadata. On 12-lead ECGs from 161K patients in the MIMIC-IV dataset, we pretrain single-lead ECG foundation models at three scales, collectively called CLEF, using only routinely collected metadata without requiring per-sample ECG annotations. We evaluate CLEF on 18 clinical classification and regression tasks across 7 held-out datasets, and benchmark against 5 foundation model baselines and 3 self-supervised algorithms. When pretrained on 12-lead ECG data and tested on lead-I data, CLEF outperforms self-supervised foundation model baselines: the medium-sized CLEF achieves average AUROC improvements of at least 2.6% in classification and average reductions in MAEs of at least 3.2% in regression. Comparing with existing self-supervised learning algorithms, CLEF improves the average AUROC by at least 1.8%. Moreover, when pretrained only on lead-I data for classification tasks, CLEF performs comparably to the state-of-the-art ECGFounder, which was trained in a supervised manner. Overall, CLEF enables more accurate and scalable single-lead ECG analysis, advancing remote health monitoring. Code and pretrained CLEF models are available at: github.com/Nokia-Bell-Labs/ecg-foundation-model.", "date": "2025-12-01", "categories": []}
{"id": "2512.02364", "title": "Tackling Tuberculosis: A Comparative Dive into Machine Learning for Tuberculosis Detection", "abstract": "This study explores the application of machine learning models, specifically a pretrained ResNet-50 model and a general SqueezeNet model, in diagnosing tuberculosis (TB) using chest X-ray images. TB, a persistent infectious disease affecting humanity for millennia, poses challenges in diagnosis, especially in resource-limited settings. Traditional methods, such as sputum smear microscopy and culture, are inefficient, prompting the exploration of advanced technologies like deep learning and computer vision. The study utilized a dataset from Kaggle, consisting of 4,200 chest X-rays, to develop and compare the performance of the two machine learning models. Preprocessing involved data splitting, augmentation, and resizing to enhance training efficiency. Evaluation metrics, including accuracy, precision, recall, and confusion matrix, were employed to assess model performance. Results showcase that the SqueezeNet achieved a loss of 32%, accuracy of 89%, precision of 98%, recall of 80%, and an F1 score of 87%. In contrast, the ResNet-50 model exhibited a loss of 54%, accuracy of 73%, precision of 88%, recall of 52%, and an F1 score of 65%. This study emphasizes the potential of machine learning in TB detection and possible implications for early identification and treatment initiation. The possibility of integrating such models into mobile devices expands their utility in areas lacking TB detection resources. However, despite promising results, the need for continued development of faster, smaller, and more accurate TB detection models remains crucial in contributing to the global efforts in combating TB.", "date": "2025-12-02", "categories": []}
{"id": "2512.02489", "title": "Hybrid(Penalized Regression and MLP) Models for Outcome Prediction in HDLSS Health Data", "abstract": "I present an application of established machine learning techniques to NHANES health survey data for predicting diabetes status. I compare baseline models (logistic regression, random forest, XGBoost) with a hybrid approach that uses an XGBoost feature encoder and a lightweight multilayer perceptron (MLP) head. Experiments show the hybrid model attains improved AUC and balanced accuracy compared to baselines on the processed NHANES subset. I release code and reproducible scripts to encourage replication.", "date": "2025-12-02", "categories": []}
{"id": "2512.02499", "title": "COPE: Chain-Of-Thought Prediction Engine for Open-Source Large Language Model Based Stroke Outcome Prediction from Clinical Notes", "abstract": "Predicting outcomes in acute ischemic stroke (AIS) guides clinical decision-making, patient counseling, and resource allocation. Clinical notes contain rich contextual information, but their unstructured nature limits their use in traditional predictive models. We developed and evaluated the Chain-of-Thought (CoT) Outcome Prediction Engine (COPE), a reasoning-enhanced large language model framework, for predicting 90-day functional outcomes after AIS from unstructured clinical notes. This study included 464 AIS patients with discharge summaries and 90-day modified Rankin Scale (mRS) scores. COPE uses a two-step CoT framework based on sequential open-source LLaMA-3-8B models: the first generates clinical reasoning, and the second outputs an mRS prediction. We compared COPE with GPT-4.1, ClinicalBERT, a structured variable-based machine learning model (Clinical ML), and a single-step LLM without CoT. Performance was evaluated using mean absolute error (MAE), accuracy within +/-1 mRS point, and exact accuracy. COPE achieved an MAE of 1.01 (95% CI 0.92-1.11), +/-1 accuracy of 74.4% (69.9, 78.8%), and exact accuracy of 32.8% (28.0, 37.6%), comparable to GPT-4.1 and superior to ClinicalBERT [MAE 1.24 (1.13-1.36)], Clinical ML [1.28 (1.18-1.39)], and the single-step LLM [1.20 (1.09-1.33)]. Subgroup analyses showed consistent performance across sex and age, with slightly higher error among older patients, those undergoing thrombectomy, and those with longer summaries. These findings demonstrate that COPE, a lightweight, interpretable, and privacy-preserving open-source framework, provides an accurate and practical solution for outcome prediction from unstructured clinical text.", "date": "2025-12-02", "categories": []}
{"id": "2405.07369", "title": "Incorporating Anatomical Awareness for Enhanced Generalizability and Progression Prediction in Deep Learning-Based Radiographic Sacroiliitis Detection", "abstract": "Purpose: To examine whether incorporating anatomical awareness into a deep learning model can improve generalizability and enable prediction of disease progression.\n  Methods: This retrospective multicenter study included conventional pelvic radiographs of 4 different patient cohorts focusing on axial spondyloarthritis (axSpA) collected at university and community hospitals. The first cohort, which consisted of 1483 radiographs, was split into training (n=1261) and validation (n=222) sets. The other cohorts comprising 436, 340, and 163 patients, respectively, were used as independent test datasets. For the second cohort, follow-up data of 311 patients was used to examine progression prediction capabilities. Two neural networks were trained, one on images cropped to the bounding box of the sacroiliac joints (anatomy-aware) and the other one on full radiographs. The performance of the models was compared using the area under the receiver operating characteristic curve (AUC), accuracy, sensitivity, and specificity.\n  Results: On the three test datasets, the standard model achieved AUC scores of 0.853, 0.817, 0.947, with an accuracy of 0.770, 0.724, 0.850. Whereas the anatomy-aware model achieved AUC scores of 0.899, 0.846, 0.957, with an accuracy of 0.821, 0.744, 0.906, respectively. The patients who were identified as high risk by the anatomy aware model had an odds ratio of 2.16 (95% CI: 1.19, 3.86) for having progression of radiographic sacroiliitis within 2 years.\n  Conclusion: Anatomical awareness can improve the generalizability of a deep learning model in detecting radiographic sacroiliitis. The model is published as fully open source alongside this study.", "date": "2024-05-12", "categories": []}
{"id": "2502.15996", "title": "Med-gte-hybrid: A contextual embedding transformer model for extracting actionable information from clinical texts", "abstract": "We introduce a novel contextual embedding model med-gte-hybrid that was derived from the gte-large sentence transformer to extract information from unstructured clinical narratives. Our model tuning strategy for med-gte-hybrid combines contrastive learning and a denoising autoencoder. To evaluate the performance of med-gte-hybrid, we investigate several clinical prediction tasks in large patient cohorts extracted from the MIMIC-IV dataset, including Chronic Kidney Disease (CKD) patient prognosis, estimated glomerular filtration rate (eGFR) prediction, and patient mortality prediction. Furthermore, we demonstrate that the med-gte-hybrid model improves patient stratification, clustering, and text retrieval, thus outperforms current state-of-the-art models on the Massive Text Embedding Benchmark (MTEB). While some of our evaluations focus on CKD, our hybrid tuning of sentence transformers could be transferred to other medical domains and has the potential to improve clinical decision-making and personalised treatment pathways in various healthcare applications.", "date": "2025-02-21", "categories": []}
{"id": "2508.20469", "title": "Prediction of Distant Metastasis in Head and Neck Cancer Patients Using Tumor and Peritumoral Multi-Modal Deep Learning", "abstract": "Although the combined treatment of surgery, radiotherapy, chemotherapy, and emerging target therapy has significantly improved the outcomes of patients with head and neck cancer, distant metastasis remains the leading cause of treatment failure. In this study, we propose a deep learning-based multimodal framework integrating CT imaging, radiomics, and clinical data to predict metastasis risk in HNSCC. A total of 1497 patients were retrospectively analyzed. Tumor and organ masks were generated from pretreatment CT scans, from which a 3D Swin Transformer extracted deep imaging features, while 1562 radiomics features were reduced to 36 via correlation filtering and random forest selection. Clinical data (age, sex, smoking, and alcohol status) were encoded and fused with imaging features, and the multimodal representation was fed into a fully connected network for prediction. Five-fold cross-validation was used to assess performance via AUC, accuracy, sensitivity, and specificity. The multimodal model outperformed all single-modality baselines. The deep learning module alone achieved an AUC of 0.715, whereas multimodal fusion significantly improved performance (AUC = 0.803, ACC = 0.752, SEN = 0.730, SPE = 0.758). Stratified analyses confirmed good generalizability across tumor subtypes. Ablation experiments demonstrated complementary contributions from each modality, and the 3D Swin Transformer provided more robust representations than conventional architectures. This multimodal deep learning model enables accurate, non-invasive metastasis prediction in HNSCC and shows strong potential for individualized treatment planning.", "date": "2025-08-28", "categories": []}
{"id": "2512.00976", "title": "Subgroup Validity in Machine Learning for Echocardiogram Data", "abstract": "Echocardiogram datasets enable training deep learning models to automate interpretation of cardiac ultrasound, thereby expanding access to accurate readings of diagnostically-useful images. However, the gender, sex, race, and ethnicity of the patients in these datasets are underreported and subgroup-specific predictive performance is unevaluated. These reporting deficiencies raise concerns about subgroup validity that must be studied and addressed before model deployment. In this paper, we show that current open echocardiogram datasets are unable to assuage subgroup validity concerns. We improve sociodemographic reporting for two datasets: TMED-2 and MIMIC-IV-ECHO. Analysis of six open datasets reveals no consideration of gender-diverse patients and insufficient patient counts for many racial and ethnic groups. We further perform an exploratory subgroup analysis of two published aortic stenosis detection models on TMED-2. We find insufficient evidence for subgroup validity for sex, racial, and ethnic subgroups. Our findings highlight that more data for underrepresented subgroups, improved demographic reporting, and subgroup-focused analyses are needed to prove subgroup validity in future work.", "date": "2025-11-30", "categories": []}
{"id": "2512.01333", "title": "Optimizing Stroke Risk Prediction: A Machine Learning Pipeline Combining ROS-Balanced Ensembles and XAI", "abstract": "Stroke is a major cause of death and permanent impairment, making it a major worldwide health concern. For prompt intervention and successful preventative tactics, early risk assessment is essential. To address this challenge, we used ensemble modeling and explainable AI (XAI) techniques to create an interpretable machine learning framework for stroke risk prediction. A thorough evaluation of 10 different machine learning models using 5-fold cross-validation across several datasets was part of our all-inclusive strategy, which also included feature engineering and data pretreatment (using Random Over-Sampling (ROS) to solve class imbalance). Our optimized ensemble model (Random Forest + ExtraTrees + XGBoost) performed exceptionally well, obtaining a strong 99.09% accuracy on the Stroke Prediction Dataset (SPD). We improved the model's transparency and clinical applicability by identifying three important clinical variables using LIME-based interpretability analysis: age, hypertension, and glucose levels. Through early prediction, this study highlights how combining ensemble learning with explainable AI (XAI) can deliver highly accurate and interpretable stroke risk assessment. By enabling data-driven prevention and personalized clinical decisions, our framework has the potential to transform stroke prediction and cardiovascular risk management.", "date": "2025-12-01", "categories": []}
{"id": "2512.01653", "title": "Cuffless Blood Pressure Estimation from Six Wearable Sensor Modalities in Multi-Motion-State Scenarios", "abstract": "Cardiovascular disease (CVD) is a leading cause of morbidity and mortality worldwide, and sustained hypertension is an often silent risk factor, making cuffless continuous blood pressure (BP) monitoring with wearable devices important for early screening and long-term management. Most existing cuffless BP estimation methods use only photoplethysmography (PPG) and electrocardiography (ECG) signals, alone or in combination. These models are typically developed under resting or quasi-static conditions and struggle to maintain robust accuracy in multi-motion-state scenarios. In this study, we propose a six-modal BP estimation framework that jointly leverages ECG, multi-channel PPG, attachment pressure, sensor temperature, and triaxial acceleration and angular velocity. Each modality is processed by a lightweight branch encoder, contrastive learning enforces cross-modal semantic alignment, and a mixture-of-experts (MoE) regression head adaptively maps the fused features to BP across motion states. Comprehensive experiments on the public Pulse Transit Time PPG Dataset, which includes running, walking, and sitting data from 22 subjects, show that the proposed method achieves mean absolute errors (MAE) of 3.60 mmHg for systolic BP (SBP) and 3.01 mmHg for diastolic BP (DBP). From a clinical perspective, it attains Grade A for SBP, DBP, and mean arterial pressure (MAP) according to the British Hypertension Society (BHS) protocol and meets the numerical criteria of the Association for the Advancement of Medical Instrumentation (AAMI) standard for mean error (ME) and standard deviation of error (SDE).", "date": "2025-12-01", "categories": []}
{"id": "2505.23055", "title": "CDR-Agent: Intelligent Selection and Execution of Clinical Decision Rules Using Large Language Model Agents", "abstract": "Clinical decision-making is inherently complex and fast-paced, particularly in emergency departments (EDs) where critical, rapid and high-stakes decisions are made. Clinical Decision Rules (CDRs) are standardized evidence-based tools that combine signs, symptoms, and clinical variables into decision trees to make consistent and accurate diagnoses. CDR usage is often hindered by the clinician's cognitive load, limiting their ability to quickly recall and apply the appropriate rules. We introduce CDR-Agent, a novel LLM-based system designed to enhance ED decision-making by autonomously identifying and applying the most appropriate CDRs based on unstructured clinical notes. To validate CDR-Agent, we curated two novel ED datasets: synthetic and CDR-Bench, although CDR-Agent is applicable to non ED clinics. CDR-Agent achieves a 56.3\\% (synthetic) and 8.7\\% (CDR-Bench) accuracy gain relative to the standalone LLM baseline in CDR selection. Moreover, CDR-Agent significantly reduces computational overhead. Using these datasets, we demonstrated that CDR-Agent not only selects relevant CDRs efficiently, but makes cautious yet effective imaging decisions by minimizing unnecessary interventions while successfully identifying most positively diagnosed cases, outperforming traditional LLM prompting approaches. Code for our work can be found at: https://github.com/zhenxianglance/medagent-cdr-agent", "date": "2025-05-29", "categories": []}
{"id": "2506.00096", "title": "PathGene: Benchmarking Driver Gene Mutations and Exon Prediction Using Multicenter Lung Cancer Histopathology Image Dataset", "abstract": "Accurately predicting gene mutations, mutation subtypes and their exons in lung cancer is critical for personalized treatment planning and prognostic assessment. Faced with regional disparities in medical resources and the high cost of genomic assays, using artificial intelligence to infer these mutations and exon variants from routine histopathology images could greatly facilitate precision therapy. Although some prior studies have shown that deep learning can accelerate the prediction of key gene mutations from lung cancer pathology slides, their performance remains suboptimal and has so far been limited mainly to early screening tasks. To address these limitations, we have assembled PathGene, which comprises histopathology images paired with next-generation sequencing reports from 1,576 patients at the Second Xiangya Hospital, Central South University, and 448 TCGA-LUAD patients. This multi-center dataset links whole-slide images to driver gene mutation status, mutation subtypes, exon, and tumor mutational burden (TMB) status, with the goal of leveraging pathology images to predict mutations, subtypes, exon locations, and TMB for early genetic screening and to advance precision oncology. Unlike existing datasets, we provide molecular-level information related to histopathology images in PathGene to facilitate the development of biomarker prediction models. We benchmarked 11 multiple-instance learning methods on PathGene for mutation, subtype, exon, and TMB prediction tasks. These experimental methods provide valuable alternatives for early genetic screening of lung cancer patients and assisting clinicians to quickly develop personalized precision targeted treatment plans for patients. Code and data are available at https://github.com/panliangrui/NIPS2025/.", "date": "2025-05-30", "categories": []}
{"id": "2506.10157", "title": "One Patient, Many Contexts: Scaling Medical AI with Contextual Intelligence", "abstract": "Medical AI, including clinical language models, vision-language models, and multimodal health record models, already summarizes notes, answers questions, and supports decisions. Their adaptation to new populations, specialties, or care settings often relies on fine-tuning, prompting, or retrieval from external knowledge bases. These strategies can scale poorly and risk contextual errors: outputs that appear plausible but miss critical patient or situational information. We envision context switching as a solution. Context switching adjusts model reasoning at inference without retraining. Generative models can tailor outputs to patient biology, care setting, or disease. Multimodal models can reason on notes, laboratory results, imaging, and genomics, even when some data are missing or delayed. Agent models can coordinate tools and roles based on tasks and users. In each case, context switching enables medical AI to adapt across specialties, populations, and geographies. It requires advances in data design, model architectures, and evaluation frameworks, and establishes a foundation for medical AI that scales to infinitely many contexts while remaining reliable and suited to real-world care.", "date": "2025-06-11", "categories": []}
{"id": "2511.21735", "title": "Closing the Performance Gap Between AI and Radiologists in Chest X-Ray Reporting", "abstract": "AI-assisted report generation offers the opportunity to reduce radiologists' workload stemming from expanded screening guidelines, complex cases and workforce shortages, while maintaining diagnostic accuracy. In addition to describing pathological findings in chest X-ray reports, interpreting lines and tubes (L&T) is demanding and repetitive for radiologists, especially with high patient volumes. We introduce MAIRA-X, a clinically evaluated multimodal AI model for longitudinal chest X-ray (CXR) report generation, that encompasses both clinical findings and L&T reporting. Developed using a large-scale, multi-site, longitudinal dataset of 3.1 million studies (comprising 6 million images from 806k patients) from Mayo Clinic, MAIRA-X was evaluated on three holdout datasets and the public MIMIC-CXR dataset, where it significantly improved AI-generated reports over the state of the art on lexical quality, clinical correctness, and L&T-related elements. A novel L&T-specific metrics framework was developed to assess accuracy in reporting attributes such as type, longitudinal change and placement. A first-of-its-kind retrospective user evaluation study was conducted with nine radiologists of varying experience, who blindly reviewed 600 studies from distinct subjects. The user study found comparable rates of critical errors (3.0% for original vs. 4.6% for AI-generated reports) and a similar rate of acceptable sentences (97.8% for original vs. 97.4% for AI-generated reports), marking a significant improvement over prior user studies with larger gaps and higher error rates. Our results suggest that MAIRA-X can effectively assist radiologists, particularly in high-volume clinical settings.", "date": "2025-11-21", "categories": []}
{"id": "2511.22199", "title": "PULSE-ICU: A Pretrained Unified Long-Sequence Encoder for Multi-task Prediction in Intensive Care Units", "abstract": "Intensive care unit (ICU) data are highly irregular, heterogeneous, and temporally fragmented, posing challenges for generalizable clinical prediction. We present PULSE-ICU, a self-supervised foundation model that learns event-level ICU representations from large-scale EHR sequences without resampling or manual feature engineering. A unified embedding module encodes event identity, continuous values, units, and temporal attributes, while a Longformer-based encoder enables efficient modeling of long trajectories. PULSE-ICU was fine-tuned across 18 prediction tasks, including mortality, intervention forecasting, and phenotype identification, achieving strong performance across task types. External validation on eICU, HiRID, and P12 showed substantial improvements with minimal fine-tuning, demonstrating robustness to domain shift and variable constraints. These findings suggest that foundation-style modeling can improve data efficiency and adaptability, providing a scalable framework for ICU decision support across diverse clinical environments.", "date": "2025-11-27", "categories": []}
{"id": "2511.22774", "title": "Alzheimer's Disease Prediction Using EffNetViTLoRA and BiLSTM with Multimodal Longitudinal MRI Data", "abstract": "Alzheimer's disease (AD) is a prevalent neurodegenerative disorder that progressively impairs memory, decision-making, and overall cognitive function. As AD is irreversible, early prediction is critical for timely intervention and management. Mild Cognitive Impairment (MCI), a transitional stage between cognitively normal (CN) aging and AD, plays a significant role in early AD diagnosis. However, predicting MCI progression remains a significant challenge, as not all individuals with MCI convert to AD. MCI subjects are categorized into stable MCI (sMCI) and progressive MCI (pMCI) based on conversion status. In this study, we propose a generalized, end-to-end deep learning model for AD prediction using MCI cases from the Alzheimer's Disease Neuroimaging Initiative (ADNI). Our hybrid architecture integrates Convolutional Neural Networks and Vision Transformers to capture both local spatial features and global contextual dependencies from Magnetic Resonance Imaging (MRI) scans. To incorporate temporal progression, we further employ Bidirectional Long Short-Term Memory (BiLSTM) networks to process features extracted from four consecutive MRI timepoints along with some other non-image biomarkers, predicting each subject's cognitive status at month 48. Our multimodal model achieved an average progression prediction accuracy of 95.05\\% between sMCI and pMCI, outperforming existing studies in AD prediction. This work demonstrates state-of-the-art performance in longitudinal AD prediction and highlights the effectiveness of combining spatial and temporal modeling for the early detection of Alzheimer's disease.", "date": "2025-11-27", "categories": []}
{"id": "2511.23082", "title": "Implementation of a Skin Lesion Detection System for Managing Children with Atopic Dermatitis Based on Ensemble Learning", "abstract": "The amendments made to the Data 3 Act and impact of COVID-19 have fostered the growth of digital healthcare market and promoted the use of medical data in artificial intelligence in South Korea. Atopic dermatitis, a chronic inflammatory skin disease, is diagnosed via subjective evaluations without using objective diagnostic methods, thereby increasing the risk of misdiagnosis. It is also similar to psoriasis in appearance, further complicating its accurate diagnosis. Existing studies on skin diseases have used high-quality dermoscopic image datasets, but such high-quality images cannot be obtained in actual clinical settings. Moreover, existing systems must ensure accuracy and fast response times. To this end, an ensemble learning-based skin lesion detection system (ENSEL) was proposed herein. ENSEL enhanced diagnostic accuracy by integrating various deep learning models via an ensemble approach. Its performance was verified by conducting skin lesion detection experiments using images of skin lesions taken by actual users. Its accuracy and response time were measured using randomly sampled skin disease images. Results revealed that ENSEL achieved high recall in most images and less than 1s s processing speed. This study contributes to the objective diagnosis of skin lesions and promotes the advancement of digital healthcare.", "date": "2025-11-28", "categories": []}
{"id": "2511.23118", "title": "Machine learning for violence prediction: a systematic review and critical appraisal", "abstract": "Purpose To conduct a systematic review of machine learning models for predicting violent behaviour by synthesising and appraising their validity, usefulness, and performance.\n  Methods We systematically searched nine bibliographic databases and Google Scholar up to September 2025 for development and/or validation studies on machine learning methods for predicting all forms of violent behaviour. We synthesised the results by summarising discrimination and calibration performance statistics and evaluated study quality by examining risk of bias and clinical utility.\n  Results We identified 38 studies reporting the development and validation of 40 models. Most studies reported Area Under the Curve (AUC) as the discrimination statistic with a range of 0.68-0.99. Only eight studies reported calibration performance, and three studies reported external validation. 31 studies had a high risk of bias, mainly in the analysis domain, and three studies had low risk of bias. The overall clinical utility of violence prediction models is poor, as indicated by risks of overfitting due to small samples, lack of transparent reporting, and low generalisability.\n  Conclusion Although black box machine learning models currently have limited applicability in clinical settings, they may show promise for identifying high-risk individuals. We recommend five key considerations for violence prediction modelling: (i) ensuring methodological quality (e.g. following guidelines) and interdisciplinary collaborations; (ii) using black box algorithms only for highly complex data; (iii) incorporating dynamic predictions to allow for risk monitoring; (iv) developing more trustworthy algorithms using explainable methods; and (v) applying causal machine learning approaches where appropriate.", "date": "2025-11-28", "categories": []}
{"id": "2409.04290", "title": "CoxKAN: Kolmogorov-Arnold Networks for Interpretable, High-Performance Survival Analysis", "abstract": "Motivation: Survival analysis is a branch of statistics that is crucial in medicine for modeling the time to critical events such as death or relapse, in order to improve treatment strategies and patient outcomes. Selecting survival models often involves a trade-off between performance and interpretability; deep learning models offer high performance but lack the transparency of more traditional approaches. This poses a significant issue in medicine, where practitioners are reluctant to use black-box models for critical patient decisions.\n  Results: We introduce CoxKAN, a Cox proportional hazards Kolmogorov-Arnold Network for interpretable, high-performance survival analysis. Kolmogorov-Arnold Networks (KANs) were recently proposed as an interpretable and accurate alternative to multi-layer perceptrons. We evaluated CoxKAN on four synthetic and nine real datasets, including five cohorts with clinical data and four with genomics biomarkers. In synthetic experiments, CoxKAN accurately recovered interpretable hazard function formulae and excelled in automatic feature selection. Evaluations on real datasets showed that CoxKAN consistently outperformed the traditional Cox proportional hazards model (by up to 4% in C-index) and matched or surpassed the performance of deep learning-based models. Importantly, CoxKAN revealed complex interactions between predictor variables and uncovered symbolic formulae, which are key capabilities that other survival analysis methods lack, to provide clear insights into the impact of key biomarkers on patient risk.\n  Availability and implementation: CoxKAN is available at GitHub and Zenodo", "date": "2024-09-06", "categories": []}
{"id": "2506.07864", "title": "Lightweight Sequential Transformers for Blood Glucose Level Prediction in Type-1 Diabetes", "abstract": "Type 1 Diabetes (T1D) affects millions worldwide, requiring continuous monitoring to prevent severe hypo- and hyperglycemic events. While continuous glucose monitoring has improved blood glucose management, deploying predictive models on wearable devices remains challenging due to computational and memory constraints. To address this, we propose a novel Lightweight Sequential Transformer model designed for blood glucose prediction in T1D. By integrating the strengths of Transformers' attention mechanisms and the sequential processing of recurrent neural networks, our architecture captures long-term dependencies while maintaining computational efficiency. The model is optimized for deployment on resource-constrained edge devices and incorporates a balanced loss function to handle the inherent data imbalance in hypo- and hyperglycemic events. Experiments on two benchmark datasets, OhioT1DM and DiaTrend, demonstrate that the proposed model outperforms state-of-the-art methods in predicting glucose levels and detecting adverse events. This work fills the gap between high-performance modeling and practical deployment, providing a reliable and efficient T1D management solution.", "date": "2025-06-09", "categories": []}
{"id": "2511.21530", "title": "The Age-specific Alzheimer 's Disease Prediction with Characteristic Constraints in Nonuniform Time Span", "abstract": "Alzheimer's disease is a debilitating disorder marked by a decline in cognitive function. Timely identification of the disease is essential for the development of personalized treatment strategies that aim to mitigate its progression. The application of generated images for the prediction of Alzheimer's disease poses challenges, particularly in accurately representing the disease's characteristics when input sequences are captured at irregular time intervals. This study presents an innovative methodology for sequential image generation, guided by quantitative metrics, to maintain the essential features indicative of disease progression. Furthermore, an age-scaling factor is integrated into the process to produce age-specific MRI images, facilitating the prediction of advanced stages of the disease. The results obtained from the ablation study suggest that the inclusion of quantitative metrics significantly improves the accuracy of MRI image synthesis. Furthermore, the application of age-scaled pixel loss contributed to the enhanced iterative generation of MRI images. In terms of long-term disease prognosis, the Structural Similarity Index reached a peak value of 0.882, indicating a substantial degree of similarity in the synthesized images.", "date": "2025-11-26", "categories": []}
{"id": "2511.21561", "title": "Machine Learning Approaches to Clinical Risk Prediction: Multi-Scale Temporal Alignment in Electronic Health Records", "abstract": "This study proposes a risk prediction method based on a Multi-Scale Temporal Alignment Network (MSTAN) to address the challenges of temporal irregularity, sampling interval differences, and multi-scale dynamic dependencies in Electronic Health Records (EHR). The method focuses on temporal feature modeling by introducing a learnable temporal alignment mechanism and a multi-scale convolutional feature extraction structure to jointly model long-term trends and short-term fluctuations in EHR sequences. At the input level, the model maps multi-source clinical features into a unified high-dimensional semantic space and employs temporal embedding and alignment modules to dynamically weight irregularly sampled data, reducing the impact of temporal distribution differences on model performance. The multi-scale feature extraction module then captures key patterns across different temporal granularities through multi-layer convolution and hierarchical fusion, achieving a fine-grained representation of patient states. Finally, an attention-based aggregation mechanism integrates global temporal dependencies to generate individual-level risk representations for disease risk prediction and health status assessment. Experiments conducted on publicly available EHR datasets show that the proposed model outperforms mainstream baselines in accuracy, recall, precision, and F1-Score, demonstrating the effectiveness and robustness of multi-scale temporal alignment in complex medical time-series analysis. This study provides a new solution for intelligent representation of high-dimensional asynchronous medical sequences and offers important technical support for EHR-driven clinical risk prediction.", "date": "2025-11-26", "categories": []}
{"id": "2510.01508", "title": "Realistic CDSS Drug Dosing with End-to-end Recurrent Q-learning for Dual Vasopressor Control", "abstract": "Reinforcement learning (RL) applications in Clinical Decision Support Systems (CDSS) frequently encounter skepticism because models may recommend inoperable dosing decisions. We propose an end-to-end offline RL framework for dual vasopressor administration in Intensive Care Units (ICUs) that directly addresses this challenge through principled action space design. Our method integrates discrete, continuous, and directional dosing strategies with conservative Q-learning and incorporates a novel recurrent modeling using a replay buffer to capture temporal dependencies in ICU time-series data. Our comparative analysis of norepinephrine dosing strategies across different action space formulations reveals that the designed action spaces improve interpretability and facilitate clinical adoption while preserving efficacy. Empirical results on eICU and MIMIC demonstrate that action space design profoundly influences learned behavioral policies. Compared with baselines, the proposed methods achieve more than 3x expected reward improvements, while aligning with established clinical protocols.", "date": "2025-10-01", "categories": []}
{"id": "2510.25628", "title": "EHR-R1: A Reasoning-Enhanced Foundational Language Model for Electronic Health Record Analysis", "abstract": "Electronic Health Records (EHRs) contain rich yet complex information, and their automated analysis is critical for clinical decision-making. Despite recent advances of large language models (LLMs) in clinical workflows, their ability to analyze EHRs remains limited due to narrow task coverage and lack of EHR-oriented reasoning capabilities. This paper aims to bridge the gap, specifically, we present EHR-Ins, a large-scale, comprehensive EHR reasoning instruction dataset, comprising 300k high-quality reasoning cases and 4M non-reasoning cases across 42 distinct EHR tasks. Its core innovation is a thinking-graph-driven framework that enables to generate high-quality reasoning data at scale. Based on it, we develop EHR-R1, a series of reasoning-enhanced LLMs with up to 72B parameters tailored for EHR analysis. Through a multi-stage training paradigm, including domain adaptation, reasoning enhancement, and reinforcement learning, EHR-R1 systematically acquires domain knowledge and diverse reasoning capabilities, enabling accurate and robust EHR analysis. Lastly, we introduce EHR-Bench, a new benchmark curated from MIMIC-IV, spanning 42 tasks, to comprehensively assess reasoning and prediction across EHR scenarios. In experiments, we show that the resulting EHR-R1 consistently outperforms state-of-the-art commercial and open-source LLMs (including DeepSeek-V3 and GPT-4o), surpassing GPT-4o by over 30 points on MIMIC-Bench and achieving a 10\\% higher zero-shot AUROC on EHRSHOT. Collectively, EHR-Ins, EHR-R1, and EHR-Bench have significantly advanced the development for more reliable and clinically relevant EHR analysis.", "date": "2025-10-29", "categories": []}
{"id": "2511.19485", "title": "OmniTFT: Omni Target Forecasting for Vital Signs and Laboratory Result Trajectories in Multi Center ICU Data", "abstract": "Accurate multivariate time-series prediction of vital signs and laboratory results is crucial for early intervention and precision medicine in intensive care units (ICUs). However, vital signs are often noisy and exhibit rapid fluctuations, while laboratory tests suffer from missing values, measurement lags, and device-specific bias, making integrative forecasting highly challenging. To address these issues, we propose OmniTFT, a deep learning framework that jointly learns and forecasts high-frequency vital signs and sparsely sampled laboratory results based on the Temporal Fusion Transformer (TFT). Specifically, OmniTFT implements four novel strategies to enhance performance: sliding window equalized sampling to balance physiological states, frequency-aware embedding shrinkage to stabilize rare-class representations, hierarchical variable selection to guide model attention toward informative feature clusters, and influence-aligned attention calibration to enhance robustness during abrupt physiological changes. By reducing the reliance on target-specific architectures and extensive feature engineering, OmniTFT enables unified modeling of multiple heterogeneous clinical targets while preserving cross-institutional generalizability. Across forecasting tasks, OmniTFT achieves substantial performance improvement for both vital signs and laboratory results on the MIMIC-III, MIMIC-IV, and eICU datasets. Its attention patterns are interpretable and consistent with known pathophysiology, underscoring its potential utility for quantitative decision support in clinical care.", "date": "2025-11-23", "categories": []}
{"id": "2511.19535", "title": "Masked Autoencoder Joint Learning for Robust Spitzoid Tumor Classification", "abstract": "Accurate diagnosis of spitzoid tumors (ST) is critical to ensure a favorable prognosis and to avoid both under- and over-treatment. Epigenetic data, particularly DNA methylation, provide a valuable source of information for this task. However, prior studies assume complete data, an unrealistic setting as methylation profiles frequently contain missing entries due to limited coverage and experimental artifacts. Our work challenges these favorable scenarios and introduces ReMAC, an extension of ReMasker designed to tackle classification tasks on high-dimensional data under complete and incomplete regimes. Evaluation on real clinical data demonstrates that ReMAC achieves strong and robust performance compared to competing classification methods in the stratification of ST. Code is available: https://github.com/roshni-mahtani/ReMAC.", "date": "2025-11-24", "categories": []}
{"id": "2511.19798", "title": "KOM: A Multi-Agent Artificial Intelligence System for Precision Management of Knee Osteoarthritis (KOA)", "abstract": "Knee osteoarthritis (KOA) affects more than 600 million individuals globally and is associated with significant pain, functional impairment, and disability. While personalized multidisciplinary interventions have the potential to slow disease progression and enhance quality of life, they typically require substantial medical resources and expertise, making them difficult to implement in resource-limited settings. To address this challenge, we developed KOM, a multi-agent system designed to automate KOA evaluation, risk prediction, and treatment prescription. This system assists clinicians in performing essential tasks across the KOA care pathway and supports the generation of tailored management plans based on individual patient profiles, disease status, risk factors, and contraindications. In benchmark experiments, KOM demonstrated superior performance compared to several general-purpose large language models in imaging analysis and prescription generation. A randomized three-arm simulation study further revealed that collaboration between KOM and clinicians reduced total diagnostic and planning time by 38.5% and resulted in improved treatment quality compared to each approach used independently. These findings indicate that KOM could help facilitate automated KOA management and, when integrated into clinical workflows, has the potential to enhance care efficiency. The modular architecture of KOM may also offer valuable insights for developing AI-assisted management systems for other chronic conditions.", "date": "2025-11-24", "categories": []}
{"id": "2511.20601", "title": "The Driver-Blindness Phenomenon: Why Deep Sequence Models Default to Autocorrelation in Blood Glucose Forecasting", "abstract": "Deep sequence models for blood glucose forecasting consistently fail to leverage clinically informative drivers--insulin, meals, and activity--despite well-understood physiological mechanisms. We term this Driver-Blindness and formalize it via $\\Delta_{\\text{drivers}}$, the performance gain of multivariate models over matched univariate baselines. Across the literature, $\\Delta_{\\text{drivers}}$ is typically near zero. We attribute this to three interacting factors: architectural biases favoring autocorrelation (C1), data fidelity gaps that render drivers noisy and confounded (C2), and physiological heterogeneity that undermines population-level models (C3). We synthesize strategies that partially mitigate Driver-Blindness--including physiological feature encoders, causal regularization, and personalization--and recommend that future work routinely report $\\Delta_{\\text{drivers}}$ to prevent driver-blind models from being considered state-of-the-art.", "date": "2025-11-25", "categories": []}
{"id": "2502.13290", "title": "Prediction of Clinical Complication Onset using Neural Point Processes", "abstract": "Predicting medical events in advance within critical care settings is paramount for patient outcomes and resource management. Utilizing predictive models, healthcare providers can anticipate issues such as cardiac arrest, sepsis, or respiratory failure before they manifest. Recently, there has been a surge in research focusing on forecasting adverse medical event onsets prior to clinical manifestation using machine learning. However, while these models provide temporal prognostic predictions for the occurrence of a specific adverse event of interest within defined time intervals, their interpretability often remains a challenge. In this work, we explore the applicability of neural temporal point processes in the context of adverse event onset prediction, with the aim of explaining clinical pathways and providing interpretable insights. Our experiments span six state-of-the-art neural point processes and six critical care datasets, each focusing on the onset of distinct adverse events. This work represents a novel application class of neural temporal point processes in event prediction.", "date": "2025-02-18", "categories": []}
{"id": "2507.04981", "title": "Classification of autoimmune diseases from Peripheral blood TCR repertoires by multimodal multi-instance learning", "abstract": "T cell receptor (TCR) repertoires encode critical immunological signatures for autoimmune diseases, yet their clinical application remains limited by sequence sparsity and low witness rates. We developed EAMil, a multi-instance deep learning framework that leverages TCR sequencing data to diagnose systemic lupus erythematosus (SLE) and rheumatoid arthritis (RA) with exceptional accuracy. By integrating PrimeSeq feature extraction with ESMonehot encoding and enhanced gate attention mechanisms, our model achieved state-of-the-art performance with AUCs of 98.95% for SLE and 97.76% for RA. EAMil successfully identified disease-associated genes with over 90% concordance with established differential analyses and effectively distinguished disease-specific TCR genes. The model demonstrated robustness in classifying multiple disease categories, utilizing the SLEDAI score to stratify SLE patients by disease severity as well as to diagnose the site of damage in SLE patients, and effectively controlling for confounding factors such as age and gender. This interpretable framework for immune receptor analysis provides new insights for autoimmune disease detection and classification with broad potential clinical applications across immune-mediated conditions.", "date": "2025-07-07", "categories": []}
{"id": "2511.09567", "title": "Let the Experts Speak: Improving Survival Prediction & Calibration via Mixture-of-Experts Heads", "abstract": "Deep mixture-of-experts models have attracted a lot of attention for survival analysis problems, particularly for their ability to cluster similar patients together. In practice, grouping often comes at the expense of key metrics such as calibration error and predictive accuracy. This is due to the restrictive inductive bias that mixture-of-experts imposes, that predictions for individual patients must look like predictions for the group they're assigned to. Might we be able to discover patient group structure, where it exists, while improving calibration and predictive accuracy? In this work, we introduce several discrete-time deep mixture-of-experts (MoE)-based architectures for survival analysis problems, one of which achieves all desiderata: clustering, calibration, and predictive accuracy. We show that a key differentiator between this array of MoEs is how expressive their experts are. We find that more expressive experts that tailor predictions per patient outperform experts that rely on fixed group prototypes.", "date": "2025-11-11", "categories": []}
{"id": "2511.11684", "title": "A Bayesian Model for Multi-stage Censoring", "abstract": "Many sequential decision settings in healthcare feature funnel structures characterized by a series of stages, such as screenings or evaluations, where the number of patients who advance to each stage progressively decreases and decisions become increasingly costly. For example, an oncologist may first conduct a breast exam, followed by a mammogram for patients with concerning exams, followed by a biopsy for patients with concerning mammograms. A key challenge is that the ground truth outcome, such as the biopsy result, is only revealed at the end of this funnel. The selective censoring of the ground truth can introduce statistical biases in risk estimation, especially in underserved patient groups, whose outcomes are more frequently censored. We develop a Bayesian model for funnel decision structures, drawing from prior work on selective labels and censoring. We first show in synthetic settings that our model is able to recover the true parameters and predict outcomes for censored patients more accurately than baselines. We then apply our model to a dataset of emergency department visits, where in-hospital mortality is observed only for those who are admitted to either the hospital or ICU. We find that there are gender-based differences in hospital and ICU admissions. In particular, our model estimates that the mortality risk threshold to admit women to the ICU is higher for women (5.1%) than for men (4.5%).", "date": "2025-11-12", "categories": []}
{"id": "2511.15393", "title": "EVA-Net: Interpretable Anomaly Detection for Brain Health via Learning Continuous Aging Prototypes from One-Class EEG Cohorts", "abstract": "The brain age is a key indicator of brain health. While electroencephalography (EEG) is a practical tool for this task, existing models struggle with the common challenge of imperfect medical data, such as learning a ``normal'' baseline from weakly supervised, healthy-only cohorts. This is a critical anomaly detection task for identifying disease, but standard models are often black boxes lacking an interpretable structure. We propose EVA-Net, a novel framework that recasts brain age as an interpretable anomaly detection problem. EVA-Net uses an efficient, sparsified-attention Transformer to model long EEG sequences. To handle noise and variability in imperfect data, it employs a Variational Information Bottleneck to learn a robust, compressed representation. For interpretability, this representation is aligned to a continuous prototype network that explicitly learns the normative healthy aging manifold. Trained on 1297 healthy subjects, EVA-Net achieves state-of-the-art accuracy. We validated its anomaly detection capabilities on an unseen cohort of 27 MCI and AD patients. This pathological group showed significantly higher brain-age gaps and a novel Prototype Alignment Error, confirming their deviation from the healthy manifold. EVA-Net provides an interpretable framework for healthcare intelligence using imperfect medical data.", "date": "2025-11-19", "categories": []}
{"id": "2511.17553", "title": "Practical Machine Learning for Aphasic Discourse Analysis", "abstract": "Analyzing spoken discourse is a valid means of quantifying language ability in persons with aphasia. There are many ways to quantify discourse, one common way being to evaluate the informativeness of the discourse. That is, given the total number of words produced, how many of those are context-relevant and accurate. This type of analysis is called Correct Information Unit (CIU) analysis and is one of the most prevalent discourse analyses used by speech-language pathologists (SLPs). Despite this, CIU analysis in the clinic remains limited due to the manual labor needed by SLPs to code and analyze collected speech. Recent advances in machine learning (ML) seek to augment such labor by automating modeling of propositional, macrostructural, pragmatic, and multimodal dimensions of discourse. To that end, this study evaluated five ML models for reliable identification of Correct Information Units (CIUs, Nicholas & Brookshire, 1993), during a picture description task. The five supervised ML models were trained using randomly selected human-coded transcripts and accompanying words and CIUs from persons with aphasia. The baseline model training produced a high accuracy across transcripts for word vs non-word, with all models achieving near perfect performance (0.995) with high AUC range (0.914 min, 0.995 max). In contrast, CIU vs non-CIU showed a greater variability, with the k-nearest neighbor (k-NN) model the highest accuracy (0.824) and second highest AUC (0.787). These findings indicate that while the supervised ML models can distinguish word from not word, identifying CIUs is challenging.", "date": "2025-11-12", "categories": []}
{"id": "2511.17605", "title": "Copula Based Fusion of Clinical and Genomic Machine Learning Risk Scores for Breast Cancer Risk Stratification", "abstract": "Clinical and genomic models are both used to predict breast cancer outcomes, but they are often combined using simple linear rules that do not account for how their risk scores relate, especially at the extremes. Using the METABRIC breast cancer cohort, we studied whether directly modeling the joint relationship between clinical and genomic machine learning risk scores could improve risk stratification for 5-year cancer-specific mortality. We created a binary 5-year cancer-death outcome and defined two sets of predictors: a clinical set (demographic, tumor, and treatment variables) and a genomic set (gene-expression $z$-scores). We trained several supervised classifiers, such as Random Forest and XGBoost, and used 5-fold cross-validated predicted probabilities as unbiased risk scores. These scores were converted to pseudo-observations on $(0,1)^2$ to fit Gaussian, Clayton, and Gumbel copulas. Clinical models showed good discrimination (AUC 0.783), while genomic models had moderate performance (AUC 0.681). The joint distribution was best captured by a Gaussian copula (bootstrap $p=0.997$), which suggests a symmetric, moderately strong positive relationship. When we grouped patients based on this relationship, Kaplan-Meier curves showed clear differences: patients who were high-risk in both clinical and genomic scores had much poorer survival than those high-risk in only one set. These results show that copula-based fusion works in real-world cohorts and that considering dependencies between scores can better identify patient subgroups with the worst prognosis.", "date": "2025-11-18", "categories": []}
{"id": "2511.17635", "title": "Upstream Probabilistic Meta-Imputation for Multimodal Pediatric Pancreatitis Classification", "abstract": "Pediatric pancreatitis is a progressive and debilitating inflammatory condition, including acute pancreatitis and chronic pancreatitis, that presents significant clinical diagnostic challenges. Machine learning-based methods also face diagnostic challenges due to limited sample availability and multimodal imaging complexity. To address these challenges, this paper introduces Upstream Probabilistic Meta-Imputation (UPMI), a light-weight augmentation strategy that operates upstream of a meta-learner in a low-dimensional meta-feature space rather than in image space. Modality-specific logistic regressions (T1W and T2W MRI radiomics) produce probability outputs that are transformed into a 7-dimensional meta-feature vector. Class-conditional Gaussian mixture models (GMMs) are then fit within each cross-validation fold to sample synthetic meta-features that, combined with real meta-features, train a Random Forest (RF) meta-classifier. On 67 pediatric subjects with paired T1W/T2W MRIs, UPMI achieves a mean AUC of 0.908 $\\pm$ 0.072, a $\\sim$5% relative gain over a real-only baseline (AUC 0.864 $\\pm$ 0.061).", "date": "2025-11-19", "categories": []}
{"id": "2511.17662", "title": "Enhancing Breast Cancer Prediction with LLM-Inferred Confounders", "abstract": "This study enhances breast cancer prediction by using large language models to infer the likelihood of confounding diseases, namely diabetes, obesity, and cardiovascular disease, from routine clinical data. These AI-generated features improved Random Forest model performance, particularly for LLMs like Gemma (3.9%) and Llama (6.4%). The approach shows promise for noninvasive prescreening and clinical integration, supporting improved early detection and shared decision-making in breast cancer diagnosis.", "date": "2025-11-20", "categories": []}
{"id": "2511.17947", "title": "Leveraging Evidence-Guided LLMs to Enhance Trustworthy Depression Diagnosis", "abstract": "Large language models (LLMs) show promise in automating clinical diagnosis, yet their non-transparent decision-making and limited alignment with diagnostic standards hinder trust and clinical adoption. We address this challenge by proposing a two-stage diagnostic framework that enhances transparency, trustworthiness, and reliability. First, we introduce Evidence-Guided Diagnostic Reasoning (EGDR), which guides LLMs to generate structured diagnostic hypotheses by interleaving evidence extraction with logical reasoning grounded in DSM-5 criteria. Second, we propose a Diagnosis Confidence Scoring (DCS) module that evaluates the factual accuracy and logical consistency of generated diagnoses through two interpretable metrics: the Knowledge Attribution Score (KAS) and the Logic Consistency Score (LCS). Evaluated on the D4 dataset with pseudo-labels, EGDR outperforms direct in-context prompting and Chain-of-Thought (CoT) across five LLMs. For instance, on OpenBioLLM, EGDR improves accuracy from 0.31 (Direct) to 0.76 and increases DCS from 0.50 to 0.67. On MedLlama, DCS rises from 0.58 (CoT) to 0.77. Overall, EGDR yields up to +45% accuracy and +36% DCS gains over baseline methods, offering a clinically grounded, interpretable foundation for trustworthy AI-assisted diagnosis.", "date": "2025-11-22", "categories": []}
{"id": "2511.18199", "title": "Improving Forecasts of Suicide Attempts for Patients with Little Data", "abstract": "Ecological Momentary Assessment provides real-time data on suicidal thoughts and behaviors, but predicting suicide attempts remains challenging due to their rarity and patient heterogeneity. We show that single models fit to all patients perform poorly, while individualized models improve performance but still overfit to patients with limited data. To address this, we introduce Latent Similarity Gaussian Processes (LSGPs) to capture patient heterogeneity, enabling those with little data to leverage similar patients' trends. Preliminary results show promise: even without kernel-design, we outperform all but one baseline while offering a new understanding of patient similarity.", "date": "2025-11-22", "categories": []}
{"id": "2511.18316", "title": "Stro-VIGRU: Defining the Vision Recurrent-Based Baseline Model for Brain Stroke Classification", "abstract": "Stroke majorly causes death and disability worldwide, and early recognition is one of the key elements of successful treatment of the same. It is common to diagnose strokes using CT scanning, which is fast and readily available, however, manual analysis may take time and may result in mistakes. In this work, a pre-trained Vision Transformer-based transfer learning framework is proposed for the early identification of brain stroke. A few of the encoder blocks of the ViT model are frozen, and the rest are allowed to be fine-tuned in order to learn brain stroke-specific features. The features that have been extracted are given as input to a single-layer Bi-GRU to perform classification. Class imbalance is handled by data augmentation. The model has achieved 94.06% accuracy in classifying brain stroke from the Stroke Dataset.", "date": "2025-11-23", "categories": []}
{"id": "2511.18334", "title": "Clinician-in-the-Loop Smart Home System to Detect Urinary Tract Infection Flare-Ups via Uncertainty-Aware Decision Support", "abstract": "Urinary tract infection (UTI) flare-ups pose a significant health risk for older adults with chronic conditions. These infections often go unnoticed until they become severe, making early detection through innovative smart home technologies crucial. Traditional machine learning (ML) approaches relying on simple binary classification for UTI detection offer limited utility to nurses and practitioners as they lack insight into prediction uncertainty, hindering informed clinical decision-making. This paper presents a clinician-in-the-loop (CIL) smart home system that leverages ambient sensor data to extract meaningful behavioral markers, train robust predictive ML models, and calibrate them to enable uncertainty-aware decision support. The system incorporates a statistically valid uncertainty quantification method called Conformal-Calibrated Interval (CCI), which quantifies uncertainty and abstains from making predictions (\"I don't know\") when the ML model's confidence is low. Evaluated on real-world data from eight smart homes, our method outperforms baseline methods in recall and other classification metrics while maintaining the lowest abstention proportion and interval width. A survey of 42 nurses confirms that our system's outputs are valuable for guiding clinical decision-making, underscoring their practical utility in improving informed decisions and effectively managing UTIs and other condition flare-ups in older adults.", "date": "2025-11-23", "categories": []}
{"id": "2511.18839", "title": "Enhancing Multi-Label Thoracic Disease Diagnosis with Deep Ensemble-Based Uncertainty Quantification", "abstract": "The utility of deep learning models, such as CheXNet, in high stakes clinical settings is fundamentally constrained by their purely deterministic nature, failing to provide reliable measures of predictive confidence. This project addresses this critical gap by integrating robust Uncertainty Quantification (UQ) into a high performance diagnostic platform for 14 common thoracic diseases on the NIH ChestX-ray14 dataset. Initial architectural development failed to stabilize performance and calibration using Monte Carlo Dropout (MCD), yielding an unacceptable Expected Calibration Error (ECE) of 0.7588. This technical failure necessitated a rigorous architectural pivot to a high diversity, 9-member Deep Ensemble (DE). This resulting DE successfully stabilized performance and delivered superior reliability, achieving a State-of-the-Art (SOTA) average Area Under the Receiver Operating Characteristic Curve (AUROC) of 0.8559 and an average F1 Score of 0.3857. Crucially, the DE demonstrated superior calibration (Mean ECE of 0.0728 and Negative Log-Likelihood (NLL) of 0.1916) and enabled the reliable decomposition of total uncertainty into its Aleatoric (irreducible data noise) and Epistemic (reducible model knowledge) components, with a mean Epistemic Uncertainty (EU) of 0.0240. These results establish the Deep Ensemble as a trustworthy and explainable platform, transforming the model from a probabilistic tool into a reliable clinical decision support system.", "date": "2025-11-24", "categories": []}
{"id": "2511.16933", "title": "A novel approach to classification of ECG arrhythmia types with latent ODEs", "abstract": "12-lead ECGs with high sampling frequency are the clinical gold standard for arrhythmia detection, but their short-term, spot-check nature often misses intermittent events. Wearable ECGs enable long-term monitoring but suffer from irregular, lower sampling frequencies due to battery constraints, making morphology analysis challenging. We present an end-to-end classification pipeline to address these issues. We train a latent ODE to model continuous ECG waveforms and create robust feature vectors from high-frequency single-channel signals. We construct three latent vectors per waveform via downsampling the initial 360 Hz ECG to 90 Hz and 45 Hz. We then use a gradient boosted tree to classify these vectors and test robustness across frequencies. Performance shows minimal degradation, with macro-averaged AUC-ROC values of 0.984, 0.978, and 0.976 at 360 Hz, 90 Hz, and 45 Hz, respectively, suggesting a way to sidestep the trade-off between signal fidelity and battery life. This enables smaller wearables, promoting long-term monitoring of cardiac health.", "date": "2025-11-21", "categories": []}
{"id": "2511.17056", "title": "Patient-level Information Extraction by Consistent Integration of Textual and Tabular Evidence with Bayesian Networks", "abstract": "Electronic health records (EHRs) form an invaluable resource for training clinical decision support systems. To leverage the potential of such systems in high-risk applications, we need large, structured tabular datasets on which we can build transparent feature-based models. While part of the EHR already contains structured information (e.g. diagnosis codes, medications, and lab results), much of the information is contained within unstructured text (e.g. discharge summaries and nursing notes). In this work, we propose a method for multi-modal patient-level information extraction that leverages both the tabular features available in the patient's EHR (using an expert-informed Bayesian network) as well as clinical notes describing the patient's symptoms (using neural text classifiers). We propose the use of virtual evidence augmented with a consistency node to provide an interpretable, probabilistic fusion of the models' predictions. The consistency node improves the calibration of the final predictions compared to virtual evidence alone, allowing the Bayesian network to better adjust the neural classifier's output to handle missing information and resolve contradictions between the tabular and text data. We show the potential of our method on the SimSUM dataset, a simulated benchmark linking tabular EHRs with clinical notes through expert knowledge.", "date": "2025-11-21", "categories": []}
{"id": "2511.17158", "title": "Exploring the added value of pretherapeutic MR descriptors in predicting breast cancer pathologic complete response to neoadjuvant chemotherapy", "abstract": "Objectives: To evaluate the association between pretreatment MRI descriptors and breast cancer (BC) pathological complete response (pCR) to neoadjuvant chemotherapy (NAC). Materials \\& Methods: Patients with BC treated by NAC with a breast MRI between 2016 and 2020 were included in this retrospective observational single-center study. MR studies were described using the standardized BI-RADS and breast edema score on T2-weighted MRI. Univariable and multivariable logistic regression analyses were performed to assess variables association with pCR according to residual cancer burden. Random forest classifiers were trained to predict pCR on a random split including 70% of the database and were validated on the remaining cases. Results: Among 129 BC, 59 (46%) achieved pCR after NAC (luminal (n=7/37, 19%), triple negative (TN) (n=30/55, 55%), HER2+ (n=22/37, 59%). Clinical and biological items associated with pCR were BC subtype (p<0.001), T stage 0/I/II (p=0.008), higher Ki67 (p=0.005) and higher tumor-infiltrating lymphocytes levels (p=0.016). Univariate analysis showed that the following MRI features, oval or round shape (p=0.047), unifocality (p=0.026), non-spiculated margins (p=0.018), no associated non-mass enhancement (NME) (p = 0.024) and a lower MRI size (p = 0.031) were significantly associated with pCR. Unifocality and non-spiculated margins remained independently associated with pCR at multivariable analysis. Adding significant MRI features to clinicobiological variables in random forest classifiers significantly increased sensitivity (0.67 versus 0.62), specificity (0.69 versus 0.67) and precision (0.71 versus 0.67) for pCR prediction. Conclusion: Non-spiculated margins and unifocality are independently associated with pCR and can increase models performance to predict BC response to NAC. Clinical Relevance Statement: A multimodal approach integrating pretreatment MRI features with clinicobiological predictors, including TILs, could be employed to develop machine learning models for identifying patients at risk of non-response. This may enable consideration of alternative therapeutic strategies to optimize treatment outcomes", "date": "2025-11-21", "categories": []}
{"id": "2407.18629", "title": "CardioLab: Laboratory Values Estimation from Electrocardiogram Features - An Exploratory Study", "abstract": "Laboratory value represents a cornerstone of medical diagnostics, but suffers from slow turnaround times, and high costs and only provides information about a single point in time. The continuous estimation of laboratory values from non-invasive data such as electrocardiogram (ECG) would therefore mark a significant frontier in healthcare monitoring. Despite its potential, this domain remains relatively underexplored. In this preliminary study, we used a publicly available dataset (MIMIC-IV-ECG) to investigate the feasibility of inferring laboratory values from ECG features and patient demographics using tree-based models (XGBoost). We define the prediction task as a binary problem of whether the lab value falls into low or high abnormalities. We assessed model performance with AUROC. Our findings demonstrate promising results in the estimation of laboratory values related to different organ systems. While further research and validation are warranted to fully assess the clinical utility and generalizability of the approach, our findings lay the groundwork for future investigations for laboratory value estimation using ECG data. Such advancements hold promise for revolutionizing predictive healthcare applications, offering faster, non-invasive, and more affordable means of patient monitoring.", "date": "2024-07-26", "categories": []}
{"id": "2408.17329", "title": "Estimation of Cardiac and Non-cardiac Diagnosis from Electrocardiogram Features", "abstract": "Ensuring timely and accurate diagnosis of medical conditions is paramount for effective patient care. Electrocardiogram (ECG) signals are fundamental for evaluating a patient's cardiac health and are readily available. Despite this, little attention has been given to the remarkable potential of ECG data in detecting non-cardiac conditions. In our study, we used publicly available datasets (MIMIC-IV-ECG-ICD and ECG-VIEW II) to investigate the feasibility of inferring general diagnostic conditions from ECG features. To this end, we trained a tree-based model (XGBoost) based on ECG features and basic demographic features to estimate a wide range of diagnoses, encompassing both cardiac and non-cardiac conditions. Our results demonstrate the reliability of estimating 23 cardiac as well as 21 non-cardiac conditions above 0.7 AUROC in a statistically significant manner across a wide range of physiological categories. Our findings underscore the predictive potential of ECG data in identifying well-known cardiac conditions. However, even more striking, this research represents a pioneering effort in systematically expanding the scope of ECG-based diagnosis to conditions not traditionally associated with the cardiac system.", "date": "2024-08-30", "categories": []}
{"id": "2412.07737", "title": "Explainable machine learning for neoplasms diagnosis via electrocardiograms: an externally validated study", "abstract": "Background: Neoplasms are a major cause of mortality globally, where early diagnosis is essential for improving outcomes. Current diagnostic methods are often invasive, expensive, and inaccessible in resource-limited settings. This study explores the potential of electrocardiogram (ECG) data, a widely available and non-invasive tool for diagnosing neoplasms through cardiovascular changes linked to neoplastic presence.\n  Methods: A diagnostic pipeline combining tree-based machine learning models with Shapley value analysis for explainability was developed. The model was trained and internally validated on a large dataset and externally validated on an independent cohort to ensure robustness and generalizability. Key ECG features contributing to predictions were identified and analyzed.\n  Results: The model achieved high diagnostic accuracy in both internal testing and external validation cohorts. Shapley value analysis highlighted significant ECG features, including novel predictors. The approach is cost-effective, scalable, and suitable for resource-limited settings, offering insights into cardiovascular changes associated with neoplasms and their therapies.\n  Conclusions: This study demonstrates the feasibility of using ECG signals and machine learning for non-invasive neoplasm diagnosis. By providing interpretable insights into cardio-neoplasm interactions, this method addresses gaps in diagnostics and supports integration into broader diagnostic and therapeutic frameworks.", "date": "2024-12-10", "categories": []}
{"id": "2511.15778", "title": "Balancing Natural Language Processing Accuracy and Normalisation in Extracting Medical Insights", "abstract": "Extracting structured medical insights from unstructured clinical text using Natural Language Processing (NLP) remains an open challenge in healthcare, particularly in non-English contexts where resources are scarce. This study presents a comparative analysis of NLP low-compute rule-based methods and Large Language Models (LLMs) for information extraction from electronic health records (EHR) obtained from the Voivodeship Rehabilitation Hospital for Children in Ameryka, Poland. We evaluate both approaches by extracting patient demographics, clinical findings, and prescribed medications while examining the effects of lack of text normalisation and translation-induced information loss. Results demonstrate that rule-based methods provide higher accuracy in information retrieval tasks, particularly for age and sex extraction. However, LLMs offer greater adaptability and scalability, excelling in drug name recognition. The effectiveness of the LLMs was compared with texts originally in Polish and those translated into English, assessing the impact of translation. These findings highlight the trade-offs between accuracy, normalisation, and computational cost when deploying NLP in healthcare settings. We argue for hybrid approaches that combine the precision of rule-based systems with the adaptability of LLMs, offering a practical path toward more reliable and resource-efficient clinical NLP in real-world hospitals.", "date": "2025-11-19", "categories": []}
{"id": "2511.15847", "title": "Transparent Early ICU Mortality Prediction with Clinical Transformer and Per-Case Modality Attribution", "abstract": "Early identification of intensive care patients at risk of in-hospital mortality enables timely intervention and efficient resource allocation. Despite high predictive performance, existing machine learning approaches lack transparency and robustness, limiting clinical adoption. We present a lightweight, transparent multimodal ensemble that fuses physiological time-series measurements with unstructured clinical notes from the first 48 hours of an ICU stay. A logistic regression model combines predictions from two modality-specific models: a bidirectional LSTM for vitals and a finetuned ClinicalModernBERT transformer for notes. This traceable architecture allows for multilevel interpretability: feature attributions within each modality and direct per-case modality attributions quantifying how vitals and notes influence each decision. On the MIMIC-III benchmark, our late-fusion ensemble improves discrimination over the best single model (AUPRC 0.565 vs. 0.526; AUROC 0.891 vs. 0.876) while maintaining well-calibrated predictions. The system remains robust through a calibrated fallback when a modality is missing. These results demonstrate competitive performance with reliable, auditable risk estimates and transparent, predictable operation, which together are crucial for clinical use.", "date": "2025-11-19", "categories": []}
{"id": "2511.16333", "title": "Beyond Generative AI: World Models for Clinical Prediction, Counterfactuals, and Planning", "abstract": "Healthcare requires AI that is predictive, reliable, and data-efficient. However, recent generative models lack physical foundation and temporal reasoning required for clinical decision support. As scaling language models show diminishing returns for grounded clinical reasoning, world models are gaining traction because they learn multimodal, temporally coherent, and action-conditioned representations that reflect the physical and causal structure of care. This paper reviews World Models for healthcare systems that learn predictive dynamics to enable multistep rollouts, counterfactual evaluation and planning. We survey recent work across three domains: (i) medical imaging and diagnostics (e.g., longitudinal tumor simulation, projection-transition modeling, and Joint Embedding Predictive Architecture i.e., JEPA-style predictive representation learning), (ii) disease progression modeling from electronic health records (generative event forecasting at scale), and (iii) robotic surgery and surgical planning (action-conditioned guidance and control). We also introduce a capability rubric: L1 temporal prediction, L2 action-conditioned prediction, L3 counterfactual rollouts for decision support, and L4 planning/control. Most reviewed systems achieve L1--L2, with fewer instances of L3 and rare L4. We identify cross-cutting gaps that limit clinical reliability; under-specified action spaces and safety constraints, weak interventional validation, incomplete multimodal state construction, and limited trajectory-level uncertainty calibration. This review outlines a research agenda for clinically robust prediction-first world models that integrate generative backbones (transformers, diffusion, VAE) with causal/mechanical foundation for safe decision support in healthcare.", "date": "2025-11-20", "categories": []}
{"id": "2511.16398", "title": "Collaborative Management for Chronic Diseases and Depression: A Double Heterogeneity-based Multi-Task Learning Method", "abstract": "Wearable sensor technologies and deep learning are transforming healthcare management. Yet, most health sensing studies focus narrowly on physical chronic diseases. This overlooks the critical need for joint assessment of comorbid physical chronic diseases and depression, which is essential for collaborative chronic care. We conceptualize multi-disease assessment, including both physical diseases and depression, as a multi-task learning (MTL) problem, where each disease assessment is modeled as a task. This joint formulation leverages inter-disease relationships to improve accuracy, but it also introduces the challenge of double heterogeneity: chronic diseases differ in their manifestation (disease heterogeneity), and patients with the same disease show varied patterns (patient heterogeneity). To address these issues, we first adopt existing techniques and propose a base method. Given the limitations of the base method, we further propose an Advanced Double Heterogeneity-based Multi-Task Learning (ADH-MTL) method that improves the base method through three innovations: (1) group-level modeling to support new patient predictions, (2) a decomposition strategy to reduce model complexity, and (3) a Bayesian network that explicitly captures dependencies while balancing similarities and differences across model components. Empirical evaluations on real-world wearable sensor data demonstrate that ADH-MTL significantly outperforms existing baselines, and each of its innovations is shown to be effective. This study contributes to health information systems by offering a computational solution for integrated physical and mental healthcare and provides design principles for advancing collaborative chronic disease management across the pre-treatment, treatment, and post-treatment phases.", "date": "2025-11-20", "categories": []}
{"id": "2511.16427", "title": "Generative Modeling of Clinical Time Series via Latent Stochastic Differential Equations", "abstract": "Clinical time series data from electronic health records and medical registries offer unprecedented opportunities to understand patient trajectories and inform medical decision-making. However, leveraging such data presents significant challenges due to irregular sampling, complex latent physiology, and inherent uncertainties in both measurements and disease progression. To address these challenges, we propose a generative modeling framework based on latent neural stochastic differential equations (SDEs) that views clinical time series as discrete-time partial observations of an underlying controlled stochastic dynamical system. Our approach models latent dynamics via neural SDEs with modality-dependent emission models, while performing state estimation and parameter learning through variational inference. This formulation naturally handles irregularly sampled observations, learns complex non-linear interactions, and captures the stochasticity of disease progression and measurement noise within a unified scalable probabilistic framework. We validate the framework on two complementary tasks: (i) individual treatment effect estimation using a simulated pharmacokinetic-pharmacodynamic (PKPD) model of lung cancer, and (ii) probabilistic forecasting of physiological signals using real-world intensive care unit (ICU) data from 12,000 patients. Results show that our framework outperforms ordinary differential equation and long short-term memory baseline models in accuracy and uncertainty estimation. These results highlight its potential for enabling precise, uncertainty-aware predictions to support clinical decision-making.", "date": "2025-11-20", "categories": []}
{"id": "2409.06525", "title": "MENSA: A Multi-Event Network for Survival Analysis with Trajectory-based Likelihood Estimation", "abstract": "Most existing time-to-event methods focus on either single-event or competing-risks settings, leaving multi-event scenarios relatively underexplored. In many healthcare applications, for example, a patient may experience multiple clinical events, that can be non-exclusive and semi-competing. A common workaround is to train independent single-event models for such multi-event problems, but this approach fails to exploit dependencies and shared structures across events. To overcome these limitations, we propose MENSA (Multi-Event Network for Survival Analysis), a deep learning model that jointly learns flexible time-to-event distributions for multiple events, whether competing or co-occurring. In addition, we introduce a novel trajectory-based likelihood term that captures the temporal ordering between events. Across four multi-event datasets, MENSA improves predictive performance over many state-of-the-art baselines. Source code is available at https://github.com/thecml/mensa.", "date": "2024-09-10", "categories": []}
{"id": "2502.04918", "title": "Explainable and externally validated machine learning for neurocognitive diagnosis via electrocardiograms", "abstract": "Background: Electrocardiogram (ECG) analysis has emerged as a promising tool for detecting physiological changes linked to non-cardiac disorders. Given the close connection between cardiovascular and neurocognitive health, ECG abnormalities may be present in individuals with co-occurring neurocognitive conditions. This highlights the potential of ECG as a biomarker to improve detection, therapy monitoring, and risk stratification in patients with neurocognitive disorders, an area that remains underexplored.\n  Methods: We aim to demonstrate the feasibility to predict neurocognitive disorders from ECG features across diverse patient populations. We utilized ECG features and demographic data to predict neurocognitive disorders defined by ICD-10 codes, focusing on dementia, delirium, and Parkinson's disease. Internal and external validations were performed using the MIMIC-IV and ECG-View datasets. Predictive performance was assessed using AUROC scores, and Shapley values were used to interpret feature contributions.\n  Results: Significant predictive performance was observed for disorders within the neurcognitive disorders. Significantly, the disorders with the highest predictive performance is F03: Dementia, with an internal AUROC of 0.848 (95% CI: 0.848-0.848) and an external AUROC of 0.865 (0.864-0.965), followed by G30: Alzheimer's, with an internal AUROC of 0.809 (95% CI: 0.808-0.810) and an external AUROC of 0.863 (95% CI: 0.863-0.864). Feature importance analysis revealed both known and novel ECG correlates. ECGs hold promise as non-invasive, explainable biomarkers for selected neurocognitive disorders. This study demonstrates robust performance across cohorts and lays the groundwork for future clinical applications, including early detection and personalized monitoring.", "date": "2025-02-07", "categories": []}
{"id": "2506.02076", "title": "A meaningful prediction of functional decline in amyotrophic lateral sclerosis based on multi-event survival analysis", "abstract": "Amyotrophic lateral sclerosis (ALS) is a degenerative disorder of the motor neurons that causes progressive paralysis in patients. Current treatment options aim to prolong survival and improve quality of life. However, due to the heterogeneity of the disease, it is often difficult to determine the optimal time for potential therapies or medical interventions. In this study, we propose a novel method to predict the time until a patient with ALS experiences significant functional impairment (ALSFRS-R <= 2) for each of five common functions: speaking, swallowing, handwriting, walking, and breathing. We formulate this task as a multi-event survival problem and validate our approach in the PRO-ACT dataset (N = 3220) by training five covariate-based survival models to estimate the probability of each event over the 500 days following the baseline visit. We then predict five event-specific individual survival distributions (ISDs) for a patient, each providing an interpretable estimate of when that event is likely to occur. The results show that covariate-based models are superior to the Kaplan-Meier estimator at predicting time-to-event outcomes in the PRO-ACT dataset. Additionally, our method enables practitioners to make individual counterfactual predictions -- where certain covariates can be changed -- to estimate their effect on the predicted outcome. In this regard, we find that Riluzole has little or no impact on predicted functional decline. However, for patients with bulbar-onset ALS, our model predicts significantly shorter time-to-event estimates for loss of speech and swallowing function compared to patients with limb-onset ALS (log-rank p<0.001, Bonferroni-adjusted alpha=0.01). The proposed method can be applied to current clinical examination data to assess the risk of functional decline and thus allow more personalized treatment planning.", "date": "2025-06-02", "categories": []}
{"id": "2511.14939", "title": "Fine-tuning Pre-trained Audio Models for COVID-19 Detection: A Technical Report", "abstract": "This technical report investigates the performance of pre-trained audio models on COVID-19 detection tasks using established benchmark datasets. We fine-tuned Audio-MAE and three PANN architectures (CNN6, CNN10, CNN14) on the Coswara and COUGHVID datasets, evaluating both intra-dataset and cross-dataset generalization. We implemented a strict demographic stratification by age and gender to prevent models from exploiting spurious correlations between demographic characteristics and COVID-19 status. Intra-dataset results showed moderate performance, with Audio-MAE achieving the strongest result on Coswara (0.82 AUC, 0.76 F1-score), while all models demonstrated limited performance on Coughvid (AUC 0.58-0.63). Cross-dataset evaluation revealed severe generalization failure across all models (AUC 0.43-0.68), with Audio-MAE showing strong performance degradation (F1-score 0.00-0.08). Our experiments demonstrate that demographic balancing, while reducing apparent model performance, provides more realistic assessment of COVID-19 detection capabilities by eliminating demographic leakage - a confounding factor that inflate performance metrics. Additionally, the limited dataset sizes after balancing (1,219-2,160 samples) proved insufficient for deep learning models that typically require substantially larger training sets. These findings highlight fundamental challenges in developing generalizable audio-based COVID-19 detection systems and underscore the importance of rigorous demographic controls for clinically robust model evaluation.", "date": "2025-11-18", "categories": []}
{"id": "2511.15048", "title": "Oversampling techniques for predicting COVID-19 patient length of stay", "abstract": "COVID-19 is a respiratory disease that caused a global pandemic in 2019. It is highly infectious and has the following symptoms: fever or chills, cough, shortness of breath, fatigue, muscle or body aches, headache, the new loss of taste or smell, sore throat, congestion or runny nose, nausea or vomiting, and diarrhea. These symptoms vary in severity; some people with many risk factors have been known to have lengthy hospital stays or die from the disease. In this paper, we analyze patients' electronic health records (EHR) to predict the severity of their COVID-19 infection using the length of stay (LOS) as our measurement of severity. This is an imbalanced classification problem, as many people have a shorter LOS rather than a longer one. To combat this problem, we synthetically create alternate oversampled training data sets. Once we have this oversampled data, we run it through an Artificial Neural Network (ANN), which during training has its hyperparameters tuned using Bayesian optimization. We select the model with the best F1 score and then evaluate it and discuss it.", "date": "2025-11-19", "categories": []}
{"id": "2511.15062", "title": "Interpretable temporal fusion network of multi- and multi-class arrhythmia classification", "abstract": "Clinical decision support systems (CDSSs) have been widely utilized to support the decisions made by cardiologists when detecting and classifying arrhythmia from electrocardiograms. However, forming a CDSS for the arrhythmia classification task is challenging due to the varying lengths of arrhythmias. Although the onset time of arrhythmia varies, previously developed methods have not considered such conditions. Thus, we propose a framework that consists of (i) local and global extraction and (ii) local-global information fusion with attention to enable arrhythmia detection and classification within a constrained input length. The framework's performance was evaluated in terms of 10-class and 4-class arrhythmia detection, focusing on identifying the onset and ending point of arrhythmia episodes and their duration using the MIT-BIH arrhythmia database (MITDB) and the MIT-BIH atrial fibrillation database (AFDB). Duration, episode, and Dice score performances resulted in overall F1-scores of 96.45%, 82.05%, and 96.31% on the MITDB and 97.57%, 98.31%, and 97.45% on the AFDB, respectively. The results demonstrated statistically superior performance compared to those of the benchmark models. To assess the generalization capability of the proposed method, an MITDB-trained model and MIT-BIH malignant ventricular arrhythmia database-trained model were tested AFDB and MITDB, respectively. Superior performance was attained compared with that of a state-of-the-art model. The proposed method effectively captures both local and global information and dynamics without significant information loss. Consequently, arrhythmias can be detected with greater accuracy, and their occurrence times can be precisely determined, enabling the clinical field to develop more accurate treatment plans based on the proposed method.", "date": "2025-11-19", "categories": []}
{"id": "2511.15357", "title": "Cost-Aware Prediction (CAP): An LLM-Enhanced Machine Learning Pipeline and Decision Support System for Heart Failure Mortality Prediction", "abstract": "Objective: Machine learning (ML) predictive models are often developed without considering downstream value trade-offs and clinical interpretability. This paper introduces a cost-aware prediction (CAP) framework that combines cost-benefit analysis assisted by large language model (LLM) agents to communicate the trade-offs involved in applying ML predictions. Materials and Methods: We developed an ML model predicting 1-year mortality in patients with heart failure (N = 30,021, 22% mortality) to identify those eligible for home care. We then introduced clinical impact projection (CIP) curves to visualize important cost dimensions - quality of life and healthcare provider expenses, further divided into treatment and error costs, to assess the clinical consequences of predictions. Finally, we used four LLM agents to generate patient-specific descriptions. The system was evaluated by clinicians for its decision support value. Results: The eXtreme gradient boosting (XGB) model achieved the best performance, with an area under the receiver operating characteristic curve (AUROC) of 0.804 (95% confidence interval (CI) 0.792-0.816), area under the precision-recall curve (AUPRC) of 0.529 (95% CI 0.502-0.558) and a Brier score of 0.135 (95% CI 0.130-0.140). Discussion: The CIP cost curves provided a population-level overview of cost composition across decision thresholds, whereas LLM-generated cost-benefit analysis at individual patient-levels. The system was well received according to the evaluation by clinicians. However, feedback emphasizes the need to strengthen the technical accuracy for speculative tasks. Conclusion: CAP utilizes LLM agents to integrate ML classifier outcomes and cost-benefit analysis for more transparent and interpretable decision support.", "date": "2025-11-19", "categories": []}
{"id": "2511.15485", "title": "A Novel CustNetGC Boosted Model with Spectral Features for Parkinson's Disease Prediction", "abstract": "Parkinson's disease is a neurodegenerative disorder that can be very tricky to diagnose and treat. Such early symptoms can include tremors, wheezy breathing, and changes in voice quality as critical indicators of neural damage. Notably, there has been growing interest in utilizing changes in vocal attributes as markers for the detection of PD early on. Based on this understanding, the present paper was designed to focus on the acoustic feature analysis based on voice recordings of patients diagnosed with PD and healthy controls (HC). In this paper, we introduce a novel classification and visualization model known as CustNetGC, combining a Convolutional Neural Network (CNN) with Custom Network Grad-CAM and CatBoost to enhance the efficiency of PD diagnosis. We use a publicly available dataset from Figshare, including voice recordings of 81 participants: 40 patients with PD and 41 healthy controls. From these recordings, we extracted the key spectral features: L-mHP and Spectral Slopes. The L-mHP feature combines three spectrogram representations: Log-Mel spectrogram, harmonic spectrogram, and percussive spectrogram, which are derived using Harmonic-Percussive Source Separation (HPSS). Grad-CAM was used to highlight the important regions in the data, thus making the PD predictions interpretable and effective. Our proposed CustNetGC model achieved an accuracy of 99.06% and precision of 95.83%, with the area under the ROC curve (AUC) recorded at 0.90 for the PD class and 0.89 for the HC class. Additionally, the combination of CatBoost, a gradient boosting algorithm, enhanced the robustness and the prediction performance by properly classifying PD and non-PD samples. Therefore, the results provide the potential improvement in the CustNetGC system in enhancing diagnostic accuracy and the interpretability of the Parkinson's Disease prediction model.", "date": "2025-11-19", "categories": []}
{"id": "2511.15632", "title": "CODE-II: A large-scale dataset for artificial intelligence in ECG analysis", "abstract": "Data-driven methods for electrocardiogram (ECG) interpretation are rapidly progressing. Large datasets have enabled advances in artificial intelligence (AI) based ECG analysis, yet limitations in annotation quality, size, and scope remain major challenges. Here we present CODE-II, a large-scale real-world dataset of 2,735,269 12-lead ECGs from 2,093,807 adult patients collected by the Telehealth Network of Minas Gerais (TNMG), Brazil. Each exam was annotated using standardized diagnostic criteria and reviewed by cardiologists. A defining feature of CODE-II is a set of 66 clinically meaningful diagnostic classes, developed with cardiologist input and routinely used in telehealth practice. We additionally provide an open available subset: CODE-II-open, a public subset of 15,000 patients, and the CODE-II-test, a non-overlapping set of 8,475 exams reviewed by multiple cardiologists for blinded evaluation. A neural network pre-trained on CODE-II achieved superior transfer performance on external benchmarks (PTB-XL and CPSC 2018) and outperformed alternatives trained on larger datasets.", "date": "2025-11-19", "categories": []}
{"id": "2511.02340", "title": "Chronic Kidney Disease Prognosis Prediction Using Transformer", "abstract": "Chronic Kidney Disease (CKD) affects nearly 10\\% of the global population and often progresses to end-stage renal failure. Accurate prognosis prediction is vital for timely interventions and resource optimization. We present a transformer-based framework for predicting CKD progression using multi-modal electronic health records (EHR) from the Seoul National University Hospital OMOP Common Data Model. Our approach (\\textbf{ProQ-BERT}) integrates demographic, clinical, and laboratory data, employing quantization-based tokenization for continuous lab values and attention mechanisms for interpretability. The model was pretrained with masked language modeling and fine-tuned for binary classification tasks predicting progression from stage 3a to stage 5 across varying follow-up and assessment periods. Evaluated on a cohort of 91,816 patients, our model consistently outperformed CEHR-BERT, achieving ROC-AUC up to 0.995 and PR-AUC up to 0.989 for short-term prediction. These results highlight the effectiveness of transformer architectures and temporal design choices in clinical prognosis modeling, offering a promising direction for personalized CKD care.", "date": "2025-11-04", "categories": []}
{"id": "2511.13757", "title": "VitalBench: A Rigorous Multi-Center Benchmark for Long-Term Vital Sign Prediction in Intraoperative Care", "abstract": "Intraoperative monitoring and prediction of vital signs are critical for ensuring patient safety and improving surgical outcomes. Despite recent advances in deep learning models for medical time-series forecasting, several challenges persist, including the lack of standardized benchmarks, incomplete data, and limited cross-center validation. To address these challenges, we introduce VitalBench, a novel benchmark specifically designed for intraoperative vital sign prediction. VitalBench includes data from over 4,000 surgeries across two independent medical centers, offering three evaluation tracks: complete data, incomplete data, and cross-center generalization. This framework reflects the real-world complexities of clinical practice, minimizing reliance on extensive preprocessing and incorporating masked loss techniques for robust and unbiased model evaluation. By providing a standardized and unified platform for model development and comparison, VitalBench enables researchers to focus on architectural innovation while ensuring consistency in data handling. This work lays the foundation for advancing predictive models for intraoperative vital sign forecasting, ensuring that these models are not only accurate but also robust and adaptable across diverse clinical environments. Our code and data are available at https://github.com/XiudingCai/VitalBench.", "date": "2025-11-14", "categories": []}
{"id": "2511.13797", "title": "MAT-MPNN: A Mobility-Aware Transformer-MPNN Model for Dynamic Spatiotemporal Prediction of HIV Diagnoses in California, Florida, and New England", "abstract": "Human Immunodeficiency Virus (HIV) has posed a major global health challenge for decades, and forecasting HIV diagnoses continues to be a critical area of research. However, capturing the complex spatial and temporal dependencies of HIV transmission remains challenging. Conventional Message Passing Neural Network (MPNN) models rely on a fixed binary adjacency matrix that only encodes geographic adjacency, which is unable to represent interactions between non-contiguous counties. Our study proposes a deep learning architecture Mobility-Aware Transformer-Message Passing Neural Network (MAT-MPNN) framework to predict county-level HIV diagnosis rates across California, Florida, and the New England region. The model combines temporal features extracted by a Transformer encoder with spatial relationships captured through a Mobility Graph Generator (MGG). The MGG improves conventional adjacency matrices by combining geographic and demographic information. Compared with the best-performing hybrid baseline, the Transformer MPNN model, MAT-MPNN reduced the Mean Squared Prediction Error (MSPE) by 27.9% in Florida, 39.1% in California, and 12.5% in New England, and improved the Predictive Model Choice Criterion (PMCC) by 7.7%, 3.5%, and 3.9%, respectively. MAT-MPNN also achieved better results than the Spatially Varying Auto-Regressive (SVAR) model in Florida and New England, with comparable performance in California. These results demonstrate that applying mobility-aware dynamic spatial structures substantially enhances predictive accuracy and calibration in spatiotemporal epidemiological prediction.", "date": "2025-11-17", "categories": []}
{"id": "2511.14110", "title": "A Patient-Independent Neonatal Seizure Prediction Model Using Reduced Montage EEG and ECG", "abstract": "Neonates are highly susceptible to seizures, often leading to short or long-term neurological impairments. However, clinical manifestations of neonatal seizures are subtle and often lead to misdiagnoses. This increases the risk of prolonged, untreated seizure activity and subsequent brain injury. Continuous video electroencephalogram (cEEG) monitoring is the gold standard for seizure detection. However, this is an expensive evaluation that requires expertise and time. In this study, we propose a convolutional neural network-based model for early prediction of neonatal seizures by distinguishing between interictal and preictal states of the EEG. Our model is patient-independent, enabling generalization across multiple subjects, and utilizes mel-frequency cepstral coefficient matrices extracted from multichannel EEG and electrocardiogram (ECG) signals as input features. Trained and validated on the Helsinki neonatal EEG dataset with 10-fold cross-validation, the proposed model achieved an average accuracy of 97.52%, sensitivity of 98.31%, specificity of 96.39%, and F1-score of 97.95%, enabling accurate seizure prediction up to 30 minutes before onset. The inclusion of ECG alongside EEG improved the F1-score by 1.42%, while the incorporation of an attention mechanism yielded an additional 0.5% improvement. To enhance transparency, we incorporated SHapley Additive exPlanations (SHAP) as an explainable artificial intelligence method to interpret the model and provided localization of seizure focus using scalp plots. The overall results demonstrate the model's potential for minimally supervised deployment in neonatal intensive care units, enabling timely and reliable prediction of neonatal seizures, while demonstrating strong generalization capability across unseen subjects through transfer learning.", "date": "2025-11-18", "categories": []}
{"id": "2511.14119", "title": "Real-Time Mobile Video Analytics for Pre-arrival Emergency Medical Services", "abstract": "Timely and accurate pre-arrival video streaming and analytics are critical for emergency medical services (EMS) to deliver life-saving interventions. Yet, current-generation EMS infrastructure remains constrained by one-to-one video streaming and limited analytics capabilities, leaving dispatchers and EMTs to manually interpret overwhelming, often noisy or redundant information in high-stress environments. We present TeleEMS, a mobile live video analytics system that enables pre-arrival multimodal inference by fusing audio and video into a unified decision-making pipeline before EMTs arrive on scene.\n  TeleEMS comprises two key components: TeleEMS Client and TeleEMS Server. The TeleEMS Client runs across phones, smart glasses, and desktops to support bystanders, EMTs en route, and 911 dispatchers. The TeleEMS Server, deployed at the edge, integrates EMS-Stream, a communication backbone that enables smooth multi-party video streaming. On top of EMSStream, the server hosts three real-time analytics modules: (1) audio-to-symptom analytics via EMSLlama, a domain-specialized LLM for robust symptom extraction and normalization; (2) video-to-vital analytics using state-of-the-art rPPG methods for heart rate estimation; and (3) joint text-vital analytics via PreNet, a multimodal multitask model predicting EMS protocols, medication types, medication quantities, and procedures.\n  Evaluation shows that EMSLlama outperforms GPT-4o (exact-match 0.89 vs. 0.57) and that text-vital fusion improves inference robustness, enabling reliable pre-arrival intervention recommendations. TeleEMS demonstrates the potential of mobile live video analytics to transform EMS operations, bridging the gap between bystanders, dispatchers, and EMTs, and paving the way for next-generation intelligent EMS infrastructure.", "date": "2025-11-18", "categories": []}
{"id": "2303.10390", "title": "HIBMatch: Hypergraph Information Bottleneck for Semi-supervised Alzheimer's Progression", "abstract": "Alzheimer's disease progression prediction is critical for patients with early Mild Cognitive Impairment (MCI) to enable timely intervention and improve their quality of life. While existing progression prediction techniques demonstrate potential with multimodal data, they are highly limited by their reliance on labelled data and fail to account for a key element of future progression prediction: not all features extracted at the current moment may be relevant for predicting progression several years later. To address these limitations in the literature, we design a novel semi-supervised multimodal learning hypergraph architecture, termed HIBMatch, by harnessing hypergraph knowledge based on information bottleneck and consistency regularisation strategies. Firstly, our framework utilises hypergraphs to represent multimodal data, encompassing both imaging and non-imaging modalities. Secondly, to harmonise relevant information from the currently captured data for future MCI conversion prediction, we propose a Hypergraph Information Bottleneck (HIB) that discriminates against irrelevant information, thereby focusing exclusively on harmonising relevant information for future MCI conversion prediction. Thirdly, our method enforces consistency regularisation between the HIB and a discriminative classifier to enhance the robustness and generalisation capabilities of HIBMatch under both topological and feature perturbations. Finally, to fully exploit the unlabeled data, HIBMatch incorporates a cross-modal contrastive loss for data efficiency. Extensive experiments on the Alzheimer's Disease Neuroimaging Initiative (ADNI) dataset demonstrate that our proposed HIBMatch framework surpasses existing state-of-the-art methods in Alzheimer's disease prognosis.", "date": "2023-03-18", "categories": []}
{"id": "2508.16839", "title": "One VLM, Two Roles: Stage-Wise Routing and Specialty-Level Deployment for Clinical Workflows", "abstract": "Clinical ML workflows are often fragmented and inefficient: triage, task selection, and model deployment are handled by a patchwork of task-specific networks. These pipelines are rarely aligned with data-science practice, reducing efficiency and increasing operational cost. They also lack data-driven model identification (from imaging/tabular inputs) and standardized delivery of model outputs. We present a framework that employs a single vision-language model (VLM) in two complementary, modular roles.\n  First (Solution 1): the VLM acts as an aware model-card matcher that routes an incoming image to the appropriate specialist model via a three-stage workflow (modality -> primary abnormality -> model-card ID). Reliability is improved by (i) stage-wise prompts enabling early termination via \"None\"/\"Other\" and (ii) a calibrated top-2 answer selector with a stage-wise cutoff. This raises routing accuracy by +9 and +11 percentage points on the training and held-out splits, respectively, compared with a baseline router, and improves held-out calibration (lower Expected Calibration Error, ECE).\n  Second (Solution 2): we fine-tune the same VLM on specialty-specific datasets so that one model per specialty covers multiple downstream tasks, simplifying deployment while maintaining performance. Across gastroenterology, hematology, ophthalmology, pathology, and radiology, this single-model deployment matches or approaches specialized baselines.\n  Together, these solutions reduce data-science effort through more accurate selection, simplify monitoring and maintenance by consolidating task-specific models, and increase transparency via per-stage justifications and calibrated thresholds. Each solution stands alone, and in combination they offer a practical, modular path from triage to deployment.", "date": "2025-08-22", "categories": []}
{"id": "2509.10517", "title": "A Comparative Benchmark of Federated Learning Strategies for Mortality Prediction on Heterogeneous and Imbalanced Clinical Data", "abstract": "Machine learning models hold significant potential for predicting in-hospital mortality, yet data privacy constraints and the statistical heterogeneity of real-world clinical data often hamper their development. Federated Learning (FL) offers a privacy-preserving solution, but its performance under non-Independent and Identically Distributed (non-IID) and imbalanced conditions requires rigorous investigation. The study presents a comparative benchmark of five federated learning strategies: FedAvg, FedProx, FedAdagrad, FedAdam, and FedCluster for mortality prediction. Using the large-scale MIMIC-IV dataset, we simulate a realistic non-IID environment by partitioning data by clinical care unit. To address the inherent class imbalance of the task, the SMOTE-Tomek technique is applied to each client's local training data. Our experiments, conducted over 50 communication rounds, reveal that the regularization-based strategy, FedProx, consistently outperformed other methods, achieving the highest F1-Score of 0.8831 while maintaining stable convergence. While the baseline FedAvg was the most computationally efficient, its predictive performance was substantially lower. Our findings indicate that regularization-based FL algorithms like FedProx offer a more robust and effective solution for heterogeneous and imbalanced clinical prediction tasks than standard or server-side adaptive aggregation methods. The work provides a crucial empirical benchmark for selecting appropriate FL strategies for real-world healthcare applications.", "date": "2025-09-03", "categories": []}
{"id": "2510.18988", "title": "Timely Clinical Diagnosis through Active Test Selection", "abstract": "There is growing interest in using machine learning (ML) to support clinical diagnosis, but most approaches rely on static, fully observed datasets and fail to reflect the sequential, resource-aware reasoning clinicians use in practice. Diagnosis remains complex and error prone, especially in high-pressure or resource-limited settings, underscoring the need for frameworks that help clinicians make timely and cost-effective decisions. We propose ACTMED (Adaptive Clinical Test selection via Model-based Experimental Design), a diagnostic framework that integrates Bayesian Experimental Design (BED) with large language models (LLMs) to better emulate real-world diagnostic reasoning. At each step, ACTMED selects the test expected to yield the greatest reduction in diagnostic uncertainty for a given patient. LLMs act as flexible simulators, generating plausible patient state distributions and supporting belief updates without requiring structured, task-specific training data. Clinicians can remain in the loop; reviewing test suggestions, interpreting intermediate outputs, and applying clinical judgment throughout. We evaluate ACTMED on real-world datasets and show it can optimize test selection to improve diagnostic accuracy, interpretability, and resource use. This represents a step toward transparent, adaptive, and clinician-aligned diagnostic systems that generalize across settings with reduced reliance on domain-specific data.", "date": "2025-10-21", "categories": []}
{"id": "2510.23639", "title": "Integrating Genomics into Multimodal EHR Foundation Models", "abstract": "This paper introduces an innovative Electronic Health Record (EHR) foundation model that integrates Polygenic Risk Scores (PRS) as a foundational data modality, moving beyond traditional EHR-only approaches to build more holistic health profiles. Leveraging the extensive and diverse data from the All of Us (AoU) Research Program, this multimodal framework aims to learn complex relationships between clinical data and genetic predispositions. The methodology extends advancements in generative AI to the EHR foundation model space, enhancing predictive capabilities and interpretability. Evaluation on AoU data demonstrates the model's predictive value for the onset of various conditions, particularly Type 2 Diabetes (T2D), and illustrates the interplay between PRS and EHR data. The work also explores transfer learning for custom classification tasks, showcasing the architecture's versatility and efficiency. This approach is pivotal for unlocking new insights into disease prediction, proactive health management, risk stratification, and personalized treatment strategies, laying the groundwork for more personalized, equitable, and actionable real-world evidence generation in healthcare.", "date": "2025-10-24", "categories": []}
{"id": "2511.11623", "title": "Early GVHD Prediction in Liver Transplantation via Multi-Modal Deep Learning on Imbalanced EHR Data", "abstract": "Graft-versus-host disease (GVHD) is a rare but often fatal complication in liver transplantation, with a very high mortality rate. By harnessing multi-modal deep learning methods to integrate heterogeneous and imbalanced electronic health records (EHR), we aim to advance early prediction of GVHD, paving the way for timely intervention and improved patient outcomes. In this study, we analyzed pre-transplant electronic health records (EHR) spanning the period before surgery for 2,100 liver transplantation patients, including 42 cases of graft-versus-host disease (GVHD), from a cohort treated at Mayo Clinic between 1992 and 2025. The dataset comprised four major modalities: patient demographics, laboratory tests, diagnoses, and medications. We developed a multi-modal deep learning framework that dynamically fuses these modalities, handles irregular records with missing values, and addresses extreme class imbalance through AUC-based optimization. The developed framework outperforms all single-modal and multi-modal machine learning baselines, achieving an AUC of 0.836, an AUPRC of 0.157, a recall of 0.768, and a specificity of 0.803. It also demonstrates the effectiveness of our approach in capturing complementary information from different modalities, leading to improved performance. Our multi-modal deep learning framework substantially improves existing approaches for early GVHD prediction. By effectively addressing the challenges of heterogeneity and extreme class imbalance in real-world EHR, it achieves accurate early prediction. Our proposed multi-modal deep learning method demonstrates promising results for early prediction of a GVHD in liver transplantation, despite the challenge of extremely imbalanced EHR data.", "date": "2025-11-06", "categories": []}
{"id": "2511.11867", "title": "Identifying Imaging Follow-Up in Radiology Reports: A Comparative Analysis of Traditional ML and LLM Approaches", "abstract": "Large language models (LLMs) have shown considerable promise in clinical natural language processing, yet few domain-specific datasets exist to rigorously evaluate their performance on radiology tasks. In this work, we introduce an annotated corpus of 6,393 radiology reports from 586 patients, each labeled for follow-up imaging status, to support the development and benchmarking of follow-up adherence detection systems. Using this corpus, we systematically compared traditional machine-learning classifiers, including logistic regression (LR), support vector machines (SVM), Longformer, and a fully fine-tuned Llama3-8B-Instruct, with recent generative LLMs. To evaluate generative LLMs, we tested GPT-4o and the open-source GPT-OSS-20B under two configurations: a baseline (Base) and a task-optimized (Advanced) setting that focused inputs on metadata, recommendation sentences, and their surrounding context. A refined prompt for GPT-OSS-20B further improved reasoning accuracy. Performance was assessed using precision, recall, and F1 scores with 95% confidence intervals estimated via non-parametric bootstrapping. Inter-annotator agreement was high (F1 = 0.846). GPT-4o (Advanced) achieved the best performance (F1 = 0.832), followed closely by GPT-OSS-20B (Advanced; F1 = 0.828). LR and SVM also performed strongly (F1 = 0.776 and 0.775), underscoring that while LLMs approach human-level agreement through prompt optimization, interpretable and resource-efficient models remain valuable baselines.", "date": "2025-11-14", "categories": []}
{"id": "2511.11883", "title": "ClinStructor: AI-Powered Structuring of Unstructured Clinical Texts", "abstract": "Clinical notes contain valuable, context-rich information, but their unstructured format introduces several challenges, including unintended biases (e.g., gender or racial bias), and poor generalization across clinical settings (e.g., models trained on one EHR system may perform poorly on another due to format differences) and poor interpretability. To address these issues, we present ClinStructor, a pipeline that leverages large language models (LLMs) to convert clinical free-text into structured, task-specific question-answer pairs prior to predictive modeling. Our method substantially enhances transparency and controllability and only leads to a modest reduction in predictive performance (a 2-3% drop in AUC), compared to direct fine-tuning, on the ICU mortality prediction task. ClinStructor lays a strong foundation for building reliable, interpretable, and generalizable machine learning models in clinical environments.", "date": "2025-11-14", "categories": []}
{"id": "2511.12241", "title": "AURA: Development and Validation of an Augmented Unplanned Removal Alert System using Synthetic ICU Videos", "abstract": "Unplanned extubation (UE) remains a critical patient safety concern in intensive care units (ICUs), often leading to severe complications or death. Real-time UE detection has been limited, largely due to the ethical and privacy challenges of obtaining annotated ICU video data. We propose Augmented Unplanned Removal Alert (AURA), a vision-based risk detection system developed and validated entirely on a fully synthetic video dataset. By leveraging text-to-video diffusion, we generated diverse and clinically realistic ICU scenarios capturing a range of patient behaviors and care contexts. The system applies pose estimation to identify two high-risk movement patterns: collision, defined as hand entry into spatial zones near airway tubes, and agitation, quantified by the velocity of tracked anatomical keypoints. Expert assessments confirmed the realism of the synthetic data, and performance evaluations showed high accuracy for collision detection and moderate performance for agitation recognition. This work demonstrates a novel pathway for developing privacy-preserving, reproducible patient safety monitoring systems with potential for deployment in intensive care settings.", "date": "2025-11-15", "categories": []}
{"id": "2511.13457", "title": "Artificial Intelligence-Enabled Spirometry for Early Detection of Right Heart Failure", "abstract": "Right heart failure (RHF) is a disease characterized by abnormalities in the structure or function of the right ventricle (RV), which is associated with high morbidity and mortality. Lung disease often causes increased right ventricular load, leading to RHF. Therefore, it is very important to screen out patients with cor pulmonale who develop RHF from people with underlying lung diseases. In this work, we propose a self-supervised representation learning method to early detecting RHF from patients with cor pulmonale, which uses spirogram time series to predict patients with RHF at an early stage. The proposed model is divided into two stages. The first stage is the self-supervised representation learning-based spirogram embedding (SLSE) network training process, where the encoder of the Variational autoencoder (VAE-encoder) learns a robust low-dimensional representation of the spirogram time series from the data-augmented unlabeled data. Second, this low-dimensional representation is fused with demographic information and fed into a CatBoost classifier for the downstream RHF prediction task. Trained and tested on a carefully selected subset of 26,617 individuals from the UK Biobank, our model achieved an AUROC of 0.7501 in detecting RHF, demonstrating strong population-level distinction ability. We further evaluated the model on high-risk clinical subgroups, achieving AUROC values of 0.8194 on a test set of 74 patients with chronic kidney disease (CKD) and 0.8413 on a set of 64 patients with valvular heart disease (VHD). These results highlight the model's potential utility in predicting RHF among clinically elevated-risk populations. In conclusion, this study presents a self-supervised representation learning approach combining spirogram time series and demographic data, demonstrating promising potential for early RHF detection in clinical practice.", "date": "2025-11-17", "categories": []}
{"id": "2511.13637", "title": "Towards Multimodal Representation Learning in Paediatric Kidney Disease", "abstract": "Paediatric kidney disease varies widely in its presentation and progression, which calls for continuous monitoring of renal function. Using electronic health records collected between 2019 and 2025 at Great Ormond Street Hospital, a leading UK paediatric hospital, we explored a temporal modelling approach that integrates longitudinal laboratory sequences with demographic information. A recurrent neural model trained on these data was used to predict whether a child would record an abnormal serum creatinine value within the following thirty days. Framed as a pilot study, this work provides an initial demonstration that simple temporal representations can capture useful patterns in routine paediatric data and lays the groundwork for future multimodal extensions using additional clinical signals and more detailed renal outcomes.", "date": "2025-11-17", "categories": []}
{"id": "2511.13703", "title": "Generalist Foundation Models Are Not Clinical Enough for Hospital Operations", "abstract": "Hospitals and healthcare systems rely on operational decisions that determine patient flow, cost, and quality of care. Despite strong performance on medical knowledge and conversational benchmarks, foundation models trained on general text may lack the specialized knowledge required for these operational decisions. We introduce Lang1, a family of models (100M-7B parameters) pretrained on a specialized corpus blending 80B clinical tokens from NYU Langone Health's EHRs and 627B tokens from the internet. To rigorously evaluate Lang1 in real-world settings, we developed the REalistic Medical Evaluation (ReMedE), a benchmark derived from 668,331 EHR notes that evaluates five critical tasks: 30-day readmission prediction, 30-day mortality prediction, length of stay, comorbidity coding, and predicting insurance claims denial. In zero-shot settings, both general-purpose and specialized models underperform on four of five tasks (36.6%-71.7% AUROC), with mortality prediction being an exception. After finetuning, Lang1-1B outperforms finetuned generalist models up to 70x larger and zero-shot models up to 671x larger, improving AUROC by 3.64%-6.75% and 1.66%-23.66% respectively. We also observed cross-task scaling with joint finetuning on multiple tasks leading to improvement on other tasks. Lang1-1B effectively transfers to out-of-distribution settings, including other clinical tasks and an external health system. Our findings suggest that predictive capabilities for hospital operations require explicit supervised finetuning, and that this finetuning process is made more efficient by in-domain pretraining on EHR. Our findings support the emerging view that specialized LLMs can compete with generalist models in specialized tasks, and show that effective healthcare systems AI requires the combination of in-domain pretraining, supervised finetuning, and real-world evaluation beyond proxy benchmarks.", "date": "2025-11-17", "categories": []}
{"id": "2510.02558", "title": "AttentiveGRUAE: An Attention-Based GRU Autoencoder for Temporal Clustering and Behavioral Characterization of Depression from Wearable Data", "abstract": "In this study, we present AttentiveGRUAE, a novel attention-based gated recurrent unit (GRU) autoencoder designed for temporal clustering and prediction of outcome from longitudinal wearable data. Our model jointly optimizes three objectives: (1) learning a compact latent representation of daily behavioral features via sequence reconstruction, (2) predicting end-of-period depression rate through a binary classification head, and (3) identifying behavioral subtypes through Gaussian Mixture Model (GMM) based soft clustering of learned embeddings. We evaluate AttentiveGRUAE on longitudinal sleep data from 372 participants (GLOBEM 2018-2019), and it demonstrates superior performance over baseline clustering, domain-aligned self-supervised, and ablated models in both clustering quality (silhouette score = 0.70 vs 0.32-0.70) and depression classification (AUC = 0.74 vs 0.50-0.67). Additionally, external validation on cross-year cohorts from 332 participants (GLOBEM 2020-2021) confirms cluster reproducibility (silhouette score = 0.63, AUC = 0.61) and stability. We further perform subtype analysis and visualize temporal attention, which highlights sleep-related differences between clusters and identifies salient time windows that align with changes in sleep regularity, yielding clinically interpretable explanations of risk.", "date": "2025-10-02", "categories": []}
{"id": "2511.10669", "title": "Forecasting Spoken Language Development in Children with Cochlear Implants Using Preimplantation MRI", "abstract": "Cochlear implants (CI) significantly improve spoken language in children with severe-to-profound sensorineural hearing loss (SNHL), yet outcomes remain more variable than in children with normal hearing. This variability cannot be reliably predicted for individual children using age at implantation or residual hearing. This study aims to compare the accuracy of traditional machine learning (ML) to deep transfer learning (DTL) algorithms to predict post-CI spoken language development of children with bilateral SNHL using a binary classification model of high versus low language improvers. A total of 278 implanted children enrolled from three centers. The accuracy, sensitivity and specificity of prediction models based upon brain neuroanatomic features using traditional ML and DTL learning. DTL prediction models using bilinear attention-based fusion strategy achieved: accuracy of 92.39% (95% CI, 90.70%-94.07%), sensitivity of 91.22% (95% CI, 89.98%-92.47%), specificity of 93.56% (95% CI, 90.91%-96.21%), and area under the curve (AUC) of 0.977 (95% CI, 0.969-0.986). DTL outperformed traditional ML models in all outcome measures. DTL was significantly improved by direct capture of discriminative and task-specific information that are advantages of representation learning enabled by this approach over ML. The results support the feasibility of a single DTL prediction model for language prediction of children served by CI programs worldwide.", "date": "2025-11-09", "categories": []}
{"id": "2511.07127", "title": "REACT-LLM: A Benchmark for Evaluating LLM Integration with Causal Features in Clinical Prognostic Tasks", "abstract": "Large Language Models (LLMs) and causal learning each hold strong potential for clinical decision making (CDM). However, their synergy remains poorly understood, largely due to the lack of systematic benchmarks evaluating their integration in clinical risk prediction. In real-world healthcare, identifying features with causal influence on outcomes is crucial for actionable and trustworthy predictions. While recent work highlights LLMs' emerging causal reasoning abilities, there lacks comprehensive benchmarks to assess their causal learning and performance informed by causal features in clinical risk prediction. To address this, we introduce REACT-LLM, a benchmark designed to evaluate whether combining LLMs with causal features can enhance clinical prognostic performance and potentially outperform traditional machine learning (ML) methods. Unlike existing LLM-clinical benchmarks that often focus on a limited set of outcomes, REACT-LLM evaluates 7 clinical outcomes across 2 real-world datasets, comparing 15 prominent LLMs, 6 traditional ML models, and 3 causal discovery (CD) algorithms. Our findings indicate that while LLMs perform reasonably in clinical prognostics, they have not yet outperformed traditional ML models. Integrating causal features derived from CD algorithms into LLMs offers limited performance gains, primarily due to the strict assumptions of many CD methods, which are often violated in complex clinical data. While the direct integration yields limited improvement, our benchmark reveals a more promising synergy.", "date": "2025-11-10", "categories": []}
{"id": "2511.09576", "title": "Prostate-VarBench: A Benchmark with Interpretable TabNet Framework for Prostate Cancer Variant Classification", "abstract": "Variants of Uncertain Significance (VUS) limit the clinical utility of prostate cancer genomics by delaying diagnosis and therapy when evidence for pathogenicity or benignity is incomplete. Progress is further limited by inconsistent annotations across sources and the absence of a prostate-specific benchmark for fair comparison. We introduce Prostate-VarBench, a curated pipeline for creating prostate-specific benchmarks that integrates COSMIC (somatic cancer mutations), ClinVar (expert-curated clinical variants), and TCGA-PRAD (prostate tumor genomics from The Cancer Genome Atlas) into a harmonized dataset of 193,278 variants supporting patient- or gene-aware splits to prevent data leakage. To ensure data integrity, we corrected a Variant Effect Predictor (VEP) issue that merged multiple transcript records, introducing ambiguity in clinical significance fields. We then standardized 56 interpretable features across eight clinically relevant tiers, including population frequency, variant type, and clinical context. AlphaMissense pathogenicity scores were incorporated to enhance missense variant classification and reduce VUS uncertainty. Building on this resource, we trained an interpretable TabNet model to classify variant pathogenicity, whose step-wise sparse masks provide per-case rationales consistent with molecular tumor board review practices. On the held-out test set, the model achieved 89.9% accuracy with balanced class metrics, and the VEP correction yields an 6.5% absolute reduction in VUS.", "date": "2025-11-12", "categories": []}
{"id": "2510.24889", "title": "Adaptive EEG-based stroke diagnosis with a GRU-TCN classifier and deep Q-learning thresholding", "abstract": "Rapid triage of suspected stroke needs accurate, bedside-deployable tools; EEG is promising but underused at first contact. We present an adaptive multitask EEG classifier that converts 32-channel signals to power spectral density features (Welch), uses a recurrent-convolutional network (GRU-TCN) to predict stroke type (healthy, ischemic, hemorrhagic), hemispheric lateralization, and severity, and applies a deep Q-network (DQN) to tune decision thresholds in real time. Using a patient-wise split of the UCLH Stroke EIT/EEG data set (44 recordings; about 26 acute stroke, 10 controls), the primary outcome was stroke-type performance; secondary outcomes were severity and lateralization. The baseline GRU-TCN reached 89.3% accuracy (F1 92.8%) for stroke type, about 96.9% (F1 95.9%) for severity, and about 96.7% (F1 97.4%) for lateralization. With DQN threshold adaptation, stroke-type accuracy increased to about 98.0% (F1 97.7%). We also tested robustness on an independent, low-density EEG cohort (ZJU4H) and report paired patient-level statistics. Analyses follow STARD 2015 guidance for diagnostic accuracy studies (index test: GRU-TCN+DQN; reference standard: radiology/clinical diagnosis; patient-wise evaluation). Adaptive thresholding shifts the operating point to clinically preferred sensitivity-specificity trade-offs, while integrated scalp-map and spectral visualizations support interpretability.", "date": "2025-10-28", "categories": []}
{"id": "2511.08636", "title": "Detecting Suicidal Ideation in Text with Interpretable Deep Learning: A CNN-BiGRU with Attention Mechanism", "abstract": "Worldwide, suicide is the second leading cause of death for adolescents with past suicide attempts to be an important predictor for increased future suicides. While some people with suicidal thoughts may try to suppress them, many signal their intentions in social media platforms. To address these issues, we propose a new type of hybrid deep learning scheme, i.e., the combination of a CNN architecture and a BiGRU technique, which can accurately identify the patterns of suicidal ideation from SN datasets. Also, we apply Explainable AI methods using SHapley Additive exPlanations to interpret the prediction results and verifying the model reliability. This integration of CNN local feature extraction, BiGRU bidirectional sequence modeling, attention mechanisms, and SHAP interpretability provides a comprehensive framework for suicide detection. Training and evaluation of the system were performed on a publicly available dataset. Several performance metrics were used for evaluating model performance. Our method was found to have achieved 93.97 accuracy in experimental results. Comparative study to different state-of-the-art Machine Learning and DL models and existing literature demonstrates the superiority of our proposed technique over all the competing methods.", "date": "2025-11-10", "categories": []}
{"id": "2511.08650", "title": "A Lightweight CNN-Attention-BiLSTM Architecture for Multi-Class Arrhythmia Classification on Standard and Wearable ECGs", "abstract": "Early and accurate detection of cardiac arrhythmias is vital for timely diagnosis and intervention. We propose a lightweight deep learning model combining 1D Convolutional Neural Networks (CNN), attention mechanisms, and Bidirectional Long Short-Term Memory (BiLSTM) for classifying arrhythmias from both 12-lead and single-lead ECGs. Evaluated on the CPSC 2018 dataset, the model addresses class imbalance using a class-weighted loss and demonstrates superior accuracy and F1- scores over baseline models. With only 0.945 million parameters, our model is well-suited for real-time deployment in wearable health monitoring systems.", "date": "2025-11-11", "categories": []}
{"id": "2511.08806", "title": "Toward Automated Cognitive Assessment in Parkinson's Disease Using Pretrained Language Models", "abstract": "Understanding how individuals with Parkinson's disease (PD) describe cognitive experiences in their daily lives can offer valuable insights into disease-related cognitive and emotional changes. However, extracting such information from unstructured patient narratives is challenging due to the subtle, overlapping nature of cognitive constructs. This study developed and evaluated natural language processing (NLP) models to automatically identify categories that reflect various cognitive processes from de-identified first-person narratives. Three model families, a Bio_ClinicalBERT-based span categorization model for nested entity recognition, a fine-tuned Meta-Llama-3-8B-Instruct model using QLoRA for instruction following, and GPT-4o mini evaluated under zero- and few-shot settings, were compared on their performance on extracting seven categories. Our findings indicated that model performance varied substantially across categories and model families. The fine-tuned Meta-Llama-3-8B-Instruct achieved the highest overall F1-scores (0.74 micro-average and 0.59 macro-average), particularly excelling in context-dependent categories such as thought and social interaction. Bio_ClinicalBERT exhibited high precision but low recall and performed comparable to Llama for some category types such as location and time but failed on other categories such as thought, emotion and social interaction. Compared to conventional information extraction tasks, this task presents a greater challenge due to the abstract and overlapping nature of narrative accounts of complex cognitive processes. Nonetheless, with continued refinement, these NLP systems hold promise for enabling low-burden, longitudinal monitoring of cognitive function and serving as a valuable complement to formal neuropsychological assessments in PD.", "date": "2025-11-11", "categories": []}
{"id": "2511.08994", "title": "Generalisable prediction model of surgical case duration: multicentre development and temporal validation", "abstract": "Background: Accurate prediction of surgical case duration underpins operating room (OR) scheduling, yet existing models often depend on site- or surgeon-specific inputs and rarely undergo external validation, limiting generalisability.\n  Methods: We undertook a retrospective multicentre study using routinely collected perioperative data from two general hospitals in Japan (development: 1 January 2021-31 December 2023; temporal test: 1 January-31 December 2024). Elective weekday procedures with American Society of Anesthesiologists (ASA) Physical Status 1-4 were included. Pre-specified preoperative predictors comprised surgical context (year, month, weekday, scheduled duration, general anaesthesia indicator, body position) and patient factors (sex, age, body mass index, allergy, infection, comorbidity, ASA). Missing data were addressed by multiple imputation by chained equations. Four learners (elastic-net, generalised additive models, random forest, gradient-boosted trees) were tuned within internal-external cross-validation (IECV; leave-one-cluster-out by centre-year) and combined by stacked generalisation to predict log-transformed duration.\n  Results: We analysed 63,206 procedures (development 45,647; temporal test 17,559). Cluster-specific and pooled errors and calibrations from IECV are provided with consistent performance across centres and years. In the 2024 temporal test cohort, calibration was good (intercept 0.423, 95%CI 0.372 to 0.474; slope 0.921, 95%CI 0.911 to 0.932).\n  Conclusions: A stacked machine-learning model using only widely available preoperative variables achieved accurate, well-calibrated predictions in temporal external validation, supporting transportability across sites and over time. Such general-purpose tools may improve OR scheduling without relying on idiosyncratic inputs.", "date": "2025-11-12", "categories": []}
{"id": "2511.09247", "title": "MedFuse: Multiplicative Embedding Fusion For Irregular Clinical Time Series", "abstract": "Clinical time series derived from electronic health records (EHRs) are inherently irregular, with asynchronous sampling, missing values, and heterogeneous feature dynamics. While numerical laboratory measurements are highly informative, existing embedding strategies usually combine feature identity and value embeddings through additive operations, which constrains their ability to capture value-dependent feature interactions. We propose MedFuse, a framework for irregular clinical time series centered on the MuFuse (Multiplicative Embedding Fusion) module. MuFuse fuses value and feature embeddings through multiplicative modulation, preserving feature-specific information while modeling higher-order dependencies across features. Experiments on three real-world datasets covering both intensive and chronic care show that MedFuse consistently outperforms state-of-the-art baselines on key predictive tasks. Analysis of the learned representations further demonstrates that multiplicative fusion enhances expressiveness and supports cross-dataset pretraining. These results establish MedFuse as a generalizable approach for modeling irregular clinical time series.", "date": "2025-11-12", "categories": []}
{"id": "2502.10388", "title": "Aspect-Oriented Summarization for Psychiatric Short-Term Readmission Prediction", "abstract": "Recent progress in large language models (LLMs) has enabled the automated processing of lengthy documents even without supervised training on a task-specific dataset. Yet, their zero-shot performance in complex tasks as opposed to straightforward information extraction tasks remains suboptimal. One feasible approach for tasks with lengthy, complex input is to first summarize the document and then apply supervised fine-tuning to the summary. However, the summarization process inevitably results in some loss of information. In this study we present a method for processing the summaries of long documents aimed to capture different important aspects of the original document. We hypothesize that LLM summaries generated with different aspect-oriented prompts contain different information signals, and we propose methods to measure these differences. We introduce approaches to effectively integrate signals from these different summaries for supervised training of transformer models. We validate our hypotheses on a high-impact task -- 30-day readmission prediction from a psychiatric discharge -- using real-world data from four hospitals, and show that our proposed method increases the prediction performance for the complex task of predicting patient outcome.", "date": "2025-02-14", "categories": []}
{"id": "2511.08260", "title": "Data-Driven Discovery of Feature Groups in Clinical Time Series", "abstract": "Clinical time series data are critical for patient monitoring and predictive modeling. These time series are typically multivariate and often comprise hundreds of heterogeneous features from different data sources. The grouping of features based on similarity and relevance to the prediction task has been shown to enhance the performance of deep learning architectures. However, defining these groups a priori using only semantic knowledge is challenging, even for domain experts. To address this, we propose a novel method that learns feature groups by clustering weights of feature-wise embedding layers. This approach seamlessly integrates into standard supervised training and discovers the groups that directly improve downstream performance on clinically relevant tasks. We demonstrate that our method outperforms static clustering approaches on synthetic data and achieves performance comparable to expert-defined groups on real-world medical data. Moreover, the learned feature groups are clinically interpretable, enabling data-driven discovery of task-relevant relationships between variables.", "date": "2025-11-11", "categories": []}
{"id": "2506.18106", "title": "CT Radiomics-Based Explainable Machine Learning Model for Accurate Differentiation of Malignant and Benign Endometrial Tumors: A Two-Center Study", "abstract": "Aimed to develop and validate a CT radiomics-based explainable machine learning model for precise diagnosing malignancy and benignity specifically in endometrial cancer (EC) patients. A total of 83 EC patients from two centers, including 46 with malignant and 37 with benign conditions, were included, with data split into a training set (n=59) and a testing set (n=24). The regions of interest (ROIs) were manually segmented from pre-surgical CT scans, and 1132 radiomic features were extracted from the pre-surgical CT scans using Pyradiomics. Six explainable machine learning (ML) modeling algorithms were implemented respectively, for determining the optimal radiomics pipeline. The diagnostic performance of the radiomic model was evaluated by using sensitivity, specificity, accuracy, precision, F1 score, AUROC, and AUPRC. To enhance clinical understanding and usability, we separately implemented SHAP analysis and feature mapping visualization, and evaluated the calibration curve and decision curve. By comparing six modeling strategies, the Random Forest model emerged as the optimal choice for diagnosing EC, with a training AUROC of 1.00 and a testing AUROC of 0.96. SHAP identified the most important radiomic features, revealing that all selected features were significantly associated with EC (P < 0.05). Radiomics feature maps also provide a feasible assessment tool for clinical applications. Decision Curve Analysis (DCA) indicated a higher net benefit for our model compared to the \"All\" and \"None\" strategies, suggesting its clinical utility in identifying high-risk cases and reducing unnecessary interventions. In conclusion, the CT radiomics-based explainable ML model achieved high diagnostic performance, which could be used as an intelligent auxiliary tool for the diagnosis of endometrial cancer.", "date": "2025-06-22", "categories": []}
{"id": "2508.12104", "title": "Generative Medical Event Models Improve with Scale", "abstract": "Realizing personalized medicine at scale calls for methods that distill insights from longitudinal patient journeys, which can be viewed as a sequence of medical events. Foundation models pretrained on large-scale medical event data represent a promising direction for scaling real-world evidence generation and generalizing to diverse downstream tasks. Using Epic Cosmos, a dataset with medical events from de-identified longitudinal health records for 16.3 billion encounters over 300 million unique patient records from 310 health systems, we introduce the Curiosity models, a family of decoder-only transformer models pretrained on 118 million patients representing 115 billion discrete medical events (151 billion tokens). We present the largest scaling-law study of medical event data, establishing a methodology for pretraining and revealing power-law scaling relationships for compute, tokens, and model size. Consequently, we pretrained a series of compute-optimal models with up to 1 billion parameters. Conditioned on a patient's real-world history, Curiosity autoregressively predicts the next medical event to simulate patient health timelines. We studied 78 real-world tasks, including diagnosis prediction, disease prognosis, and healthcare operations. Remarkably for a foundation model with generic pretraining and simulation-based inference, Curiosity generally outperformed or matched task-specific supervised models on these tasks, without requiring task-specific fine-tuning or few-shot examples. Curiosity's predictive power consistently improves as the model and pretraining scale. Our results show that Curiosity, a generative medical event foundation model, can effectively capture complex clinical dynamics, providing an extensible and generalizable framework to support clinical decision-making, streamline healthcare operations, and improve patient outcomes.", "date": "2025-08-16", "categories": []}
{"id": "2511.05696", "title": "AI-assisted workflow enables rapid, high-fidelity breast cancer clinical trial eligibility prescreening", "abstract": "Clinical trials play an important role in cancer care and research, yet participation rates remain low. We developed MSK-MATCH (Memorial Sloan Kettering Multi-Agent Trial Coordination Hub), an AI system for automated eligibility screening from clinical text. MSK-MATCH integrates a large language model with a curated oncology trial knowledge base and retrieval-augmented architecture providing explanations for all AI predictions grounded in source text. In a retrospective dataset of 88,518 clinical documents from 731 patients across six breast cancer trials, MSK-MATCH automatically resolved 61.9% of cases and triaged 38.1% for human review. This AI-assisted workflow achieved 98.6% accuracy, 98.4% sensitivity, and 98.7% specificity for patient-level eligibility classification, matching or exceeding performance of the human-only and AI-only comparisons. For the triaged cases requiring manual review, prepopulating eligibility screens with AI-generated explanations reduced screening time from 20 minutes to 43 seconds at an average cost of $0.96 per patient-trial pair.", "date": "2025-11-07", "categories": []}
{"id": "2511.05973", "title": "Explainable Deep Learning-based Classification of Wolff-Parkinson-White Electrocardiographic Signals", "abstract": "Wolff-Parkinson-White (WPW) syndrome is a cardiac electrophysiology (EP) disorder caused by the presence of an accessory pathway (AP) that bypasses the atrioventricular node, faster ventricular activation rate, and provides a substrate for atrio-ventricular reentrant tachycardia (AVRT). Accurate localization of the AP is critical for planning and guiding catheter ablation procedures. While traditional diagnostic tree (DT) methods and more recent machine learning (ML) approaches have been proposed to predict AP location from surface electrocardiogram (ECG), they are often constrained by limited anatomical localization resolution, poor interpretability, and the use of small clinical datasets. In this study, we present a Deep Learning (DL) model for the localization of single manifest APs across 24 cardiac regions, trained on a large, physiologically realistic database of synthetic ECGs generated using a personalized virtual heart model. We also integrate eXplainable Artificial Intelligence (XAI) methods, Guided Backpropagation, Grad-CAM, and Guided Grad-CAM, into the pipeline. This enables interpretation of DL decision-making and addresses one of the main barriers to clinical adoption: lack of transparency in ML predictions. Our model achieves localization accuracy above 95%, with a sensitivity of 94.32% and specificity of 99.78%. XAI outputs are physiologically validated against known depolarization patterns, and a novel index is introduced to identify the most informative ECG leads for AP localization. Results highlight lead V2 as the most critical, followed by aVF, V1, and aVL. This work demonstrates the potential of combining cardiac digital twins with explainable DL to enable accurate, transparent, and non-invasive AP localization.", "date": "2025-11-08", "categories": []}
{"id": "2511.06215", "title": "Explicit Knowledge-Guided In-Context Learning for Early Detection of Alzheimer's Disease", "abstract": "Detecting Alzheimer's Disease (AD) from narrative transcripts remains a challenging task for large language models (LLMs), particularly under out-of-distribution (OOD) and data-scarce conditions. While in-context learning (ICL) provides a parameter-efficient alternative to fine-tuning, existing ICL approaches often suffer from task recognition failure, suboptimal demonstration selection, and misalignment between label words and task objectives, issues that are amplified in clinical domains like AD detection. We propose Explicit Knowledge In-Context Learners (EK-ICL), a novel framework that integrates structured explicit knowledge to enhance reasoning stability and task alignment in ICL. EK-ICL incorporates three knowledge components: confidence scores derived from small language models (SLMs) to ground predictions in task-relevant patterns, parsing feature scores to capture structural differences and improve demo selection, and label word replacement to resolve semantic misalignment with LLM priors. In addition, EK-ICL employs a parsing-based retrieval strategy and ensemble prediction to mitigate the effects of semantic homogeneity in AD transcripts. Extensive experiments across three AD datasets demonstrate that EK-ICL significantly outperforms state-of-the-art fine-tuning and ICL baselines. Further analysis reveals that ICL performance in AD detection is highly sensitive to the alignment of label semantics and task-specific context, underscoring the importance of explicit knowledge in clinical reasoning under low-resource conditions.", "date": "2025-11-09", "categories": []}
{"id": "2511.06230", "title": "Overview of CHIP 2025 Shared Task 2: Discharge Medication Recommendation for Metabolic Diseases Based on Chinese Electronic Health Records", "abstract": "Discharge medication recommendation plays a critical role in ensuring treatment continuity, preventing readmission, and improving long-term management for patients with chronic metabolic diseases. This paper present an overview of the CHIP 2025 Shared Task 2 competition, which aimed to develop state-of-the-art approaches for automatically recommending appro-priate discharge medications using real-world Chinese EHR data. For this task, we constructed CDrugRed, a high-quality dataset consisting of 5,894 de-identified hospitalization records from 3,190 patients in China. This task is challenging due to multi-label nature of medication recommendation, het-erogeneous clinical text, and patient-specific variability in treatment plans. A total of 526 teams registered, with 167 and 95 teams submitting valid results to the Phase A and Phase B leaderboards, respectively. The top-performing team achieved the highest overall performance on the final test set, with a Jaccard score of 0.5102, F1 score of 0.6267, demonstrating the potential of advanced large language model (LLM)-based ensemble systems. These re-sults highlight both the promise and remaining challenges of applying LLMs to medication recommendation in Chinese EHRs. The post-evaluation phase remains open at https://tianchi.aliyun.com/competition/entrance/532411/.", "date": "2025-11-09", "categories": []}
{"id": "2511.06681", "title": "An Adaptive Machine Learning Triage Framework for Predicting Alzheimer's Disease Progression", "abstract": "Accurate predictions of conversion from mild cognitive impairment (MCI) to Alzheimer's disease (AD) can enable effective personalized therapy. While cognitive tests and clinical data are routinely collected, they lack the predictive power of PET scans and CSF biomarker analysis, which are prohibitively expensive to obtain for every patient. To address this cost-accuracy dilemma, we design a two-stage machine learning framework that selectively obtains advanced, costly features based on their predicted \"value of information\". We apply our framework to predict AD progression for MCI patients using data from the Alzheimer's Disease Neuroimaging Initiative (ADNI). Our framework reduces the need for advanced testing by 20% while achieving a test AUROC of 0.929, comparable to the model that uses both basic and advanced features (AUROC=0.915, p=0.1010). We also provide an example interpretability analysis showing how one may explain the triage decision. Our work presents an interpretable, data-driven framework that optimizes AD diagnostic pathways and balances accuracy with cost, representing a step towards making early, reliable AD prediction more accessible in real-world practice. Future work should consider multiple categories of advanced features and larger-scale validation.", "date": "2025-11-10", "categories": []}
{"id": "2511.07011", "title": "Multilingual Lexical Feature Analysis of Spoken Language for Predicting Major Depression Symptom Severity", "abstract": "Background: Captured between clinical appointments using mobile devices, spoken language has potential for objective, more regular assessment of symptom severity and earlier detection of relapse in major depressive disorder. However, research to date has largely been in non-clinical cross-sectional samples of written language using complex machine learning (ML) approaches with limited interpretability.\n  Methods: We describe an initial exploratory analysis of longitudinal speech data and PHQ-8 assessments from 5,836 recordings of 586 participants in the UK, Netherlands, and Spain, collected in the RADAR-MDD study. We sought to identify interpretable lexical features associated with MDD symptom severity with linear mixed-effects modelling. Interpretable features and high-dimensional vector embeddings were also used to test the prediction performance of four regressor ML models.\n  Results: In English data, MDD symptom severity was associated with 7 features including lexical diversity measures and absolutist language. In Dutch, associations were observed with words per sentence and positive word frequency; no associations were observed in recordings collected in Spain. The predictive power of lexical features and vector embeddings was near chance level across all languages.\n  Limitations: Smaller samples in non-English speech and methodological choices, such as the elicitation prompt, may have also limited the effect sizes observable. A lack of NLP tools in languages other than English restricted our feature choice.\n  Conclusion: To understand the value of lexical markers in clinical research and practice, further research is needed in larger samples across several languages using improved protocols, and ML models that account for within- and between-individual variations in language.", "date": "2025-11-10", "categories": []}
{"id": "2511.07311", "title": "ACE-ICD: Acronym Expansion As Data Augmentation For Automated ICD Coding", "abstract": "Automatic ICD coding, the task of assigning disease and procedure codes to electronic medical records, is crucial for clinical documentation and billing. While existing methods primarily enhance model understanding of code hierarchies and synonyms, they often overlook the pervasive use of medical acronyms in clinical notes, a key factor in ICD code inference. To address this gap, we propose a novel effective data augmentation technique that leverages large language models to expand medical acronyms, allowing models to be trained on their full form representations. Moreover, we incorporate consistency training to regularize predictions by enforcing agreement between the original and augmented documents. Extensive experiments on the MIMIC-III dataset demonstrate that our approach, ACE-ICD establishes new state-of-the-art performance across multiple settings, including common codes, rare codes, and full-code assignments. Our code is publicly available.", "date": "2025-11-10", "categories": []}
{"id": "2506.11137", "title": "Scalable Medication Extraction and Discontinuation Identification from Electronic Health Records Using Large Language Models", "abstract": "Identifying medication discontinuations in electronic health records (EHRs) is vital for patient safety but is often hindered by information being buried in unstructured notes. This study aims to evaluate the capabilities of advanced open-sourced and proprietary large language models (LLMs) in extracting medications and classifying their medication status from EHR notes, focusing on their scalability on medication information extraction without human annotation. We collected three EHR datasets from diverse sources to build the evaluation benchmark. We evaluated 12 advanced LLMs and explored multiple LLM prompting strategies. Performance on medication extraction, medication status classification, and their joint task (extraction then classification) was systematically compared across all experiments. We found that LLMs showed promising performance on the medication extraction and discontinuation classification from EHR notes. GPT-4o consistently achieved the highest average F1 scores in all tasks under zero-shot setting - 94.0% for medication extraction, 78.1% for discontinuation classification, and 72.7% for the joint task. Open-sourced models followed closely, Llama-3.1-70B-Instruct achieved the highest performance in medication status classification on the MIV-Med dataset (68.7%) and in the joint task on both the Re-CASI (76.2%) and MIV-Med (60.2%) datasets. Medical-specific LLMs demonstrated lower performance compared to advanced general-domain LLMs. Few-shot learning generally improved performance, while CoT reasoning showed inconsistent gains. LLMs demonstrate strong potential for medication extraction and discontinuation identification on EHR notes, with open-sourced models offering scalable alternatives to proprietary systems and few-shot can further improve LLMs' capability.", "date": "2025-06-10", "categories": []}
{"id": "2511.04789", "title": "Conditional Neural ODE for Longitudinal Parkinson's Disease Progression Forecasting", "abstract": "Parkinson's disease (PD) shows heterogeneous, evolving brain-morphometry patterns. Modeling these longitudinal trajectories enables mechanistic insight, treatment development, and individualized 'digital-twin' forecasting. However, existing methods usually adopt recurrent neural networks and transformer architectures, which rely on discrete, regularly sampled data while struggling to handle irregular and sparse magnetic resonance imaging (MRI) in PD cohorts. Moreover, these methods have difficulty capturing individual heterogeneity including variations in disease onset, progression rate, and symptom severity, which is a hallmark of PD. To address these challenges, we propose CNODE (Conditional Neural ODE), a novel framework for continuous, individualized PD progression forecasting. The core of CNODE is to model morphological brain changes as continuous temporal processes using a neural ODE model. In addition, we jointly learn patient-specific initial time and progress speed to align individual trajectories into a shared progression trajectory. We validate CNODE on the Parkinson's Progression Markers Initiative (PPMI) dataset. Experimental results show that our method outperforms state-of-the-art baselines in forecasting longitudinal PD progression.", "date": "2025-11-06", "categories": []}
{"id": "2511.04948", "title": "A benchmark multimodal oro-dental dataset for large vision-language models", "abstract": "The advancement of artificial intelligence in oral healthcare relies on the availability of large-scale multimodal datasets that capture the complexity of clinical practice. In this paper, we present a comprehensive multimodal dataset, comprising 8775 dental checkups from 4800 patients collected over eight years (2018-2025), with patients ranging from 10 to 90 years of age. The dataset includes 50000 intraoral images, 8056 radiographs, and detailed textual records, including diagnoses, treatment plans, and follow-up notes. The data were collected under standard ethical guidelines and annotated for benchmarking. To demonstrate its utility, we fine-tuned state-of-the-art large vision-language models, Qwen-VL 3B and 7B, and evaluated them on two tasks: classification of six oro-dental anomalies and generation of complete diagnostic reports from multimodal inputs. We compared the fine-tuned models with their base counterparts and GPT-4o. The fine-tuned models achieved substantial gains over these baselines, validating the dataset and underscoring its effectiveness in advancing AI-driven oro-dental healthcare solutions. The dataset is publicly available, providing an essential resource for future research in AI dentistry.", "date": "2025-11-07", "categories": []}
{"id": "2511.04971", "title": "Risk Prediction of Cardiovascular Disease for Diabetic Patients with Machine Learning and Deep Learning Techniques", "abstract": "Accurate prediction of cardiovascular disease (CVD) risk is crucial for healthcare institutions. This study addresses the growing prevalence of diabetes and its strong link to heart disease by proposing an efficient CVD risk prediction model for diabetic patients using machine learning (ML) and hybrid deep learning (DL) approaches. The BRFSS dataset was preprocessed by removing duplicates, handling missing values, identifying categorical and numerical features, and applying Principal Component Analysis (PCA) for feature extraction. Several ML models, including Decision Trees (DT), Random Forest (RF), k-Nearest Neighbors (KNN), Support Vector Machine (SVM), AdaBoost, and XGBoost, were implemented, with XGBoost achieving the highest accuracy of 0.9050. Various DL models, such as Artificial Neural Networks (ANN), Deep Neural Networks (DNN), Recurrent Neural Networks (RNN), Convolutional Neural Networks (CNN), Long Short-Term Memory (LSTM), Bidirectional LSTM (BiLSTM), and Gated Recurrent Unit (GRU), as well as hybrid models combining CNN with LSTM, BiLSTM, and GRU, were also explored. Some of these models achieved perfect recall (1.00), with the LSTM model achieving the highest accuracy of 0.9050. Our research highlights the effectiveness of ML and DL models in predicting CVD risk among diabetic patients, automating and enhancing clinical decision-making. High accuracy and F1 scores demonstrate these models' potential to improve personalized risk management and preventive strategies.", "date": "2025-11-07", "categories": []}
{"id": "2511.04983", "title": "Predicting Cognitive Assessment Scores in Older Adults with Cognitive Impairment Using Wearable Sensors", "abstract": "Background and Objectives: This paper focuses on using AI to assess the cognitive function of older adults with mild cognitive impairment or mild dementia using physiological data provided by a wearable device. Cognitive screening tools are disruptive, time-consuming, and only capture brief snapshots of activity. Wearable sensors offer an attractive alternative by continuously monitoring physiological signals. This study investigated whether physiological data can accurately predict scores on established cognitive tests. Research Design and Methods: We recorded physiological signals from 23 older adults completing three NIH Toolbox Cognitive Battery tests, which assess working memory, processing speed, and attention. The Empatica EmbracePlus, a wearable device, measured blood volume pulse, skin conductance, temperature, and movement. Statistical features were extracted using wavelet-based and segmentation methods. We then applied supervised learning and validated predictions via cross-validation, hold-out testing, and bootstrapping. Results: Our models showed strong performance with Spearman's \\rho of 0.73-0.82 and mean absolute errors of 0.14-0.16, significantly outperforming a naive mean predictor. Sensor roles varied: heart-related signals combined with movement and temperature best predicted working memory, movement paired with skin conductance was most informative for processing speed, and heart in tandem with skin conductance worked best for attention. Discussion and Implications: These findings suggest that wearable sensors paired with AI tools such as supervised learning and feature engineering can noninvasively track specific cognitive functions in older adults, enabling continuous monitoring. Our study demonstrates how AI can be leveraged when the data sample is small. This approach may support remote assessments and facilitate clinical interventions.", "date": "2025-11-07", "categories": []}
{"id": "2511.04998", "title": "BiPETE: A Bi-Positional Embedding Transformer Encoder for Risk Assessment of Alcohol and Substance Use Disorder with Electronic Health Records", "abstract": "Transformer-based deep learning models have shown promise for disease risk prediction using electronic health records(EHRs), but modeling temporal dependencies remains a key challenge due to irregular visit intervals and lack of uniform structure. We propose a Bi-Positional Embedding Transformer Encoder or BiPETE for single-disease prediction, which integrates rotary positional embeddings to encode relative visit timing and sinusoidal embeddings to preserve visit order. Without relying on large-scale pretraining, BiPETE is trained on EHR data from two mental health cohorts-depressive disorder and post-traumatic stress disorder (PTSD)-to predict the risk of alcohol and substance use disorders (ASUD). BiPETE outperforms baseline models, improving the area under the precision-recall curve (AUPRC) by 34% and 50% in the depression and PTSD cohorts, respectively. An ablation study further confirms the effectiveness of the dual positional encoding strategy. We apply the Integrated Gradients method to interpret model predictions, identifying key clinical features associated with ASUD risk and protection, such as abnormal inflammatory, hematologic, and metabolic markers, as well as specific medications and comorbidities. Overall, these key clinical features identified by the attribution methods contribute to a deeper understanding of the risk assessment process and offer valuable clues for mitigating potential risks. In summary, our study presents a practical and interpretable framework for disease risk prediction using EHR data, which can achieve strong performance.", "date": "2025-11-07", "categories": []}
{"id": "2511.05169", "title": "Multimodal Deep Learning for Prediction of Progression-Free Survival in Patients with Neuroendocrine Tumors Undergoing 177Lu-based Peptide Receptor Radionuclide Therapy", "abstract": "Peptide receptor radionuclide therapy (PRRT) is an established treatment for metastatic neuroendocrine tumors (NETs), yet long-term disease control occurs only in a subset of patients. Predicting progression-free survival (PFS) could support individualized treatment planning. This study evaluates laboratory, imaging, and multimodal deep learning models for PFS prediction in PRRT-treated patients. In this retrospective, single-center study 116 patients with metastatic NETs undergoing 177Lu-DOTATOC were included. Clinical characteristics, laboratory values, and pretherapeutic somatostatin receptor positron emission tomography/computed tomographies (SR-PET/CT) were collected. Seven models were trained to classify low- vs. high-PFS groups, including unimodal (laboratory, SR-PET, or CT) and multimodal fusion approaches. Explainability was evaluated by feature importance analysis and gradient maps. Forty-two patients (36%) had short PFS (< 1 year), 74 patients long PFS (>1 year). Groups were similar in most characteristics, except for higher baseline chromogranin A (p = 0.003), elevated gamma-GT (p = 0.002), and fewer PRRT cycles (p < 0.001) in short-PFS patients. The Random Forest model trained only on laboratory biomarkers reached an AUROC of 0.59 +- 0.02. Unimodal three-dimensional convolutional neural networks using SR-PET or CT performed worse (AUROC 0.42 +- 0.03 and 0.54 +- 0.01, respectively). A multimodal fusion model laboratory values, SR-PET, and CT -augmented with a pretrained CT branch - achieved the best results (AUROC 0.72 +- 0.01, AUPRC 0.80 +- 0.01). Multimodal deep learning combining SR-PET, CT, and laboratory biomarkers outperformed unimodal approaches for PFS prediction after PRRT. Upon external validation, such models may support risk-adapted follow-up strategies.", "date": "2025-11-07", "categories": []}
{"id": "2511.05485", "title": "MIMIC-SR-ICD11: A Dataset for Narrative-Based Diagnosis", "abstract": "Disease diagnosis is a central pillar of modern healthcare, enabling early detection and timely intervention for acute conditions while guiding lifestyle adjustments and medication regimens to prevent or slow chronic disease. Self-reports preserve clinically salient signals that templated electronic health record (EHR) documentation often attenuates or omits, especially subtle but consequential details. To operationalize this shift, we introduce MIMIC-SR-ICD11, a large English diagnostic dataset built from EHR discharge notes and natively aligned to WHO ICD-11 terminology. We further present LL-Rank, a likelihood-based re-ranking framework that computes a length-normalized joint likelihood of each label given the clinical report context and subtracts the corresponding report-free prior likelihood for that label. Across seven model backbones, LL-Rank consistently outperforms a strong generation-plus-mapping baseline (GenMap). Ablation experiments show that LL-Rank's gains primarily stem from its PMI-based scoring, which isolates semantic compatibility from label frequency bias.", "date": "2025-11-07", "categories": []}
{"id": "2505.18246", "title": "Will Large Language Models Transform Clinical Prediction?", "abstract": "Objective: Large language models (LLMs) are attracting increasing interest in healthcare. This commentary evaluates the potential of LLMs to improve clinical prediction models (CPMs) for diagnostic and prognostic tasks, with a focus on their ability to process longitudinal electronic health record (EHR) data.\n  Findings: LLMs show promise in handling multimodal and longitudinal EHR data and can support multi-outcome predictions for diverse health conditions. However, methodological, validation, infrastructural, and regulatory chal- lenges remain. These include inadequate methods for time-to-event modelling, poor calibration of predictions, limited external validation, and bias affecting underrepresented groups. High infrastructure costs and the absence of clear regulatory frameworks further prevent adoption.\n  Implications: Further work and interdisciplinary collaboration are needed to support equitable and effective integra- tion into the clinical prediction. Developing temporally aware, fair, and explainable models should be a priority focus for transforming clinical prediction workflow.", "date": "2025-05-23", "categories": []}
{"id": "2507.13956", "title": "Cross-modal Causal Intervention for Alzheimer's Disease Prediction", "abstract": "Mild Cognitive Impairment (MCI) serves as a prodromal stage of Alzheimer's Disease (AD), where early identification and intervention can effectively slow the progression to dementia. However, diagnosing AD remains a significant challenge in neurology due to the confounders caused mainly by the selection bias of multi-modal data and the complex relationships between variables. To address these issues, we propose a novel visual-language causality-inspired framework named Cross-modal Causal Intervention with Mediator for Alzheimer's Disease Diagnosis (MediAD) for diagnostic assistance. Our MediAD employs Large Language Models (LLMs) to summarize clinical data under strict templates, therefore enriching textual inputs. The MediAD model utilizes Magnetic Resonance Imaging (MRI), clinical data, and textual data enriched by LLMs to classify participants into Cognitively Normal (CN), MCI, and AD categories. Because of the presence of confounders, such as cerebral vascular lesions and age-related biomarkers, non-causal models are likely to capture spurious input-output correlations, generating less reliable results. Our framework implicitly mitigates the effect of both observable and unobservable confounders through a unified causal intervention method. Experimental results demonstrate the outstanding performance of our method in distinguishing CN/MCI/AD cases, outperforming other methods in most evaluation metrics. The study showcases the potential of integrating causal reasoning with multi-modal learning for neurological disease diagnosis.", "date": "2025-07-18", "categories": []}
{"id": "2509.01794", "title": "A Multi-target Bayesian Transformer Framework for Predicting Cardiovascular Disease Biomarkers during Pandemics", "abstract": "The COVID-19 pandemic disrupted healthcare systems worldwide, disproportionately impacting individuals with chronic conditions such as cardiovascular disease (CVD). These disruptions -- through delayed care and behavioral changes, affected key CVD biomarkers, including LDL cholesterol (LDL-C), HbA1c, BMI, and systolic blood pressure (SysBP). Accurate modeling of these changes is crucial for predicting disease progression and guiding preventive care. However, prior work has not addressed multi-target prediction of CVD biomarker from Electronic Health Records (EHRs) using machine learning (ML), while jointly capturing biomarker interdependencies, temporal patterns, and predictive uncertainty. In this paper, we propose MBT-CB, a Multi-target Bayesian Transformer (MBT) with pre-trained BERT-based transformer framework to jointly predict LDL-C, HbA1c, BMI and SysBP CVD biomarkers from EHR data. The model leverages Bayesian Variational Inference to estimate uncertainties, embeddings to capture temporal relationships and a DeepMTR model to capture biomarker inter-relationships. We evaluate MBT-CT on retrospective EHR data from 3,390 CVD patient records (304 unique patients) in Central Massachusetts during the Covid-19 pandemic. MBT-CB outperformed a comprehensive set of baselines including other BERT-based ML models, achieving an MAE of 0.00887, RMSE of 0.0135 and MSE of 0.00027, while effectively capturing data and model uncertainty, patient biomarker inter-relationships, and temporal dynamics via its attention and embedding mechanisms. MBT-CB's superior performance highlights its potential to improve CVD biomarker prediction and support clinical decision-making during pandemics.", "date": "2025-09-01", "categories": []}
{"id": "2509.22993", "title": "Hemorica: A Comprehensive CT Scan Dataset for Automated Brain Hemorrhage Classification, Segmentation, and Detection", "abstract": "Timely diagnosis of Intracranial hemorrhage (ICH) on Computed Tomography (CT) scans remains a clinical priority, yet the development of robust Artificial Intelligence (AI) solutions is still hindered by fragmented public data. To close this gap, we introduce Hemorica, a publicly available collection of 372 head CT examinations acquired between 2012 and 2024. Each scan has been exhaustively annotated for five ICH subtypes-epidural (EPH), subdural (SDH), subarachnoid (SAH), intraparenchymal (IPH), and intraventricular (IVH)-yielding patient-wise and slice-wise classification labels, subtype-specific bounding boxes, two-dimensional pixel masks and three-dimensional voxel masks. A double-reading workflow, preceded by a pilot consensus phase and supported by neurosurgeon adjudication, maintained low inter-rater variability. Comprehensive statistical analysis confirms the clinical realism of the dataset. To establish reference baselines, standard convolutional and transformer architectures were fine-tuned for binary slice classification and hemorrhage segmentation. With only minimal fine-tuning, lightweight models such as MobileViT-XS achieved an F1 score of 87.8% in binary classification, whereas a U-Net with a DenseNet161 encoder reached a Dice score of 85.5% for binary lesion segmentation that validate both the quality of the annotations and the sufficiency of the sample size. Hemorica therefore offers a unified, fine-grained benchmark that supports multi-task and curriculum learning, facilitates transfer to larger but weakly labelled cohorts, and facilitates the process of designing an AI-based assistant for ICH detection and quantification systems.", "date": "2025-09-26", "categories": []}
{"id": "2511.03986", "title": "Use of Continuous Glucose Monitoring with Machine Learning to Identify Metabolic Subphenotypes and Inform Precision Lifestyle Changes", "abstract": "The classification of diabetes and prediabetes by static glucose thresholds obscures the pathophysiological dysglycemia heterogeneity, primarily driven by insulin resistance (IR), beta-cell dysfunction, and incretin deficiency. This review demonstrates that continuous glucose monitoring and wearable technologies enable a paradigm shift towards non-invasive, dynamic metabolic phenotyping. We show evidence that machine learning models can leverage high-resolution glucose data from at-home, CGM-enabled oral glucose tolerance tests to accurately predict gold-standard measures of muscle IR and beta-cell function. This personalized characterization extends to real-world nutrition, where an individual's unique postprandial glycemic response (PPGR) to standardized meals, such as the relative glucose spike to potatoes versus grapes, could serve as a biomarker for their metabolic subtype. Moreover, integrating wearable data reveals that habitual diet, sleep, and physical activity patterns, particularly their timing, are uniquely associated with specific metabolic dysfunctions, informing precision lifestyle interventions. The efficacy of dietary mitigators in attenuating PPGR is also shown to be phenotype-dependent. Collectively, this evidence demonstrates that CGM can deconstruct the complexity of early dysglycemia into distinct, actionable subphenotypes. This approach moves beyond simple glycemic control, paving the way for targeted nutritional, behavioral, and pharmacological strategies tailored to an individual's core metabolic defects, thereby paving the way for a new era of precision diabetes prevention.", "date": "2025-11-06", "categories": []}
{"id": "2511.04158", "title": "Deep Learning Approach for Clinical Risk Identification Using Transformer Modeling of Heterogeneous EHR Data", "abstract": "This study proposes a Transformer-based longitudinal modeling method to address challenges in clinical risk classification with heterogeneous Electronic Health Record (EHR) data, including irregular temporal patterns, large modality differences, and complex semantic structures. The method takes multi-source medical features as input and employs a feature embedding layer to achieve a unified representation of structured and unstructured data. A learnable temporal encoding mechanism is introduced to capture dynamic evolution under uneven sampling intervals. The core model adopts a multi-head self-attention structure to perform global dependency modeling on longitudinal sequences, enabling the aggregation of long-term trends and short-term fluctuations across different temporal scales. To enhance semantic representation, a semantic-weighted pooling module is designed to assign adaptive importance to key medical events, improving the discriminative ability of risk-related features. Finally, a linear mapping layer generates individual-level risk scores. Experimental results show that the proposed model outperforms traditional machine learning and temporal deep learning models in accuracy, recall, precision, and F1-Score, achieving stable and precise risk identification in multi-source heterogeneous EHR environments and providing an efficient and reliable framework for clinical intelligent decision-making.", "date": "2025-11-06", "categories": []}
{"id": "2511.04476", "title": "Probabilistic Textual Time Series Depression Detection", "abstract": "Accurate and interpretable predictions of depression severity are essential for clinical decision support, yet existing models often lack uncertainty estimates and temporal modeling. We propose PTTSD, a Probabilistic Textual Time Series Depression Detection framework that predicts PHQ-8 scores from utterance-level clinical interviews while modeling uncertainty over time. PTTSD includes sequence-to-sequence and sequence-to-one variants, both combining bidirectional LSTMs, self-attention, and residual connections with Gaussian or Student-t output heads trained via negative log-likelihood. Evaluated on E-DAIC and DAIC-WOZ, PTTSD achieves state-of-the-art performance among text-only systems (e.g., MAE = 3.85 on E-DAIC, 3.55 on DAIC) and produces well-calibrated prediction intervals. Ablations confirm the value of attention and probabilistic modeling, while comparisons with MentalBERT establish generality. A three-part calibration analysis and qualitative case studies further highlight the interpretability and clinical relevance of uncertainty-aware forecasting.", "date": "2025-11-06", "categories": []}
{"id": "2510.12214", "title": "DE3S: Dual-Enhanced Soft-Sparse-Shape Learning for Medical Early Time-Series Classification", "abstract": "Early Time Series Classification (ETSC) is critical in time-sensitive medical applications such as sepsis, yet it presents an inherent trade-off between accuracy and earliness. This trade-off arises from two core challenges: 1) models should effectively model inherently weak and noisy early-stage snippets, and 2) they should resolve the complex, dual requirement of simultaneously capturing local, subject-specific variations and overarching global temporal patterns. Existing methods struggle to overcome these underlying challenges, often forcing a severe compromise: sacrificing accuracy to achieve earliness, or vice-versa. We propose \\textbf{DE3S}, a \\textbf{D}ual-\\textbf{E}nhanced \\textbf{S}oft-\\textbf{S}parse \\textbf{S}equence Learning framework, which systematically solves these challenges. A dual enhancement mechanism is proposed to enhance the modeling of weak, early signals. Then, an attention-based patch module is introduced to preserve discriminative information while reducing noise and complexity. A dual-path fusion architecture is designed, using a sparse mixture of experts to model local, subject-specific variations. A multi-scale inception module is also employed to capture global dependencies. Experiments on six real-world medical datasets show the competitive performance of DE3S, particularly in early prediction windows. Ablation studies confirm the effectiveness of each component in addressing its targeted challenge. The source code is available \\href{https://github.com/kuxit/DE3S}{\\textbf{here}}.", "date": "2025-10-14", "categories": []}
{"id": "2511.02846", "title": "Spatio-Temporal Attention Network for Epileptic Seizure Prediction", "abstract": "In this study, we present a deep learning framework that learns complex spatio-temporal correlation structures of EEG signals through a Spatio-Temporal Attention Network (STAN) for accurate predictions of onset of seizures for Epilepsy patients. Unlike existing methods, which rely on feature engineering and/or assume fixed preictal durations, our approach simultaneously models spatio-temporal correlations through STAN and employs an adversarial discriminator to distinguish preictal from interictal attention patterns, enabling patient-specific learning. Evaluation on CHB-MIT and MSSM datasets demonstrates 96.6\\% sensitivity with 0.011/h false detection rate on CHB-MIT, and 94.2% sensitivity with 0.063/h FDR on MSSM, significantly outperforming state-of-the-art methods. The framework reliably detects preictal states at least 15 minutes before an onset, with patient-specific windows extending to 45 minutes, providing sufficient intervention time for clinical applications.", "date": "2025-10-24", "categories": []}
{"id": "2511.02853", "title": "Consciousness-ECG Transformer for Conscious State Estimation System with Real-Time Monitoring", "abstract": "Conscious state estimation is important in various medical settings, including sleep staging and anesthesia management, to ensure patient safety and optimize health outcomes. Traditional methods predominantly utilize electroencephalography (EEG), which faces challenges such as high sensitivity to noise and the requirement for controlled environments. In this study, we propose the consciousness-ECG transformer that leverages electrocardiography (ECG) signals for non-invasive and reliable conscious state estimation. Our approach employs a transformer with decoupled query attention to effectively capture heart rate variability features that distinguish between conscious and unconscious states. We implemented the conscious state estimation system with real-time monitoring and validated our system on datasets involving sleep staging and anesthesia level monitoring during surgeries. Experimental results demonstrate that our model outperforms baseline models, achieving accuracies of 0.877 on sleep staging and 0.880 on anesthesia level monitoring. Moreover, our model achieves the highest area under curve values of 0.786 and 0.895 on sleep staging and anesthesia level monitoring, respectively. The proposed system offers a practical and robust alternative to EEG-based methods, particularly suited for dynamic clinical environments. Our results highlight the potential of ECG-based consciousness monitoring to enhance patient safety and advance our understanding of conscious states.", "date": "2025-10-31", "categories": []}
{"id": "2510.15748", "title": "Towards Relaxed Multimodal Inputs for Gait-based Parkinson's Disease Assessment", "abstract": "Parkinson's disease assessment has garnered growing interest in recent years, particularly with the advent of sensor data and machine learning techniques. Among these, multimodal approaches have demonstrated strong performance by effectively integrating complementary information from various data sources. However, two major limitations hinder their practical application: (1) the need to synchronize all modalities during training, and (2) the dependence on all modalities during inference. To address these issues, we propose the first Parkinson's assessment system that formulates multimodal learning as a multi-objective optimization (MOO) problem. This not only allows for more flexible modality requirements during both training and inference, but also handles modality collapse issue during multimodal information fusion. In addition, to mitigate the imbalance within individual modalities, we introduce a margin-based class rebalancing strategy to enhance category learning. We conduct extensive experiments on three public datasets under both synchronous and asynchronous settings. The results show that our framework-Towards Relaxed InPuts (TRIP)-achieves state-of-the-art performance, outperforming the best baselines by 16.48, 6.89, and 11.55 percentage points in the asynchronous setting, and by 4.86 and 2.30 percentage points in the synchronous setting, highlighting its effectiveness and adaptability.", "date": "2025-10-17", "categories": []}
{"id": "2511.01945", "title": "Learning a Distance for the Clustering of Patients with Amyotrophic Lateral Sclerosis", "abstract": "Amyotrophic lateral sclerosis (ALS) is a severe disease with a typical survival of 3-5 years after symptom onset. Current treatments offer only limited life extension, and the variability in patient responses highlights the need for personalized care. However, research is hindered by small, heterogeneous cohorts, sparse longitudinal data, and the lack of a clear definition for clinically meaningful patient clusters. Existing clustering methods remain limited in both scope and number. To address this, we propose a clustering approach that groups sequences using a disease progression declarative score. Our approach integrates medical expertise through multiple descriptive variables, investigating several distance measures combining such variables, both by reusing off-the-shelf distances and employing a weak-supervised learning method. We pair these distances with clustering methods and benchmark them against state-of-the-art techniques. The evaluation of our approach on a dataset of 353 ALS patients from the University Hospital of Tours, shows that our method outperforms state-of-the-art methods in survival analysis while achieving comparable silhouette scores. In addition, the learned distances enhance the relevance and interpretability of results for medical experts.", "date": "2025-11-03", "categories": []}
{"id": "2511.01947", "title": "Interpretable Heart Disease Prediction via a Weighted Ensemble Model: A Large-Scale Study with SHAP and Surrogate Decision Trees", "abstract": "Cardiovascular disease (CVD) remains a critical global health concern, demanding reliable and interpretable predictive models for early risk assessment. This study presents a large-scale analysis using the Heart Disease Health Indicators Dataset, developing a strategically weighted ensemble model that combines tree-based methods (LightGBM, XGBoost) with a Convolutional Neural Network (CNN) to predict CVD risk. The model was trained on a preprocessed dataset of 229,781 patients where the inherent class imbalance was managed through strategic weighting and feature engineering enhanced the original 22 features to 25. The final ensemble achieves a statistically significant improvement over the best individual model, with a Test AUC of 0.8371 (p=0.003) and is particularly suited for screening with a high recall of 80.0%. To provide transparency and clinical interpretability, surrogate decision trees and SHapley Additive exPlanations (SHAP) are used. The proposed model delivers a combination of robust predictive performance and clinical transparency by blending diverse learning architectures and incorporating explainability through SHAP and surrogate decision trees, making it a strong candidate for real-world deployment in public health screening.", "date": "2025-11-03", "categories": []}
{"id": "2511.02047", "title": "A Dual-Use Framework for Clinical Gait Analysis: Attention-Based Sensor Optimization and Automated Dataset Auditing", "abstract": "Objective gait analysis using wearable sensors and AI is critical for managing neurological and orthopedic conditions. However, models are vulnerable to hidden dataset biases, and task-specific sensor optimization remains a challenge. We propose a multi-stream attention-based deep learning framework that functions as both a sensor optimizer and an automated data auditor. Applied to the Voisard et al. (2025) multi-cohort gait dataset on four clinical tasks (PD, OA, CVA screening; PD vs CVA differential), the model's attention mechanism quantitatively discovered a severe dataset confound. For OA and CVA screening, tasks where bilateral assessment is clinically essential, the model assigned more than 70 percent attention to the Right Foot while statistically ignoring the Left Foot (less than 0.1 percent attention, 95 percent CI [0.0-0.1]). This was not a clinical finding but a direct reflection of a severe laterality bias (for example, 15 of 15 right-sided OA) in the public dataset. The primary contribution of this work is methodological, demonstrating that an interpretable framework can automatically audit dataset integrity. As a secondary finding, the model proposes novel, data-driven sensor synergies (for example, Head plus Foot for PD screening) as hypotheses for future optimized protocols.", "date": "2025-11-03", "categories": []}
{"id": "2511.02228", "title": "Collaborative Attention and Consistent-Guided Fusion of MRI and PET for Alzheimer's Disease Diagnosis", "abstract": "Alzheimer's disease (AD) is the most prevalent form of dementia, and its early diagnosis is essential for slowing disease progression. Recent studies on multimodal neuroimaging fusion using MRI and PET have achieved promising results by integrating multi-scale complementary features. However, most existing approaches primarily emphasize cross-modal complementarity while overlooking the diagnostic importance of modality-specific features. In addition, the inherent distributional differences between modalities often lead to biased and noisy representations, degrading classification performance. To address these challenges, we propose a Collaborative Attention and Consistent-Guided Fusion framework for MRI and PET based AD diagnosis. The proposed model introduces a learnable parameter representation (LPR) block to compensate for missing modality information, followed by a shared encoder and modality-independent encoders to preserve both shared and specific representations. Furthermore, a consistency-guided mechanism is employed to explicitly align the latent distributions across modalities. Experimental results on the ADNI dataset demonstrate that our method achieves superior diagnostic performance compared with existing fusion strategies.", "date": "2025-11-04", "categories": []}
{"id": "2511.02379", "title": "H-Infinity Filter Enhanced CNN-LSTM for Arrhythmia Detection from Heart Sound Recordings", "abstract": "Early detection of heart arrhythmia can prevent severe future complications in cardiac patients. While manual diagnosis still remains the clinical standard, it relies heavily on visual interpretation and is inherently subjective. In recent years, deep learning has emerged as a powerful tool to automate arrhythmia detection, offering improved accuracy, consistency, and efficiency. Several variants of convolutional and recurrent neural network architectures have been widely explored to capture spatial and temporal patterns in physiological signals. However, despite these advancements, current models often struggle to generalize well in real-world scenarios, especially when dealing with small or noisy datasets, which are common challenges in biomedical applications. In this paper, a novel CNN-H-Infinity-LSTM architecture is proposed to identify arrhythmic heart signals from heart sound recordings. This architecture introduces trainable parameters inspired by the H-Infinity filter from control theory, enhancing robustness and generalization. Extensive experimentation on the PhysioNet CinC Challenge 2016 dataset, a public benchmark of heart audio recordings, demonstrates that the proposed model achieves stable convergence and outperforms existing benchmarks, with a test accuracy of 99.42% and an F1 score of 98.85%.", "date": "2025-11-04", "categories": []}
{"id": "2511.02392", "title": "Fuzzy Soft Set Theory based Expert System for the Risk Assessment in Breast Cancer Patients", "abstract": "Breast cancer remains one of the leading causes of mortality among women worldwide, with early diagnosis being critical for effective treatment and improved survival rates. However, timely detection continues to be a challenge due to the complex nature of the disease and variability in patient risk factors. This study presents a fuzzy soft set theory-based expert system designed to assess the risk of breast cancer in patients using measurable clinical and physiological parameters. The proposed system integrates Body Mass Index, Insulin Level, Leptin Level, Adiponectin Level, and age as input variables to estimate breast cancer risk through a set of fuzzy inference rules and soft set computations. These parameters can be obtained from routine blood analyses, enabling a non-invasive and accessible method for preliminary assessment. The dataset used for model development and validation was obtained from the UCI Machine Learning Repository. The proposed expert system aims to support healthcare professionals in identifying high-risk patients and determining the necessity of further diagnostic procedures such as biopsies.", "date": "2025-11-04", "categories": []}
{"id": "2410.07129", "title": "Exploring Large Language Models for Detecting Mental Disorders", "abstract": "This paper compares the effectiveness of traditional machine learning methods, encoder-based models, and large language models (LLMs) on the task of detecting depression and anxiety. Five Russian-language datasets were considered, each differing in format and in the method used to define the target pathology class. We tested AutoML models based on linguistic features, several variations of encoder-based Transformers such as BERT, and state-of-the-art LLMs as pathology classification models. The results demonstrated that LLMs outperform traditional methods, particularly on noisy and small datasets where training examples vary significantly in text length and genre. However, psycholinguistic features and encoder-based models can achieve performance comparable to language models when trained on texts from individuals with clinically confirmed depression, highlighting their potential effectiveness in targeted clinical applications.", "date": "2024-10-09", "categories": []}
{"id": "2505.00189", "title": "Chronic Diseases Prediction using Machine Learning and Deep Learning Methods", "abstract": "Chronic diseases, such as cardiovascular disease, diabetes, chronic kidney disease, and thyroid disorders, are the leading causes of premature mortality worldwide. Early detection and intervention are crucial for improving patient outcomes, yet traditional diagnostic methods often fail due to the complex nature of these conditions. This study explores the application of machine learning (ML) and deep learning (DL) techniques to predict chronic disease and thyroid disorders. We used a variety of models, including Logistic Regression (LR), Random Forest (RF), Gradient Boosted Trees (GBT), Neural Networks (NN), Decision Trees (DT) and Native Bayes (NB), to analyze and predict disease outcomes. Our methodology involved comprehensive data pre-processing, including handling missing values, categorical encoding, and feature aggregation, followed by model training and evaluation. Performance metrics such ad precision, recall, accuracy, F1-score, and Area Under the Curve (AUC) were used to assess the effectiveness of each model. The results demonstrated that ensemble methods like Random Forest and Gradient Boosted Trees consistently outperformed. Neutral Networks also showed superior performance, particularly in capturing complex data patterns. The findings highlight the potential of ML and DL in revolutionizing chronic disease prediction, enabling early diagnosis and personalized treatment strategies. However, challenges such as data quality, model interpretability, and the need for advanced computational techniques in healthcare to improve patient outcomes and reduce the burden of chronic diseases. This study was conducted as part of Big Data class project under the supervision of our professors Mr. Abderrahmane EZ-ZAHOUT and Mr. Abdessamad ESSAIDI.", "date": "2025-04-30", "categories": []}
{"id": "2511.00728", "title": "Validating Deep Models for Alzheimer's 18F-FDG PET Diagnosis Across Populations: A Study with Latin American Data", "abstract": "Deep learning models have shown strong performance in diagnosing Alzheimer's disease (AD) using neuroimaging data, particularly 18F-FDG PET scans, with training datasets largely composed of North American cohorts such as those in the Alzheimer's Disease Neuroimaging Initiative (ADNI). However, their generalization to underrepresented populations remains underexplored. In this study, we benchmark convolutional and Transformer-based models on the ADNI dataset and assess their generalization performance on a novel Latin American clinical cohort from the FLENI Institute in Buenos Aires, Argentina. We show that while all models achieve high AUCs on ADNI (up to .96, .97), their performance drops substantially on FLENI (down to .82, .80, respectively), revealing a significant domain shift. The tested architectures demonstrated similar performance, calling into question the supposed advantages of transformers for this specific task. Through ablation studies, we identify per-image normalization and a correct sampling selection as key factors for generalization. Occlusion sensitivity analysis further reveals that models trained on ADNI, generally attend to canonical hypometabolic regions for the AD class, but focus becomes unclear for the other classes and for FLENI scans. These findings highlight the need for population-aware validation of diagnostic AI models and motivate future work on domain adaptation and cohort diversification.", "date": "2025-11-01", "categories": []}
{"id": "2511.00782", "title": "Count-Based Approaches Remain Strong: A Benchmark Against Transformer and LLM Pipelines on Structured EHR", "abstract": "Structured electronic health records (EHR) are essential for clinical prediction. While count-based learners continue to perform strongly on such data, no benchmarking has directly compared them against more recent mixture-of-agents LLM pipelines, which have been reported to outperform single LLMs in various NLP tasks. In this study, we evaluated three categories of methodologies for EHR prediction using the EHRSHOT dataset: count-based models built from ontology roll-ups with two time bins, based on LightGBM and the tabular foundation model TabPFN; a pretrained sequential transformer (CLMBR); and a mixture-of-agents pipeline that converts tabular histories to natural-language summaries followed by a text classifier. We assessed eight outcomes using the EHRSHOT dataset. Across the eight evaluation tasks, head-to-head wins were largely split between the count-based and the mixture-of-agents methods. Given their simplicity and interpretability, count-based models remain a strong candidate for structured EHR benchmarking. The source code is available at: https://github.com/cristea-lab/Structured_EHR_Benchmark.", "date": "2025-11-02", "categories": []}
{"id": "2511.00949", "title": "Motion-Robust Multimodal Fusion of PPG and Accelerometer Signals for Three-Class Heart Rhythm Classification", "abstract": "Atrial fibrillation (AF) is a leading cause of stroke and mortality, particularly in elderly patients. Wrist-worn photoplethysmography (PPG) enables non-invasive, continuous rhythm monitoring, yet suffers from significant vulnerability to motion artifacts and physiological noise. Many existing approaches rely solely on single-channel PPG and are limited to binary AF detection, often failing to capture the broader range of arrhythmias encountered in clinical settings. We introduce RhythmiNet, a residual neural network enhanced with temporal and channel attention modules that jointly leverage PPG and accelerometer (ACC) signals. The model performs three-class rhythm classification: AF, sinus rhythm (SR), and Other. To assess robustness across varying movement conditions, test data are stratified by accelerometer-based motion intensity percentiles without excluding any segments. RhythmiNet achieved a 4.3% improvement in macro-AUC over the PPG-only baseline. In addition, performance surpassed a logistic regression model based on handcrafted HRV features by 12%, highlighting the benefit of multimodal fusion and attention-based learning in noisy, real-world clinical data.", "date": "2025-11-02", "categories": []}
{"id": "2511.01018", "title": "AI for pRedicting Exacerbations in KIDs with aSthma (AIRE-KIDS)", "abstract": "Recurrent exacerbations remain a common yet preventable outcome for many children with asthma. Machine learning (ML) algorithms using electronic medical records (EMR) could allow accurate identification of children at risk for exacerbations and facilitate referral for preventative comprehensive care to avoid this morbidity. We developed ML algorithms to predict repeat severe exacerbations (i.e. asthma-related emergency department (ED) visits or future hospital admissions) for children with a prior asthma ED visit at a tertiary care children's hospital.\n  Retrospective pre-COVID19 (Feb 2017 - Feb 2019, N=2716) Epic EMR data from the Children's Hospital of Eastern Ontario (CHEO) linked with environmental pollutant exposure and neighbourhood marginalization information was used to train various ML models. We used boosted trees (LGBM, XGB) and 3 open-source large language model (LLM) approaches (DistilGPT2, Llama 3.2 1B and Llama-8b-UltraMedical). Models were tuned and calibrated then validated in a second retrospective post-COVID19 dataset (Jul 2022 - Apr 2023, N=1237) from CHEO. Models were compared using the area under the curve (AUC) and F1 scores, with SHAP values used to determine the most predictive features.\n  The LGBM ML model performed best with the most predictive features in the final AIRE-KIDS_ED model including prior asthma ED visit, the Canadian triage acuity scale, medical complexity, food allergy, prior ED visits for non-asthma respiratory diagnoses, and age for an AUC of 0.712, and F1 score of 0.51. This is a nontrivial improvement over the current decision rule which has F1=0.334. While the most predictive features in the AIRE-KIDS_HOSP model included medical complexity, prior asthma ED visit, average wait time in the ED, the pediatric respiratory assessment measure score at triage and food allergy.", "date": "2025-11-02", "categories": []}
{"id": "2306.03833", "title": "Decoding Virtual Healthcare Success through Knowledge-Aware and Multimodal Predictive Modeling", "abstract": "Online healthcare consultations have transformed how patients seek medical advice, offering convenience while introducing new challenges for ensuring consultation success. Predicting whether an online consultation will be successful is critical for improving patient experiences and sustaining platform competitiveness. Yet, such prediction is inherently difficult due to the fragmented nature of patients' care journeys and the lack of integration between virtual and traditional healthcare systems. Furthermore, the data collected from online platforms, including textual conversations, interaction sequences, and behavioral traces, are often sparse and incomplete. This study develops a predictive modeling approach that fuses multimodal data and dynamically constructed knowledge networks to capture latent relationships among patients, physicians, and consultation contexts. By integrating heterogeneous information sources and uncovering the evolving structure of digital interactions, the model enhances the accuracy and interpretability of consultation success prediction. The findings offer implications for designing hybrid healthcare ecosystems that combine online and offline services through data-driven intelligence.", "date": "2023-06-06", "categories": []}
{"id": "2507.23607", "title": "Deep Learning-based Prediction of Clinical Trial Enrollment with Uncertainty Estimates", "abstract": "Clinical trials are a systematic endeavor to assess the safety and efficacy of new drugs or treatments. Conducting such trials typically demands significant financial investment and meticulous planning, highlighting the need for accurate predictions of trial outcomes. Accurately predicting patient enrollment, a key factor in trial success, is one of the primary challenges during the planning phase. In this work, we propose a novel deep learning-based method to address this critical challenge. Our method, implemented as a neural network model, leverages pre-trained language models (PLMs) to capture the complexities and nuances of clinical documents, transforming them into expressive representations. These representations are then combined with encoded tabular features via an attention mechanism. To account for uncertainties in enrollment prediction, we enhance the model with a probabilistic layer based on the Gamma distribution, which enables range estimation. We apply the proposed model to predict clinical trial duration, assuming site-level enrollment follows a Poisson-Gamma process. We carry out extensive experiments on real-world clinical trial data, and show that the proposed method can effectively predict the number of patients enrolled at a number of sites for a given clinical trial, outperforming established baseline models.", "date": "2025-07-31", "categories": []}
{"id": "2510.16026", "title": "A tutorial on discovering and quantifying the effect of latent causal sources of multimodal EHR data", "abstract": "We provide an accessible description of a peer-reviewed generalizable causal machine learning pipeline to (i) discover latent causal sources of large-scale electronic health records observations, and (ii) quantify the source causal effects on clinical outcomes. We illustrate how imperfect multimodal clinical data can be processed, decomposed into probabilistic independent latent sources, and used to train taskspecific causal models from which individual causal effects can be estimated. We summarize the findings of the two real-world applications of the approach to date as a demonstration of its versatility and utility for medical discovery at scale.", "date": "2025-10-15", "categories": []}
{"id": "2510.26807", "title": "Diabetes Lifestyle Medicine Treatment Assistance Using Reinforcement Learning", "abstract": "Type 2 diabetes prevention and treatment can benefit from personalized lifestyle prescriptions. However, the delivery of personalized lifestyle medicine prescriptions is limited by the shortage of trained professionals and the variability in physicians' expertise. We propose an offline contextual bandit approach that learns individualized lifestyle prescriptions from the aggregated NHANES profiles of 119,555 participants by minimizing the Magni glucose risk-reward function. The model encodes patient status and generates lifestyle medicine prescriptions, which are trained using a mixed-action Soft Actor-Critic algorithm. The task is treated as a single-step contextual bandit. The model is validated against lifestyle medicine prescriptions issued by three certified physicians from Xiangya Hospital. These results demonstrate that offline mixed-action SAC can generate risk-aware lifestyle medicine prescriptions from cross-sectional NHANES data, warranting prospective clinical validation.", "date": "2025-10-19", "categories": []}
{"id": "2510.26808", "title": "A Machine Learning-Based Framework to Shorten the Questionnaire for Assessing Autism Intervention", "abstract": "Caregivers of individuals with autism spectrum disorder (ASD) often find the 77-item Autism Treatment Evaluation Checklist (ATEC) burdensome, limiting its use for routine monitoring. This study introduces a generalizable machine learning framework that seeks to shorten assessments while maintaining evaluative accuracy. Using longitudinal ATEC data from 60 autistic children receiving therapy, we applied feature selection and cross-validation techniques to identify the most predictive items across two assessment goals: longitudinal therapy tracking and point-in-time severity estimation. For progress monitoring, the framework identified 16 items (21% of the original questionnaire) that retained strong correlation with total score change and full subdomain coverage. We also generated smaller subsets (1-7 items) for efficient approximations. For point-in-time severity assessment, our model achieved over 80% classification accuracy using just 13 items (17% of the original set). While demonstrated on ATEC, the methodology-based on subset optimization, model interpretability, and statistical rigor-is broadly applicable to other high-dimensional psychometric tools. The resulting framework could potentially enable more accessible, frequent, and scalable assessments and offer a data-driven approach for AI-supported interventions across neurodevelopmental and psychiatric contexts.", "date": "2025-10-22", "categories": []}
{"id": "2510.27535", "title": "Patient-Centered Summarization Framework for AI Clinical Summarization: A Mixed-Methods Design", "abstract": "Large Language Models (LLMs) are increasingly demonstrating the potential to reach human-level performance in generating clinical summaries from patient-clinician conversations. However, these summaries often focus on patients' biology rather than their preferences, values, wishes, and concerns. To achieve patient-centered care, we propose a new standard for Artificial Intelligence (AI) clinical summarization tasks: Patient-Centered Summaries (PCS). Our objective was to develop a framework to generate PCS that capture patient values and ensure clinical utility and to assess whether current open-source LLMs can achieve human-level performance in this task. We used a mixed-methods process. Two Patient and Public Involvement groups (10 patients and 8 clinicians) in the United Kingdom participated in semi-structured interviews exploring what personal and contextual information should be included in clinical summaries and how it should be structured for clinical use. Findings informed annotation guidelines used by eight clinicians to create gold-standard PCS from 88 atrial fibrillation consultations. Sixteen consultations were used to refine a prompt aligned with the guidelines. Five open-source LLMs (Llama-3.2-3B, Llama-3.1-8B, Mistral-8B, Gemma-3-4B, and Qwen3-8B) generated summaries for 72 consultations using zero-shot and few-shot prompting, evaluated with ROUGE-L, BERTScore, and qualitative metrics. Patients emphasized lifestyle routines, social support, recent stressors, and care values. Clinicians sought concise functional, psychosocial, and emotional context. The best zero-shot performance was achieved by Mistral-8B (ROUGE-L 0.189) and Llama-3.1-8B (BERTScore 0.673); the best few-shot by Llama-3.1-8B (ROUGE-L 0.206, BERTScore 0.683). Completeness and fluency were similar between experts and models, while correctness and patient-centeredness favored human PCS.", "date": "2025-10-31", "categories": []}
{"id": "2509.09695", "title": "Machine-learning competition to grade EEG background patterns in newborns with hypoxic-ischaemic encephalopathy", "abstract": "Machine learning (ML) has the potential to support and improve expert performance in monitoring the brain function of at-risk newborns. Developing accurate and reliable ML models depends on access to high-quality, annotated data, a resource in short supply. ML competitions address this need by providing researchers access to expertly annotated datasets, fostering shared learning through direct model comparisons, and leveraging the benefits of crowdsourcing diverse expertise. We compiled a retrospective dataset containing 353 hours of EEG from 102 individual newborns from a multi-centre study. The data was fully anonymised and divided into training, testing, and held-out validation datasets. EEGs were graded for the severity of abnormal background patterns. Next, we created a web-based competition platform and hosted a machine learning competition to develop ML models for classifying the severity of EEG background patterns in newborns. After the competition closed, the top 4 performing models were evaluated offline on a separate held-out validation dataset. Although a feature-based model ranked first on the testing dataset, deep learning models generalised better on the validation sets. All methods had a significant decline in validation performance compared to the testing performance. This highlights the challenges for model generalisation on unseen data, emphasising the need for held-out validation datasets in ML studies with neonatal EEG. The study underscores the importance of training ML models on large and diverse datasets to ensure robust generalisation. The competition's outcome demonstrates the potential for open-access data and collaborative ML development to foster a collaborative research environment and expedite the development of clinical decision-support tools for neonatal neuromonitoring.", "date": "2025-08-27", "categories": []}
{"id": "2510.26014", "title": "Dual Mixture-of-Experts Framework for Discrete-Time Survival Analysis", "abstract": "Survival analysis is a task to model the time until an event of interest occurs, widely used in clinical and biomedical research. A key challenge is to model patient heterogeneity while also adapting risk predictions to both individual characteristics and temporal dynamics. We propose a dual mixture-of-experts (MoE) framework for discrete-time survival analysis. Our approach combines a feature-encoder MoE for subgroup-aware representation learning with a hazard MoE that leverages patient features and time embeddings to capture temporal dynamics. This dual-MoE design flexibly integrates with existing deep learning based survival pipelines. On METABRIC and GBSG breast cancer datasets, our method consistently improves performance, boosting the time-dependent C-index up to 0.04 on the test sets, and yields further gains when incorporated into the Consurv framework.", "date": "2025-10-29", "categories": []}
{"id": "2510.26188", "title": "Predicting All-Cause Hospital Readmissions from Medical Claims Data of Hospitalised Patients", "abstract": "Reducing preventable hospital readmissions is a national priority for payers, providers, and policymakers seeking to improve health care and lower costs. The rate of readmission is being used as a benchmark to determine the quality of healthcare provided by the hospitals. In thisproject, we have used machine learning techniques like Logistic Regression, Random Forest and Support Vector Machines to analyze the health claims data and identify demographic and medical factors that play a crucial role in predicting all-cause readmissions. As the health claims data is high dimensional, we have used Principal Component Analysis as a dimension reduction technique and used the results for building regression models. We compared and evaluated these models based on the Area Under Curve (AUC) metric. Random Forest model gave the highest performance followed by Logistic Regression and Support Vector Machine models. These models can be used to identify the crucial factors causing readmissions and help identify patients to focus on to reduce the chances of readmission, ultimately bringing down the cost and increasing the quality of healthcare provided to the patients.", "date": "2025-10-30", "categories": []}
{"id": "2510.26444", "title": "Personalized Treatment Outcome Prediction from Scarce Data via Dual-Channel Knowledge Distillation and Adaptive Fusion", "abstract": "Personalized treatment outcome prediction based on trial data for small-sample and rare patient groups is critical in precision medicine. However, the costly trial data limit the prediction performance. To address this issue, we propose a cross-fidelity knowledge distillation and adaptive fusion network (CFKD-AFN), which leverages abundant but low-fidelity simulation data to enhance predictions on scarce but high-fidelity trial data. CFKD-AFN incorporates a dual-channel knowledge distillation module to extract complementary knowledge from the low-fidelity model, along with an attention-guided fusion module to dynamically integrate multi-source information. Experiments on treatment outcome prediction for the chronic obstructive pulmonary disease demonstrates significant improvements of CFKD-AFN over state-of-the-art methods in prediction accuracy, ranging from 6.67\\% to 74.55\\%, and strong robustness to varying high-fidelity dataset sizes. Furthermore, we extend CFKD-AFN to an interpretable variant, enabling the exploration of latent medical semantics to support clinical decision-making.", "date": "2025-10-30", "categories": []}
{"id": "2510.17851", "title": "Pre to Post-Treatment Glioblastoma MRI Prediction using a Latent Diffusion Model", "abstract": "Glioblastoma (GBM) is an aggressive primary brain tumor with a median survival of approximately 15 months. In clinical practice, the Stupp protocol serves as the standard first-line treatment. However, patients exhibit highly heterogeneous therapeutic responses which required at least two months before first visual impact can be observed, typically with MRI. Early prediction treatment response is crucial for advancing personalized medicine. Disease Progression Modeling (DPM) aims to capture the trajectory of disease evolution, while Treatment Response Prediction (TRP) focuses on assessing the impact of therapeutic interventions. Whereas most TRP approaches primarly rely on timeseries data, we consider the problem of early visual TRP as a slice-to-slice translation model generating post-treatment MRI from a pre-treatment MRI, thus reflecting the tumor evolution. To address this problem we propose a Latent Diffusion Model with a concatenation-based conditioning from the pre-treatment MRI and the tumor localization, and a classifier-free guidance to enhance generation quality using survival information, in particular post-treatment tumor evolution. Our model were trained and tested on a local dataset consisting of 140 GBM patients collected at Centre Fran\\c{c}ois Baclesse. For each patient we collected pre and post T1-Gd MRI, tumor localization manually delineated in the pre-treatment MRI by medical experts, and survival information.", "date": "2025-10-13", "categories": []}
{"id": "2510.21779", "title": "What Causes Postoperative Aspiration?", "abstract": "Background: Aspiration, the inhalation of foreign material into the lungs, significantly impacts surgical patient morbidity and mortality. This study develops a machine learning (ML) model to predict postoperative aspiration, enabling timely preventative interventions.\n  Methods: From the MIMIC-IV database of over 400,000 hospital admissions, we identified 826 surgical patients (mean age: 62, 55.7\\% male) who experienced aspiration within seven days post-surgery, along with a matched non-aspiration cohort. Three ML models: XGBoost, Multilayer Perceptron, and Random Forest were trained using pre-surgical hospitalization data to predict postoperative aspiration. To investigate causation, we estimated Average Treatment Effects (ATE) using Augmented Inverse Probability Weighting.\n  Results: Our ML model achieved an AUROC of 0.86 and 77.3\\% sensitivity on a held-out test set. Maximum daily opioid dose, length of stay, and patient age emerged as the most important predictors. ATE analysis identified significant causative factors: opioids (0.25 +/- 0.06) and operative site (neck: 0.20 +/- 0.13, head: 0.19 +/- 0.13). Despite equal surgery rates across genders, men were 1.5 times more likely to aspirate and received 27\\% higher maximum daily opioid dosages compared to women.\n  Conclusion: ML models can effectively predict postoperative aspiration risk, enabling targeted preventative measures. Maximum daily opioid dosage and operative site significantly influence aspiration risk. The gender disparity in both opioid administration and aspiration rates warrants further investigation. These findings have important implications for improving postoperative care protocols and aspiration prevention strategies.", "date": "2025-10-18", "categories": []}
{"id": "2510.24385", "title": "When are radiology reports useful for training medical image classifiers?", "abstract": "Medical images used to train machine learning models are often accompanied by radiology reports containing rich expert annotations. However, relying on these reports as inputs for clinical prediction requires the timely manual work of a trained radiologist. This raises a natural question: when can radiology reports be leveraged during training to improve image-only classification? Prior works are limited to evaluating pre-trained image representations by fine-tuning them to predict diagnostic labels, often extracted from reports, ignoring tasks with labels that are weakly associated with the text. To address this gap, we conduct a systematic study of how radiology reports can be used during both pre-training and fine-tuning, across diagnostic and prognostic tasks (e.g., 12-month readmission), and under varying training set sizes. Our findings reveal that: (1) Leveraging reports during pre-training is beneficial for downstream classification tasks where the label is well-represented in the text; however, pre-training through explicit image-text alignment can be detrimental in settings where it's not; (2) Fine-tuning with reports can lead to significant improvements and even have a larger impact than the pre-training method in certain settings. These results provide actionable insights into when and how to leverage privileged text data to train medical image classifiers while highlighting gaps in current research.", "date": "2025-10-28", "categories": []}
{"id": "2510.25199", "title": "AI-Powered Early Detection of Critical Diseases using Image Processing and Audio Analysis", "abstract": "Early diagnosis of critical diseases can significantly improve patient survival and reduce treatment costs. However, existing diagnostic techniques are often costly, invasive, and inaccessible in low-resource regions. This paper presents a multimodal artificial intelligence (AI) diagnostic framework integrating image analysis, thermal imaging, and audio signal processing for early detection of three major health conditions: skin cancer, vascular blood clots, and cardiopulmonary abnormalities. A fine-tuned MobileNetV2 convolutional neural network was trained on the ISIC 2019 dataset for skin lesion classification, achieving 89.3% accuracy, 91.6% sensitivity, and 88.2% specificity. A support vector machine (SVM) with handcrafted features was employed for thermal clot detection, achieving 86.4% accuracy (AUC = 0.89) on synthetic and clinical data. For cardiopulmonary analysis, lung and heart sound datasets from PhysioNet and Pascal were processed using Mel-Frequency Cepstral Coefficients (MFCC) and classified via Random Forest, reaching 87.2% accuracy and 85.7% sensitivity. Comparative evaluation against state-of-the-art models demonstrates that the proposed system achieves competitive results while remaining lightweight and deployable on low-cost devices. The framework provides a promising step toward scalable, real-time, and accessible AI-based pre-diagnostic healthcare solutions.", "date": "2025-10-29", "categories": []}
{"id": "2405.19000", "title": "FedMAP: Personalised Federated Learning for Real Large-Scale Healthcare Systems", "abstract": "Federated learning (FL) promises to enable collaborative machine learning across healthcare sites whilst preserving data privacy. Practical deployment remains limited by statistical heterogeneity arising from differences in patient demographics, treatments, and outcomes, and infrastructure constraints. We introduce FedMAP, a personalised FL (PFL) framework that addresses heterogeneity through local Maximum a Posteriori (MAP) estimation with Input Convex Neural Network priors. These priors represent global knowledge gathered from other sites that guides the model while adapting to local data, and we provide a formal proof of convergence. Unlike many PFL methods that rely on fixed regularisation, FedMAP's prior adaptively learns patterns that capture complex inter-site relationships. We demonstrate improved performance compared to local training, FedAvg, and several PFL methods across three large-scale clinical datasets: 10-year cardiovascular risk prediction (CPRD, 387 general practitioner practices, 258,688 patients), iron deficiency detection (INTERVAL, 4 donor centres, 31,949 blood donors), and mortality prediction (eICU, 150 hospitals, 44,842 patients). FedMAP incorporates a three-tier design that enables participation across healthcare sites with varying infrastructure and technical capabilities, from full federated training to inference-only deployment. Geographical analysis reveals substantial equity improvements, with underperforming regions achieving up to 14.3% performance gains. This framework provides the first practical pathway for large-scale healthcare FL deployment, which ensures clinical sites at all scales can benefit, equity is enhanced, and privacy is retained.", "date": "2024-05-29", "categories": []}
{"id": "2505.18195", "title": "Acoustic and Machine Learning Methods for Speech-Based Suicide Risk Assessment: A Systematic Review", "abstract": "Suicide remains a public health challenge, necessitating improved detection methods to facilitate timely intervention and treatment. This systematic review evaluates the role of Artificial Intelligence (AI) and Machine Learning (ML) in assessing suicide risk through acoustic analysis of speech. Following PRISMA guidelines, we analyzed 33 articles selected from PubMed, Cochrane, Scopus, and Web of Science databases. The last search was conducted in February 2025. Risk of bias was assessed using the PROBAST tool. Studies analyzing acoustic features between individuals at risk of suicide (RS) and those not at risk (NRS) were included, while studies lacking acoustic data, a suicide-related focus, or sufficient methodological details were excluded. Sample sizes varied widely and were reported in terms of participants or speech segments, depending on the study. Results were synthesized narratively based on acoustic features and classifier performance. Findings consistently showed significant acoustic feature variations between RS and NRS populations, particularly involving jitter, fundamental frequency (F0), Mel-frequency cepstral coefficients (MFCC), and power spectral density (PSD). Classifier performance varied based on algorithms, modalities, and speech elicitation methods, with multimodal approaches integrating acoustic, linguistic, and metadata features demonstrating superior performance. Among the 29 classifier-based studies, reported AUC values ranged from 0.62 to 0.985 and accuracies from 60% to 99.85%. Most datasets were imbalanced in favor of NRS, and performance metrics were rarely reported separately by group, limiting clear identification of direction of effect.", "date": "2025-05-20", "categories": []}
{"id": "2510.23884", "title": "Language Models for Longitudinal Clinical Prediction", "abstract": "We explore a lightweight framework that adapts frozen large language models to analyze longitudinal clinical data. The approach integrates patient history and context within the language model space to generate accurate forecasts without model fine-tuning. Applied to neuropsychological assessments, it achieves accurate and reliable performance even with minimal training data, showing promise for early-stage Alzheimer's monitoring.", "date": "2025-10-27", "categories": []}
{"id": "2510.23946", "title": "Leveraging LLMs for Early Alzheimer's Prediction", "abstract": "We present a connectome-informed LLM framework that encodes dynamic fMRI connectivity as temporal sequences, applies robust normalization, and maps these data into a representation suitable for a frozen pre-trained LLM for clinical prediction. Applied to early Alzheimer's detection, our method achieves sensitive prediction with error rates well below clinically recognized margins, with implications for timely Alzheimer's intervention.", "date": "2025-10-27", "categories": []}
{"id": "2510.24287", "title": "Towards actionable hypotension prediction -- predicting catecholamine therapy initiation in the intensive care unit", "abstract": "Hypotension in critically ill ICU patients is common and life-threatening. Escalation to catecholamine therapy marks a key management step, with both undertreatment and overtreatment posing risks. Most machine learning (ML) models predict hypotension using fixed MAP thresholds or MAP forecasting, overlooking the clinical decision behind treatment escalation. Predicting catecholamine initiation, the start of vasoactive or inotropic agent administration offers a more clinically actionable target reflecting real decision-making. Using the MIMIC-III database, we modeled catecholamine initiation as a binary event within a 15-minute prediction window. Input features included statistical descriptors from a two-hour sliding MAP context window, along with demographics, biometrics, comorbidities, and ongoing treatments. An Extreme Gradient Boosting (XGBoost) model was trained and interpreted via SHapley Additive exPlanations (SHAP). The model achieved an AUROC of 0.822 (0.813-0.830), outperforming the hypotension baseline (MAP < 65, AUROC 0.686 [0.675-0.699]). SHAP analysis highlighted recent MAP values, MAP trends, and ongoing treatments (e.g., sedatives, electrolytes) as dominant predictors. Subgroup analysis showed higher performance in males, younger patients (<53 years), those with higher BMI (>32), and patients without comorbidities or concurrent medications. Predicting catecholamine initiation based on MAP dynamics, treatment context, and patient characteristics supports the critical decision of when to escalate therapy, shifting focus from threshold-based alarms to actionable decision support. This approach is feasible across a broad ICU cohort under natural event imbalance. Future work should enrich temporal and physiological context, extend label definitions to include therapy escalation, and benchmark against existing hypotension prediction systems.", "date": "2025-10-28", "categories": []}
{"id": "2510.24500", "title": "MIMIC-Sepsis: A Curated Benchmark for Modeling and Learning from Sepsis Trajectories in the ICU", "abstract": "Sepsis is a leading cause of mortality in intensive care units (ICUs), yet existing research often relies on outdated datasets, non-reproducible preprocessing pipelines, and limited coverage of clinical interventions. We introduce MIMIC-Sepsis, a curated cohort and benchmark framework derived from the MIMIC-IV database, designed to support reproducible modeling of sepsis trajectories. Our cohort includes 35,239 ICU patients with time-aligned clinical variables and standardized treatment data, including vasopressors, fluids, mechanical ventilation and antibiotics. We describe a transparent preprocessing pipeline-based on Sepsis-3 criteria, structured imputation strategies, and treatment inclusion-and release it alongside benchmark tasks focused on early mortality prediction, length-of-stay estimation, and shock onset classification. Empirical results demonstrate that incorporating treatment variables substantially improves model performance, particularly for Transformer-based architectures. MIMIC-Sepsis serves as a robust platform for evaluating predictive and sequential models in critical care research.", "date": "2025-10-28", "categories": []}
{"id": "2507.01068", "title": "Wearable Sensor-Based IoT XAI Framework for Predicting Freezing of Gait in Parkinsons Disease", "abstract": "This research discusses the critical need for early detection and treatment for early prediction of Freezing of Gaits (FOG) utilizing a wearable sensor technology powered with LoRa communication. The system consisted of an Esp-32 microcontroller, in which the trained model is utilized utilizing the Micromlgen Python library. The research investigates accurate FOG classification based on pertinent clinical data by utilizing machine learning (ML) algorithms like Catboost, XGBoost, and Extra Tree classifiers. The XGBoost could classify with approximately 97% accuracy, along with 96% for the catboost and 90% for the Extra Trees Classifier model. The SHAP analysis interpretability shows that GYR SI degree is the most affecting factor in the prediction of the diseases. These results show the possibility of monitoring and identifying the affected person with tracking location on GPS and providing aid as an assistive technology for aiding the affected. The developed sensor-based technology has great potential for real-world problem solving in the field of healthcare and biomedical technology enhancements.", "date": "2025-07-01", "categories": []}
{"id": "2507.22963", "title": "FedCVD++: Communication-Efficient Federated Learning for Cardiovascular Risk Prediction with Parametric and Non-Parametric Model Optimization", "abstract": "Cardiovascular diseases (CVD) cause over 17 million deaths annually worldwide, highlighting the urgent need for privacy-preserving predictive systems. We introduce FedCVD++, an enhanced federated learning (FL) framework that integrates both parametric models (logistic regression, SVM, neural networks) and non-parametric models (Random Forest, XGBoost) for coronary heart disease risk prediction. To address key FL challenges, we propose: (1) tree-subset sampling that reduces Random Forest communication overhead by 70%, (2) XGBoost-based feature extraction enabling lightweight federated ensembles, and (3) federated SMOTE synchronization for resolving cross-institutional class imbalance.\n  Evaluated on the Framingham dataset (4,238 records), FedCVD++ achieves state-of-the-art results: federated XGBoost (F1 = 0.80) surpasses its centralized counterpart (F1 = 0.78), and federated Random Forest (F1 = 0.81) matches non-federated performance. Additionally, our communication-efficient strategies reduce bandwidth consumption by 3.2X while preserving 95% accuracy.\n  Compared to existing FL frameworks, FedCVD++ delivers up to 15% higher F1-scores and superior scalability for multi-institutional deployment. This work represents the first practical integration of non-parametric models into federated healthcare systems, providing a privacy-preserving solution validated under real-world clinical constraints.", "date": "2025-07-30", "categories": []}
{"id": "2507.23491", "title": "Explainable artificial intelligence model predicting the risk of all-cause mortality in patients with type 2 diabetes mellitus", "abstract": "Objective. Type 2 diabetes mellitus (T2DM) is a highly prevalent non-communicable chronic disease that substantially reduces life expectancy. Accurate estimation of all-cause mortality risk in T2DM patients is crucial for personalizing and optimizing treatment strategies. Research Design and Methods. This study analyzed a cohort of 554 patients (aged 40-87 years) with diagnosed T2DM over a maximum follow-up period of 16.8 years, during which 202 patients (36%) died. Key survival-associated features were identified, and multiple machine learning (ML) models were trained and validated to predict all-cause mortality risk. To improve model interpretability, Shapley additive explanations (SHAP) was applied to the best-performing model. Results. The extra survival trees (EST) model, incorporating ten key features, demonstrated the best predictive performance. The model achieved a C-statistic of 0.776, with the area under the receiver operating characteristic curve (AUC) values of 0.86, 0.80, 0.841, and 0.826 for 5-, 10-, 15-, and 16.8-year all-cause mortality predictions, respectively. The SHAP approach was employed to interpret the model's individual decision-making processes. Conclusions. The developed model exhibited strong predictive performance for mortality risk assessment. Its clinically interpretable outputs enable potential bedside application, improving the identification of high-risk patients and supporting timely treatment optimization.", "date": "2025-07-31", "categories": []}
{"id": "2510.19879", "title": "Automated HIV Screening on Dutch Electronic Health Records with Large Language Models", "abstract": "Efficient screening and early diagnosis of HIV are critical for reducing onward transmission. Although large scale laboratory testing is not feasible, the widespread adoption of Electronic Health Records (EHRs) offers new opportunities to address this challenge. Existing research primarily focuses on applying machine learning methods to structured data, such as patient demographics, for improving HIV diagnosis. However, these approaches often overlook unstructured text data such as clinical notes, which potentially contain valuable information relevant to HIV risk. In this study, we propose a novel pipeline that leverages a Large Language Model (LLM) to analyze unstructured EHR text and determine a patient's eligibility for further HIV testing. Experimental results on clinical data from Erasmus University Medical Center Rotterdam demonstrate that our pipeline achieved high accuracy while maintaining a low false negative rate.", "date": "2025-10-22", "categories": []}
{"id": "2510.22285", "title": "Supervised Fine-Tuning or In-Context Learning? Evaluating LLMs for Clinical NER", "abstract": "We study clinical Named Entity Recognition (NER) on the CADEC corpus and compare three families of approaches: (i) BERT-style encoders (BERT Base, BioClinicalBERT, RoBERTa-large), (ii) GPT-4o used with few-shot in-context learning (ICL) under simple vs.\\ complex prompts, and (iii) GPT-4o with supervised fine-tuning (SFT). All models are evaluated on standard NER metrics over CADEC's five entity types (ADR, Drug, Disease, Symptom, Finding). RoBERTa-large and BioClinicalBERT offer limited improvements over BERT Base, showing the limit of these family of models. Among LLM settings, simple ICL outperforms a longer, instruction-heavy prompt, and SFT achieves the strongest overall performance (F1 $\\approx$ 87.1%), albeit with higher cost. We find that the LLM achieve higher accuracy on simplified tasks, restricting classification to two labels.", "date": "2025-10-25", "categories": []}
{"id": "2510.22878", "title": "Limits of Generative Pre-Training in Structured EMR Trajectories with Irregular Sampling", "abstract": "Foundation models refer to architectures trained on vast datasets using autoregressive pre-training from natural language processing to capture intricate patterns and motifs. They were originally developed to transfer such learned knowledge to downstream predictive tasks. Recently, however, some studies repurpose these learned representations for phenotype discovery without rigorous validation, risking superficially realistic but clinically incoherent embeddings. To test this mismatch, we trained two autoregressive models -- a sequence-to-sequence LSTM and a reduced Transformer -- on longitudinal ART for HIV and Acute Hypotension datasets. Controlled irregularity was added during training via random inter-visit gaps, while test sequences stayed complete. Patient-trajectory synthesis evaluated distributional and correlational fidelity. Both reproduced feature distributions but failed to preserve cross-feature structure -- showing that generative pre-training yields local realism but limited clinical coherence. These results highlight the need for domain-specific evaluation and support trajectory synthesis as a practical probe before fine-tuning or deployment.", "date": "2025-10-27", "categories": []}
{"id": "2303.05389", "title": "Depression Detection Using Digital Traces on Social Media: A Knowledge-aware Deep Learning Approach", "abstract": "Depression is a common disease worldwide. It is difficult to diagnose and continues to be underdiagnosed. Because depressed patients constantly share their symptoms, major life events, and treatments on social media, researchers are turning to user-generated digital traces on social media for depression detection. Such methods have distinct advantages in combating depression because they can facilitate innovative approaches to fight depression and alleviate its social and economic burden. However, most existing studies lack effective means to incorporate established medical domain knowledge in depression detection or suffer from feature extraction difficulties that impede greater performance. Following the design science research paradigm, we propose a Deep Knowledge-aware Depression Detection (DKDD) framework to accurately detect social media users at risk of depression and explain the critical factors that contribute to such detection. Extensive empirical studies with real-world data demonstrate that, by incorporating domain knowledge, our method outperforms existing state-of-the-art methods. Our work has significant implications for IS research in knowledge-aware machine learning, digital traces utilization, and NLP research in IS. Practically, by providing early detection and explaining the critical factors, DKDD can supplement clinical depression screening and enable large-scale evaluations of a population's mental health status.", "date": "2023-03-06", "categories": []}
{"id": "2508.08646", "title": "P-CAFE: Personalized Cost-Aware Incremental Feature Selection For Electronic Health Records", "abstract": "Electronic Health Records (EHR) have revolutionized healthcare by digitizing patient data, improving accessibility, and streamlining clinical workflows. However, extracting meaningful insights from these complex and multimodal datasets remains a significant challenge for researchers. Traditional feature selection methods often struggle with the inherent sparsity and heterogeneity of EHR data, especially when accounting for patient-specific variations and feature costs in clinical applications. To address these challenges, we propose a novel personalized, online and cost-aware feature selection framework tailored specifically for EHR datasets. The features are aquired in an online fashion for individual patients, incorporating budgetary constraints and feature variability costs. The framework is designed to effectively manage sparse and multimodal data, ensuring robust and scalable performance in diverse healthcare contexts. A primary application of our proposed method is to support physicians' decision making in patient screening scenarios. By guiding physicians toward incremental acquisition of the most informative features within budget constraints, our approach aims to increase diagnostic confidence while optimizing resource utilization.", "date": "2025-08-12", "categories": []}
{"id": "2510.20857", "title": "Lightweight Classifier for Detecting Intracranial Hemorrhage in Ultrasound Data", "abstract": "Intracranial hemorrhage (ICH) secondary to Traumatic Brain Injury (TBI) represents a critical diagnostic challenge, with approximately 64,000 TBI-related deaths annually in the United States. Current diagnostic modalities including Computed Tomography (CT) and Magnetic Resonance Imaging (MRI) have significant limitations: high cost, limited availability, and infrastructure dependence, particularly in resource-constrained environments. This study investigates machine learning approaches for automated ICH detection using Ultrasound Tissue Pulsatility Imaging (TPI), a portable technique measuring tissue displacement from hemodynamic forces during cardiac cycles. We analyze ultrasound TPI signals comprising 30 temporal frames per cardiac cycle with recording angle information, collected from TBI patients with CT-confirmed ground truth labels. Our preprocessing pipeline employs z-score normalization and Principal Component Analysis (PCA) for dimensionality reduction, retaining components explaining 95% of cumulative variance. We systematically evaluate multiple classification algorithms spanning probabilistic, kernel-based, neural network, and ensemble learning approaches across three feature representations: original 31-dimensional space, reduced subset, and PCA-transformed space. Results demonstrate that PCA transformation substantially improves classifier performance, with ensemble methods achieving 98.0% accuracy and F1-score of 0.890, effectively balancing precision and recall despite class imbalance. These findings establish the feasibility of machine learning-based ICH detection in TBI patients using portable ultrasound devices, with applications in emergency medicine, rural healthcare, and military settings where traditional imaging is unavailable.", "date": "2025-10-22", "categories": []}
{"id": "2510.21596", "title": "Automated interictal epileptic spike detection from simple and noisy annotations in MEG data", "abstract": "In drug-resistant epilepsy, presurgical evaluation of epilepsy can be considered. Magnetoencephalography (MEG) has been shown to be an effective exam to inform the localization of the epileptogenic zone through the localization of interictal epileptic spikes. Manual detection of these pathological biomarkers remains a fastidious and error-prone task due to the high dimensionality of MEG recordings, and interrater agreement has been reported to be only moderate. Current automated methods are unsuitable for clinical practice, either requiring extensively annotated data or lacking robustness on non-typical data. In this work, we demonstrate that deep learning models can be used for detecting interictal spikes in MEG recordings, even when only temporal and single-expert annotations are available, which represents real-world clinical practice. We propose two model architectures: a feature-based artificial neural network (ANN) and a convolutional neural network (CNN), trained on a database of 59 patients, and evaluated against a state-of-the-art model to classify short time windows of signal. In addition, we employ an interactive machine learning strategy to iteratively improve our data annotation quality using intermediary model outputs. Both proposed models outperform the state-of-the-art model (F1-scores: CNN=0.46, ANN=0.44) when tested on 10 holdout test patients. The interactive machine learning strategy demonstrates that our models are robust to noisy annotations. Overall, results highlight the robustness of models with simple architectures when analyzing complex and imperfectly annotated data. Our method of interactive machine learning offers great potential for faster data annotation, while our models represent useful and efficient tools for automated interictal spikes detection.", "date": "2025-10-24", "categories": []}
{"id": "2510.20085", "title": "Hierarchical Dual-Head Model for Suicide Risk Assessment via MentalRoBERTa", "abstract": "Social media platforms have become important sources for identifying suicide risk, but automated detection systems face multiple challenges including severe class imbalance, temporal complexity in posting patterns, and the dual nature of risk levels as both ordinal and categorical. This paper proposes a hierarchical dual-head neural network based on MentalRoBERTa for suicide risk classification into four levels: indicator, ideation, behavior, and attempt. The model employs two complementary prediction heads operating on a shared sequence representation: a CORAL (Consistent Rank Logits) head that preserves ordinal relationships between risk levels, and a standard classification head that enables flexible categorical distinctions. A 3-layer Transformer encoder with 8-head multi-head attention models temporal dependencies across post sequences, while explicit time interval embeddings capture posting behavior dynamics. The model is trained with a combined loss function (0.5 CORAL + 0.3 Cross-Entropy + 0.2 Focal Loss) that simultaneously addresses ordinal structure preservation, overconfidence reduction, and class imbalance. To improve computational efficiency, we freeze the first 6 layers (50%) of MentalRoBERTa and employ mixed-precision training. The model is evaluated using 5-fold stratified cross-validation with macro F1 score as the primary metric.", "date": "2025-10-23", "categories": []}
{"id": "2510.20239", "title": "Tri-Modal Severity Fused Diagnosis across Depression and Post-traumatic Stress Disorders", "abstract": "Depression and post traumatic stress disorder (PTSD) often co-occur with connected symptoms, complicating automated assessment, which is often binary and disorder specific. Clinically useful diagnosis needs severity aware cross disorder estimates and decision support explanations. Our unified tri modal affective severity framework synchronizes and fuses interview text with sentence level transformer embeddings, audio with log Mel statistics with deltas, and facial signals with action units, gaze, head and pose descriptors to output graded severities for diagnosing both depression (PHQ-8; 5 classes) and PTSD (3 classes). Standardized features are fused via a calibrated late fusion classifier, yielding per disorder probabilities and feature-level attributions. This severity aware tri-modal affective fusion approach is demoed on multi disorder concurrent depression and PTSD assessment. Stratified cross validation on DAIC derived corpora outperforms unimodal/ablation baselines. The fused model matches the strongest unimodal baseline on accuracy and weighted F1, while improving decision curve utility and robustness under noisy or missing modalities. For PTSD specifically, fusion reduces regression error and improves class concordance. Errors cluster between adjacent severities; extreme classes are identified reliably. Ablations show text contributes most to depression severity, audio and facial cues are critical for PTSD, whereas attributions align with linguistic and behavioral markers. Our approach offers reproducible evaluation and clinician in the loop support for affective clinical decision making.", "date": "2025-10-23", "categories": []}
{"id": "2510.20629", "title": "Equitable Survival Prediction: A Fairness-Aware Survival Modeling (FASM) Approach", "abstract": "As machine learning models become increasingly integrated into healthcare, structural inequities and social biases embedded in clinical data can be perpetuated or even amplified by data-driven models. In survival analysis, censoring and time dynamics can further add complexity to fair model development. Additionally, algorithmic fairness approaches often overlook disparities in cross-group rankings, e.g., high-risk Black patients may be ranked below lower-risk White patients who do not experience the event of mortality. Such misranking can reinforce biological essentialism and undermine equitable care. We propose a Fairness-Aware Survival Modeling (FASM), designed to mitigate algorithmic bias regarding both intra-group and cross-group risk rankings over time. Using breast cancer prognosis as a representative case and applying FASM to SEER breast cancer data, we show that FASM substantially improves fairness while preserving discrimination performance comparable to fairness-unaware survival models. Time-stratified evaluations show that FASM maintains stable fairness over a 10-year horizon, with the greatest improvements observed during the mid-term of follow-up. Our approach enables the development of survival models that prioritize both accuracy and equity in clinical decision-making, advancing fairness as a core principle in clinical care.", "date": "2025-10-23", "categories": []}
{"id": "2403.05229", "title": "Developing Federated Time-to-Event Scores Using Heterogeneous Real-World Survival Data", "abstract": "Survival analysis serves as a fundamental component in numerous healthcare applications, where the determination of the time to specific events (such as the onset of a certain disease or death) for patients is crucial for clinical decision-making. Scoring systems are widely used for swift and efficient risk prediction. However, existing methods for constructing survival scores presume that data originates from a single source, posing privacy challenges in collaborations with multiple data owners. We propose a novel framework for building federated scoring systems for multi-site survival outcomes, ensuring both privacy and communication efficiency. We applied our approach to sites with heterogeneous survival data originating from emergency departments in Singapore and the United States. Additionally, we independently developed local scores at each site. In testing datasets from each participant site, our proposed federated scoring system consistently outperformed all local models, evidenced by higher integrated area under the receiver operating characteristic curve (iAUC) values, with a maximum improvement of 11.6%. Additionally, the federated score's time-dependent AUC(t) values showed advantages over local scores, exhibiting narrower confidence intervals (CIs) across most time points. The model developed through our proposed method exhibits effective performance on each local site, signifying noteworthy implications for healthcare research. Sites participating in our proposed federated scoring model training gained benefits by acquiring survival models with enhanced prediction accuracy and efficiency. This study demonstrates the effectiveness of our privacy-preserving federated survival score generation framework and its applicability to real-world heterogeneous survival data.", "date": "2024-03-08", "categories": []}
{"id": "2409.08303", "title": "Interpretable Features for the Assessment of Neurodegenerative Diseases through Handwriting Analysis", "abstract": "Motor dysfunction is a common sign of neurodegenerative diseases (NDs) such as Parkinson's disease (PD) and Alzheimer's disease (AD), but may be difficult to detect, especially in the early stages. In this work, we examine the behavior of a wide array of interpretable features extracted from the handwriting signals of 113 subjects performing multiple tasks on a digital tablet, as part of the Neurological Signals dataset. The aim is to measure their effectiveness in characterizing NDs, including AD and PD. To this end, task-agnostic and task-specific features are extracted from 14 distinct tasks. Subsequently, through statistical analysis and a series of classification experiments, we investigate which features provide greater discriminative power between NDs and healthy controls and amongst different NDs. Preliminary results indicate that the tasks at hand can all be effectively leveraged to distinguish between the considered set of NDs, specifically by measuring the stability, the speed of writing, the time spent not writing, and the pressure variations between groups from our handcrafted interpretable features, which shows a statistically significant difference between groups, across multiple tasks. Using various binary classification algorithms on the computed features, we obtain up to 87% accuracy for the discrimination between AD and healthy controls (CTL), and up to 69% for the discrimination between PD and CTL.", "date": "2024-09-10", "categories": []}
{"id": "2503.11331", "title": "Cardiomyopathy Diagnosis Model from Endomyocardial Biopsy Specimens: Appropriate Feature Space and Class Boundary in Small Sample Size Data", "abstract": "As the number of patients with heart failure increases, machine learning (ML) has garnered attention in cardiomyopathy diagnosis, driven by the shortage of pathologists. However, endomyocardial biopsy specimens are often small sample size and require techniques such as feature extraction and dimensionality reduction. This study aims to determine whether texture features are effective for feature extraction in the pathological diagnosis of cardiomyopathy. Furthermore, model designs that contribute toward improving generalization performance are examined by applying feature selection (FS) and dimensional compression (DC) to several ML models. The obtained results were verified by visualizing the inter-class distribution differences and conducting statistical hypothesis testing based on texture features. Additionally, they were evaluated using predictive performance across different model designs with varying combinations of FS and DC (applied or not) and decision boundaries. The obtained results confirmed that texture features may be effective for the pathological diagnosis of cardiomyopathy. Moreover, when the ratio of features to the sample size is high, a multi-step process involving FS and DC improved the generalization performance, with the linear kernel support vector machine achieving the best results. This process was demonstrated to be potentially effective for models with reduced complexity, regardless of whether the decision boundaries were linear, curved, perpendicular, or parallel to the axes. These findings are expected to facilitate the development of an effective cardiomyopathy diagnostic model for its rapid adoption in medical practice.", "date": "2025-03-14", "categories": []}
{"id": "2504.21795", "title": "Balancing Interpretability and Flexibility in Modeling Diagnostic Trajectories with an Embedded Neural Hawkes Process Model", "abstract": "The Hawkes process (HP) is commonly used to model event sequences with self-reinforcing dynamics, including electronic health records (EHRs). Traditional HPs capture self-reinforcement via parametric impact functions that can be inspected to understand how each event modulates the intensity of others. Neural network-based HPs offer greater flexibility, resulting in improved fit and prediction performance, but at the cost of interpretability, which is often critical in healthcare. In this work, we aim to understand and improve upon this tradeoff. We propose a novel HP formulation in which impact functions are modeled by defining a flexible impact kernel, instantiated as a neural network, in event embedding space, which allows us to model large-scale event sequences with many event types. This approach is more flexible than traditional HPs yet more interpretable than other neural network approaches, and allows us to explicitly trade flexibility for interpretability by adding transformer encoder layers to further contextualize the event embeddings. Results show that our method accurately recovers impact functions in simulations, achieves competitive performance on MIMIC-IV procedure dataset, and gains clinically meaningful interpretation on Duke-EHR with children diagnosis dataset even without transformer layers. This suggests that our flexible impact kernel is often sufficient to capture self-reinforcing dynamics in EHRs and other data effectively, implying that interpretability can be maintained without loss of performance.", "date": "2025-04-30", "categories": []}
{"id": "2506.22498", "title": "ViFusionTST: Deep Fusion of Time-Series Image Representations from Load Signals for Early Bed-Exit Prediction", "abstract": "Bed-related falls remain a major source of injury in hospitals and long-term care facilities, yet many commercial alarms trigger only after a patient has already left the bed. We show that early bed-exit intent can be predicted using only one low-cost load cell mounted under a bed leg. The resulting load signals are first converted into a compact set of complementary images: an RGB line plot that preserves raw waveforms and three texture maps-recurrence plot, Markov transition field, and Gramian angular field-that expose higher-order dynamics. We introduce ViFusionTST, a dual-stream Swin Transformer that processes the line plot and texture maps in parallel and fuses them through cross-attention to learn data-driven modality weights. To provide a realistic benchmark, we collected six months of continuous data from 95 beds in a long-term-care facility. On this real-world dataset ViFusionTST reaches an accuracy of 0.885 and an F1 score of 0.794, surpassing recent 1D and 2D time-series baselines across F1, recall, accuracy, and AUPRC. The results demonstrate that image-based fusion of load-sensor signals for time series classification is a practical and effective solution for real-time, privacy-preserving fall prevention.", "date": "2025-06-25", "categories": []}
{"id": "2510.17810", "title": "Exploring Complexity Changes in Diseased ECG Signals for Enhanced Classification", "abstract": "The complex dynamics of the heart are reflected in its electrical activity, captured through electrocardiograms (ECGs). In this study we use nonlinear time series analysis to understand how ECG complexity varies with cardiac pathology. Using the large PTB-XL dataset, we extracted nonlinear measures from lead II ECGs, and cross-channel metrics (leads II, V2, AVL) using Spearman correlations and mutual information. Significant differences between diseased and healthy individuals were found in almost all measures between healthy and diseased classes, and between 5 diagnostic superclasses ($p<.001$). Moreover, incorporating these complexity quantifiers into machine learning models substantially improved classification accuracy measured using area under the ROC curve (AUC) from 0.86 (baseline) to 0.87 (nonlinear measures) and 0.90 (including cross-time series metrics).", "date": "2025-09-24", "categories": []}
{"id": "2510.18103", "title": "Enhancing mortality prediction in cardiac arrest ICU patients through meta-modeling of structured clinical data from MIMIC-IV", "abstract": "Accurate early prediction of in-hospital mortality in intensive care units (ICUs) is essential for timely clinical intervention and efficient resource allocation. This study develops and evaluates machine learning models that integrate both structured clinical data and unstructured textual information, specifically discharge summaries and radiology reports, from the MIMIC-IV database. We used LASSO and XGBoost for feature selection, followed by a multivariate logistic regression trained on the top features identified by both models. Incorporating textual features using TF-IDF and BERT embeddings significantly improved predictive performance. The final logistic regression model, which combined structured and textual input, achieved an AUC of 0.918, compared to 0.753 when using structured data alone, a relative improvement 22%. The analysis of the decision curve demonstrated a superior standardized net benefit in a wide range of threshold probabilities (0.2-0.8), confirming the clinical utility of the model. These results underscore the added prognostic value of unstructured clinical notes and support their integration into interpretable feature-driven risk prediction models for ICU patients.", "date": "2025-10-20", "categories": []}
{"id": "2510.18841", "title": "A Hybrid Enumeration Framework for Optimal Counterfactual Generation in Post-Acute COVID-19 Heart Failure", "abstract": "Counterfactual inference provides a mathematical framework for reasoning about hypothetical outcomes under alternative interventions, bridging causal reasoning and predictive modeling. We present a counterfactual inference framework for individualized risk estimation and intervention analysis, illustrated through a clinical application to post-acute sequelae of COVID-19 (PASC) among patients with pre-existing heart failure (HF). Using longitudinal diagnosis, laboratory, and medication data from a large health-system cohort, we integrate regularized predictive modeling with counterfactual search to identify actionable pathways to PASC-related HF hospital admissions. The framework combines exact enumeration with optimization-based methods, including the Nearest Instance Counterfactual Explanations (NICE) and Multi-Objective Counterfactuals (MOC) algorithms, to efficiently explore high-dimensional intervention spaces. Applied to more than 2700 individuals with confirmed SARS-CoV-2 infection and prior HF, the model achieved strong discriminative performance (AUROC: 0.88, 95% CI: 0.84-0.91) and generated interpretable, patient-specific counterfactuals that quantify how modifying comorbidity patterns or treatment factors could alter predicted outcomes. This work demonstrates how counterfactual reasoning can be formalized as an optimization problem over predictive functions, offering a rigorous, interpretable, and computationally efficient approach to personalized inference in complex biomedical systems.", "date": "2025-10-21", "categories": []}
{"id": "2311.13925", "title": "Predicting Patient Recovery or Mortality Using Deep Neural Decision Tree and Forest", "abstract": "Objective: Identifying patients at high risk of mortality is crucial for emergency physicians to allocate hospital resources effectively, particularly in regions with limited medical services. This need becomes even more pressing during global health crises that lead to significant morbidity and mortality. This study aimed to present the usability deep neural decision forest and deep neural decision tree to predict mortality among Coronavirus disease 2019 (COVID-19) patients. To this end, We used patient data encompassing Coronavirus disease 2019 diagnosis, demographics, health indicators, and occupational risk factors to analyze disease severity and outcomes. The dataset was partitioned using a stratified sampling method, ensuring that 80% was allocated for training and 20% for testing. Nine machine learning and deep learning methods were employed to build predictive models. The models were evaluated across all stages to determine their effectiveness in predicting patient outcomes. Results: Among the models, the deep neural decision forest consistently outperformed others. Results indicated that using only clinical data yielded an accuracy of 80% by deep neural decision forest, demonstrating it as a reliable predictor of patient mortality. Moreover, the results suggest that clinical data alone may be the most accurate diagnostic tool for predicting mortality.", "date": "2023-11-23", "categories": []}
{"id": "2505.06945", "title": "Challenges and proposed solutions in modeling multimodal data: A systematic review", "abstract": "Multimodal data modeling has emerged as a powerful approach in clinical research, enabling the integration of diverse data types such as imaging, genomics, wearable sensors, and electronic health records. Despite its potential to improve diagnostic accuracy and support personalized care, modeling such heterogeneous data presents significant technical challenges. This systematic review synthesizes findings from 69 studies to identify common obstacles, including missing modalities, limited sample sizes, dimensionality imbalance, interpretability issues, and finding the optimal fusion techniques. We highlight recent methodological advances, such as transfer learning, generative models, attention mechanisms, and neural architecture search that offer promising solutions. By mapping current trends and innovations, this review provides a comprehensive overview of the field and offers practical insights to guide future research and development in multimodal modeling for medical applications.", "date": "2025-05-11", "categories": []}
{"id": "2508.05830", "title": "\"Mirror\" Language AI Models of Depression are Criterion-Contaminated", "abstract": "Recent studies show near-perfect language-based predictions of depression scores (R2 = .70), but these \"Mirror\" models rely on language responses directly from depression assessments to predict depression assessment scores. These methods suffer from criterion contamination that inflate prediction estimates. We compare \"Mirror\" models to \"Non-Mirror\" models, which use other external language to predict depression scores. 110 participants completed both structured diagnostic (Mirror condition) and life history (Non-Mirror condition) interviews. LLMs were prompted to predict diagnostic depression scores. As expected, Mirror models were near-perfect. However, Non-Mirror models also displayed prediction sizes considered large in psychology. Further, both Mirror and Non-Mirror predictions correlated with other questionnaire-based depression symptoms at similar sizes, suggesting bias in Mirror models. Topic modeling revealed different theme structures across model types. As language models for depression continue to evolve, incorporating Non-Mirror approaches may support more valid and clinically useful language-based AI applications in psychological assessment.", "date": "2025-08-07", "categories": []}
{"id": "2510.15950", "title": "Cross-dataset Multivariate Time-series Model for Parkinson's Diagnosis via Keyboard Dynamics", "abstract": "Parkinson's disease (PD) presents a growing global challenge, affecting over 10 million individuals, with prevalence expected to double by 2040. Early diagnosis remains difficult due to the late emergence of motor symptoms and limitations of traditional clinical assessments. In this study, we propose a novel pipeline that leverages keystroke dynamics as a non-invasive and scalable biomarker for remote PD screening and telemonitoring. Our methodology involves three main stages: (i) preprocessing of data from four distinct datasets, extracting four temporal signals and addressing class imbalance through the comparison of three methods; (ii) pre-training eight state-of-the-art deep-learning architectures on the two largest datasets, optimizing temporal windowing, stride, and other hyperparameters; (iii) fine-tuning on an intermediate-sized dataset and performing external validation on a fourth, independent cohort. Our results demonstrate that hybrid convolutional-recurrent and transformer-based models achieve strong external validation performance, with AUC-ROC scores exceeding 90% and F1-Score over 70%. Notably, a temporal convolutional model attains an AUC-ROC of 91.14% in external validation, outperforming existing methods that rely solely on internal validation. These findings underscore the potential of keystroke dynamics as a reliable digital biomarker for PD, offering a promising avenue for early detection and continuous monitoring.", "date": "2025-10-10", "categories": []}
{"id": "2510.16677", "title": "Renaissance of RNNs in Streaming Clinical Time Series: Compact Recurrence Remains Competitive with Transformers", "abstract": "We present a compact, strictly causal benchmark for streaming clinical time series on the MIT--BIH Arrhythmia Database using per-second heart rate. Two tasks are studied under record-level, non-overlapping splits: near-term tachycardia risk (next ten seconds) and one-step heart rate forecasting. We compare a GRU-D (RNN) and a Transformer under matched training budgets against strong non-learned baselines. Evaluation is calibration-aware for classification and proper for forecasting, with temperature scaling and grouped bootstrap confidence intervals. On MIT-BIH, GRU-D slightly surpasses the Transformer for tachycardia risk, while the Transformer clearly lowers forecasting error relative to GRU-D and persistence. Our results show that, in longitudinal monitoring, model choice is task-dependent: compact RNNs remain competitive for short-horizon risk scoring, whereas compact Transformers deliver clearer gains for point forecasting.", "date": "2025-10-19", "categories": []}
{"id": "2510.17039", "title": "Click, Predict, Trust: Clinician-in-the-Loop AI Segmentation for Lung Cancer CT-Based Prognosis within the Knowledge-to-Action Framework", "abstract": "Lung cancer remains the leading cause of cancer mortality, with CT imaging central to screening, prognosis, and treatment. Manual segmentation is variable and time-intensive, while deep learning (DL) offers automation but faces barriers to clinical adoption. Guided by the Knowledge-to-Action framework, this study develops a clinician-in-the-loop DL pipeline to enhance reproducibility, prognostic accuracy, and clinical trust. Multi-center CT data from 999 patients across 12 public datasets were analyzed using five DL models (3D Attention U-Net, ResUNet, VNet, ReconNet, SAM-Med3D), benchmarked against expert contours on whole and click-point cropped images. Segmentation reproducibility was assessed using 497 PySERA-extracted radiomic features via Spearman correlation, ICC, Wilcoxon tests, and MANOVA, while prognostic modeling compared supervised (SL) and semi-supervised learning (SSL) across 38 dimensionality reduction strategies and 24 classifiers. Six physicians qualitatively evaluated masks across seven domains, including clinical meaningfulness, boundary quality, prognostic value, trust, and workflow integration. VNet achieved the best performance (Dice = 0.83, IoU = 0.71), radiomic stability (mean correlation = 0.76, ICC = 0.65), and predictive accuracy under SSL (accuracy = 0.88, F1 = 0.83). SSL consistently outperformed SL across models. Radiologists favored VNet for peritumoral representation and smoother boundaries, preferring AI-generated initial masks for refinement rather than replacement. These results demonstrate that integrating VNet with SSL yields accurate, reproducible, and clinically trusted CT-based lung cancer prognosis, highlighting a feasible path toward physician-centered AI translation.", "date": "2025-10-19", "categories": []}
{"id": "2502.17460", "title": "Finetuning and Quantization of EEG-Based Foundational BioSignal Models on ECG and PPG Data for Blood Pressure Estimation", "abstract": "Blood pressure (BP) is a key indicator of cardiovascular health. As hypertension remains a global cause of morbidity and mortality, accurate, continuous, and non-invasive BP monitoring is therefore of paramount importance. Photoplethysmography (PPG) and electrocardiography (ECG) can potentially enable continuous BP monitoring, yet training accurate and robust machine learning (ML) models remains challenging due to variability in data quality and patient-specific factors. Recently, multiple research groups explored Electroencephalographic (EEG)--based foundation models and demonstrated their exceptional ability to learn rich temporal resolution. Considering the morphological similarities between different biosignals, the question arises of whether a model pre-trained on one modality can effectively be exploited to improve the accuracy of a different signal type. In this work, we take an initial step towards generalized biosignal foundation models by investigating whether model representations learned from abundant EEG data can effectively be transferred to ECG/PPG data solely with fine-tuning, without the need for large-scale additional pre-training, for the BP estimation task. Evaluations on the MIMIC-III and VitalDB datasets demonstrate that our approach achieves near state-of-the-art accuracy for diastolic BP (mean absolute error of 1.57 mmHg) and surpasses by 1.5x the accuracy of prior works for systolic BP (mean absolute error 2.72 mmHg). Additionally, we perform dynamic INT8 quantization, reducing the smallest model size by over 3.5x (from 13.73 MB down to 3.83 MB) while preserving performance, thereby enabling unobtrusive, real-time BP monitoring on resource-constrained wearable devices.", "date": "2025-02-10", "categories": []}
{"id": "2507.02998", "title": "A Weakly Supervised Transformer for Rare Disease Diagnosis and Subphenotyping from EHRs with Pulmonary Case Studies", "abstract": "Rare diseases affect an estimated 300-400 million people worldwide, yet individual conditions remain underdiagnosed and poorly characterized due to their low prevalence and limited clinician familiarity. Computational phenotyping offers a scalable approach to improving rare disease detection, but algorithm development is hindered by the scarcity of high-quality labeled data for training. Expert-labeled datasets from chart reviews and registries are clinically accurate but limited in scope and availability, whereas labels derived from electronic health records (EHRs) provide broader coverage but are often noisy or incomplete. To address these challenges, we propose WEST (WEakly Supervised Transformer for rare disease phenotyping and subphenotyping from EHRs), a framework that combines routinely collected EHR data with a limited set of expert-validated cases and controls to enable large-scale phenotyping. At its core, WEST employs a weakly supervised transformer model trained on extensive probabilistic silver-standard labels - derived from both structured and unstructured EHR features - that are iteratively refined during training to improve model calibration. We evaluate WEST on two rare pulmonary diseases using EHR data from Boston Children's Hospital and show that it outperforms existing methods in phenotype classification, identification of clinically meaningful subphenotypes, and prediction of disease progression. By reducing reliance on manual annotation, WEST enables data-efficient rare disease phenotyping that improves cohort definition, supports earlier and more accurate diagnosis, and accelerates data-driven discovery for the rare disease community.", "date": "2025-07-01", "categories": []}
{"id": "2510.14997", "title": "Evaluation and Implementation of Machine Learning Algorithms to Predict Early Detection of Kidney and Heart Disease in Diabetic Patients", "abstract": "Cardiovascular disease and chronic kidney disease are major complications of diabetes, leading to high morbidity and mortality. Early detection of these conditions is critical, yet traditional diagnostic markers often lack sensitivity in the initial stages. This study integrates conventional statistical methods with machine learning approaches to improve early diagnosis of CKD and CVD in diabetic patients. Descriptive and inferential statistics were computed in SPSS to explore associations between diseases and clinical or demographic factors. Patients were categorized into four groups: Group A both CKD and CVD, Group B CKD only, Group C CVD only, and Group D no disease. Statistical analysis revealed significant correlations: Serum Creatinine and Hypertension with CKD, and Cholesterol, Triglycerides, Myocardial Infarction, Stroke, and Hypertension with CVD. These results guided the selection of predictive features for machine learning models. Logistic Regression, Support Vector Machine, and Random Forest algorithms were implemented, with Random Forest showing the highest accuracy, particularly for CKD prediction. Ensemble models outperformed single classifiers in identifying high-risk diabetic patients. SPSS results further validated the significance of the key parameters integrated into the models. While challenges such as interpretability and class imbalance remain, this hybrid statistical machine learning framework offers a promising advancement toward early detection and risk stratification of diabetic complications compared to conventional diagnostic approaches.", "date": "2025-10-12", "categories": []}
{"id": "2510.15179", "title": "An Advanced Two-Stage Model with High Sensitivity and Generalizability for Prediction of Hip Fracture Risk Using Multiple Datasets", "abstract": "Hip fractures are a major cause of disability, mortality, and healthcare burden in older adults, underscoring the need for early risk assessment. However, commonly used tools such as the DXA T-score and FRAX often lack sensitivity and miss individuals at high risk, particularly those without prior fractures or with osteopenia. To address this limitation, we propose a sequential two-stage model that integrates clinical and imaging information to improve prediction accuracy. Using data from the Osteoporotic Fractures in Men Study (MrOS), the Study of Osteoporotic Fractures (SOF), and the UK Biobank, Stage 1 (Screening) employs clinical, demographic, and functional variables to estimate baseline risk, while Stage 2 (Imaging) incorporates DXA-derived features for refinement. The model was rigorously validated through internal and external testing, showing consistent performance and adaptability across cohorts. Compared to T-score and FRAX, the two-stage framework achieved higher sensitivity and reduced missed cases, offering a cost-effective and personalized approach for early hip fracture risk assessment.\n  Keywords: Hip Fracture, Two-Stage Model, Risk Prediction, Sensitivity, DXA, FRAX", "date": "2025-10-16", "categories": []}
{"id": "2510.13843", "title": "Serialized EHR make for good text representations", "abstract": "The emergence of foundation models in healthcare has opened new avenues for learning generalizable representations from large scale clinical data. Yet, existing approaches often struggle to reconcile the tabular and event based nature of Electronic Health Records (EHRs) with the sequential priors of natural language models. This structural mismatch limits their ability to capture longitudinal dependencies across patient encounters. We introduce SerialBEHRT, a domain aligned foundation model that extends SciBERT through additional pretraining on structured EHR sequences. SerialBEHRT is designed to encode temporal and contextual relationships among clinical events, thereby producing richer patient representations. We evaluate its effectiveness on the task of antibiotic susceptibility prediction, a clinically meaningful problem in antibiotic stewardship. Through extensive benchmarking against state of the art EHR representation strategies, we demonstrate that SerialBEHRT achieves superior and more consistent performance, highlighting the importance of temporal serialization in foundation model pretraining for healthcare.", "date": "2025-10-11", "categories": []}
{"id": "2510.13922", "title": "LTR-ICD: A Learning-to-Rank Approach for Automatic ICD Coding", "abstract": "Clinical notes contain unstructured text provided by clinicians during patient encounters. These notes are usually accompanied by a sequence of diagnostic codes following the International Classification of Diseases (ICD). Correctly assigning and ordering ICD codes are essential for medical diagnosis and reimbursement. However, automating this task remains challenging. State-of-the-art methods treated this problem as a classification task, leading to ignoring the order of ICD codes that is essential for different purposes. In this work, as a first attempt, we approach this task from a retrieval system perspective to consider the order of codes, thus formulating this problem as a classification and ranking task. Our results and analysis show that the proposed framework has a superior ability to identify high-priority codes compared to other methods. For instance, our model accuracy in correctly ranking primary diagnosis codes is 47%, compared to 20% for the state-of-the-art classifier. Additionally, in terms of classification metrics, the proposed model achieves a micro- and macro-F1 scores of 0.6065 and 0.2904, respectively, surpassing the previous best model with scores of 0.597 and 0.2660.", "date": "2025-10-15", "categories": []}
{"id": "2510.14254", "title": "Generalist vs Specialist Time Series Foundation Models: Investigating Potential Emergent Behaviors in Assessing Human Health Using PPG Signals", "abstract": "Foundation models are large-scale machine learning models that are pre-trained on massive amounts of data and can be adapted for various downstream tasks. They have been extensively applied to tasks in Natural Language Processing and Computer Vision with models such as GPT, BERT, and CLIP. They are now also increasingly gaining attention in time-series analysis, particularly for physiological sensing. However, most time series foundation models are specialist models - with data in pre-training and testing of the same type, such as Electrocardiogram, Electroencephalogram, and Photoplethysmogram (PPG). Recent works, such as MOMENT, train a generalist time series foundation model with data from multiple domains, such as weather, traffic, and electricity. This paper aims to conduct a comprehensive benchmarking study to compare the performance of generalist and specialist models, with a focus on PPG signals. Through an extensive suite of total 51 tasks covering cardiac state assessment, laboratory value estimation, and cross-modal inference, we comprehensively evaluate both models across seven dimensions, including win score, average performance, feature quality, tuning gain, performance variance, transferability, and scalability. These metrics jointly capture not only the models' capability but also their adaptability, robustness, and efficiency under different fine-tuning strategies, providing a holistic understanding of their strengths and limitations for diverse downstream scenarios. In a full-tuning scenario, we demonstrate that the specialist model achieves a 27% higher win score. Finally, we provide further analysis on generalization, fairness, attention visualizations, and the importance of training data choice.", "date": "2025-10-16", "categories": []}
{"id": "2510.14286", "title": "Stable Prediction of Adverse Events in Medical Time-Series Data", "abstract": "Early event prediction (EEP) systems continuously estimate a patient's imminent risk to support clinical decision-making. For bedside trust, risk trajectories must be accurate and temporally stable, shifting only with new, relevant evidence. However, current benchmarks (a) ignore stability of risk scores and (b) evaluate mainly on tabular inputs, leaving trajectory behavior untested. To address this gap, we introduce CAREBench, an EEP benchmark that evaluates deployability using multi-modal inputs-tabular EHR, ECG waveforms, and clinical text-and assesses temporal stability alongside predictive accuracy. We propose a stability metric that quantifies short-term variability in per-patient risk and penalizes abrupt oscillations based on local-Lipschitz constants. CAREBench spans six prediction tasks such as sepsis onset and compares classical learners, deep sequence models, and zero-shot LLMs. Across tasks, existing methods, especially LLMs, struggle to jointly optimize accuracy and stability, with notably poor recall at high-precision operating points. These results highlight the need for models that produce evidence-aligned, stable trajectories to earn clinician trust in continuous monitoring settings. (Code: https://github.com/SeewonChoi/CAREBench.)", "date": "2025-10-16", "categories": []}
{"id": "2510.14332", "title": "A Robust Classification Method using Hybrid Word Embedding for Early Diagnosis of Alzheimer's Disease", "abstract": "Early detection of Alzheimer's Disease (AD) is greatly beneficial to AD patients, leading to early treatments that lessen symptoms and alleviating financial burden of health care. As one of the leading signs of AD, language capability changes can be used for early diagnosis of AD. In this paper, I develop a robust classification method using hybrid word embedding and fine-tuned hyperparameters to achieve state-of-the-art accuracy in the early detection of AD. Specifically, we create a hybrid word embedding based on word vectors from Doc2Vec and ELMo to obtain perplexity scores of the sentences. The scores identify whether a sentence is fluent or not and capture semantic context of the sentences. I enrich the word embedding by adding linguistic features to analyze syntax and semantics. Further, we input an embedded feature vector into logistic regression and fine tune hyperparameters throughout the pipeline. By tuning hyperparameters of the machine learning pipeline (e.g., model regularization parameter, learning rate and vector size of Doc2Vec, and vector size of ELMo), I achieve 91% classification accuracy and an Area Under the Curve (AUC) of 97% in distinguishing early AD from healthy subjects. Based on my knowledge, my model with 91% accuracy and 97% AUC outperforms the best existing NLP model for AD diagnosis with an accuracy of 88% [32]. I study the model stability through repeated experiments and find that the model is stable even though the training data is split randomly (standard deviation of accuracy = 0.0403; standard deviation of AUC = 0.0174). This affirms our proposed method is accurate and stable. This model can be used as a large-scale screening method for AD, as well as a complementary examination for doctors to detect AD.", "date": "2025-10-16", "categories": []}
{"id": "2510.14800", "title": "Morphology-Aware Prognostic model for Five-Year Survival Prediction in Colorectal Cancer from H&E Whole Slide Images", "abstract": "Colorectal cancer (CRC) remains the third most prevalent malignancy globally, with approximately 154,000 new cases and 54,000 projected deaths anticipated for 2025. The recent advancement of foundation models in computational pathology has been largely propelled by task agnostic methodologies that can overlook organ-specific crucial morphological patterns that represent distinct biological processes that can fundamentally influence tumor behavior, therapeutic response, and patient outcomes. The aim of this study is to develop a novel, interpretable AI model, PRISM (Prognostic Representation of Integrated Spatial Morphology), that incorporates a continuous variability spectrum within each distinct morphology to characterize phenotypic diversity and reflecting the principle that malignant transformation occurs through incremental evolutionary processes rather than abrupt phenotypic shifts. PRISM is trained on 8.74 million histological images extracted from surgical resection specimens of 424 patients with stage III CRC. PRISM achieved superior prognostic performance for five-year OS (AUC = 0.70 +- 0.04; accuracy = 68.37% +- 4.75%; HR = 3.34, 95% CI = 2.28-4.90; p < 0.0001), outperforming existing CRC-specific methods by 15% and AI foundation models by ~23% accuracy. It showed sex-agnostic robustness (AUC delta = 0.02; accuracy delta = 0.15%) and stable performance across clinicopathological subgroups, with minimal accuracy fluctuation (delta = 1.44%) between 5FU/LV and CPT-11/5FU/LV regimens, replicating the Alliance cohort finding of no survival difference between treatments.", "date": "2025-10-16", "categories": []}
{"id": "2510.14937", "title": "AI-Powered Early Diagnosis of Mental Health Disorders from Real-World Clinical Conversations", "abstract": "Mental health disorders remain among the leading cause of disability worldwide, yet conditions such as depression, anxiety, and Post-Traumatic Stress Disorder (PTSD) are frequently underdiagnosed or misdiagnosed due to subjective assessments, limited clinical resources, and stigma and low awareness. In primary care settings, studies show that providers misidentify depression or anxiety in over 60% of cases, highlighting the urgent need for scalable, accessible, and context-aware diagnostic tools that can support early detection and intervention. In this study, we evaluate the effectiveness of machine learning models for mental health screening using a unique dataset of 553 real-world, semistructured interviews, each paried with ground-truth diagnoses for major depressive episodes (MDE), anxiety disorders, and PTSD. We benchmark multiple model classes, including zero-shot prompting with GPT-4.1 Mini and MetaLLaMA, as well as fine-tuned RoBERTa models using LowRank Adaptation (LoRA). Our models achieve over 80% accuracy across diagnostic categories, with especially strongperformance on PTSD (up to 89% accuracy and 98% recall). We also find that using shorter context, focused context segments improves recall, suggesting that focused narrative cues enhance detection sensitivity. LoRA fine-tuning proves both efficient and effective, with lower-rank configurations (e.g., rank 8 and 16) maintaining competitive performance across evaluation metrics. Our results demonstrate that LLM-based models can offer substantial improvements over traditional self-report screening tools, providing a path toward low-barrier, AI-powerd early diagnosis. This work lays the groundwork for integrating machine learning into real-world clinical workflows, particularly in low-resource or high-stigma environments where access to timely mental health care is most limited.", "date": "2025-10-16", "categories": []}
{"id": "2507.23227", "title": "Enabling Few-Shot Alzheimer's Disease Diagnosis on Biomarker Data with Tabular LLMs", "abstract": "Early and accurate diagnosis of Alzheimer's disease (AD), a complex neurodegenerative disorder, requires analysis of heterogeneous biomarkers (e.g., neuroimaging, genetic risk factors, cognitive tests, and cerebrospinal fluid proteins) typically represented in a tabular format. With flexible few-shot reasoning, multimodal integration, and natural-language-based interpretability, large language models (LLMs) offer unprecedented opportunities for prediction with structured biomedical data. We propose a novel framework called TAP-GPT, Tabular Alzheimer's Prediction GPT, that adapts TableGPT2, a multimodal tabular-specialized LLM originally developed for business intelligence tasks, for AD diagnosis using structured biomarker data with small sample sizes. Our approach constructs few-shot tabular prompts using in-context learning examples from structured biomedical data and finetunes TableGPT2 using the parameter-efficient qLoRA adaption for a clinical binary classification task of AD or cognitively normal (CN). The TAP-GPT framework harnesses the powerful tabular understanding ability of TableGPT2 and the encoded prior knowledge of LLMs to outperform more advanced general-purpose LLMs and a tabular foundation model (TFM) developed for prediction tasks. To our knowledge, this is the first application of LLMs to the prediction task using tabular biomarker data, paving the way for future LLM-driven multi-agent frameworks in biomedical informatics.", "date": "2025-07-31", "categories": []}
{"id": "2509.07330", "title": "General Demographic Foundation Models for Enhancing Predictive Performance Across Diseases and Populations", "abstract": "Demographic attributes are universally present in electronic health records. They are the most widespread information across populations and diseases, and serve as vital predictors in clinical risk stratification and treatment decisions. Despite their significance, these attributes are often treated as auxiliaries in model design, with limited attention being paid to learning their representations. This study explored the development of a General Demographic Pre-trained (GDP) model as a foundational model tailored to demographic attributes, focusing on age and gender. The model is pre-trained and evaluated using datasets with diverse diseases and populations compositions from different geographic regions. The composition of GDP architecture was explored through examining combinations of ordering approaches and encoding methods to transform tabular demographic inputs into effective latent embeddings. Results demonstrate the feasibility of GDP to generalize across task, diseases, and populations. In detailed composition, the sequential ordering substantially improves model performance in discrimination, calibration, and the corresponding information gain at each decision tree split, particularly in diseases where age and gender contribute significantly to risk stratification. Even in datasets where demographic attributes hold relatively low predictive value, GDP enhances the representational importance, increasing their influence in downstream gradient boosting models. The findings suggest that foundation models for tabular demographic attributes offer a promising direction for improving predictive performance in healthcare applications.", "date": "2025-09-09", "categories": []}
{"id": "2408.09873", "title": "AI-powered skin spectral imaging enables instant sepsis diagnosis and outcome prediction in critically ill patients", "abstract": "With sepsis remaining a leading cause of mortality, early identification of patients with sepsis and those at high risk of death is a challenge of high socioeconomic importance. Given the potential of hyperspectral imaging (HSI) to monitor microcirculatory alterations, we propose a deep learning approach to automated sepsis diagnosis and mortality prediction using a single HSI cube acquired within seconds. In a prospective observational study, we collected HSI data from the palms and fingers of more than 480 intensive care unit patients. Neural networks applied to HSI measurements predicted sepsis and mortality with areas under the receiver operating characteristic curve (AUROCs) of 0.80 and 0.72, respectively. Performance improved substantially with additional clinical data, reaching AUROCs of 0.94 for sepsis and 0.83 for mortality. We conclude that deep learning-based HSI analysis enables rapid and noninvasive prediction of sepsis and mortality, with a potential clinical value for enhancing diagnosis and treatment.", "date": "2024-08-19", "categories": []}
{"id": "2506.03750", "title": "MoodAngels: A Retrieval-augmented Multi-agent Framework for Psychiatry Diagnosis", "abstract": "The application of AI in psychiatric diagnosis faces significant challenges, including the subjective nature of mental health assessments, symptom overlap across disorders, and privacy constraints limiting data availability. To address these issues, we present MoodAngels, the first specialized multi-agent framework for mood disorder diagnosis. Our approach combines granular-scale analysis of clinical assessments with a structured verification process, enabling more accurate interpretation of complex psychiatric data. Complementing this framework, we introduce MoodSyn, an open-source dataset of 1,173 synthetic psychiatric cases that preserves clinical validity while ensuring patient privacy. Experimental results demonstrate that MoodAngels outperforms conventional methods, with our baseline agent achieving 12.3% higher accuracy than GPT-4o on real-world cases, and our full multi-agent system delivering further improvements. Evaluation in the MoodSyn dataset demonstrates exceptional fidelity, accurately reproducing both the core statistical patterns and complex relationships present in the original data while maintaining strong utility for machine learning applications. Together, these contributions provide both an advanced diagnostic tool and a critical research resource for computational psychiatry, bridging important gaps in AI-assisted mental health assessment.", "date": "2025-06-04", "categories": []}
{"id": "2508.17554", "title": "Bridging Graph and State-Space Modeling for Intensive Care Unit Length of Stay Prediction", "abstract": "Predicting a patient's length of stay (LOS) in the intensive care unit (ICU) is a critical task for hospital resource management, yet remains challenging due to the heterogeneous and irregularly sampled nature of electronic health records (EHRs). In this work, we propose S$^2$G-Net, a novel neural architecture that unifies state-space sequence modeling with multi-view Graph Neural Networks (GNNs) for ICU LOS prediction. The temporal path employs Mamba state-space models (SSMs) to capture patient trajectories, while the graph path leverages an optimized GraphGPS backbone, designed to integrate heterogeneous patient similarity graphs derived from diagnostic, administrative, and semantic features. Experiments on the large-scale MIMIC-IV cohort dataset show that S$^2$G-Net consistently outperforms sequence models (BiLSTM, Mamba, Transformer), graph models (classic GNNs, GraphGPS), and hybrid approaches across all primary metrics. Extensive ablation studies and interpretability analyses highlight the complementary contributions of each component of our architecture and underscore the importance of principled graph construction. These results demonstrate that S$^2$G-Net provides an effective and scalable solution for ICU LOS prediction with multi-modal clinical data. The code can be found at https://github.com/ShuqiZi1/S2G-Net.", "date": "2025-08-24", "categories": []}
{"id": "2510.10364", "title": "Transformer Model Detects Antidepressant Use From a Single Night of Sleep, Unlocking an Adherence Biomarker", "abstract": "Antidepressant nonadherence is pervasive, driving relapse, hospitalization, suicide risk, and billions in avoidable costs. Clinicians need tools that detect adherence lapses promptly, yet current methods are either invasive (serum assays, neuroimaging) or proxy-based and inaccurate (pill counts, pharmacy refills). We present the first noninvasive biomarker that detects antidepressant intake from a single night of sleep. A transformer-based model analyzes sleep data from a consumer wearable or contactless wireless sensor to infer antidepressant intake, enabling remote, effortless, daily adherence assessment at home. Across six datasets comprising 62,000 nights from >20,000 participants (1,800 antidepressant users), the biomarker achieved AUROC = 0.84, generalized across drug classes, scaled with dose, and remained robust to concomitant psychotropics. Longitudinal monitoring captured real-world initiation, tapering, and lapses. This approach offers objective, scalable adherence surveillance with potential to improve depression care and outcomes.", "date": "2025-10-11", "categories": []}
{"id": "2510.10464", "title": "Post-TIPS Prediction via Multimodal Interaction: A Multi-Center Dataset and Framework for Survival, Complication, and Portal Pressure Assessment", "abstract": "Transjugular intrahepatic portosystemic shunt (TIPS) is an established procedure for portal hypertension, but provides variable survival outcomes and frequent overt hepatic encephalopathy (OHE), indicating the necessity of accurate preoperative prognostic modeling. Current studies typically build machine learning models from preoperative CT images or clinical characteristics, but face three key challenges: (1) labor-intensive region-of-interest (ROI) annotation, (2) poor reliability and generalizability of unimodal methods, and (3) incomplete assessment from single-endpoint prediction. Moreover, the lack of publicly accessible datasets constrains research in this field. Therefore, we present MultiTIPS, the first public multi-center dataset for TIPS prognosis, and propose a novel multimodal prognostic framework based on it. The framework comprises three core modules: (1) dual-option segmentation, which integrates semi-supervised and foundation model-based pipelines to achieve robust ROI segmentation with limited annotations and facilitate subsequent feature extraction; (2) multimodal interaction, where three techniques, multi-grained radiomics attention (MGRA), progressive orthogonal disentanglement (POD), and clinically guided prognostic enhancement (CGPE), are introduced to enable cross-modal feature interaction and complementary representation integration, thus improving model accuracy and robustness; and (3) multi-task prediction, where a staged training strategy is used to perform stable optimization of survival, portal pressure gradient (PPG), and OHE prediction for comprehensive prognostic assessment. Extensive experiments on MultiTIPS demonstrate the superiority of the proposed method over state-of-the-art approaches, along with strong cross-domain generalization and interpretability, indicating its promise for clinical application. The dataset and code are available.", "date": "2025-10-12", "categories": []}
{"id": "2510.10558", "title": "Multi-scale Frequency-Aware Adversarial Network for Parkinson's Disease Assessment Using Wearable Sensors", "abstract": "Severity assessment of Parkinson's disease (PD) using wearable sensors offers an effective, objective basis for clinical management. However, general-purpose time series models often lack pathological specificity in feature extraction, making it difficult to capture subtle signals highly correlated with PD.Furthermore, the temporal sparsity of PD symptoms causes key diagnostic features to be easily \"diluted\" by traditional aggregation methods, further complicating assessment. To address these issues, we propose the Multi-scale Frequency-Aware Adversarial Multi-Instance Network (MFAM). This model enhances feature specificity through a frequency decomposition module guided by medical prior knowledge. Furthermore, by introducing an attention-based multi-instance learning (MIL) framework, the model can adaptively focus on the most diagnostically valuable sparse segments.We comprehensively validated MFAM on both the public PADS dataset for PD versus differential diagnosis (DD) binary classification and a private dataset for four-class severity assessment. Experimental results demonstrate that MFAM outperforms general-purpose time series models in handling complex clinical time series with specificity, providing a promising solution for automated assessment of PD severity.", "date": "2025-10-12", "categories": []}
{"id": "2510.11112", "title": "Multimodal Disease Progression Modeling via Spatiotemporal Disentanglement and Multiscale Alignment", "abstract": "Longitudinal multimodal data, including electronic health records (EHR) and sequential chest X-rays (CXRs), is critical for modeling disease progression, yet remains underutilized due to two key challenges: (1) redundancy in consecutive CXR sequences, where static anatomical regions dominate over clinically-meaningful dynamics, and (2) temporal misalignment between sparse, irregular imaging and continuous EHR data. We introduce $\\texttt{DiPro}$, a novel framework that addresses these challenges through region-aware disentanglement and multi-timescale alignment. First, we disentangle static (anatomy) and dynamic (pathology progression) features in sequential CXRs, prioritizing disease-relevant changes. Second, we hierarchically align these static and dynamic CXR features with asynchronous EHR data via local (pairwise interval-level) and global (full-sequence) synchronization to model coherent progression pathways. Extensive experiments on the MIMIC dataset demonstrate that $\\texttt{DiPro}$ could effectively extract temporal clinical dynamics and achieve state-of-the-art performance on both disease progression identification and general ICU prediction tasks.", "date": "2025-10-13", "categories": []}
{"id": "2510.11257", "title": "MIEO: encoding clinical data to enhance cardiovascular event prediction", "abstract": "As clinical data are becoming increasingly available, machine learning methods have been employed to extract knowledge from them and predict clinical events. While promising, approaches suffer from at least two main issues: low availability of labelled data and data heterogeneity leading to missing values. This work proposes the use of self-supervised auto-encoders to efficiently address these challenges. We apply our methodology to a clinical dataset from patients with ischaemic heart disease. Patient data is embedded in a latent space, built using unlabelled data, which is then used to train a neural network classifier to predict cardiovascular death. Results show improved balanced accuracy compared to applying the classifier directly to the raw data, demonstrating that this solution is promising, especially in conditions where availability of unlabelled data could increase.", "date": "2025-10-13", "categories": []}
{"id": "2510.11275", "title": "SeFEF: A Seizure Forecasting Evaluation Framework", "abstract": "The lack of standardization in seizure forecasting slows progress in the field and limits the clinical translation of forecasting models. In this work, we introduce a Python-based framework aimed at streamlining the development, assessment, and documentation of individualized seizure forecasting algorithms.\n  The framework automates data labeling, cross-validation splitting, forecast post-processing, performance evaluation, and reporting. It supports various forecasting horizons and includes a model card that documents implementation details, training and evaluation settings, and performance metrics. Three different models were implemented as a proof-of-concept. The models leveraged features extracted from time series data and seizure periodicity. Model performance was assessed using time series cross-validation and key deterministic and probabilistic metrics.\n  Implementation of the three models was successful, demonstrating the flexibility of the framework. The results also emphasize the importance of careful model interpretation due to variations in probability scaling, calibration, and subject-specific differences. Although formal usability metrics were not recorded, empirical observations suggest reduced development time and methodological consistency, minimizing unintentional variations that could affect the comparability of different approaches.\n  As a proof-of-concept, this validation is inherently limited, relying on a single-user experiment without statistical analyses or replication across independent datasets. At this stage, our objective is to make the framework publicly available to foster community engagement, facilitate experimentation, and gather feedback. In the long term, we aim to contribute to the establishment of a consensus on a standardized methodology for the development and validation of seizure forecasting algorithms in people with epilepsy.", "date": "2025-10-13", "categories": []}
{"id": "2507.12269", "title": "Site-Level Fine-Tuning with Progressive Layer Freezing: Towards Robust Prediction of Bronchopulmonary Dysplasia from Day-1 Chest Radiographs in Extremely Preterm Infants", "abstract": "Bronchopulmonary dysplasia (BPD) is a chronic lung disease affecting 35% of extremely low birth weight infants. Defined by oxygen dependence at 36 weeks postmenstrual age, it causes lifelong respiratory complications. However, preventive interventions carry severe risks, including neurodevelopmental impairment, ventilator-induced lung injury, and systemic complications. Therefore, early BPD prognosis and prediction of BPD outcome is crucial to avoid unnecessary toxicity in low risk infants. Admission radiographs of extremely preterm infants are routinely acquired within 24h of life and could serve as a non-invasive prognostic tool. In this work, we developed and investigated a deep learning approach using chest X-rays from 163 extremely low-birth-weight infants ($\\leq$32 weeks gestation, 401-999g) obtained within 24 hours of birth. We fine-tuned a ResNet-50 pretrained specifically on adult chest radiographs, employing progressive layer freezing with discriminative learning rates to prevent overfitting and evaluated a CutMix augmentation and linear probing. For moderate/severe BPD outcome prediction, our best performing model with progressive freezing, linear probing and CutMix achieved an AUROC of 0.78 $\\pm$ 0.10, balanced accuracy of 0.69 $\\pm$ 0.10, and an F1-score of 0.67 $\\pm$ 0.11. In-domain pre-training significantly outperformed ImageNet initialization (p = 0.031) which confirms domain-specific pretraining to be important for BPD outcome prediction. Routine IRDS grades showed limited prognostic value (AUROC 0.57 $\\pm$ 0.11), confirming the need of learned markers. Our approach demonstrates that domain-specific pretraining enables accurate BPD prediction from routine day-1 radiographs. Through progressive freezing and linear probing, the method remains computationally feasible for site-level implementation and future federated learning deployments.", "date": "2025-07-16", "categories": []}
{"id": "2510.09159", "title": "Cross-Representation Benchmarking in Time-Series Electronic Health Records for Clinical Outcome Prediction", "abstract": "Electronic Health Records (EHRs) enable deep learning for clinical predictions, but the optimal method for representing patient data remains unclear due to inconsistent evaluation practices. We present the first systematic benchmark to compare EHR representation methods, including multivariate time-series, event streams, and textual event streams for LLMs. This benchmark standardises data curation and evaluation across two distinct clinical settings: the MIMIC-IV dataset for ICU tasks (mortality, phenotyping) and the EHRSHOT dataset for longitudinal care (30-day readmission, 1-year pancreatic cancer). For each paradigm, we evaluate appropriate modelling families--including Transformers, MLP, LSTMs and Retain for time-series, CLMBR and count-based models for event streams, 8-20B LLMs for textual streams--and analyse the impact of feature pruning based on data missingness. Our experiments reveal that event stream models consistently deliver the strongest performance. Pre-trained models like CLMBR are highly sample-efficient in few-shot settings, though simpler count-based models can be competitive given sufficient data. Furthermore, we find that feature selection strategies must be adapted to the clinical setting: pruning sparse features improves ICU predictions, while retaining them is critical for longitudinal tasks. Our results, enabled by a unified and reproducible pipeline, provide practical guidance for selecting EHR representations based on the clinical context and data regime.", "date": "2025-10-10", "categories": []}
{"id": "2510.09493", "title": "Performance Analysis of Machine Learning Algorithms in Chronic Kidney Disease Prediction", "abstract": "Kidneys are the filter of the human body. About 10% of the global population is thought to be affected by Chronic Kidney Disease (CKD), which causes kidney function to decline. To protect in danger patients from additional kidney damage, effective risk evaluation of CKD and appropriate CKD monitoring are crucial. Due to quick and precise detection capabilities, Machine Learning models can help practitioners accomplish this goal efficiently; therefore, an enormous number of diagnosis systems and processes in the healthcare sector nowadays are relying on machine learning due to its disease prediction capability. In this study, we designed and suggested disease predictive computer-aided designs for the diagnosis of CKD. The dataset for CKD is attained from the repository of machine learning of UCL, with a few missing values; those are filled in using \"mean-mode\" and \"Random sampling method\" strategies. After successfully achieving the missing data, eight ML techniques (Random Forest, SVM, Naive Bayes, Logistic Regression, KNN, XGBoost, Decision Tree, and AdaBoost) were used to establish models, and the performance evaluation comparisons among the result accuracies are measured by the techniques to find the machine learning models with the highest accuracy. Among them, Random Forest as well as Logistic Regression showed an outstanding 99% accuracy, followed by the Ada Boost, XGBoost, Naive Bayes, Decision Tree, and SVM, whereas the KNN classifier model stands last with an accuracy of 73%.", "date": "2025-10-10", "categories": []}
{"id": "2510.09513", "title": "Interpretable Generative and Discriminative Learning for Multimodal and Incomplete Clinical Data", "abstract": "Real-world clinical problems are often characterized by multimodal data, usually associated with incomplete views and limited sample sizes in their cohorts, posing significant limitations for machine learning algorithms. In this work, we propose a Bayesian approach designed to efficiently handle these challenges while providing interpretable solutions. Our approach integrates (1) a generative formulation to capture cross-view relationships with a semi-supervised strategy, and (2) a discriminative task-oriented formulation to identify relevant information for specific downstream objectives. This dual generative-discriminative formulation offers both general understanding and task-specific insights; thus, it provides an automatic imputation of the missing views while enabling robust inference across different data sources. The potential of this approach becomes evident when applied to the multimodal clinical data, where our algorithm is able to capture and disentangle the complex interactions among biological, psychological, and sociodemographic modalities.", "date": "2025-10-10", "categories": []}
{"id": "2409.00310", "title": "Objective Features Extracted from Motor Activity Time Series for Food Addiction Analysis Using Machine Learning -- A Pilot Study", "abstract": "Wearable sensors and IoT/IoMT platforms enable continuous, real-time monitoring, but objective digital markers for eating disorders are limited. In this study, we examined whether actimetry and machine learning (ML) could provide objective criteria for food addiction (FA) and symptom counts (SC). In 78 participants (mean age 22.1 +/- 9.5 y; 73.1% women), one week of non-dominant wrist actimetry and psychometric data (YFAS, DEBQ, ZSDS) were collected. The time series were segmented into daytime activity and nighttime rest, and statistical and entropy descriptors (FuzzyEn, DistEn, SVDEn, PermEn, PhaseEn; 256 features) were calculated. The mean Matthews correlation coefficient (MCC) was used as the primary metric in a K-nearest neighbors (KNN) pipeline with five-fold stratified cross-validation (one hundred repetitions; 500 evaluations); SHAP was used to assist in interpretation. For binary FA, activity-segment features performed best (MCC = 0.78 +/- 0.02; Accuracy ~ 95.3% +/- 0.5; Sensitivity ~ 0.77 +/- 0.03; Specificity ~ 0.98 +/- 0.004), exceeding OaS (Objective and Subjective Features) (MCC = 0.69 +/- 0.03) and rest-only (MCC = 0.50 +/- 0.03). For SC (four classes), OaS slightly surpassed actimetry (MCC = 0.40 +/- 0.01 vs 0.38 +/- 0.01; Accuracy ~ 58.1% vs 56.9%). Emotional and restrained eating were correlated with actimetric features. These findings support wrist-worn actimetry as a digital biomarker of FA that complements questionnaires and may facilitate privacy-preserving clinical translation.", "date": "2024-08-31", "categories": []}
{"id": "2501.12193", "title": "MyDigiTwin: A Privacy-Preserving Framework for Personalized Cardiovascular Risk Prediction and Scenario Exploration", "abstract": "Cardiovascular disease (CVD) remains a leading cause of death, and primary prevention through personalized interventions is crucial. This paper introduces MyDigiTwin, a framework that integrates health digital twins with personal health environments to empower patients in exploring personalized health scenarios while ensuring data privacy. MyDigiTwin uses federated learning to train predictive models across distributed datasets without transferring raw data, and a novel data harmonization framework addresses semantic and format inconsistencies in health data. A proof-of-concept demonstrates the feasibility of harmonizing and using cohort data to train privacy-preserving CVD prediction models. This framework offers a scalable solution for proactive, personalized cardiovascular care and sets the stage for future applications in real-world healthcare settings.", "date": "2025-01-21", "categories": []}
{"id": "2509.14830", "title": "ProtoMedX: Towards Explainable Multi-Modal Prototype Learning for Bone Health Classification", "abstract": "Bone health studies are crucial in medical practice for the early detection and treatment of Osteopenia and Osteoporosis. Clinicians usually make a diagnosis based on densitometry (DEXA scans) and patient history. The applications of AI in this field are ongoing research. Most successful methods rely on deep learning models that use vision alone (DEXA/X-ray imagery) and focus on prediction accuracy, while explainability is often disregarded and left to post hoc assessments of input contributions. We propose ProtoMedX, a multi-modal (multimodal) model that uses both DEXA scans of the lumbar spine and patient records. ProtoMedX's prototype-based architecture is explainable by design, which is crucial for medical applications, especially in the context of the upcoming EU AI Act, as it allows explicit analysis of model decisions, including incorrect ones. ProtoMedX demonstrates state-of-the-art performance in bone health classification while also providing explanations that can be visually understood by clinicians. Using a dataset of 4,160 real NHS patients, the proposed ProtoMedX achieves 87.58% accuracy in vision-only tasks and 89.8% in its multi-modal variant, both surpassing existing published methods.", "date": "2025-09-18", "categories": []}
{"id": "2510.07477", "title": "HEMERA: A Human-Explainable Transformer Model for Estimating Lung Cancer Risk using GWAS Data", "abstract": "Lung cancer (LC) is the third most common cancer and the leading cause of cancer deaths in the US. Although smoking is the primary risk factor, the occurrence of LC in never-smokers and familial aggregation studies highlight a genetic component. Genetic biomarkers identified through genome-wide association studies (GWAS) are promising tools for assessing LC risk. We introduce HEMERA (Human-Explainable Transformer Model for Estimating Lung Cancer Risk using GWAS Data), a new framework that applies explainable transformer-based deep learning to GWAS data of single nucleotide polymorphisms (SNPs) for predicting LC risk. Unlike prior approaches, HEMERA directly processes raw genotype data without clinical covariates, introducing additive positional encodings, neural genotype embeddings, and refined variant filtering. A post hoc explainability module based on Layer-wise Integrated Gradients enables attribution of model predictions to specific SNPs, aligning strongly with known LC risk loci. Trained on data from 27,254 Million Veteran Program participants, HEMERA achieved >99% AUC (area under receiver characteristics) score. These findings support transparent, hypothesis-generating models for personalized LC risk assessment and early intervention.", "date": "2025-10-08", "categories": []}
{"id": "2510.07524", "title": "EEG Sleep Stage Classification with Continuous Wavelet Transform and Deep Learning", "abstract": "Accurate classification of sleep stages is crucial for the diagnosis and management of sleep disorders. Conventional approaches for sleep scoring rely on manual annotation or features extracted from EEG signals in the time or frequency domain. This study proposes a novel framework for automated sleep stage scoring using time-frequency analysis based on the wavelet transform. The Sleep-EDF Expanded Database (sleep-cassette recordings) was used for evaluation. The continuous wavelet transform (CWT) generated time-frequency maps that capture both transient and oscillatory patterns across frequency bands relevant to sleep staging. Experimental results demonstrate that the proposed wavelet-based representation, combined with ensemble learning, achieves an overall accuracy of 88.37 percent and a macro-averaged F1 score of 73.15, outperforming conventional machine learning methods and exhibiting comparable or superior performance to recent deep learning approaches. These findings highlight the potential of wavelet analysis for robust, interpretable, and clinically applicable sleep stage classification.", "date": "2025-10-08", "categories": []}
{"id": "2510.07629", "title": "Toward Reliable Clinical Coding with Language Models: Verification and Lightweight Adaptation", "abstract": "Accurate clinical coding is essential for healthcare documentation, billing, and decision-making. While prior work shows that off-the-shelf LLMs struggle with this task, evaluations based on exact match metrics often overlook errors where predicted codes are hierarchically close but incorrect. Our analysis reveals that such hierarchical misalignments account for a substantial portion of LLM failures. We show that lightweight interventions, including prompt engineering and small-scale fine-tuning, can improve accuracy without the computational overhead of search-based methods. To address hierarchically near-miss errors, we introduce clinical code verification as both a standalone task and a pipeline component. To mitigate the limitations in existing datasets, such as incomplete evidence and inpatient bias in MIMIC, we release an expert double-annotated benchmark of outpatient clinical notes with ICD-10 codes. Our results highlight verification as an effective and reliable step toward improving LLM-based medical coding.", "date": "2025-10-08", "categories": []}
{"id": "2406.15222", "title": "A Deep Learning System for Rapid and Accurate Warning of Acute Aortic Syndrome on Non-contrast CT in China", "abstract": "The accurate and timely diagnosis of acute aortic syndromes (AAS) in patients presenting with acute chest pain remains a clinical challenge. Aortic CT angiography (CTA) is the imaging protocol of choice in patients with suspected AAS. However, due to economic and workflow constraints in China, the majority of suspected patients initially undergo non-contrast CT as the initial imaging testing, and CTA is reserved for those at higher risk. In this work, we present an artificial intelligence-based warning system, iAorta, using non-contrast CT for AAS identification in China, which demonstrates remarkably high accuracy and provides clinicians with interpretable warnings. iAorta was evaluated through a comprehensive step-wise study. In the multi-center retrospective study (n = 20,750), iAorta achieved a mean area under the receiver operating curve (AUC) of 0.958 (95% CI 0.950-0.967). In the large-scale real-world study (n = 137,525), iAorta demonstrated consistently high performance across various non-contrast CT protocols, achieving a sensitivity of 0.913-0.942 and a specificity of 0.991-0.993. In the prospective comparative study (n = 13,846), iAorta demonstrated the capability to significantly shorten the time to correct diagnostic pathway. For the prospective pilot deployment that we conducted, iAorta correctly identified 21 out of 22 patients with AAS among 15,584 consecutive patients presenting with acute chest pain and under non-contrast CT protocol in the emergency department (ED) and enabled the average diagnostic time of these 21 AAS positive patients to be 102.1 (75-133) mins. Last, the iAorta can help avoid delayed or missed diagnosis of AAS in settings where non-contrast CT remains the unavoidable the initial or only imaging test in resource-constrained regions and in patients who cannot or did not receive intravenous contrast.", "date": "2024-06-14", "categories": []}
{"id": "2510.06887", "title": "Lung Infection Severity Prediction Using Transformers with Conditional TransMix Augmentation and Cross-Attention", "abstract": "Lung infections, particularly pneumonia, pose serious health risks that can escalate rapidly, especially during pandemics. Accurate AI-based severity prediction from medical imaging is essential to support timely clinical decisions and optimize patient outcomes. In this work, we present a novel method applicable to both CT scans and chest X-rays for assessing lung infection severity. Our contributions are twofold: (i) QCross-Att-PVT, a Transformer-based architecture that integrates parallel encoders, a cross-gated attention mechanism, and a feature aggregator to capture rich multi-scale features; and (ii) Conditional Online TransMix, a custom data augmentation strategy designed to address dataset imbalance by generating mixed-label image patches during training. Evaluated on two benchmark datasets, RALO CXR and Per-COVID-19 CT, our method consistently outperforms several state-of-the-art deep learning models. The results emphasize the critical role of data augmentation and gated attention in improving both robustness and predictive accuracy. This approach offers a reliable, adaptable tool to support clinical diagnosis, disease monitoring, and personalized treatment planning. The source code of this work is available at https://github.com/bouthainas/QCross-Att-PVT.", "date": "2025-10-08", "categories": []}
{"id": "2510.05183", "title": "Aneurysm Growth Time Series Reconstruction Using Physics-informed Autoencoder", "abstract": "Arterial aneurysm (Fig.1) is a bulb-shape local expansion of human arteries, the rupture of which is a leading cause of morbidity and mortality in US. Therefore, the prediction of arterial aneurysm rupture is of great significance for aneurysm management and treatment selection. The prediction of aneurysm rupture depends on the analysis of the time series of aneurysm growth history. However, due to the long time scale of aneurysm growth, the time series of aneurysm growth is not always accessible. We here proposed a method to reconstruct the aneurysm growth time series directly from patient parameters. The prediction is based on data pairs of [patient parameters, patient aneurysm growth time history]. To obtain the mapping from patient parameters to patient aneurysm growth time history, we first apply autoencoder to obtain a compact representation of the time series for each patient. Then a mapping is learned from patient parameters to the corresponding compact representation of time series via a five-layer neural network. Moving average and convolutional output layer are implemented to explicitly taking account the time dependency of the time series.\n  Apart from that, we also propose to use prior knowledge about the mechanism of aneurysm growth to improve the time series reconstruction results. The prior physics-based knowledge is incorporated as constraints for the optimization problem associated with autoencoder. The model can handle both algebraic and differential constraints. Our results show that including physical model information about the data will not significantly improve the time series reconstruction results if the training data is error-free. However, in the case of training data with noise and bias error, incorporating physical model constraints can significantly improve the predicted time series.", "date": "2025-10-05", "categories": []}
{"id": "2510.05410", "title": "Aligning Language Models with Clinical Expertise: DPO for Heart Failure Nursing Documentation in Critical Care", "abstract": "Nursing documentation in intensive care units (ICUs) provides essential clinical intelligence but often suffers from inconsistent terminology, informal styles, and lack of standardization, challenges that are particularly critical in heart failure care. This study applies Direct Preference Optimization (DPO) to adapt Mistral-7B, a locally deployable language model, using 8,838 heart failure nursing notes from the MIMIC-III database and 21,210 preference pairs derived from expert-verified GPT outputs, model generations, and original notes. Evaluation across BLEU, ROUGE, BERTScore, Perplexity, and expert qualitative assessments demonstrates that DPO markedly enhances documentation quality. Specifically, BLEU increased by 84% (0.173 to 0.318), BERTScore improved by 7.6% (0.828 to 0.891), and expert ratings rose across accuracy (+14.4 points), completeness (+14.5 points), logical consistency (+14.1 points), readability (+11.1 points), and structural clarity (+6.0 points). These results indicate that DPO can align lightweight clinical language models with expert standards, supporting privacy-preserving, AI-assisted documentation within electronic health record systems to reduce administrative burden and improve ICU patient safety.", "date": "2025-10-06", "categories": []}
{"id": "2510.05511", "title": "EEG-Based Acute Pain Classification: Machine Learning Model Comparison and Real-Time Clinical Feasibility", "abstract": "Current pain assessment within hospitals often relies on self-reporting or non-specific EKG vital signs. This system leaves critically ill, sedated, and cognitively impaired patients vulnerable to undertreated pain and opioid overuse. Electroencephalography (EEG) offers a noninvasive method of measuring brain activity. This technology could potentially be applied as an assistive tool to highlight nociceptive processing in order to mitigate this issue. In this study, we compared machine learning models for classifying high-pain versus low/no-pain EEG epochs using data from fifty-two healthy adults exposed to laser-evoked pain at three intensities (low, medium, high). Each four-second epoch was transformed into a 537-feature vector spanning spectral power, band ratios, Hjorth parameters, entropy measures, coherence, wavelet energies, and peak-frequency metrics. Nine traditional machine learning models were evaluated with leave-one-participant-out cross-validation. A support vector machine with radial basis function kernel achieved the best offline performance with 88.9% accuracy and sub-millisecond inference time (1.02 ms). Our Feature importance analysis was consistent with current canonical pain physiology, showing contralateral alpha suppression, midline theta/alpha enhancement, and frontal gamma bursts. The real-time XGBoost model maintained an end-to-end latency of about 4 ms and 94.2% accuracy, demonstrating that an EEG-based pain monitor is technically feasible within a clinical setting and provides a pathway towards clinical validation.", "date": "2025-10-07", "categories": []}
{"id": "2407.18525", "title": "ClinicRealm: Re-evaluating Large Language Models with Conventional Machine Learning for Non-Generative Clinical Prediction Tasks", "abstract": "Large Language Models (LLMs) are increasingly deployed in medicine. However, their utility in non-generative clinical prediction, often presumed inferior to specialized models, remains under-evaluated, leading to ongoing debate within the field and potential for misuse, misunderstanding, or over-reliance due to a lack of systematic benchmarking. Our ClinicRealm study addresses this by benchmarking 15 GPT-style LLMs, 5 BERT-style models, and 11 traditional methods on unstructured clinical notes and structured Electronic Health Records (EHR), while also assessing their reasoning, reliability, and fairness. Key findings reveal a significant shift: for clinical note predictions, leading LLMs (e.g., DeepSeek-V3.1-Think, GPT-5) in zero-shot settings now decisively outperform finetuned BERT models. On structured EHRs, while specialized models excel with ample data, advanced LLMs (e.g., GPT-5, DeepSeek-V3.1-Think) show potent zero-shot capabilities, often surpassing conventional models in data-scarce settings. Notably, leading open-source LLMs can match or exceed proprietary counterparts. These results provide compelling evidence that modern LLMs are competitive tools for non-generative clinical prediction, particularly with unstructured text and offering data-efficient structured data options, thus necessitating a re-evaluation of model selection strategies. This research should serve as an important insight for medical informaticists, AI developers, and clinical researchers, potentially prompting a reassessment of current assumptions and inspiring new approaches to LLM application in predictive healthcare.", "date": "2024-07-26", "categories": []}
{"id": "2507.09630", "title": "Brain Stroke Detection and Classification Using CT Imaging with Transformer Models and Explainable AI", "abstract": "Stroke is one of the leading causes of death globally, making early and accurate diagnosis essential for improving patient outcomes, particularly in emergency settings where timely intervention is critical. CT scans are the key imaging modality because of their speed, accessibility, and cost-effectiveness. This study proposed an artificial intelligence framework for multiclass stroke classification (ischemic, hemorrhagic, and no stroke) using CT scan images from a dataset provided by the Republic of Turkey's Ministry of Health. The proposed method adopted MaxViT, a state-of-the-art Vision Transformer, as the primary deep learning model for image-based stroke classification, with additional transformer variants (vision transformer, transformer-in-transformer, and ConvNext). To enhance model generalization and address class imbalance, we applied data augmentation techniques, including synthetic image generation. The MaxViT model trained with augmentation achieved the best performance, reaching an accuracy and F1-score of 98.00%, outperforming all other evaluated models and the baseline methods. The primary goal of this study was to distinguish between stroke types with high accuracy while addressing crucial issues of transparency and trust in artificial intelligence models. To achieve this, Explainable Artificial Intelligence (XAI) was integrated into the framework, particularly Grad-CAM++. It provides visual explanations of the model's decisions by highlighting relevant stroke regions in the CT scans and establishing an accurate, interpretable, and clinically applicable solution for early stroke detection. This research contributed to the development of a trustworthy AI-assisted diagnostic tool for stroke, facilitating its integration into clinical practice and enhancing access to timely and optimal stroke diagnosis in emergency departments, thereby saving more lives.", "date": "2025-07-13", "categories": []}
{"id": "2510.03780", "title": "A Benchmark Study of Deep Learning Methods for Multi-Label Pediatric Electrocardiogram-Based Cardiovascular Disease Classification", "abstract": "Cardiovascular disease (CVD) is a major pediatric health burden, and early screening is of critical importance. Electrocardiography (ECG), as a noninvasive and accessible tool, is well suited for this purpose. This paper presents the first benchmark study of deep learning for multi-label pediatric CVD classification on the recently released ZZU-pECG dataset, comprising 3716 recordings with 19 CVD categories. We systematically evaluate four representative paradigms--ResNet-1D, BiLSTM, Transformer, and Mamba 2--under both 9-lead and 12-lead configurations. All models achieved strong results, with Hamming Loss as low as 0.0069 and F1-scores above 85% in most settings. ResNet-1D reached a macro-F1 of 94.67% on the 12-lead subset, while BiLSTM and Transformer also showed competitive performance. Per-class analysis indicated challenges for rare conditions such as hypertrophic cardiomyopathy in the 9-lead subset, reflecting the effect of limited positive samples. This benchmark establishes reusable baselines and highlights complementary strengths across paradigms. It further points to the need for larger-scale, multi-center validation, age-stratified analysis, and broader disease coverage to support real-world pediatric ECG applications.", "date": "2025-10-04", "categories": []}
{"id": "2510.04312", "title": "CARE-PD: A Multi-Site Anonymized Clinical Dataset for Parkinson's Disease Gait Assessment", "abstract": "Objective gait assessment in Parkinson's Disease (PD) is limited by the absence of large, diverse, and clinically annotated motion datasets. We introduce CARE-PD, the largest publicly available archive of 3D mesh gait data for PD, and the first multi-site collection spanning 9 cohorts from 8 clinical centers. All recordings (RGB video or motion capture) are converted into anonymized SMPL meshes via a harmonized preprocessing pipeline. CARE-PD supports two key benchmarks: supervised clinical score prediction (estimating Unified Parkinson's Disease Rating Scale, UPDRS, gait scores) and unsupervised motion pretext tasks (2D-to-3D keypoint lifting and full-body 3D reconstruction). Clinical prediction is evaluated under four generalization protocols: within-dataset, cross-dataset, leave-one-dataset-out, and multi-dataset in-domain adaptation. To assess clinical relevance, we compare state-of-the-art motion encoders with a traditional gait-feature baseline, finding that encoders consistently outperform handcrafted features. Pretraining on CARE-PD reduces MPJPE (from 60.8mm to 7.5mm) and boosts PD severity macro-F1 by 17 percentage points, underscoring the value of clinically curated, diverse training data. CARE-PD and all benchmark code are released for non-commercial research at https://neurips2025.care-pd.ca/.", "date": "2025-10-05", "categories": []}
{"id": "2510.04386", "title": "SSM-CGM: Interpretable State-Space Forecasting Model of Continuous Glucose Monitoring for Personalized Diabetes Management", "abstract": "Continuous glucose monitoring (CGM) generates dense data streams critical for diabetes management, but most used forecasting models lack interpretability for clinical use. We present SSM-CGM, a Mamba-based neural state-space forecasting model that integrates CGM and wearable activity signals from the AI-READI cohort. SSM-CGM improves short-term accuracy over a Temporal Fusion Transformer baseline, adds interpretability through variable selection and temporal attribution, and enables counterfactual forecasts simulating how planned changes in physiological signals (e.g., heart rate, respiration) affect near-term glucose. Together, these features make SSM-CGM an interpretable, physiologically grounded framework for personalized diabetes management.", "date": "2025-10-05", "categories": []}
{"id": "2510.02416", "title": "Cross-Platform DNA Methylation Classifier for the Eight Molecular Subtypes of Group 3 & 4 Medulloblastoma", "abstract": "Medulloblastoma is a malignant pediatric brain cancer, and the discovery of molecular subgroups is enabling personalized treatment strategies. In 2019, a consensus identified eight novel subtypes within Groups 3 and 4, each displaying heterogeneous characteristics. Classifiers are essential for translating these findings into clinical practice by supporting clinical trials, personalized therapy development and application, and patient monitoring. This study presents a DNA methylation-based, cross-platform machine learning classifier capable of distinguishing these subtypes on both HM450 and EPIC methylation array samples. Across two independent test sets, the model achieved weighted F1 = 0.95 and balanced accuracy = 0.957, consistent across platforms. As the first cross-platform solution, it provides backward compatibility while extending applicability to a newer platform, also enhancing accessibility. It also has the potential to become the first publicly available classifier for these subtypes once deployed through a web application, as planned in the future. This work overall takes steps in the direction of advancing precision medicine and improving clinical outcomes for patients within the majority prevalence medulloblastoma subgroups, groups 3 and 4.", "date": "2025-10-02", "categories": []}
{"id": "2510.02936", "title": "RAxSS: Retrieval-Augmented Sparse Sampling for Explainable Variable-Length Medical Time Series Classification", "abstract": "Medical time series analysis is challenging due to data sparsity, noise, and highly variable recording lengths. Prior work has shown that stochastic sparse sampling effectively handles variable-length signals, while retrieval-augmented approaches improve explainability and robustness to noise and weak temporal correlations. In this study, we generalize the stochastic sparse sampling framework for retrieval-informed classification. Specifically, we weight window predictions by within-channel similarity and aggregate them in probability space, yielding convex series-level scores and an explicit evidence trail for explainability. Our method achieves competitive iEEG classification performance and provides practitioners with greater transparency and explainability. We evaluate our method in iEEG recordings collected in four medical centers, demonstrating its potential for reliable and explainable clinical variable-length time series classification.", "date": "2025-10-03", "categories": []}
{"id": "2501.16388", "title": "Development and Validation of a Dynamic Kidney Failure Prediction Model based on Deep Learning: A Real-World Study with External Validation", "abstract": "Background: Chronic kidney disease (CKD), a progressive disease with high morbidity and mortality, has become a significant global public health problem. Most existing models are static and fail to capture temporal trends in disease progression, limiting their ability to inform timely interventions. We address this gap by developing a dynamic model that leverages common longitudinal clinical indicators from real-world Electronic Health Records (EHRs) for real-time kidney failure prediction.\n  Findings: A retrospective cohort of 4,587 patients from Yinzhou, China, was used for model development (2,752 patients for training, 917 patients for validation) and internal validation (918 patients), while external validation was conducted on a prospective PKUFH cohort (934 patients). The model demonstrated competitive performance across datasets, with an AUROC of 0.9311 (95%CI, 0.8873-0.9749) in the internal validation cohort and 0.8141 (95%CI, 0.7728-0.8554) in the external validation cohort, alongside progressively improving dynamic predictions, good calibration, and clinically consistent interpretability. KFDeep has been deployed on an open-access website and in primary care settings.\n  Interpretation: The KFDeep model enables dynamic prediction of kidney failure without increasing clinical examination costs. It has been integrated into existing hospital systems, providing physicians with a continuously updated decision-support tool in routine care.", "date": "2025-01-25", "categories": []}
{"id": "2510.01558", "title": "CardioRAG: A Retrieval-Augmented Generation Framework for Multimodal Chagas Disease Detection", "abstract": "Chagas disease affects nearly 6 million people worldwide, with Chagas cardiomyopathy representing its most severe complication. In regions where serological testing capacity is limited, AI-enhanced electrocardiogram (ECG) screening provides a critical diagnostic alternative. However, existing machine learning approaches face challenges such as limited accuracy, reliance on large labeled datasets, and more importantly, weak integration with evidence-based clinical diagnostic indicators. We propose a retrieval-augmented generation framework, CardioRAG, integrating large language models with interpretable ECG-based clinical features, including right bundle branch block, left anterior fascicular block, and heart rate variability metrics. The framework uses variational autoencoder-learned representations for semantic case retrieval, providing contextual cases to guide clinical reasoning. Evaluation demonstrated high recall performance of 89.80%, with a maximum F1 score of 0.68 for effective identification of positive cases requiring prioritized serological testing. CardioRAG provides an interpretable, clinical evidence-based approach particularly valuable for resource-limited settings, demonstrating a pathway for embedding clinical indicators into trustworthy medical AI systems.", "date": "2025-10-02", "categories": []}
{"id": "2510.00706", "title": "AttentionDep: Domain-Aware Attention for Explainable Depression Severity Assessment", "abstract": "In today's interconnected society, social media platforms provide a window into individuals' thoughts, emotions, and mental states. This paper explores the use of platforms like Facebook, X (formerly Twitter), and Reddit for depression severity detection. We propose AttentionDep, a domain-aware attention model that drives explainable depression severity estimation by fusing contextual and domain knowledge. Posts are encoded hierarchically using unigrams and bigrams, with attention mechanisms highlighting clinically relevant tokens. Domain knowledge from a curated mental health knowledge graph is incorporated through a cross-attention mechanism, enriching the contextual features. Finally, depression severity is predicted using an ordinal regression framework that respects the clinical-relevance and natural ordering of severity levels. Our experiments demonstrate that AttentionDep outperforms state-of-the-art baselines by over 5% in graded F1 score across datasets, while providing interpretable insights into its predictions. This work advances the development of trustworthy and transparent AI systems for mental health assessment from social media.", "date": "2025-10-01", "categories": []}
{"id": "2510.01074", "title": "Predicting Diabetic Retinopathy Using a Two-Level Ensemble Model", "abstract": "Preprint Note: This is the author preprint version of a paper accepted for presentation at the IISE Annual Conference & Expo 2025. The final version will appear in the official proceedings.\n  Diabetic retinopathy (DR) is a leading cause of blindness in working-age adults, and current diagnostic methods rely on resource-intensive eye exams and specialized equipment. Image-based AI tools have shown limitations in early-stage detection, motivating the need for alternative approaches. We propose a non-image-based, two-level ensemble model for DR prediction using routine laboratory test results. In the first stage, base models (Linear SVC, Random Forest, Gradient Boosting, and XGBoost) are hyperparameter tuned and internally stacked across different configurations to optimize metrics such as accuracy, recall, and precision. In the second stage, predictions are aggregated using Random Forest as a meta-learner. This hierarchical stacking strategy improves generalization, balances performance across multiple metrics, and remains computationally efficient compared to deep learning approaches. The model achieved Accuracy 0.9433, F1 Score 0.9425, Recall 0.9207, Precision 0.9653, ROC-AUC 0.9844, and AUPRC 0.9875, surpassing one-level stacking and FCN baselines. These results highlight the model potential for accurate and interpretable DR risk prediction in clinical settings.", "date": "2025-10-01", "categories": []}
{"id": "2505.04931", "title": "Fair Uncertainty Quantification for Depression Prediction", "abstract": "Trustworthy depression prediction based on deep learning, incorporating both predictive reliability and algorithmic fairness across diverse demographic groups, is crucial for clinical application. Recently, achieving reliable depression predictions through uncertainty quantification has attracted increasing attention. However, few studies have focused on the fairness of uncertainty quantification (UQ) in depression prediction. In this work, we investigate the algorithmic fairness of UQ, namely Equal Opportunity Coverage (EOC) fairness, and propose Fair Uncertainty Quantification (FUQ) for depression prediction. FUQ pursues reliable and fair depression predictions through group-based analysis. Specifically, we first group all the participants by different sensitive attributes and leverage conformal prediction to quantify uncertainty within each demographic group, which provides a theoretically guaranteed and valid way to quantify uncertainty for depression prediction and facilitates the investigation of fairness across different demographic groups. Furthermore, we propose a fairness-aware optimization strategy that formulates fairness as a constrained optimization problem under EOC constraints. This enables the model to preserve predictive reliability while adapting to the heterogeneous uncertainty levels across demographic groups, thereby achieving optimal fairness. Through extensive evaluations on several visual and audio depression datasets, our approach demonstrates its effectiveness.", "date": "2025-05-08", "categories": []}
{"id": "2509.25381", "title": "Deep Survival Analysis for Competing Risk Modeling with Functional Covariates and Missing Data Imputation", "abstract": "We introduce the Functional Competing Risk Net (FCRN), a unified deep-learning framework for discrete-time survival analysis under competing risks, which seamlessly integrates functional covariates and handles missing data within an end-to-end model. By combining a micro-network Basis Layer for functional data representation with a gradient-based imputation module, FCRN simultaneously learns to impute missing values and predict event-specific hazards. Evaluated on multiple simulated datasets and a real-world ICU case study using the MIMIC-IV and Cleveland Clinic datasets, FCRN demonstrates substantial improvements in prediction accuracy over random survival forests and traditional competing risks models. This approach advances prognostic modeling in critical care by more effectively capturing dynamic risk factors and static predictors while accommodating irregular and incomplete data.", "date": "2025-09-29", "categories": []}
{"id": "2509.25591", "title": "Building the EHR Foundation Model via Next Event Prediction", "abstract": "Electronic Health Records (EHRs) contain rich temporal dynamics that conventional encoding approaches fail to adequately capture. While Large Language Models (LLMs) show promise for EHR modeling, they struggle to reason about sequential clinical events and temporal dependencies. We propose Next Event Prediction (NEP), a framework that enhances LLMs' temporal reasoning through autoregressive fine-tuning on clinical event sequences. By reformulating EHRs as timestamped event chains and predicting future medical events, NEP explicitly models disease progression patterns and causal relationships. Extensive evaluations across oncology survival prediction and clinical diagnosis tasks demonstrate NEP's superiority, outperforming specialized EHR models by 4.6% AUROC and general-purpose LLMs by 7.2% C-index in temporal reasoning tasks. Our analyses reveal dual benefits: state-of-the-art prediction accuracy combined with clinically interpretable attention patterns that align with known disease pathways.", "date": "2025-09-29", "categories": []}
{"id": "2509.25791", "title": "EchoingECG: An Electrocardiogram Cross-Modal Model for Echocardiogram Tasks", "abstract": "Electrocardiogram (ECG) is a widely used tool for assessing cardiac function due to its low cost and accessibility. Emergent research shows that ECGs can help make predictions on key outcomes traditionally derived from more complex modalities such as echocardiograms (ECHO), enabling the use of ECGs as a more accessible method to predict broader measurements of cardiac function. ECHO, in particular, are of great importance because they require considerable hospital resources while playing a key role in clinical cardiac assessment. To aid this use case, we introduce EchoingECG, a probabilistic student-teacher model that leverages uncertainty-aware ECG embeddings and ECHO supervision to improve ECG-based cardiac function prediction. Our approach integrates Probabilistic Cross-Modal Embeddings (PCME++), a probabilistic contrastive framework, with ECHO-CLIP, a vision-language pre-trained model trained on ECHO-text pairs, to distill ECHO knowledge into ECG representations. Through experiments and external validation, we showed that EchoingECG outperforms state-of-the-art foundation ECG models in zero-shot, few-shot, and fine-tune settings for ECHO predictions based on ECG. We also highlighted that variance estimation (enabled through our method) enhanced our understanding of model performance by identifying underlying regions of uncertainty within ECGs. The code is available: https://github.com/mcintoshML/EchoingECG.", "date": "2025-09-30", "categories": []}
{"id": "2509.26474", "title": "The Average Patient Fallacy", "abstract": "Machine learning in medicine is typically optimized for population averages. This frequency weighted training privileges common presentations and marginalizes rare yet clinically critical cases, a bias we call the average patient fallacy. In mixture models, gradients from rare cases are suppressed by prevalence, creating a direct conflict with precision medicine. Clinical vignettes in oncology, cardiology, and ophthalmology show how this yields missed rare responders, delayed recognition of atypical emergencies, and underperformance on vision-threatening variants. We propose operational fixes: Rare Case Performance Gap, Rare Case Calibration Error, a prevalence utility definition of rarity, and clinically weighted objectives that surface ethical priorities. Weight selection should follow structured deliberation. AI in medicine must detect exceptional cases because of their significance.", "date": "2025-09-30", "categories": []}
{"id": "2206.10381", "title": "TabText: Language-Based Representations of Tabular Health Data for Predictive Modelling", "abstract": "Tabular medical records remain the most readily available data format for applying machine learning in healthcare. However, traditional data preprocessing ignores valuable contextual information in tables and requires substantial manual cleaning and harmonisation, creating a bottleneck for model development. We introduce TabText, a preprocessing and feature extraction method that leverages contextual information and streamlines the curation of tabular medical data. This method converts tables into contextual language and applies pretrained large language models (LLMs) to generate task-independent numerical representations. These fixed embeddings are then used as input for various predictive tasks. TabText was evaluated on nine inpatient flow prediction tasks (e.g., ICU admission, discharge, mortality) using electronic medical records across six hospitals from a US health system, and on nine publicly available datasets from the UCI Machine Learning Repository, covering tasks such as cancer diagnosis, recurrence, and survival. TabText models trained on unprocessed data from a single hospital (572,964 patient-days, Jan 2018-Dec 2020) achieved accurate performance (AUC 0.75-0.94) when tested prospectively on 265,917 patient-days from Jan 2021-Apr 2022, and generalised well to five additional hospitals not used for training. When augmenting preprocessed tabular records with these contextual embeddings, out-of-sample AUC improved by up to 4 additive percentage points in challenging tasks such as ICU transfer and breast cancer recurrence, while providing little to no benefit for already high-performing tasks. Findings were consistent across both private and public datasets.", "date": "2022-06-21", "categories": []}
{"id": "2503.12642", "title": "COVID-19 Pneumonia Diagnosis Using Medical Images: Deep Learning-Based Transfer Learning Approach", "abstract": "SARS-CoV-2, the causative agent of COVID-19, remains a global health concern due to its high transmissibility and evolving variants. Although vaccination efforts and therapeutic advancements have mitigated disease severity, emerging mutations continue to challenge diagnostics and containment strategies. As of mid-February 2025, global test positivity has risen to 11%, marking the highest level in over six months despite widespread immunization efforts. Newer variants demonstrate enhanced host cell binding, increasing both infectivity and diagnostic complexity. This study evaluates the effectiveness of deep transfer learning in delivering rapid, accurate, and mutation-resilient COVID-19 diagnosis from medical imaging, with a focus on scalability and accessibility. We developed an automated detection system using state-of-the-art CNNs, including VGG16, ResNet50, ConvNetXtTiny, MobileNet, NASNetMobile, and DenseNet121 among others, to detect COVID-19 from chest X-ray and CT images. Among all the models evaluated, DenseNet121 emerged as the best-performing architecture for COVID-19 diagnosis using CT and X-ray images. It achieved an impressive accuracy of 98%, with 96.9% precision, 98.9% recall, 97.9% F1-score and 99.8% AUC score, indicating a high degree of consistency and reliability in both detecting positive and negative cases. The confusion matrix showed minimal false positives and false negatives, underscoring the model's robustness in real-world diagnostic scenarios.", "date": "2025-03-16", "categories": []}
{"id": "2505.16288", "title": "No Black Boxes: Interpretable and Interactable Predictive Healthcare with Knowledge-Enhanced Agentic Causal Discovery", "abstract": "Deep learning models trained on extensive Electronic Health Records (EHR) data have achieved high accuracy in diagnosis prediction, offering the potential to assist clinicians in decision-making and treatment planning. However, these models lack two crucial features that clinicians highly value: interpretability and interactivity. The ``black-box'' nature of these models makes it difficult for clinicians to understand the reasoning behind predictions, limiting their ability to make informed decisions. Additionally, the absence of interactive mechanisms prevents clinicians from incorporating their own knowledge and experience into the decision-making process. To address these limitations, we propose II-KEA, a knowledge-enhanced agent-driven causal discovery framework that integrates personalized knowledge databases and agentic LLMs. II-KEA enhances interpretability through explicit reasoning and causal analysis, while also improving interactivity by allowing clinicians to inject their knowledge and experience through customized knowledge bases and prompts. II-KEA is evaluated on both MIMIC-III and MIMIC-IV, demonstrating superior performance along with enhanced interpretability and interactivity, as evidenced by its strong results from extensive case studies.", "date": "2025-05-22", "categories": []}
{"id": "2509.22673", "title": "PISA: An AI Pipeline for Interpretable-by-design Survival Analysis Providing Multiple Complexity-Accuracy Trade-off Models", "abstract": "Survival analysis is central to clinical research, informing patient prognoses, guiding treatment decisions, and optimising resource allocation. Accurate time-to-event predictions not only improve quality of life but also reveal risk factors that shape clinical practice. For these models to be relevant in healthcare, interpretability is critical: predictions must be traceable to patient-specific characteristics, and risk factors should be identifiable to generate actionable insights for both clinicians and researchers. Traditional survival models often fail to capture non-linear interactions, while modern deep learning approaches, though powerful, are limited by poor interpretability.\n  We propose a Pipeline for Interpretable Survival Analysis (PISA) - a pipeline that provides multiple survival analysis models that trade off complexity and performance. Using multiple-feature, multi-objective feature engineering, PISA transforms patient characteristics and time-to-event data into multiple survival analysis models, providing valuable insights into the survival prediction task. Crucially, every model is converted into simple patient stratification flowcharts supported by Kaplan-Meier curves, whilst not compromising on performance. While PISA is model-agnostic, we illustrate its flexibility through applications of Cox regression and shallow survival trees, the latter avoiding proportional hazards assumptions.\n  Applied to two clinical benchmark datasets, PISA produced interpretable survival models and intuitive stratification flowcharts whilst achieving state-of-the-art performances. Revisiting a prior departmental study further demonstrated its capacity to automate survival analysis workflows in real-world clinical research.", "date": "2025-09-13", "categories": []}
{"id": "2509.23720", "title": "A Self-Adaptive Frequency Domain Network for Continuous Intraoperative Hypotension Prediction", "abstract": "Intraoperative hypotension (IOH) is strongly associated with postoperative complications, including postoperative delirium and increased mortality, making its early prediction crucial in perioperative care. While several artificial intelligence-based models have been developed to provide IOH warnings, existing methods face limitations in incorporating both time and frequency domain information, capturing short- and long-term dependencies, and handling noise sensitivity in biosignal data. To address these challenges, we propose a novel Self-Adaptive Frequency Domain Network (SAFDNet). Specifically, SAFDNet integrates an adaptive spectral block, which leverages Fourier analysis to extract frequency-domain features and employs self-adaptive thresholding to mitigate noise. Additionally, an interactive attention block is introduced to capture both long-term and short-term dependencies in the data. Extensive internal and external validations on two large-scale real-world datasets demonstrate that SAFDNet achieves up to 97.3\\% AUROC in IOH early warning, outperforming state-of-the-art models. Furthermore, SAFDNet exhibits robust predictive performance and low sensitivity to noise, making it well-suited for practical clinical applications.", "date": "2025-09-28", "categories": []}
{"id": "2509.23930", "title": "A University of Texas Medical Branch Case Study on Aortic Calcification Detection", "abstract": "This case study details The University of Texas Medical Branch (UTMB)'s partnership with Zauron Labs, Inc. to enhance detection and coding of aortic calcifications (ACs) using chest radiographs. ACs are often underreported despite their significant prognostic value for cardiovascular disease, and UTMB partnered with Zauron to apply its advanced AI tools, including a high-performing image model (AUC = 0.938) and a fine-tuned language model based on Meta's Llama 3.2, to retrospectively analyze imaging and report data. The effort identified 495 patients out of 3,988 unique patients assessed (5,000 total exams) whose reports contained indications of aortic calcifications that were not properly coded for reimbursement (12.4% miscode rate) as well as an additional 84 patients who had aortic calcifications that were missed during initial review (2.1% misdiagnosis rate). Identification of these patients provided UTMB with the potential to impact clinical care for these patients and pursue $314k in missed annual revenue. These findings informed UTMB's decision to adopt Zauron's Guardian Pro software system-wide to ensure accurate, AI-enhanced peer review and coding, improving both patient care and financial solvency. This study is covered under University of Texas Health San Antonio's Institutional Review Board Study ID 00001887.", "date": "2025-09-28", "categories": []}
{"id": "2509.23999", "title": "TREAT-Net: Tabular-Referenced Echocardiography Analysis for Acute Coronary Syndrome Treatment Prediction", "abstract": "Coronary angiography remains the gold standard for diagnosing Acute Coronary Syndrome (ACS). However, its resource-intensive and invasive nature can expose patients to procedural risks and diagnostic delays, leading to postponed treatment initiation. In this work, we introduce TREAT-Net, a multimodal deep learning framework for ACS treatment prediction that leverages non-invasive modalities, including echocardiography videos and structured clinical records. TREAT-Net integrates tabular-guided cross-attention to enhance video interpretation, along with a late fusion mechanism to align predictions across modalities. Trained on a dataset of over 9000 ACS cases, the model outperforms unimodal and non-fused baselines, achieving a balanced accuracy of 67.6% and an AUROC of 71.1%. Cross-modality agreement analysis demonstrates 88.6% accuracy for intervention prediction. These findings highlight the potential of TREAT-Net as a non-invasive tool for timely and accurate patient triage, particularly in underserved populations with limited access to coronary angiography.", "date": "2025-09-28", "categories": []}
{"id": "2509.24118", "title": "HyMaTE: A Hybrid Mamba and Transformer Model for EHR Representation Learning", "abstract": "Electronic health Records (EHRs) have become a cornerstone in modern-day healthcare. They are a crucial part for analyzing the progression of patient health; however, their complexity, characterized by long, multivariate sequences, sparsity, and missing values poses significant challenges in traditional deep learning modeling. While Transformer-based models have demonstrated success in modeling EHR data and predicting clinical outcomes, their quadratic computational complexity and limited context length hinder their efficiency and practical applications. On the other hand, State Space Models (SSMs) like Mamba present a promising alternative offering linear-time sequence modeling and improved efficiency for handling long sequences, but focus mostly on mixing sequence-level information rather than channel-level data. To overcome these challenges, we propose HyMaTE (A Hybrid Mamba and Transformer Model for EHR Representation Learning), a novel hybrid model tailored for representing longitudinal data, combining the strengths of SSMs with advanced attention mechanisms. By testing the model on predictive tasks on multiple clinical datasets, we demonstrate HyMaTE's ability to capture an effective, richer, and more nuanced unified representation of EHR data. Additionally, the interpretability of the outcomes achieved by self-attention illustrates the effectiveness of our model as a scalable and generalizable solution for real-world healthcare applications. Codes are available at: https://github.com/healthylaife/HyMaTE.", "date": "2025-09-28", "categories": []}
{"id": "2509.24860", "title": "ELPG-DTFS: Prior-Guided Adaptive Time-Frequency Graph Neural Network for EEG Depression Diagnosis", "abstract": "Timely and objective screening of major depressive disorder (MDD) is vital, yet diagnosis still relies on subjective scales. Electroencephalography (EEG) provides a low-cost biomarker, but existing deep models treat spectra as static images, fix inter-channel graphs, and ignore prior knowledge, limiting accuracy and interpretability. We propose ELPG-DTFS, a prior-guided adaptive time-frequency graph neural network that introduces: (1) channel-band attention with cross-band mutual information, (2) a learnable adjacency matrix for dynamic functional links, and (3) a residual knowledge-graph pathway injecting neuroscience priors. On the 128-channel MODMA dataset (53 subjects), ELPG-DTFS achieves 97.63% accuracy and 97.33% F1, surpassing the 2025 state-of-the-art ACM-GNN. Ablation shows that removing any module lowers F1 by up to 4.35, confirming their complementary value. ELPG-DTFS thus offers a robust and interpretable framework for next-generation EEG-based MDD diagnostics.", "date": "2025-09-29", "categories": []}
{"id": "2407.02737", "title": "Development of Machine Learning Classifiers for Blood-based Diagnosis and Prognosis of Suspected Acute Infections and Sepsis", "abstract": "We applied machine learning to the unmet medical need of rapid and accurate diagnosis and prognosis of acute infections and sepsis in emergency departments. Our solution consists of a Myrna (TM) Instrument and embedded TriVerity (TM) classifiers. The instrument measures abundances of 29 messenger RNAs in patient's blood, subsequently used as features for machine learning. The classifiers convert the input features to an intuitive test report comprising the separate likelihoods of (1) a bacterial infection (2) a viral infection, and (3) severity (need for Intensive Care Unit-level care). In internal validation, the system achieved AUROC = 0.83 on the three-class disease diagnosis (bacterial, viral, or non-infected) and AUROC = 0.77 on binary prognosis of disease severity. The Myrna, TriVerity system was granted breakthrough device designation by the United States Food and Drug Administration (FDA). This engineering manuscript teaches the standard and novel machine learning methods used to translate an academic research concept to a clinical product aimed at improving patient care, and discusses lessons learned.", "date": "2024-07-03", "categories": []}
{"id": "2502.12158", "title": "Mining Social Determinants of Health for Heart Failure Patient 30-Day Readmission via Large Language Model", "abstract": "Heart Failure (HF) affects millions of Americans and leads to high readmission rates, posing significant healthcare challenges. While Social Determinants of Health (SDOH) such as socioeconomic status and housing stability play critical roles in health outcomes, they are often underrepresented in structured EHRs and hidden in unstructured clinical notes. This study leverages advanced large language models (LLMs) to extract SDOHs from clinical text and uses logistic regression to analyze their association with HF readmissions. By identifying key SDOHs (e.g. tobacco usage, limited transportation) linked to readmission risk, this work also offers actionable insights for reducing readmissions and improving patient care.", "date": "2025-01-23", "categories": []}
{"id": "2505.16044", "title": "Multimodal Biomarkers for Schizophrenia: Towards Individual Symptom Severity Estimation", "abstract": "Studies on schizophrenia assessments using deep learning typically treat it as a classification task to detect the presence or absence of the disorder, oversimplifying the condition and reducing its clinical applicability. This traditional approach overlooks the complexity of schizophrenia, limiting its practical value in healthcare settings. This study shifts the focus to individual symptom severity estimation using a multimodal approach that integrates speech, video, and text inputs. We develop unimodal models for each modality and a multimodal framework to improve accuracy and robustness. By capturing a more detailed symptom profile, this approach can help in enhancing diagnostic precision and support personalized treatment, offering a scalable and objective tool for mental health assessment.", "date": "2025-05-21", "categories": []}
{"id": "2506.00209", "title": "Intercept Cancer: Cancer Pre-Screening with Large Scale Healthcare Foundation Models", "abstract": "Cancer screening, leading to early detection, saves lives. Unfortunately, existing screening techniques require expensive and intrusive medical procedures, not globally available, resulting in too many lost would-be-saved lives. We present CATCH-FM, CATch Cancer early with Healthcare Foundation Models, a cancer pre-screening methodology that identifies high-risk patients for further screening solely based on their historical medical records. With millions of electronic healthcare records (EHR), we establish the scaling law of EHR foundation models pretrained on medical code sequences, pretrain compute-optimal foundation models of up to 2.4 billion parameters, and finetune them on clinician-curated cancer risk prediction cohorts. In our retrospective evaluation comprising of thirty thousand patients, CATCH-FM achieves strong efficacy, with 50% sensitivity in predicting first cancer risks at 99% specificity cutoff, and outperforming feature-based tree models and both general and medical LLMs by up to 20% AUPRC. Despite significant demographic, healthcare system, and EHR coding differences, CATCH-FM achieves state-of-the-art pancreatic cancer risk prediction on the EHRSHOT few-shot leaderboard, outperforming EHR foundation models pretrained using on-site patient data. Our analysis demonstrates the robustness of CATCH-FM in various patient distributions, the benefits of operating in the ICD code space, and its ability to capture non-trivial cancer risk factors. Our code will be open-sourced.", "date": "2025-05-30", "categories": []}
{"id": "2506.13920", "title": "Integrating Knowledge Graphs and Bayesian Networks: A Hybrid Approach for Explainable Disease Risk Prediction", "abstract": "Multimodal electronic health record (EHR) data is useful for disease risk prediction based on medical domain knowledge. However, general medical knowledge must be adapted to specific healthcare settings and patient populations to achieve practical clinical use. Additionally, risk prediction systems must handle uncertainty from incomplete data and non-deterministic health outcomes while remaining explainable. These challenges can be alleviated by the integration of knowledge graphs (KGs) and Bayesian networks (BNs). We present a novel approach for constructing BNs from ontology-based KGs and multimodal EHR data for explainable disease risk prediction. Through an application use case of atrial fibrillation and real-world EHR data, we demonstrate that the approach balances generalised medical knowledge with patient-specific context, effectively handles uncertainty, is highly explainable, and achieves good predictive performance.", "date": "2025-06-16", "categories": []}
{"id": "2509.21383", "title": "The LongiMam model for improved breast cancer risk prediction using longitudinal mammograms", "abstract": "Risk-adapted breast cancer screening requires robust models that leverage longitudinal imaging data. Most current deep learning models use single or limited prior mammograms and lack adaptation for real-world settings marked by imbalanced outcome distribution and heterogeneous follow-up. We developed LongiMam, an end-to-end deep learning model that integrates both current and up to four prior mammograms. LongiMam combines a convolutional and a recurrent neural network to capture spatial and temporal patterns predictive of breast cancer. The model was trained and evaluated using a large, population-based screening dataset with disproportionate case-to-control ratio typical of clinical screening. Across several scenarios that varied in the number and composition of prior exams, LongiMam consistently improved prediction when prior mammograms were included. The addition of prior and current visits outperformed single-visit models, while priors alone performed less well, highlighting the importance of combining historical and recent information. Subgroup analyses confirmed the model's efficacy across key risk groups, including women with dense breasts and those aged 55 years or older. Moreover, the model performed best in women with observed changes in mammographic density over time. These findings demonstrate that longitudinal modeling enhances breast cancer prediction and support the use of repeated mammograms to refine risk stratification in screening programs. LongiMam is publicly available as open-source software.", "date": "2025-09-23", "categories": []}
{"id": "2509.21530", "title": "Expert-guided Clinical Text Augmentation via Query-Based Model Collaboration", "abstract": "Data augmentation is a widely used strategy to improve model robustness and generalization by enriching training datasets with synthetic examples. While large language models (LLMs) have demonstrated strong generative capabilities for this purpose, their applications in high-stakes domains like healthcare present unique challenges due to the risk of generating clinically incorrect or misleading information. In this work, we propose a novel query-based model collaboration framework that integrates expert-level domain knowledge to guide the augmentation process to preserve critical medical information. Experiments on clinical prediction tasks demonstrate that our lightweight collaboration-based approach consistently outperforms existing LLM augmentation methods while improving safety through reduced factual errors. This framework addresses the gap between LLM augmentation potential and the safety requirements of specialized domains.", "date": "2025-09-25", "categories": []}
{"id": "2509.21695", "title": "Wav2Arrest 2.0: Long-Horizon Cardiac Arrest Prediction with Time-to-Event Modeling, Identity-Invariance, and Pseudo-Lab Alignment", "abstract": "High-frequency physiological waveform modality offers deep, real-time insights into patient status. Recently, physiological foundation models based on Photoplethysmography (PPG), such as PPG-GPT, have been shown to predict critical events, including Cardiac Arrest (CA). However, their powerful representation still needs to be leveraged suitably, especially when the downstream data/label is scarce. We offer three orthogonal improvements to improve PPG-only CA systems by using minimal auxiliary information. First, we propose to use time-to-event modeling, either through simple regression to the event onset time or by pursuing fine-grained discrete survival modeling. Second, we encourage the model to learn CA-focused features by making them patient-identity invariant. This is achieved by first training the largest-scale de-identified biometric identification model, referred to as the p-vector, and subsequently using it adversarially to deconfound cues, such as person identity, that may cause overfitting through memorization. Third, we propose regression on the pseudo-lab values generated by pre-trained auxiliary estimator networks. This is crucial since true blood lab measurements, such as lactate, sodium, troponin, and potassium, are collected sparingly. Via zero-shot prediction, the auxiliary networks can enrich cardiac arrest waveform labels and generate pseudo-continuous estimates as targets. Our proposals can independently improve the 24-hour time-averaged AUC from the 0.74 to the 0.78-0.80 range. We primarily improve over longer time horizons with minimal degradation near the event, thus pushing the Early Warning System research. Finally, we pursue multi-task formulation and diagnose it with a high gradient conflict rate among competing losses, which we alleviate via the PCGrad optimization technique.", "date": "2025-09-25", "categories": []}
{"id": "2509.21770", "title": "Machine Learning and AI Applied to fNIRS Data Reveals Novel Brain Activity Biomarkers in Stable Subclinical Multiple Sclerosis", "abstract": "People with Multiple Sclerosis (MS) complain of problems with hand dexterity and cognitive fatigue. However, in many cases, impairments are subtle and difficult to detect. Functional near-infrared spectroscopy (fNIRS) is a non-invasive neuroimaging technique that measures brain hemodynamic responses during cognitive or motor tasks. We aimed to detect brain activity biomarkers that could explain subjective reports of cognitive fatigue while completing dexterous tasks and provide targets for future brain stimulation treatments. We recruited 15 people with MS who did not have a hand (Nine Hole Peg Test [NHPT]), mobility, or cognitive impairment, and 12 age- and sex-matched controls. Participants completed two types of hand dexterity tasks with their dominant hand, single task and dual task (NHPT while holding a ball between the fifth finger and hypothenar eminence of the same hand). We analyzed fNIRS data (oxygenated and deoxygenated hemoglobin levels) using a machine learning framework to classify MS patients from controls based on their brain activation patterns in bilateral prefrontal and sensorimotor cortices. The K-Nearest Neighbor classifier achieved an accuracy of 75.0% for single manual dexterity tasks and 66.7% for the more complex dual manual dexterity tasks. Using XAI, we found that the most important brain regions contributing to the machine learning model were the supramarginal/angular gyri and the precentral gyrus (sensory integration and motor regions) of the ipsilateral hemisphere, with suppressed activity and slower neurovascular response in the MS group. During both tasks, deoxygenated hemoglobin levels were better predictors than the conventional measure of oxygenated hemoglobin. This nonconventional method of fNIRS data analysis revealed novel brain activity biomarkers that can help develop personalized brain stimulation targets.", "date": "2025-09-26", "categories": []}
{"id": "2509.22121", "title": "Mind the Missing: Variable-Aware Representation Learning for Irregular EHR Time Series using Large Language Models", "abstract": "Irregular sampling and high missingness are intrinsic challenges in modeling time series derived from electronic health records (EHRs),where clinical variables are measured at uneven intervals depending on workflow and intervention timing. To address this, we propose VITAL, a variable-aware, large language model (LLM) based framework tailored for learning from irregularly sampled physiological time series. VITAL differentiates between two distinct types of clinical variables: vital signs, which are frequently recorded and exhibit temporal patterns, and laboratory tests, which are measured sporadically and lack temporal structure. It reprograms vital signs into the language space, enabling the LLM to capture temporal context and reason over missing values through explicit encoding. In contrast, laboratory variables are embedded either using representative summary values or a learnable [Not measured] token, depending on their availability. Extensive evaluations on the benchmark datasets from the PhysioNet demonstrate that VITAL outperforms state of the art methods designed for irregular time series. Furthermore, it maintains robust performance under high levels of missingness, which is prevalent in real world clinical scenarios where key variables are often unavailable.", "date": "2025-09-26", "categories": []}
{"id": "2509.22484", "title": "A Machine Learning Pipeline for Multiple Sclerosis Biomarker Discovery: Comparing explainable AI and Traditional Statistical Approaches", "abstract": "We present a machine learning pipeline for biomarker discovery in Multiple Sclerosis (MS), integrating eight publicly available microarray datasets from Peripheral Blood Mononuclear Cells (PBMC). After robust preprocessing we trained an XGBoost classifier optimized via Bayesian search. SHapley Additive exPlanations (SHAP) were used to identify key features for model prediction, indicating thus possible biomarkers. These were compared with genes identified through classical Differential Expression Analysis (DEA). Our comparison revealed both overlapping and unique biomarkers between SHAP and DEA, suggesting complementary strengths. Enrichment analysis confirmed the biological relevance of SHAP-selected genes, linking them to pathways such as sphingolipid signaling, Th1/Th2/Th17 cell differentiation, and Epstein-Barr virus infection all known to be associated with MS. This study highlights the value of combining explainable AI (xAI) with traditional statistical methods to gain deeper insights into disease mechanism.", "date": "2025-09-26", "categories": []}
{"id": "2409.11064", "title": "A Hybrid Multi-Factor Network with Dynamic Sequence Modeling for Early Warning of Intraoperative Hypotension", "abstract": "Intraoperative hypotension (IOH) prediction using past physiological signals is crucial, as IOH may lead to inadequate organ perfusion and significantly elevate the risk of severe complications and mortality. However, current methods often rely on static modeling, overlooking the complex temporal dependencies and the inherently non-stationary nature of physiological signals. We propose a Hybrid Multi-Factor (HMF) network that formulates IOH prediction as a dynamic sequence forecasting task, explicitly capturing both temporal dependencies and physiological non-stationarity. We represent signal dynamics as multivariate time series and decompose them into trend and seasonal components, enabling separate modeling of long-term and periodic variations. Each component is encoded with a patch-based Transformer to balance computational efficiency and feature representation. To address distributional drift from evolving signals, we introduce a symmetric normalization mechanism. Experiments on both public and real-world clinical datasets show that HMF significantly outperforms competitive baselines. We hope HMF offers new insights into IOH prediction and ultimately promotes safer surgical care. Our code is available at https://github.com/Mingyue-Cheng/HMF.", "date": "2024-09-17", "categories": []}
{"id": "2506.17844", "title": "THCM-CAL: Temporal-Hierarchical Causal Modelling with Conformal Calibration for Clinical Risk Prediction", "abstract": "Automated clinical risk prediction from electronic health records (EHRs) demands modeling both structured diagnostic codes and unstructured narrative notes. However, most prior approaches either handle these modalities separately or rely on simplistic fusion strategies that ignore the directional, hierarchical causal interactions by which narrative observations precipitate diagnoses and propagate risk across admissions. In this paper, we propose THCM-CAL, a Temporal-Hierarchical Causal Model with Conformal Calibration. Our framework constructs a multimodal causal graph where nodes represent clinical entities from two modalities: Textual propositions extracted from notes and ICD codes mapped to textual descriptions. Through hierarchical causal discovery, THCM-CAL infers three clinically grounded interactions: intra-slice same-modality sequencing, intra-slice cross-modality triggers, and inter-slice risk propagation. To enhance prediction reliability, we extend conformal prediction to multi-label ICD coding, calibrating per-code confidence intervals under complex co-occurrences. Experimental results on MIMIC-III and MIMIC-IV demonstrate the superiority of THCM-CAL.", "date": "2025-06-21", "categories": []}
{"id": "2509.11941", "title": "How to Evaluate Medical AI", "abstract": "The integration of artificial intelligence (AI) into medical diagnostic workflows requires robust and consistent evaluation methods to ensure reliability, clinical relevance, and the inherent variability in expert judgments. Traditional metrics like precision and recall often fail to account for the inherent variability in expert judgments, leading to inconsistent assessments of AI performance. Inter-rater agreement statistics like Cohen's Kappa are more reliable but they lack interpretability. We introduce Relative Precision and Recall of Algorithmic Diagnostics (RPAD and RRAD) - a new evaluation metrics that compare AI outputs against multiple expert opinions rather than a single reference. By normalizing performance against inter-expert disagreement, these metrics provide a more stable and realistic measure of the quality of predicted diagnosis. In addition to the comprehensive analysis of diagnostic quality measures, our study contains a very important side result. Our evaluation methodology allows us to avoid selecting diagnoses from a limited list when evaluating a given case. Instead, both the models being tested and the examiners verifying them arrive at a free-form diagnosis. In this automated methodology for establishing the identity of free-form clinical diagnoses, a remarkable 98% accuracy becomes attainable. We evaluate our approach using 360 medical dialogues, comparing multiple large language models (LLMs) against a panel of physicians. Large-scale study shows that top-performing models, such as DeepSeek-V3, achieve consistency on par with or exceeding expert consensus. Moreover, we demonstrate that expert judgments exhibit significant variability - often greater than that between AI and humans. This finding underscores the limitations of any absolute metrics and supports the need to adopt relative metrics in medical AI.", "date": "2025-09-15", "categories": []}
{"id": "2509.20565", "title": "Generalizable Diabetes Risk Stratification via Hybrid Machine Learning Models", "abstract": "Background/Purpose: Diabetes affects over 537 million people worldwide and is projected to reach 783 million by 2045. Early risk stratification can benefit from machine learning. We compare two hybrid classifiers and assess their generalizability on an external cohort.\n  Methods: Two hybrids were built: (i) XGBoost + Random Forest (XGB-RF) and (ii) Support Vector Machine + Logistic Regression (SVM-LR). A leakage-safe, standardized pipeline (encoding, imputation, min-max scaling; SMOTE on training folds only; probability calibration for SVM) was fit on the primary dataset and frozen. Evaluation prioritized threshold-independent discrimination (AUROC/AUPRC) and calibration (Brier, slope/intercept). External validation used the PIMA cohort (N=768) with the frozen pipeline; any thresholded metrics on PIMA were computed at the default rule tau = 0.5.\n  Results: On the primary dataset (PR baseline = 0.50), XGB-RF achieved AUROC ~0.995 and AUPRC ~0.998, outperforming SVM-LR (AUROC ~0.978; AUPRC ~0.947). On PIMA (PR baseline ~0.349), XGB-RF retained strong performance (AUROC ~0.990; AUPRC ~0.959); SVM-LR was lower (AUROC ~0.963; AUPRC ~0.875). Thresholded metrics on PIMA at tau = 0.5 were XGB-RF (Accuracy 0.960; Precision 0.941; Recall 0.944; F1 0.942) and SVM-LR (Accuracy 0.900; Precision 0.855; Recall 0.858; F1 0.857).\n  Conclusions: Across internal and external cohorts, XGB-RF consistently dominated SVM-LR and exhibited smaller external attenuation on ROC/PR with acceptable calibration. These results support gradient-boosting-based hybridization as a robust, transferable approach for diabetes risk stratification and motivate prospective, multi-site validation with deployment-time threshold selection based on clinical trade-offs.", "date": "2025-09-24", "categories": []}
{"id": "2509.20842", "title": "Robust Multi-Omics Integration from Incomplete Modalities Significantly Improves Prediction of Alzheimer's Disease", "abstract": "Multi-omics data capture complex biomolecular interactions and provide insights into metabolism and disease. However, missing modalities hinder integrative analysis across heterogeneous omics. To address this, we present MOIRA (Multi-Omics Integration with Robustness to Absent modalities), an early integration method enabling robust learning from incomplete omics data via representation alignment and adaptive aggregation. MOIRA leverages all samples, including those with missing modalities, by projecting each omics dataset onto a shared embedding space where a learnable weighting mechanism fuses them. Evaluated on the Religious Order Study and Memory and Aging Project (ROSMAP) dataset for Alzheimer's Disease (AD), MOIRA outperformed existing approaches, and further ablation studies confirmed modality-wise contributions. Feature importance analysis revealed AD-related biomarkers consistent with prior literature, highlighting the biological relevance of our approach.", "date": "2025-09-25", "categories": []}
{"id": "2509.20867", "title": "Federated Markov Imputation: Privacy-Preserving Temporal Imputation in Multi-Centric ICU Environments", "abstract": "Missing data is a persistent challenge in federated learning on electronic health records, particularly when institutions collect time-series data at varying temporal granularities. To address this, we propose Federated Markov Imputation (FMI), a privacy-preserving method that enables Intensive Care Units (ICUs) to collaboratively build global transition models for temporal imputation. We evaluate FMI on a real-world sepsis onset prediction task using the MIMIC-IV dataset and show that it outperforms local imputation baselines, especially in scenarios with irregular sampling intervals across ICUs.", "date": "2025-09-25", "categories": []}
{"id": "2509.20885", "title": "Improving Early Sepsis Onset Prediction Through Federated Learning", "abstract": "Early and accurate prediction of sepsis onset remains a major challenge in intensive care, where timely detection and subsequent intervention can significantly improve patient outcomes. While machine learning models have shown promise in this domain, their success is often limited by the amount and diversity of training data available to individual hospitals and Intensive Care Units (ICUs). Federated Learning (FL) addresses this issue by enabling collaborative model training across institutions without requiring data sharing, thus preserving patient privacy. In this work, we propose a federated, attention-enhanced Long Short-Term Memory model for sepsis onset prediction, trained on multi-centric ICU data. Unlike existing approaches that rely on fixed prediction windows, our model supports variable prediction horizons, enabling both short- and long-term forecasting in a single unified model. During analysis, we put particular emphasis on the improvements through our approach in terms of early sepsis detection, i.e., predictions with large prediction windows by conducting an in-depth temporal analysis. Our results prove that using FL does not merely improve overall prediction performance (with performance approaching that of a centralized model), but is particularly beneficial for early sepsis onset prediction. Finally, we show that our choice of employing a variable prediction window rather than a fixed window does not hurt performance significantly but reduces computational, communicational, and organizational overhead.", "date": "2025-09-25", "categories": []}
{"id": "2509.19308", "title": "Graph-Based Spatio-temporal Attention and Multi-Scale Fusion for Clinically Interpretable, High-Fidelity Fetal ECG Extraction", "abstract": "Congenital Heart Disease (CHD) is the most common neonatal anomaly, highlighting the urgent need for early detection to improve outcomes. Yet, fetal ECG (fECG) signals in abdominal ECG (aECG) are often masked by maternal ECG and noise, challenging conventional methods under low signal-to-noise ratio (SNR) conditions. We propose FetalHealthNet (FHNet), a deep learning framework that integrates Graph Neural Networks with a multi-scale enhanced transformer to dynamically model spatiotemporal inter-lead correlations and extract clean fECG signals. On benchmark aECG datasets, FHNet consistently outperforms long short-term memory (LSTM) models, standard transformers, and state-of-the-art models, achieving R2>0.99 and RMSE = 0.015 even under severe noise. Interpretability analyses highlight physiologically meaningful temporal and lead contributions, supporting model transparency and clinical trust. FHNet illustrates the potential of AI-driven modeling to advance fetal monitoring and enable early CHD screening, underscoring the transformative impact of next-generation biomedical signal processing.", "date": "2025-09-05", "categories": []}
{"id": "2509.19397", "title": "Self-Alignment Learning to Improve Myocardial Infarction Detection from Single-Lead ECG", "abstract": "Myocardial infarction is a critical manifestation of coronary artery disease, yet detecting it from single-lead electrocardiogram (ECG) remains challenging due to limited spatial information. An intuitive idea is to convert single-lead into multiple-lead ECG for classification by pre-trained models, but generative methods optimized at the signal level in most cases leave a large latent space gap, ultimately degrading diagnostic performance. This naturally raises the question of whether latent space alignment could help. However, most prior ECG alignment methods focus on learning transformation invariance, which mismatches the goal of single-lead detection. To address this issue, we propose SelfMIS, a simple yet effective alignment learning framework to improve myocardial infarction detection from single-lead ECG. Discarding manual data augmentations, SelfMIS employs a self-cutting strategy to pair multiple-lead ECG with their corresponding single-lead segments and directly align them in the latent space. This design shifts the learning objective from pursuing transformation invariance to enriching the single-lead representation, explicitly driving the single-lead ECG encoder to learn a representation capable of inferring global cardiac context from the local signal. Experimentally, SelfMIS achieves superior performance over baseline models across nine myocardial infarction types while maintaining a simpler architecture and lower computational overhead, thereby substantiating the efficacy of direct latent space alignment. Our code and checkpoint will be publicly available after acceptance.", "date": "2025-09-23", "categories": []}
{"id": "2509.19577", "title": "MAGIC: Multi-task Gaussian process for joint imputation and classification in healthcare time series", "abstract": "Time series analysis has emerged as an important tool for improving patient diagnosis and management in healthcare applications. However, these applications commonly face two critical challenges: time misalignment and data sparsity. Traditional approaches address these issues through a two-step process of imputation followed by prediction. We propose MAGIC (Multi-tAsk Gaussian Process for Imputation and Classification), a novel unified framework that simultaneously performs class-informed missing value imputation and label prediction within a hierarchical multi-task Gaussian process coupled with functional logistic regression. To handle intractable likelihood components, MAGIC employs Taylor expansion approximations with bounded error analysis, and parameter estimation is performed using EM algorithm with block coordinate optimization supported by convergence analysis. We validate MAGIC through two healthcare applications: prediction of post-traumatic headache improvement following mild traumatic brain injury and prediction of in-hospital mortality within 48 hours after ICU admission. In both applications, MAGIC achieves superior predictive accuracy compared to existing methods. The ability to generate real-time and accurate predictions with limited samples facilitates early clinical assessment and treatment planning, enabling healthcare providers to make more informed treatment decisions.", "date": "2025-09-23", "categories": []}
{"id": "2509.19705", "title": "Causal Machine Learning for Surgical Interventions", "abstract": "Surgical decision-making is complex and requires understanding causal relationships between patient characteristics, interventions, and outcomes. In high-stakes settings like spinal fusion or scoliosis correction, accurate estimation of individualized treatment effects (ITEs) remains limited due to the reliance on traditional statistical methods that struggle with complex, heterogeneous data. In this study, we develop a multi-task meta-learning framework, X-MultiTask, for ITE estimation that models each surgical decision (e.g., anterior vs. posterior approach, surgery vs. no surgery) as a distinct task while learning shared representations across tasks. To strengthen causal validity, we incorporate the inverse probability weighting (IPW) into the training objective. We evaluate our approach on two datasets: (1) a public spinal fusion dataset (1,017 patients) to assess the effect of anterior vs. posterior approaches on complication severity; and (2) a private AIS dataset (368 patients) to analyze the impact of posterior spinal fusion (PSF) vs. non-surgical management on patient-reported outcomes (PROs). Our model achieves the highest average AUC (0.84) in the anterior group and maintains competitive performance in the posterior group (0.77). It outperforms baselines in treatment effect estimation with the lowest overall $\\epsilon_{\\text{NN-PEHE}}$ (0.2778) and $\\epsilon_{\\text{ATE}}$ (0.0763). Similarly, when predicting PROs in AIS, X-MultiTask consistently shows superior performance across all domains, with $\\epsilon_{\\text{NN-PEHE}}$ = 0.2551 and $\\epsilon_{\\text{ATE}}$ = 0.0902. By providing robust, patient-specific causal estimates, X-MultiTask offers a powerful tool to advance personalized surgical care and improve patient outcomes. The code is available at https://github.com/Wizaaard/X-MultiTask.", "date": "2025-09-24", "categories": []}
{"id": "2509.19750", "title": "Cuffless Blood Pressure Prediction from Speech Sentences using Deep Learning Methods", "abstract": "This research presents a novel method for noninvasive arterial blood pressure ABP prediction using speech signals employing a BERT based regression model Arterial blood pressure is a vital indicator of cardiovascular health and accurate monitoring is essential in preventing hypertension related complications Traditional cuff based methods often yield inconsistent results due to factors like whitecoat and masked hypertension Our approach leverages the acoustic characteristics of speech capturing voice features to establish correlations with blood pressure levels Utilizing advanced deep learning techniques we analyze speech signals to extract relevant patterns enabling real time monitoring without the discomfort of conventional methods In our study we employed a dataset comprising recordings from 95 participants ensuring diverse representation The BERT model was fine tuned on extracted features from speech leading to impressive performance metrics achieving a mean absolute error MAE of 136 mmHg for systolic blood pressure SBP and 124 mmHg for diastolic blood pressure DBP with R scores of 099 and 094 respectively These results indicate the models robustness in accurately predicting blood pressure levels Furthermore the training and validation loss analysis demonstrates effective learning and minimal overfitting Our findings suggest that integrating deep learning with speech analysis presents a viable alternative for blood pressure monitoring paving the way for improved applications in telemedicine and remote health monitoring By providing a user friendly and accurate method for blood pressure assessment this research has significant implications for enhancing patient care and proactive management of cardiovascular health", "date": "2025-09-24", "categories": []}
{"id": "2509.19885", "title": "Towards Self-Supervised Foundation Models for Critical Care Time Series", "abstract": "Domain-specific foundation models for healthcare have expanded rapidly in recent years, yet foundation models for critical care time series remain relatively underexplored due to the limited size and availability of datasets. In this work, we introduce an early-stage pre-trained foundation model for critical care time-series based on the Bi-Axial Transformer (BAT), trained on pooled electronic health record datasets. We demonstrate effective transfer learning by fine-tuning the model on a dataset distinct from the training sources for mortality prediction, where it outperforms supervised baselines, particularly for small datasets ($<5,000$). These contributions highlight the potential of self-supervised foundation models for critical care times series to support generalizable and robust clinical applications in resource-limited settings.", "date": "2025-09-24", "categories": []}
{"id": "2505.12344", "title": "Early Prediction of In-Hospital ICU Mortality Using Innovative First-Day Data: A Review", "abstract": "The intensive care unit (ICU) manages critically ill patients, many of whom face a high risk of mortality. Early and accurate prediction of in-hospital mortality within the first 24 hours of ICU admission is crucial for timely clinical interventions, resource optimization, and improved patient outcomes. Traditional scoring systems, while useful, often have limitations in predictive accuracy and adaptability. Objective: This review aims to systematically evaluate and benchmark innovative methodologies that leverage data available within the first day of ICU admission for predicting in-hospital mortality. We focus on advancements in machine learning, novel biomarker applications, and the integration of diverse data types.", "date": "2025-05-18", "categories": []}
{"id": "2509.18145", "title": "Early Prediction of Multi-Label Care Escalation Triggers in the Intensive Care Unit Using Electronic Health Records", "abstract": "Intensive Care Unit (ICU) patients often present with complex, overlapping signs of physiological deterioration that require timely escalation of care. Traditional early warning systems, such as SOFA or MEWS, are limited by their focus on single outcomes and fail to capture the multi-dimensional nature of clinical decline. This study proposes a multi-label classification framework to predict Care Escalation Triggers (CETs), including respiratory failure, hemodynamic instability, renal compromise, and neurological deterioration, using the first 24 hours of ICU data. Using the MIMIC-IV database, CETs are defined through rule-based criteria applied to data from hours 24 to 72 (for example, oxygen saturation below 90, mean arterial pressure below 65 mmHg, creatinine increase greater than 0.3 mg/dL, or a drop in Glasgow Coma Scale score greater than 2). Features are extracted from the first 24 hours and include vital sign aggregates, laboratory values, and static demographics. We train and evaluate multiple classification models on a cohort of 85,242 ICU stays (80 percent training: 68,193; 20 percent testing: 17,049). Evaluation metrics include per-label precision, recall, F1-score, and Hamming loss. XGBoost, the best performing model, achieves F1-scores of 0.66 for respiratory, 0.72 for hemodynamic, 0.76 for renal, and 0.62 for neurologic deterioration, outperforming baseline models. Feature analysis shows that clinically relevant parameters such as respiratory rate, blood pressure, and creatinine are the most influential predictors, consistent with the clinical definitions of the CETs. The proposed framework demonstrates practical potential for early, interpretable clinical alerts without requiring complex time-series modeling or natural language processing.", "date": "2025-09-15", "categories": []}
{"id": "2509.18457", "title": "GluMind: Multimodal Parallel Attention and Knowledge Retention for Robust Cross-Population Blood Glucose Forecasting", "abstract": "This paper proposes GluMind, a transformer-based multimodal framework designed for continual and long-term blood glucose forecasting. GluMind devises two attention mechanisms, including cross-attention and multi-scale attention, which operate in parallel and deliver accurate predictive performance. Cross-attention effectively integrates blood glucose data with other physiological and behavioral signals such as activity, stress, and heart rate, addressing challenges associated with varying sampling rates and their adverse impacts on robust prediction. Moreover, the multi-scale attention mechanism captures long-range temporal dependencies. To mitigate catastrophic forgetting, GluMind incorporates a knowledge retention technique into the transformer-based forecasting model. The knowledge retention module not only enhances the model's ability to retain prior knowledge but also boosts its overall forecasting performance. We evaluate GluMind on the recently released AIREADI dataset, which contains behavioral and physiological data collected from healthy people, individuals with prediabetes, and those with type 2 diabetes. We examine the performance stability and adaptability of GluMind in learning continuously as new patient cohorts are introduced. Experimental results show that GluMind consistently outperforms other state-of-the-art forecasting models, achieving approximately 15% and 9% improvements in root mean squared error (RMSE) and mean absolute error (MAE), respectively.", "date": "2025-09-22", "categories": []}
{"id": "2403.09548", "title": "Breast Cancer Classification Using Gradient Boosting Algorithms Focusing on Reducing the False Negative and SHAP for Explainability", "abstract": "Cancer is one of the diseases that kill the most women in the world, with breast cancer being responsible for the highest number of cancer cases and consequently deaths. However, it can be prevented by early detection and, consequently, early treatment. Any development for detection or perdition this kind of cancer is important for a better healthy life. Many studies focus on a model with high accuracy in cancer prediction, but sometimes accuracy alone may not always be a reliable metric. This study implies an investigative approach to studying the performance of different machine learning algorithms based on boosting to predict breast cancer focusing on the recall metric. Boosting machine learning algorithms has been proven to be an effective tool for detecting medical diseases. The dataset of the University of California, Irvine (UCI) repository has been utilized to train and test the model classifier that contains their attributes. The main objective of this study is to use state-of-the-art boosting algorithms such as AdaBoost, XGBoost, CatBoost and LightGBM to predict and diagnose breast cancer and to find the most effective metric regarding recall, ROC-AUC, and confusion matrix. Furthermore, our study is the first to use these four boosting algorithms with Optuna, a library for hyperparameter optimization, and the SHAP method to improve the interpretability of our model, which can be used as a support to identify and predict breast cancer. We were able to improve AUC or recall for all the models and reduce the False Negative for AdaBoost and LigthGBM the final AUC were more than 99.41\\% for all models.", "date": "2024-03-14", "categories": []}
{"id": "2504.03699", "title": "Enhancing Clinical Decision-Making: Integrating Multi-Agent Systems with Ethical AI Governance", "abstract": "Recent advances in the data-driven medicine approach, which integrates ethically managed and explainable artificial intelligence into clinical decision support systems (CDSS), are critical to ensure reliable and effective patient care. This paper focuses on comparing novel agent system designs that use modular agents to analyze laboratory results, vital signs, and clinical context, and to predict and validate results. We implement our agent system with the eICU database, including running lab analysis, vitals-only interpreters, and contextual reasoners agents first, then sharing the memory into the integration agent, prediction agent, transparency agent, and a validation agent. Our results suggest that the multi-agent system (MAS) performed better than the single-agent system (SAS) with mortality prediction accuracy (59\\%, 56\\%) and the mean error for length of stay (LOS)(4.37 days, 5.82 days), respectively. However, the transparency score for the SAS (86.21) is slightly better than the transparency score for MAS (85.5). Finally, this study suggests that our agent-based framework not only improves process transparency and prediction accuracy but also strengthens trustworthy AI-assisted decision support in an intensive care setting.", "date": "2025-03-25", "categories": []}
{"id": "2509.16345", "title": "Estimating Clinical Lab Test Result Trajectories from PPG using Physiological Foundation Model and Patient-Aware State Space Model -- a UNIPHY+ Approach", "abstract": "Clinical laboratory tests provide essential biochemical measurements for diagnosis and treatment, but are limited by intermittent and invasive sampling. In contrast, photoplethysmogram (PPG) is a non-invasive, continuously recorded signal in intensive care units (ICUs) that reflects cardiovascular dynamics and can serve as a proxy for latent physiological changes. We propose UNIPHY+Lab, a framework that combines a large-scale PPG foundation model for local waveform encoding with a patient-aware Mamba model for long-range temporal modeling. Our architecture addresses three challenges: (1) capturing extended temporal trends in laboratory values, (2) accounting for patient-specific baseline variation via FiLM-modulated initial states, and (3) performing multi-task estimation for interrelated biomarkers. We evaluate our method on the two ICU datasets for predicting the five key laboratory tests. The results show substantial improvements over the LSTM and carry-forward baselines in MAE, RMSE, and $R^2$ among most of the estimation targets. This work demonstrates the feasibility of continuous, personalized lab value estimation from routine PPG monitoring, offering a pathway toward non-invasive biochemical surveillance in critical care.", "date": "2025-09-19", "categories": []}
{"id": "2509.16348", "title": "A Unified AI Approach for Continuous Monitoring of Human Health and Diseases from Intensive Care Unit to Home with Physiological Foundation Models (UNIPHY+)", "abstract": "We present UNIPHY+, a unified physiological foundation model (physioFM) framework designed to enable continuous human health and diseases monitoring across care settings using ubiquitously obtainable physiological data. We propose novel strategies for incorporating contextual information during pretraining, fine-tuning, and lightweight model personalization via multi-modal learning, feature fusion-tuning, and knowledge distillation. We advocate testing UNIPHY+ with a broad set of use cases from intensive care to ambulatory monitoring in order to demonstrate that UNIPHY+ can empower generalizable, scalable, and personalized physiological AI to support both clinical decision-making and long-term health monitoring.", "date": "2025-09-19", "categories": []}
{"id": "2509.17228", "title": "Causal Representation Learning from Multimodal Clinical Records under Non-Random Modality Missingness", "abstract": "Clinical notes contain rich patient information, such as diagnoses or medications, making them valuable for patient representation learning. Recent advances in large language models have further improved the ability to extract meaningful representations from clinical texts. However, clinical notes are often missing. For example, in our analysis of the MIMIC-IV dataset, 24.5% of patients have no available discharge summaries. In such cases, representations can be learned from other modalities such as structured data, chest X-rays, or radiology reports. Yet the availability of these modalities is influenced by clinical decision-making and varies across patients, resulting in modality missing-not-at-random (MMNAR) patterns. We propose a causal representation learning framework that leverages observed data and informative missingness in multimodal clinical records. It consists of: (1) an MMNAR-aware modality fusion component that integrates structured data, imaging, and text while conditioning on missingness patterns to capture patient health and clinician-driven assignment; (2) a modality reconstruction component with contrastive learning to ensure semantic sufficiency in representation learning; and (3) a multitask outcome prediction model with a rectifier that corrects for residual bias from specific modality observation patterns. Comprehensive evaluations across MIMIC-IV and eICU show consistent gains over the strongest baselines, achieving up to 13.8% AUC improvement for hospital readmission and 13.1% for ICU admission.", "date": "2025-09-21", "categories": []}
{"id": "2509.17802", "title": "TS-P$^2$CL: Plug-and-Play Dual Contrastive Learning for Vision-Guided Medical Time Series Classification", "abstract": "Medical time series (MedTS) classification is pivotal for intelligent healthcare, yet its efficacy is severely limited by poor cross-subject generation due to the profound cross-individual heterogeneity. Despite advances in architectural innovations and transfer learning techniques, current methods remain constrained by modality-specific inductive biases that limit their ability to learn universally invariant representations. To overcome this, we propose TS-P$^2$CL, a novel plug-and-play framework that leverages the universal pattern recognition capabilities of pre-trained vision models. We introduce a vision-guided paradigm that transforms 1D physiological signals into 2D pseudo-images, establishing a bridge to the visual domain. This transformation enables implicit access to rich semantic priors learned from natural images. Within this unified space, we employ a dual-contrastive learning strategy: intra-modal consistency enforces temporal coherence, while cross-modal alignment aligns time-series dynamics with visual semantics, thereby mitigating individual-specific biases and learning robust, domain-invariant features. Extensive experiments on six MedTS datasets demonstrate that TS-P$^2$CL consistently outperforms fourteen methods in both subject-dependent and subject-independent settings.", "date": "2025-09-22", "categories": []}
{"id": "2509.17924", "title": "Medical priority fusion: achieving dual optimization of sensitivity and interpretability in nipt anomaly detection", "abstract": "Clinical machine learning faces a critical dilemma in high-stakes medical applications: algorithms achieving optimal diagnostic performance typically sacrifice the interpretability essential for physician decision-making, while interpretable methods compromise sensitivity in complex scenarios. This paradox becomes particularly acute in non-invasive prenatal testing (NIPT), where missed chromosomal abnormalities carry profound clinical consequences yet regulatory frameworks mandate explainable AI systems. We introduce Medical Priority Fusion (MPF), a constrained multi-objective optimization framework that resolves this fundamental trade-off by systematically integrating Naive Bayes probabilistic reasoning with Decision Tree rule-based logic through mathematically-principled weighted fusion under explicit medical constraints. Rigorous validation on 1,687 real-world NIPT samples characterized by extreme class imbalance (43.4:1 normal-to-abnormal ratio) employed stratified 5-fold cross-validation with comprehensive ablation studies and statistical hypothesis testing using McNemar's paired comparisons. MPF achieved simultaneous optimization of dual objectives: 89.3% sensitivity (95% CI: 83.9-94.7%) with 80% interpretability score, significantly outperforming individual algorithms (McNemar's test, p < 0.001). The optimal fusion configuration achieved Grade A clinical deployment criteria with large effect size (d = 1.24), establishing the first clinically-deployable solution that maintains both diagnostic accuracy and decision transparency essential for prenatal care. This work demonstrates that medical-constrained algorithm fusion can resolve the interpretability-performance trade-off, providing a mathematical framework for developing high-stakes medical decision support systems that meet both clinical efficacy and explainability requirements.", "date": "2025-09-22", "categories": []}
{"id": "2509.17991", "title": "ReDepress: A Cognitive Framework for Detecting Depression Relapse from Social Media", "abstract": "Almost 50% depression patients face the risk of going into relapse. The risk increases to 80% after the second episode of depression. Although, depression detection from social media has attained considerable attention, depression relapse detection has remained largely unexplored due to the lack of curated datasets and the difficulty of distinguishing relapse and non-relapse users. In this work, we present ReDepress, the first clinically validated social media dataset focused on relapse, comprising 204 Reddit users annotated by mental health professionals. Unlike prior approaches, our framework draws on cognitive theories of depression, incorporating constructs such as attention bias, interpretation bias, memory bias and rumination into both annotation and modeling. Through statistical analyses and machine learning experiments, we demonstrate that cognitive markers significantly differentiate relapse and non-relapse groups, and that models enriched with these features achieve competitive performance, with transformer-based temporal models attaining an F1 of 0.86. Our findings validate psychological theories in real-world textual data and underscore the potential of cognitive-informed computational methods for early relapse detection, paving the way for scalable, low-cost interventions in mental healthcare.", "date": "2025-09-22", "categories": []}
{"id": "2502.19668", "title": "SuPreME: A Supervised Pre-training Framework for Multimodal ECG Representation Learning", "abstract": "Cardiovascular diseases are a leading cause of death and disability worldwide. Electrocardiogram (ECG) is critical for diagnosing and monitoring cardiac health, but obtaining large-scale annotated ECG datasets is labor-intensive and time-consuming. Recent ECG Self-Supervised Learning (eSSL) methods mitigate this by learning features without extensive labels but fail to capture fine-grained clinical semantics and require extensive task-specific fine-tuning. To address these challenges, we propose $\\textbf{SuPreME}$, a $\\textbf{Su}$pervised $\\textbf{Pre}$-training framework for $\\textbf{M}$ultimodal $\\textbf{E}$CG representation learning. SuPreME is pre-trained using structured diagnostic labels derived from ECG report entities through a one-time offline extraction with Large Language Models (LLMs), which help denoise, standardize cardiac concepts, and improve clinical representation learning. By fusing ECG signals with textual cardiac queries instead of fixed labels, SuPreME enables zero-shot classification of unseen conditions without further fine-tuning. We evaluate SuPreME on six downstream datasets covering 106 cardiac conditions, achieving superior zero-shot AUC performance of $77.20\\%$, surpassing state-of-the-art eSSLs by $4.98\\%$. Results demonstrate SuPreME's effectiveness in leveraging structured, clinically relevant knowledge for high-quality ECG representations.", "date": "2025-02-27", "categories": []}
{"id": "2503.19285", "title": "No Black Box Anymore: Demystifying Clinical Predictive Modeling with Temporal-Feature Cross Attention Mechanism", "abstract": "Despite the outstanding performance of deep learning models in clinical prediction tasks, explainability remains a significant challenge. Inspired by transformer architectures, we introduce the Temporal-Feature Cross Attention Mechanism (TFCAM), a novel deep learning framework designed to capture dynamic interactions among clinical features across time, enhancing both predictive accuracy and interpretability. In an experiment with 1,422 patients with Chronic Kidney Disease, predicting progression to End-Stage Renal Disease, TFCAM outperformed LSTM and RETAIN baselines, achieving an AUROC of 0.95 and an F1-score of 0.69. Beyond performance gains, TFCAM provides multi-level explainability by identifying critical temporal periods, ranking feature importance, and quantifying how features influence each other across time before affecting predictions. Our approach addresses the \"black box\" limitations of deep learning in healthcare, offering clinicians transparent insights into disease progression mechanisms while maintaining state-of-the-art predictive performance.", "date": "2025-03-25", "categories": []}
{"id": "2505.09380", "title": "Examining Deployment and Refinement of the VIOLA-AI Intracranial Hemorrhage Model Using an Interactive NeoMedSys Platform", "abstract": "Background: There are many challenges and opportunities in the clinical deployment of AI tools in radiology. The current study describes a radiology software platform called NeoMedSys that can enable efficient deployment and refinements of AI models. We evaluated the feasibility and effectiveness of running NeoMedSys for three months in real-world clinical settings and focused on improvement performance of an in-house developed AI model (VIOLA-AI) designed for intracranial hemorrhage (ICH) detection.\n  Methods: NeoMedSys integrates tools for deploying, testing, and optimizing AI models with a web-based medical image viewer, annotation system, and hospital-wide radiology information systems. A prospective pragmatic investigation was deployed using clinical cases of patients presenting to the largest Emergency Department in Norway (site-1) with suspected traumatic brain injury (TBI) or patients with suspected stroke (site-2). We assessed ICH classification performance as VIOLA-AI encountered new data and underwent pre-planned model retraining. Performance metrics included sensitivity, specificity, accuracy, and the area under the receiver operating characteristic curve (AUC).\n  Results: NeoMedSys facilitated iterative improvements in the AI model, significantly enhancing its diagnostic accuracy. Automated bleed detection and segmentation were reviewed in near real-time to facilitate re-training VIOLA-AI. The iterative refinement process yielded a marked improvement in classification sensitivity, rising to 90.3% (from 79.2%), and specificity that reached 89.3% (from 80.7%). The bleed detection ROC analysis for the entire sample demonstrated a high area-under-the-curve (AUC) of 0.949 (from 0.873). Model refinement stages were associated with notable gains, highlighting the value of real-time radiologist feedback.", "date": "2025-05-14", "categories": []}
{"id": "2506.00337", "title": "Channel-Imposed Fusion: A Simple yet Effective Method for Medical Time Series Classification", "abstract": "The automatic classification of medical time series signals, such as electroencephalogram (EEG) and electrocardiogram (ECG), plays a pivotal role in clinical decision support and early detection of diseases. Although Transformer based models have achieved notable performance by implicitly modeling temporal dependencies through self-attention mechanisms, their inherently complex architectures and opaque reasoning processes undermine their trustworthiness in high stakes clinical settings. In response to these limitations, this study shifts focus toward a modeling paradigm that emphasizes structural transparency, aligning more closely with the intrinsic characteristics of medical data. We propose a novel method, Channel Imposed Fusion (CIF), which enhances the signal-to-noise ratio through cross-channel information fusion, effectively reduces redundancy, and improves classification performance. Furthermore, we integrate CIF with the Temporal Convolutional Network (TCN), known for its structural simplicity and controllable receptive field, to construct an efficient and explicit classification framework. Experimental results on multiple publicly available EEG and ECG datasets demonstrate that the proposed method not only outperforms existing state-of-the-art (SOTA) approaches in terms of various classification metrics, but also significantly enhances the transparency of the classification process, offering a novel perspective for medical time series classification.", "date": "2025-05-31", "categories": []}
{"id": "2509.15259", "title": "IEFS-GMB: Gradient Memory Bank-Guided Feature Selection Based on Information Entropy for EEG Classification of Neurological Disorders", "abstract": "Deep learning-based EEG classification is crucial for the automated detection of neurological disorders, improving diagnostic accuracy and enabling early intervention. However, the low signal-to-noise ratio of EEG signals limits model performance, making feature selection (FS) vital for optimizing representations learned by neural network encoders. Existing FS methods are seldom designed specifically for EEG diagnosis; many are architecture-dependent and lack interpretability, limiting their applicability. Moreover, most rely on single-iteration data, resulting in limited robustness to variability. To address these issues, we propose IEFS-GMB, an Information Entropy-based Feature Selection method guided by a Gradient Memory Bank. This approach constructs a dynamic memory bank storing historical gradients, computes feature importance via information entropy, and applies entropy-based weighting to select informative EEG features. Experiments on four public neurological disease datasets show that encoders enhanced with IEFS-GMB achieve accuracy improvements of 0.64% to 6.45% over baseline models. The method also outperforms four competing FS techniques and improves model interpretability, supporting its practical use in clinical settings.", "date": "2025-09-18", "categories": []}
{"id": "2509.15895", "title": "From Data to Diagnosis: A Large, Comprehensive Bone Marrow Dataset and AI Methods for Childhood Leukemia Prediction", "abstract": "Leukemia diagnosis primarily relies on manual microscopic analysis of bone marrow morphology supported by additional laboratory parameters, making it complex and time consuming. While artificial intelligence (AI) solutions have been proposed, most utilize private datasets and only cover parts of the diagnostic pipeline. Therefore, we present a large, high-quality, publicly available leukemia bone marrow dataset spanning the entire diagnostic process, from cell detection to diagnosis. Using this dataset, we further propose methods for cell detection, cell classification, and diagnosis prediction. The dataset comprises 246 pediatric patients with diagnostic, clinical and laboratory information, over 40 000 cells with bounding box annotations and more than 28 000 of these with high-quality class labels, making it the most comprehensive dataset publicly available. Evaluation of the AI models yielded an average precision of 0.96 for the cell detection, an area under the curve of 0.98, and an F1-score of 0.61 for the 33-class cell classification, and a mean F1-score of 0.90 for the diagnosis prediction using predicted cell counts. While the proposed approaches demonstrate their usefulness for AI-assisted diagnostics, the dataset will foster further research and development in the field, ultimately contributing to more precise diagnoses and improved patient outcomes.", "date": "2025-09-19", "categories": []}
{"id": "2509.15990", "title": "DAFTED: Decoupled Asymmetric Fusion of Tabular and Echocardiographic Data for Cardiac Hypertension Diagnosis", "abstract": "Multimodal data fusion is a key approach for enhancing diagnosis in medical applications. We propose an asymmetric fusion strategy starting from a primary modality and integrating secondary modalities by disentangling shared and modality-specific information. Validated on a dataset of 239 patients with echocardiographic time series and tabular records, our model outperforms existing methods, achieving an AUC over 90%. This improvement marks a crucial benchmark for clinical use.", "date": "2025-09-19", "categories": []}
{"id": "2509.16126", "title": "Network-Based Detection of Autism Spectrum Disorder Using Sustainable and Non-invasive Salivary Biomarkers", "abstract": "Autism Spectrum Disorder (ASD) lacks reliable biological markers, delaying early diagnosis. Using 159 salivary samples analyzed by ATR-FTIR spectroscopy, we developed GANet, a genetic algorithm-based network optimization framework leveraging PageRank and Degree for importance-based feature characterization. GANet systematically optimizes network structure to extract meaningful patterns from high-dimensional spectral data. It achieved superior performance compared to linear discriminant analysis, support vector machines, and deep learning models, reaching 0.78 accuracy, 0.61 sensitivity, 0.90 specificity, and a 0.74 harmonic mean. These results demonstrate GANet's potential as a robust, bio-inspired, non-invasive tool for precise ASD detection and broader spectral-based health applications.", "date": "2025-09-19", "categories": []}
{"id": "2509.14242", "title": "Artificial Intelligence-derived Cardiotocography Age as a Digital Biomarker for Predicting Future Adverse Pregnancy Outcomes", "abstract": "Cardiotocography (CTG) is a low-cost, non-invasive fetal health assessment technique used globally, especially in underdeveloped countries. However, it is currently mainly used to identify the fetus's current status (e.g., fetal acidosis or hypoxia), and the potential of CTG in predicting future adverse pregnancy outcomes has not been fully explored. We aim to develop an AI-based model that predicts biological age from CTG time series (named CTGage), then calculate the age gap between CTGage and actual age (named CTGage-gap), and use this gap as a new digital biomarker for future adverse pregnancy outcomes. The CTGage model is developed using 61,140 records from 11,385 pregnant women, collected at Peking University People's Hospital between 2018 and 2022. For model training, a structurally designed 1D convolutional neural network is used, incorporating distribution-aligned augmented regression technology. The CTGage-gap is categorized into five groups: < -21 days (underestimation group), -21 to -7 days, -7 to 7 days (normal group), 7 to 21 days, and > 21 days (overestimation group). We further defined the underestimation group and overestimation group together as the high-risk group. We then compare the incidence of adverse outcomes and maternal diseases across these groups. The average absolute error of the CTGage model is 10.91 days. When comparing the overestimation group with the normal group, premature infants incidence is 5.33% vs. 1.42% (p < 0.05) and gestational diabetes mellitus (GDM) incidence is 31.93% vs. 20.86% (p < 0.05). When comparing the underestimation group with the normal group, low birth weight incidence is 0.17% vs. 0.15% (p < 0.05) and anaemia incidence is 37.51% vs. 34.74% (p < 0.05). Artificial intelligence-derived CTGage can predict the future risk of adverse pregnancy outcomes and hold potential as a novel, non-invasive, and easily accessible digital biomarker.", "date": "2025-09-03", "categories": []}
{"id": "2509.14283", "title": "Predicting Antibiotic Resistance Patterns Using Sentence-BERT: A Machine Learning Approach", "abstract": "Antibiotic resistance poses a significant threat in in-patient settings with high mortality. Using MIMIC-III data, we generated Sentence-BERT embeddings from clinical notes and applied Neural Networks and XGBoost to predict antibiotic susceptibility. XGBoost achieved an average F1 score of 0.86, while Neural Networks scored 0.84. This study is among the first to use document embeddings for predicting antibiotic resistance, offering a novel pathway for improving antimicrobial stewardship.", "date": "2025-09-16", "categories": []}
{"id": "2509.14945", "title": "Data-Driven Prediction of Maternal Nutritional Status in Ethiopia Using Ensemble Machine Learning Models", "abstract": "Malnutrition among pregnant women is a major public health challenge in Ethiopia, increasing the risk of adverse maternal and neonatal outcomes. Traditional statistical approaches often fail to capture the complex and multidimensional determinants of nutritional status. This study develops a predictive model using ensemble machine learning techniques, leveraging data from the Ethiopian Demographic and Health Survey (2005-2020), comprising 18,108 records with 30 socio-demographic and health attributes. Data preprocessing included handling missing values, normalization, and balancing with SMOTE, followed by feature selection to identify key predictors. Several supervised ensemble algorithms including XGBoost, Random Forest, CatBoost, and AdaBoost were applied to classify nutritional status. Among them, the Random Forest model achieved the best performance, classifying women into four categories (normal, moderate malnutrition, severe malnutrition, and overnutrition) with 97.87% accuracy, 97.88% precision, 97.87% recall, 97.87% F1-score, and 99.86% ROC AUC. These findings demonstrate the effectiveness of ensemble learning in capturing hidden patterns from complex datasets and provide timely insights for early detection of nutritional risks. The results offer practical implications for healthcare providers, policymakers, and researchers, supporting data-driven strategies to improve maternal nutrition and health outcomes in Ethiopia.", "date": "2025-09-18", "categories": []}
{"id": "2509.01319", "title": "Towards Trustworthy Vital Sign Forecasting: Leveraging Uncertainty for Prediction Intervals", "abstract": "Vital signs, such as heart rate and blood pressure, are critical indicators of patient health and are widely used in clinical monitoring and decision-making. While deep learning models have shown promise in forecasting these signals, their deployment in healthcare remains limited in part because clinicians must be able to trust and interpret model outputs. Without reliable uncertainty quantification -- particularly calibrated prediction intervals (PIs) -- it is unclear whether a forecasted abnormality constitutes a meaningful warning or merely reflects model noise, hindering clinical decision-making. To address this, we present two methods for deriving PIs from the Reconstruction Uncertainty Estimate (RUE), an uncertainty measure well-suited to vital-sign forecasting due to its sensitivity to data shifts and support for label-free calibration. Our parametric approach assumes that prediction errors and uncertainty estimates follow a Gaussian copula distribution, enabling closed-form PI computation. Our non-parametric approach, based on k-nearest neighbours (KNN), empirically estimates the conditional error distribution using similar validation instances. We evaluate these methods on two large public datasets with minute- and hour-level sampling, representing high- and low-frequency health signals. Experiments demonstrate that the Gaussian copula method consistently outperforms conformal prediction baselines on low-frequency data, while the KNN approach performs best on high-frequency data. These results underscore the clinical promise of RUE-derived PIs for delivering interpretable, uncertainty-aware vital sign forecasts.", "date": "2025-09-01", "categories": []}
{"id": "2509.13696", "title": "Integrating Text and Time-Series into (Large) Language Models to Predict Medical Outcomes", "abstract": "Large language models (LLMs) excel at text generation, but their ability to handle clinical classification tasks involving structured data, such as time series, remains underexplored. In this work, we adapt instruction-tuned LLMs using DSPy-based prompt optimization to process clinical notes and structured EHR inputs jointly. Our results show that this approach achieves performance on par with specialized multimodal systems while requiring less complexity and offering greater adaptability across tasks.", "date": "2025-09-17", "categories": []}
{"id": "2509.13725", "title": "WatchAnxiety: A Transfer Learning Approach for State Anxiety Prediction from Smartwatch Data", "abstract": "Social anxiety is a common mental health condition linked to significant challenges in academic, social, and occupational functioning. A core feature is elevated momentary (state) anxiety in social situations, yet little prior work has measured or predicted fluctuations in this anxiety throughout the day. Capturing these intra-day dynamics is critical for designing real-time, personalized interventions such as Just-In-Time Adaptive Interventions (JITAIs). To address this gap, we conducted a study with socially anxious college students (N=91; 72 after exclusions) using our custom smartwatch-based system over an average of 9.03 days (SD = 2.95). Participants received seven ecological momentary assessments (EMAs) per day to report state anxiety. We developed a base model on over 10,000 days of external heart rate data, transferred its representations to our dataset, and fine-tuned it to generate probabilistic predictions. These were combined with trait-level measures in a meta-learner. Our pipeline achieved 60.4% balanced accuracy in state anxiety detection in our dataset. To evaluate generalizability, we applied the training approach to a separate hold-out set from the TILES-18 dataset-the same dataset used for pretraining. On 10,095 once-daily EMAs, our method achieved 59.1% balanced accuracy, outperforming prior work by at least 7%.", "date": "2025-09-17", "categories": []}
{"id": "2509.13974", "title": "Personalization on a Budget: Minimally-Labeled Continual Learning for Resource-Efficient Seizure Detection", "abstract": "Objective: Epilepsy, a prevalent neurological disease, demands careful diagnosis and continuous care. Seizure detection remains challenging, as current clinical practice relies on expert analysis of electroencephalography, which is a time-consuming process and requires specialized knowledge. Addressing this challenge, this paper explores automated epileptic seizure detection using deep learning, focusing on personalized continual learning models that adapt to each patient's unique electroencephalography signal features, which evolve over time. Methods: In this context, our approach addresses the challenge of integrating new data into existing models without catastrophic forgetting, a common issue in static deep learning models. We propose EpiSMART, a continual learning framework for seizure detection that uses a size-constrained replay buffer and an informed sample selection strategy to incrementally adapt to patient-specific electroencephalography signals. By selectively retaining high-entropy and seizure-predicted samples, our method preserves critical past information while maintaining high performance with minimal memory and computational requirements. Results: Validation on the CHB-MIT dataset, shows that EpiSMART achieves a 21% improvement in the F1 score over a trained baseline without updates in all other patients. On average, EpiSMART requires only 6.46 minutes of labeled data and 6.28 updates per day, making it suitable for real-time deployment in wearable systems. Conclusion:EpiSMART enables robust and personalized seizure detection under realistic and resource-constrained conditions by effectively integrating new data into existing models without degrading past knowledge. Significance: This framework advances automated seizure detection by providing a continual learning approach that supports patient-specific adaptation and practical deployment in wearable healthcare systems.", "date": "2025-09-17", "categories": []}
{"id": "2504.19075", "title": "HoloDx: Knowledge- and Data-Driven Multimodal Diagnosis of Alzheimer's Disease", "abstract": "Accurate diagnosis of Alzheimer's disease (AD) requires effectively integrating multimodal data and clinical expertise. However, existing methods often struggle to fully utilize multimodal information and lack structured mechanisms to incorporate dynamic domain knowledge. To address these limitations, we propose HoloDx, a knowledge- and data-driven framework that enhances AD diagnosis by aligning domain knowledge with multimodal clinical data. HoloDx incorporates a knowledge injection module with a knowledge-aware gated cross-attention, allowing the model to dynamically integrate domain-specific insights from both large language models (LLMs) and clinical expertise. Also, a memory injection module with a designed prototypical memory attention enables the model to retain and retrieve subject-specific information, ensuring consistency in decision-making. By jointly leveraging these mechanisms, HoloDx enhances interpretability, improves robustness, and effectively aligns prior knowledge with current subject data. Evaluations on five AD datasets demonstrate that HoloDx outperforms state-of-the-art methods, achieving superior diagnostic accuracy and strong generalization across diverse cohorts. The source code will be released upon publication acceptance.", "date": "2025-04-27", "categories": []}
{"id": "2507.21875", "title": "Tiny-BioMoE: a Lightweight Embedding Model for Biosignal Analysis", "abstract": "Pain is a complex and pervasive condition that affects a significant portion of the population. Accurate and consistent assessment is essential for individuals suffering from pain, as well as for developing effective management strategies in a healthcare system. Automatic pain assessment systems enable continuous monitoring, support clinical decision-making, and help minimize patient distress while mitigating the risk of functional deterioration. Leveraging physiological signals offers objective and precise insights into a person's state, and their integration in a multimodal framework can further enhance system performance. This study has been submitted to the Second Multimodal Sensing Grand Challenge for Next-Gen Pain Assessment (AI4PAIN). The proposed approach introduces Tiny-BioMoE, a lightweight pretrained embedding model for biosignal analysis. Trained on 4.4 million biosignal image representations and consisting of only 7.3 million parameters, it serves as an effective tool for extracting high-quality embeddings for downstream tasks. Extensive experiments involving electrodermal activity, blood volume pulse, respiratory signals, peripheral oxygen saturation, and their combinations highlight the model's effectiveness across diverse modalities in automatic pain recognition tasks. The model's architecture (code) and weights are available at https://github.com/GkikasStefanos/Tiny-BioMoE.", "date": "2025-07-29", "categories": []}
{"id": "2509.12234", "title": "Flexible Multimodal Neuroimaging Fusion for Alzheimer's Disease Progression Prediction", "abstract": "Alzheimer's disease (AD) is a progressive neurodegenerative disease with high inter-patient variance in rate of cognitive decline. AD progression prediction aims to forecast patient cognitive decline and benefits from incorporating multiple neuroimaging modalities. However, existing multimodal models fail to make accurate predictions when many modalities are missing during inference, as is often the case in clinical settings. To increase multimodal model flexibility under high modality missingness, we introduce PerM-MoE, a novel sparse mixture-of-experts method that uses independent routers for each modality in place of the conventional, single router. Using T1-weighted MRI, FLAIR, amyloid beta PET, and tau PET neuroimaging data from the Alzheimer's Disease Neuroimaging Initiative (ADNI), we evaluate PerM-MoE, state-of-the-art Flex-MoE, and unimodal neuroimaging models on predicting two-year change in Clinical Dementia Rating-Sum of Boxes (CDR-SB) scores under varying levels of modality missingness. PerM-MoE outperforms the state of the art in most variations of modality missingness and demonstrates more effective utility of experts than Flex-MoE.", "date": "2025-09-08", "categories": []}
{"id": "2509.12241", "title": "CNN-BiLSTM for sustainable and non-invasive COVID-19 detection via salivary ATR-FTIR spectroscopy", "abstract": "The COVID-19 pandemic has placed unprecedented strain on healthcare systems and remains a global health concern, especially with the emergence of new variants. Although real-time polymerase chain reaction (RT-PCR) is considered the gold standard for COVID-19 detection, it is expensive, time-consuming, labor-intensive, and sensitive to issues with RNA extraction. In this context, ATR-FTIR spectroscopy analysis of biofluids offers a reagent-free, cost-effective alternative for COVID-19 detection. We propose a novel architecture that combines Convolutional Neural Networks (CNN) with Bidirectional Long Short-Term Memory (BiLSTM) networks, referred to as CNN-BiLSTM, to process spectra generated by ATR-FTIR spectroscopy and diagnose COVID-19 from spectral samples. We compare the performance of this architecture against a standalone CNN and other state-of-the-art machine learning techniques. Experimental results demonstrate that our CNN-BiLSTM model outperforms all other models, achieving an average accuracy and F1-score of 0.80 on a challenging real-world COVID-19 dataset. The addition of the BiLSTM layer to the CNN architecture significantly enhances model performance, making CNN-BiLSTM a more accurate and reliable choice for detecting COVID-19 using ATR-FTIR spectra of non-invasive saliva samples.", "date": "2025-09-10", "categories": []}
{"id": "2509.12259", "title": "Quantum-Inspired Stacked Integrated Concept Graph Model (QISICGM) for Diabetes Risk Prediction", "abstract": "The Quantum-Inspired Stacked Integrated Concept Graph Model (QISICGM) is an innovative machine learning framework that harnesses quantum-inspired techniques to predict diabetes risk with exceptional accuracy and efficiency. Utilizing the PIMA Indians Diabetes dataset augmented with 2,000 synthetic samples to mitigate class imbalance (total: 2,768 samples, 1,949 positives), QISICGM integrates a self-improving concept graph with a stacked ensemble comprising Random Forests (RF), Extra Trees (ET), transformers, convolutional neural networks (CNNs), and feed-forward neural networks (FFNNs). This approach achieves an out-of-fold (OOF) F1 score of 0.8933 and an AUC of 0.8699, outperforming traditional methods. Quantum inspired elements, such as phase feature mapping and neighborhood sequence modeling, enrich feature representations, enabling CPU-efficient inference at 8.5 rows per second. This paper presents a detailed architecture, theoretical foundations, code insights, and performance evaluations, including visualizations from the outputs subfolder. The open-source implementation (v1.0.0) is available at https://github.com/keninayoung/QISICGM, positioning QISICGM as a potential benchmark for AI-assisted clinical triage in diabetes and beyond. Ultimately, this work emphasizes trustworthy AI through calibration, interpretability, and open-source reproducibility.", "date": "2025-09-12", "categories": []}
{"id": "2509.12453", "title": "Two-Stage Decoupling Framework for Variable-Length Glaucoma Prognosis", "abstract": "Glaucoma is one of the leading causes of irreversible blindness worldwide. Glaucoma prognosis is essential for identifying at-risk patients and enabling timely intervention to prevent blindness. Many existing approaches rely on historical sequential data but are constrained by fixed-length inputs, limiting their flexibility. Additionally, traditional glaucoma prognosis methods often employ end-to-end models, which struggle with the limited size of glaucoma datasets. To address these challenges, we propose a Two-Stage Decoupling Framework (TSDF) for variable-length glaucoma prognosis. In the first stage, we employ a feature representation module that leverages self-supervised learning to aggregate multiple glaucoma datasets for training, disregarding differences in their supervisory information. This approach enables datasets of varying sizes to learn better feature representations. In the second stage, we introduce a temporal aggregation module that incorporates an attention-based mechanism to process sequential inputs of varying lengths, ensuring flexible and efficient utilization of all available data. This design significantly enhances model performance while maintaining a compact parameter size. Extensive experiments on two benchmark glaucoma datasets:the Ocular Hypertension Treatment Study (OHTS) and the Glaucoma Real-world Appraisal Progression Ensemble (GRAPE),which differ significantly in scale and clinical settings,demonstrate the effectiveness and robustness of our approach.", "date": "2025-09-15", "categories": []}
{"id": "2509.12600", "title": "A Multimodal Foundation Model to Enhance Generalizability and Data Efficiency for Pan-cancer Prognosis Prediction", "abstract": "Multimodal data provides heterogeneous information for a holistic understanding of the tumor microenvironment. However, existing AI models often struggle to harness the rich information within multimodal data and extract poorly generalizable representations. Here we present MICE (Multimodal data Integration via Collaborative Experts), a multimodal foundation model that effectively integrates pathology images, clinical reports, and genomics data for precise pan-cancer prognosis prediction. Instead of conventional multi-expert modules, MICE employs multiple functionally diverse experts to comprehensively capture both cross-cancer and cancer-specific insights. Leveraging data from 11,799 patients across 30 cancer types, we enhanced MICE's generalizability by coupling contrastive and supervised learning. MICE outperformed both unimodal and state-of-the-art multi-expert-based multimodal models, demonstrating substantial improvements in C-index ranging from 3.8% to 11.2% on internal cohorts and 5.8% to 8.8% on independent cohorts, respectively. Moreover, it exhibited remarkable data efficiency across diverse clinical scenarios. With its enhanced generalizability and data efficiency, MICE establishes an effective and scalable foundation for pan-cancer prognosis prediction, holding strong potential to personalize tailored therapies and improve treatment outcomes.", "date": "2025-09-16", "categories": []}
{"id": "2509.12704", "title": "NORA: A Nephrology-Oriented Representation Learning Approach Towards Chronic Kidney Disease Classification", "abstract": "Chronic Kidney Disease (CKD) affects millions of people worldwide, yet its early detection remains challenging, especially in outpatient settings where laboratory-based renal biomarkers are often unavailable. In this work, we investigate the predictive potential of routinely collected non-renal clinical variables for CKD classification, including sociodemographic factors, comorbid conditions, and urinalysis findings. We introduce the Nephrology-Oriented Representation leArning (NORA) approach, which combines supervised contrastive learning with a nonlinear Random Forest classifier. NORA first derives discriminative patient representations from tabular EHR data, which are then used for downstream CKD classification. We evaluated NORA on a clinic-based EHR dataset from Riverside Nephrology Physicians. Our results demonstrated that NORA improves class separability and overall classification performance, particularly enhancing the F1-score for early-stage CKD. Additionally, we assessed the generalizability of NORA on the UCI CKD dataset, demonstrating its effectiveness for CKD risk stratification across distinct patient cohorts.", "date": "2025-09-16", "categories": []}
{"id": "2509.12772", "title": "MEGAN: Mixture of Experts for Robust Uncertainty Estimation in Endoscopy Videos", "abstract": "Reliable uncertainty quantification (UQ) is essential in medical AI. Evidential Deep Learning (EDL) offers a computationally efficient way to quantify model uncertainty alongside predictions, unlike traditional methods such as Monte Carlo (MC) Dropout and Deep Ensembles (DE). However, all these methods often rely on a single expert's annotations as ground truth for model training, overlooking the inter-rater variability in healthcare. To address this issue, we propose MEGAN, a Multi-Expert Gating Network that aggregates uncertainty estimates and predictions from multiple AI experts via EDL models trained with diverse ground truths and modeling strategies. MEGAN's gating network optimally combines predictions and uncertainties from each EDL model, enhancing overall prediction confidence and calibration. We extensively benchmark MEGAN on endoscopy videos for Ulcerative colitis (UC) disease severity estimation, assessed by visual labeling of Mayo Endoscopic Subscore (MES), where inter-rater variability is prevalent. In large-scale prospective UC clinical trial, MEGAN achieved a 3.5% improvement in F1-score and a 30.5% reduction in Expected Calibration Error (ECE) compared to existing methods. Furthermore, MEGAN facilitated uncertainty-guided sample stratification, reducing the annotation burden and potentially increasing efficiency and consistency in UC trials.", "date": "2025-09-16", "categories": []}
{"id": "2407.21039", "title": "Mapping Patient Trajectories: Understanding and Visualizing Sepsis Prognostic Pathways from Patients Clinical Narratives", "abstract": "In recent years, healthcare professionals are increasingly emphasizing on personalized and evidence-based patient care through the exploration of prognostic pathways. To study this, structured clinical variables from Electronic Health Records (EHRs) data have traditionally been employed by many researchers. Presently, Natural Language Processing models have received great attention in clinical research which expanded the possibilities of using clinical narratives. In this paper, we propose a systematic methodology for developing sepsis prognostic pathways derived from clinical notes, focusing on diverse patient subgroups identified by exploring comorbidities associated with sepsis and generating explanations of these subgroups using SHAP. The extracted prognostic pathways of these subgroups provide valuable insights into the dynamic trajectories of sepsis severity over time. Visualizing these pathways sheds light on the likelihood and direction of disease progression across various contexts and reveals patterns and pivotal factors or biomarkers influencing the transition between sepsis stages, whether toward deterioration or improvement. This empowers healthcare providers to implement more personalized and effective healthcare strategies for individual patients.", "date": "2024-07-20", "categories": []}
{"id": "2509.06516", "title": "QualityFM: a Multimodal Physiological Signal Foundation Model with Self-Distillation for Signal Quality Challenges in Critically Ill Patients", "abstract": "Photoplethysmogram (PPG) and electrocardiogram (ECG) are commonly recorded in intesive care unit (ICU) and operating room (OR). However, the high incidence of poor, incomplete, and inconsistent signal quality, can lead to false alarms or diagnostic inaccuracies. The methods explored so far suffer from limited generalizability, reliance on extensive labeled data, and poor cross-task transferability. To overcome these challenges, we introduce QualityFM, a novel multimodal foundation model for these physiological signals, designed to acquire a general-purpose understanding of signal quality. Our model is pre-trained on an large-scale dataset comprising over 21 million 30-second waveforms and 179,757 hours of data. Our approach involves a dual-track architecture that processes paired physiological signals of differing quality, leveraging a self-distillation strategy where an encoder for high-quality signals is used to guide the training of an encoder for low-quality signals. To efficiently handle long sequential signals and capture essential local quasi-periodic patterns, we integrate a windowed sparse attention mechanism within our Transformer-based model. Furthermore, a composite loss function, which combines direct distillation loss on encoder outputs with indirect reconstruction loss based on power and phase spectra, ensures the preservation of frequency-domain characteristics of the signals. We pre-train three models with varying parameter counts (9.6 M to 319 M) and demonstrate their efficacy and practical value through transfer learning on three distinct clinical tasks: false alarm of ventricular tachycardia detection, the identification of atrial fibrillation and the estimation of arterial blood pressure (ABP) from PPG and ECG signals.", "date": "2025-09-08", "categories": []}
{"id": "2507.07373", "title": "Atherosclerosis through Hierarchical Explainable Neural Network Analysis", "abstract": "In this work, we study the problem pertaining to personalized classification of subclinical atherosclerosis by developing a hierarchical graph neural network framework to leverage two characteristic modalities of a patient: clinical features within the context of the cohort, and molecular data unique to individual patients. Current graph-based methods for disease classification detect patient-specific molecular fingerprints, but lack consistency and comprehension regarding cohort-wide features, which are an essential requirement for understanding pathogenic phenotypes across diverse atherosclerotic trajectories. Furthermore, understanding patient subtypes often considers clinical feature similarity in isolation, without integration of shared pathogenic interdependencies among patients. To address these challenges, we introduce ATHENA: Atherosclerosis Through Hierarchical Explainable Neural Network Analysis, which constructs a novel hierarchical network representation through integrated modality learning; subsequently, it optimizes learned patient-specific molecular fingerprints that reflect individual omics data, enforcing consistency with cohort-wide patterns. With a primary clinical dataset of 391 patients, we demonstrate that this heterogeneous alignment of clinical features with molecular interaction patterns has significantly boosted subclinical atherosclerosis classification performance across various baselines by up to 13% in area under the receiver operating curve (AUC) and 20% in F1 score. Taken together, ATHENA enables mechanistically-informed patient subtype discovery through explainable AI (XAI)-driven subnetwork clustering; this novel integration framework strengthens personalized intervention strategies, thereby improving the prediction of atherosclerotic disease progression and management of their clinical actionable outcomes.", "date": "2025-07-10", "categories": []}
{"id": "2509.09699", "title": "Structured Information Matters: Explainable ICD Coding with Patient-Level Knowledge Graphs", "abstract": "Mapping clinical documents to standardised clinical vocabularies is an important task, as it provides structured data for information retrieval and analysis, which is essential to clinical research, hospital administration and improving patient care. However, manual coding is both difficult and time-consuming, making it impractical at scale. Automated coding can potentially alleviate this burden, improving the availability and accuracy of structured clinical data. The task is difficult to automate, as it requires mapping to high-dimensional and long-tailed target spaces, such as the International Classification of Diseases (ICD). While external knowledge sources have been readily utilised to enhance output code representation, the use of external resources for representing the input documents has been underexplored. In this work, we compute a structured representation of the input documents, making use of document-level knowledge graphs (KGs) that provide a comprehensive structured view of a patient's condition. The resulting knowledge graph efficiently represents the patient-centred input documents with 23\\% of the original text while retaining 90\\% of the information. We assess the effectiveness of this graph for automated ICD-9 coding by integrating it into the state-of-the-art ICD coding architecture PLM-ICD. Our experiments yield improved Macro-F1 scores by up to 3.20\\% on popular benchmarks, while improving training efficiency. We attribute this improvement to different types of entities and relationships in the KG, and demonstrate the improved explainability potential of the approach over the text-only baseline.", "date": "2025-09-04", "categories": []}
{"id": "2509.10369", "title": "Data distribution impacts the performance and generalisability of contrastive learning-based foundation models of electrocardiograms", "abstract": "Contrastive learning is a widely adopted self-supervised pretraining strategy, yet its dependence on cohort composition remains underexplored. We present Contrasting by Patient Augmented Electrocardiograms (CAPE) foundation model and pretrain on four cohorts (n = 5,203,352), from diverse populations across three continents (North America, South America, Asia). We systematically assess how cohort demographics, health status, and population diversity influence the downstream performance for prediction tasks also including two additional cohorts from another continent (Europe). We find that downstream performance depends on the distributional properties of the pretraining cohort, including demographics and health status. Moreover, while pretraining with a multi-centre, demographically diverse cohort improves in-distribution accuracy, it reduces out-of-distribution (OOD) generalisation of our contrastive approach by encoding cohort-specific artifacts. To address this, we propose the In-Distribution Batch (IDB) strategy, which preserves intra-cohort consistency during pretraining and enhances OOD robustness. This work provides important insights for developing clinically fair and generalisable foundation models.", "date": "2025-09-12", "categories": []}
{"id": "2509.10453", "title": "SSL-AD: Spatiotemporal Self-Supervised Learning for Generalizability and Adaptability Across Alzheimer's Prediction Tasks and Datasets", "abstract": "Alzheimer's disease is a progressive, neurodegenerative disorder that causes memory loss and cognitive decline. While there has been extensive research in applying deep learning models to Alzheimer's prediction tasks, these models remain limited by lack of available labeled data, poor generalization across datasets, and inflexibility to varying numbers of input scans and time intervals between scans. In this study, we adapt three state-of-the-art temporal self-supervised learning (SSL) approaches for 3D brain MRI analysis, and add novel extensions designed to handle variable-length inputs and learn robust spatial features. We aggregate four publicly available datasets comprising 3,161 patients for pre-training, and show the performance of our model across multiple Alzheimer's prediction tasks including diagnosis classification, conversion detection, and future conversion prediction. Importantly, our SSL model implemented with temporal order prediction and contrastive learning outperforms supervised learning on six out of seven downstream tasks. It demonstrates adaptability and generalizability across tasks and number of input images with varying time intervals, highlighting its capacity for robust performance across clinical applications. We release our code and model publicly at https://github.com/emilykaczmarek/SSL-AD.", "date": "2025-09-12", "categories": []}
{"id": "2503.15221", "title": "A Vector-Quantized Foundation Model for Patient Behavior Monitoring", "abstract": "Foundation models have achieved remarkable success across various domains, yet their adoption in healthcare remains limited. While significant advances have been made in medical imaging, genetic biomarkers, and time series from electronic health records, the potential of foundation models for patient behavior monitoring through personal digital devices remains underexplored. The data generated by these devices are inherently heterogeneous, multisource, and often exhibit high rates of missing data, posing unique challenges. This paper introduces a novel foundation model based on a modified vector quantized variational autoencoder, specifically designed to process real-world data from smartphones and wearable devices. We leveraged the discrete latent representation of this model to effectively perform two downstream tasks, suicide risk assessment and emotional state prediction, on different held-out clinical cohorts without the need of fine-tuning. We also highlight the existence of a trade-off between discrete and continuous latent structures, suggesting that hybrid models may be optimal for balancing accuracy across various supervised and unsupervised tasks.", "date": "2025-03-19", "categories": []}
{"id": "2508.03762", "title": "Scaling Artificial Intelligence for Prostate Cancer Detection on MRI towards Organized Screening and Primary Diagnosis in a Global, Multiethnic Population (Study Protocol)", "abstract": "In this intercontinental, confirmatory study, we include a retrospective cohort of 22,481 MRI examinations (21,288 patients; 46 cities in 22 countries) to train and externally validate the PI-CAI-2B model, i.e., an efficient, next-generation iteration of the state-of-the-art AI system that was developed for detecting Gleason grade group $\\geq$2 prostate cancer on MRI during the PI-CAI study. Of these examinations, 20,471 cases (19,278 patients; 26 cities in 14 countries) from two EU Horizon projects (ProCAncer-I, COMFORT) and 12 independent centers based in Europe, North America, Asia and Africa, are used for training and internal testing. Additionally, 2010 cases (2010 patients; 20 external cities in 12 countries) from population-based screening (STHLM3-MRI, IP1-PROSTAGRAM trials) and primary diagnostic settings (PRIME trial) based in Europe, North and South Americas, Asia and Australia, are used for external testing. Primary endpoint is the proportion of AI-based assessments in agreement with the standard of care diagnoses (i.e., clinical assessments made by expert uropathologists on histopathology, if available, or at least two expert urogenital radiologists in consensus; with access to patient history and peer consultation) in the detection of Gleason grade group $\\geq$2 prostate cancer within the external testing cohorts. Our statistical analysis plan is prespecified with a hypothesis of diagnostic interchangeability to the standard of care at the PI-RADS $\\geq$3 (primary diagnosis) or $\\geq$4 (screening) cut-off, considering an absolute margin of 0.05 and reader estimates derived from the PI-CAI observer study (62 radiologists reading 400 cases). Secondary measures comprise the area under the receiver operating characteristic curve (AUROC) of the AI system stratified by imaging quality, patient age and patient ethnicity to identify underlying biases (if any).", "date": "2025-08-04", "categories": []}
{"id": "2509.08830", "title": "A Masked Representation Learning to Model Cardiac Functions Using Multiple Physiological Signals", "abstract": "In clinical settings, monitoring hemodynamics is crucial for managing patient prognosis, necessitating the integrated analysis of multiple physiological signals. While recent research has analyzed single signals such as electrocardiography (ECG) or photoplethysmography (PPG), there has yet to be a proposal for an approach that encompasses the complex signal analysis required in actual clinical scenarios. In this study, we introduce the SNUPHY-M (Seoul National University hospital PHYsiological signal Masked representation learning) model extracts physiological features reflecting the electrical, pressure, and fluid characteristics of the cardiac cycle in the process of restoring three masked physiological signals based on self-supervised learning (SSL): ECG, PPG, and arterial blood pressure (ABP) signals. By employing multiple physical characteristics, the model can extract more enriched features only using non-invasive signals. We evaluated the model's performance in clinical downstream tasks such as hypotension, stroke volume, systolic blood pressure, diastolic blood pressure, and age prediction. Our results showed that the SNUPHY-M significantly outperformed supervised or SSL models, especially in prediction tasks using non-invasive signals. To the best of our knowledge, SNUPHY-M is the first model to apply multi-modal SSL to cardiovascular analysis involving ECG, PPG, and ABP signals. This approach effectively supports clinical decision-making and enables precise diagnostics, contributing significantly to the early diagnosis and management of hemodynamics without invasiveness.", "date": "2025-08-26", "categories": []}
{"id": "2412.01306", "title": "Multimodal Medical Disease Classification with LLaMA II", "abstract": "Medical patient data is always multimodal. Images, text, age, gender, histopathological data are only few examples for different modalities in this context. Processing and integrating this multimodal data with deep learning based methods is of utmost interest due to its huge potential for medical procedure such as diagnosis and patient treatment planning. In this work we retrain a multimodal transformer-based model for disease classification. To this end we use the text-image pair dataset from OpenI consisting of 2D chest X-rays associated with clinical reports. Our focus is on fusion methods for merging text and vision information extracted from medical datasets. Different architecture structures with a LLaMA II backbone model are tested. Early fusion of modality specific features creates better results with the best model reaching 97.10% mean AUC than late fusion from a deeper level of the architecture (best model: 96.67% mean AUC). Both outperform former classification models tested on the same multimodal dataset. The newly introduced multimodal architecture can be applied to other multimodal datasets with little effort and can be easily adapted for further research, especially, but not limited to, the field of medical AI.", "date": "2024-12-02", "categories": []}
{"id": "2509.08012", "title": "Validation of a CT-brain analysis tool for measuring global cortical atrophy in older patient cohorts", "abstract": "Quantification of brain atrophy currently requires visual rating scales which are time consuming and automated brain image analysis is warranted. We validated our automated deep learning (DL) tool measuring the Global Cerebral Atrophy (GCA) score against trained human raters, and associations with age and cognitive impairment, in representative older (>65 years) patients. CT-brain scans were obtained from patients in acute medicine (ORCHARD-EPR), acute stroke (OCS studies) and a legacy sample. Scans were divided in a 60/20/20 ratio for training, optimisation and testing. CT-images were assessed by two trained raters (rater-1=864 scans, rater-2=20 scans). Agreement between DL tool-predicted GCA scores (range 0-39) and the visual ratings was evaluated using mean absolute error (MAE) and Cohen's weighted kappa. Among 864 scans (ORCHARD-EPR=578, OCS=200, legacy scans=86), MAE between the DL tool and rater-1 GCA scores was 3.2 overall, 3.1 for ORCHARD-EPR, 3.3 for OCS and 2.6 for the legacy scans and half had DL-predicted GCA error between -2 and 2. Inter-rater agreement was Kappa=0.45 between the DL-tool and rater-1, and 0.41 between the tool and rater- 2 whereas it was lower at 0.28 for rater-1 and rater-2. There was no difference in GCA scores from the DL-tool and the two raters (one-way ANOVA, p=0.35) or in mean GCA scores between the DL-tool and rater-1 (paired t-test, t=-0.43, p=0.66), the tool and rater-2 (t=1.35, p=0.18) or between rater-1 and rater-2 (t=0.99, p=0.32). DL-tool GCA scores correlated with age and cognitive scores (both p<0.001). Our DL CT-brain analysis tool measured GCA score accurately and without user input in real-world scans acquired from older patients. Our tool will enable extraction of standardised quantitative measures of atrophy at scale for use in health data research and will act as proof-of-concept towards a point-of-care clinically approved tool.", "date": "2025-09-08", "categories": []}
{"id": "2509.08679", "title": "Signal Fidelity Index-Aware Calibration for Dementia Predictions Across Heterogeneous Real-World Data", "abstract": "\\textbf{Background:} Machine learning models trained on electronic health records (EHRs) often degrade across healthcare systems due to distributional shift. A fundamental but underexplored factor is diagnostic signal decay: variability in diagnostic quality and consistency across institutions, which affects the reliability of codes used for training and prediction.\n  \\textbf{Objective:} To develop a Signal Fidelity Index (SFI) quantifying diagnostic data quality at the patient level in dementia, and to test SFI-aware calibration for improving model performance across heterogeneous datasets without outcome labels.\n  \\textbf{Methods:} We built a simulation framework generating 2,500 synthetic datasets, each with 1,000 patients and realistic demographics, encounters, and coding patterns based on dementia risk factors. The SFI was derived from six interpretable components: diagnostic specificity, temporal consistency, entropy, contextual concordance, medication alignment, and trajectory stability. SFI-aware calibration applied a multiplicative adjustment, optimized across 50 simulation batches.\n  \\textbf{Results:} At the optimal parameter ($\\alpha$ = 2.0), SFI-aware calibration significantly improved all metrics (p $<$ 0.001). Gains ranged from 10.3\\% for Balanced Accuracy to 32.5\\% for Recall, with notable increases in Precision (31.9\\%) and F1-score (26.1\\%). Performance approached reference standards, with F1-score and Recall within 1\\% and Balanced Accuracy and Detection Rate improved by 52.3\\% and 41.1\\%, respectively.\n  \\textbf{Conclusions:} Diagnostic signal decay is a tractable barrier to model generalization. SFI-aware calibration provides a practical, label-free strategy to enhance prediction across healthcare contexts, particularly for large-scale administrative datasets lacking outcome labels.", "date": "2025-09-10", "categories": []}
{"id": "2509.08756", "title": "Using AI to Optimize Patient Transfer and Resource Utilization During Mass-Casualty Incidents: A Simulation Platform", "abstract": "Mass casualty incidents (MCIs) overwhelm healthcare systems and demand rapid, accurate patient-hospital allocation decisions under extreme pressure. Here, we developed and validated a deep reinforcement learning-based decision-support AI agent to optimize patient transfer decisions during simulated MCIs by balancing patient acuity levels, specialized care requirements, hospital capacities, and transport logistics. To integrate this AI agent, we developed MasTER, a web-accessible command dashboard for MCI management simulations. Through a controlled user study with 30 participants (6 trauma experts and 24 non-experts), we evaluated three interaction approaches with the AI agent (human-only, human-AI collaboration, and AI-only) across 20- and 60-patient MCI scenarios in the Greater Toronto Area. Results demonstrate that increasing AI involvement significantly improves decision quality and consistency. The AI agent outperforms trauma surgeons (p < 0.001) and enables non-experts to achieve expert-level performance when assisted, contrasting sharply with their significantly inferior unassisted performance (p < 0.001). These findings establish the potential for our AI-driven decision support to enhance both MCI preparedness training and real-world emergency response management.", "date": "2025-09-10", "categories": []}
{"id": "2509.07042", "title": "PUUMA (Placental patch and whole-Uterus dual-branch U-Mamba-based Architecture): Functional MRI Prediction of Gestational Age at Birth and Preterm Risk", "abstract": "Preterm birth is a major cause of mortality and lifelong morbidity in childhood. Its complex and multifactorial origins limit the effectiveness of current clinical predictors and impede optimal care. In this study, a dual-branch deep learning architecture (PUUMA) was developed to predict gestational age (GA) at birth using T2* fetal MRI data from 295 pregnancies, encompassing a heterogeneous and imbalanced population. The model integrates both global whole-uterus and local placental features. Its performance was benchmarked against linear regression using cervical length measurements obtained by experienced clinicians from anatomical MRI and other Deep Learning architectures. The GA at birth predictions were assessed using mean absolute error. Accuracy, sensitivity, and specificity were used to assess preterm classification. Both the fully automated MRI-based pipeline and the cervical length regression achieved comparable mean absolute errors (3 weeks) and good sensitivity (0.67) for detecting preterm birth, despite pronounced class imbalance in the dataset. These results provide a proof of concept for automated prediction of GA at birth from functional MRI, and underscore the value of whole-uterus functional imaging in identifying at-risk pregnancies. Additionally, we demonstrate that manual, high-definition cervical length measurements derived from MRI, not currently routine in clinical practice, offer valuable predictive information. Future work will focus on expanding the cohort size and incorporating additional organ-specific imaging to improve generalisability and predictive performance.", "date": "2025-09-08", "categories": []}
{"id": "2509.07723", "title": "BDPM: A Machine Learning-Based Feature Extractor for Parkinson's Disease Classification via Gut Microbiota Analysis", "abstract": "Background: Parkinson's disease remains a major neurodegenerative disorder with high misdiagnosis rates, primarily due to reliance on clinical rating scales. Recent studies have demonstrated a strong association between gut microbiota and Parkinson's disease, suggesting that microbial composition may serve as a promising biomarker. Although deep learning models based ongut microbiota show potential for early prediction, most approaches rely on single classifiers and often overlook inter-strain correlations or temporal dynamics. Therefore, there is an urgent need for more robust feature extraction methods tailored to microbiome data. Methods: We proposed BDPM (A Machine Learning-Based Feature Extractor for Parkinson's Disease Classification via Gut Microbiota Analysis). First, we collected gut microbiota profiles from 39 Parkinson's patients and their healthy spouses to identify differentially abundant taxa. Second, we developed an innovative feature selection framework named RFRE (Random Forest combined with Recursive Feature Elimination), integrating ecological knowledge to enhance biological interpretability. Finally, we designed a hybrid classification model to capture temporal and spatial patterns in microbiome data.", "date": "2025-09-09", "categories": []}
{"id": "2509.06396", "title": "AI-based response assessment and prediction in longitudinal imaging for brain metastases treated with stereotactic radiosurgery", "abstract": "Brain Metastases (BM) are a large contributor to mortality of patients with cancer. They are treated with Stereotactic Radiosurgery (SRS) and monitored with Magnetic Resonance Imaging (MRI) at regular follow-up intervals according to treatment guidelines. Analyzing and quantifying this longitudinal imaging represents an intractable workload for clinicians. As a result, follow-up images are not annotated and merely assessed by observation. Response to treatment in longitudinal imaging is being studied, to better understand growth trajectories and ultimately predict treatment success or toxicity as early as possible. In this study, we implement an automated pipeline to curate a large longitudinal dataset of SRS treatment data, resulting in a cohort of 896 BMs in 177 patients who were monitored for >360 days at approximately two-month intervals at Lausanne University Hospital (CHUV). We use a data-driven clustering to identify characteristic trajectories. In addition, we predict 12 months lesion-level response using classical as well as graph machine learning Graph Machine Learning (GML). Clustering revealed 5 dominant growth trajectories with distinct final response categories. Response prediction reaches up to 0.90 AUC (CI95%=0.88-0.92) using only pre-treatment and first follow-up MRI with gradient boosting. Similarly, robust predictive performance of up to 0.88 AUC (CI95%=0.86-0.90) was obtained using GML, offering more flexibility with a single model for multiple input time-points configurations. Our results suggest potential automation and increased precision for the comprehensive assessment and prediction of BM response to SRS in longitudinal MRI. The proposed pipeline facilitates scalable data curation for the investigation of BM growth patterns, and lays the foundation for clinical decision support systems aiming at optimizing personalized care.", "date": "2025-09-08", "categories": []}
{"id": "2509.06540", "title": "Predicting Fetal Outcomes from Cardiotocography Signals Using a Supervised Variational Autoencoder", "abstract": "Objective: To develop and interpret a supervised variational autoencoder (VAE) model for classifying cardiotocography (CTG) signals based on pregnancy outcomes, addressing interpretability limits of current deep learning approaches. Methods: The OxMat CTG dataset was used to train a VAE on five-minute fetal heart rate (FHR) segments, labeled with postnatal outcomes. The model was optimised for signal reconstruction and outcome prediction, incorporating Kullback-Leibler divergence and total correlation (TC) constraints to structure the latent space. Performance was evaluated using area under the receiver operating characteristic curve (AUROC) and mean squared error (MSE). Interpretability was assessed using coefficient of determination, latent traversals and unsupervised component analyses. Results: The model achieved an AUROC of 0.752 at the segment level and 0.779 at the CTG level, where predicted scores were aggregated. Relaxing TC constraints improved both reconstruction and classification. Latent analysis showed that baseline-related features (e.g., FHR baseline, baseline shift) were well represented and aligned with model scores, while metrics like short- and long-term variability were less strongly encoded. Traversals revealed clear signal changes for baseline features, while other properties were entangled or subtle. Unsupervised decompositions corroborated these patterns. Findings: This work demonstrates that supervised VAEs can achieve competitive fetal outcome prediction while partially encoding clinically meaningful CTG features. The irregular, multi-timescale nature of FHR signals poses challenges for disentangling physiological components, distinguishing CTG from more periodic signals such as ECG. Although full interpretability was not achieved, the model supports clinically useful outcome prediction and provides a basis for future interpretable, generative models.", "date": "2025-09-08", "categories": []}
{"id": "2501.18724", "title": "Large Language Models with Temporal Reasoning for Longitudinal Clinical Summarization and Prediction", "abstract": "Recent advances in large language models (LLMs) have shown potential in clinical text summarization, but their ability to handle long patient trajectories with multi-modal data spread across time remains underexplored. This study systematically evaluates several state-of-the-art open-source LLMs, their Retrieval Augmented Generation (RAG) variants and chain-of-thought (CoT) prompting on long-context clinical summarization and prediction. We examine their ability to synthesize structured and unstructured Electronic Health Records (EHR) data while reasoning over temporal coherence, by re-engineering existing tasks, including discharge summarization and diagnosis prediction from two publicly available EHR datasets. Our results indicate that long context windows improve input integration but do not consistently enhance clinical reasoning, and LLMs are still struggling with temporal progression and rare disease prediction. While RAG shows improvements in hallucination in some cases, it does not fully address these limitations. Our work fills the gap in long clinical text summarization, establishing a foundation for evaluating LLMs with multi-modal data and temporal reasoning.", "date": "2025-01-30", "categories": []}
{"id": "2509.04485", "title": "ASCENDgpt: A Phenotype-Aware Transformer Model for Cardiovascular Risk Prediction from Electronic Health Records", "abstract": "We present ASCENDgpt, a transformer-based model specifically designed for cardiovascular risk prediction from longitudinal electronic health records (EHRs). Our approach introduces a novel phenotype-aware tokenization scheme that maps 47,155 raw ICD codes to 176 clinically meaningful phenotype tokens, achieving 99.6\\% consolidation of diagnosis codes while preserving semantic information. This phenotype mapping contributes to a total vocabulary of 10,442 tokens - a 77.9\\% reduction when compared with using raw ICD codes directly. We pretrain ASCENDgpt on sequences derived from 19402 unique individuals using a masked language modeling objective, then fine-tune for time-to-event prediction of five cardiovascular outcomes: myocardial infarction (MI), stroke, major adverse cardiovascular events (MACE), cardiovascular death, and all-cause mortality. Our model achieves excellent discrimination on the held-out test set with an average C-index of 0.816, demonstrating strong performance across all outcomes (MI: 0.792, stroke: 0.824, MACE: 0.800, cardiovascular death: 0.842, all-cause mortality: 0.824). The phenotype-based approach enables clinically interpretable predictions while maintaining computational efficiency. Our work demonstrates the effectiveness of domain-specific tokenization and pretraining for EHR-based risk prediction tasks.", "date": "2025-08-31", "categories": []}
{"id": "2509.04497", "title": "A Narrative-Driven Computational Framework for Clinician Burnout Surveillance", "abstract": "Clinician burnout poses a substantial threat to patient safety, particularly in high-acuity intensive care units (ICUs). Existing research predominantly relies on retrospective survey tools or broad electronic health record (EHR) metadata, often overlooking the valuable narrative information embedded in clinical notes. In this study, we analyze 10,000 ICU discharge summaries from MIMIC-IV, a publicly available database derived from the electronic health records of Beth Israel Deaconess Medical Center. The dataset encompasses diverse patient data, including vital signs, medical orders, diagnoses, procedures, treatments, and deidentified free-text clinical notes. We introduce a hybrid pipeline that combines BioBERT sentiment embeddings fine-tuned for clinical narratives, a lexical stress lexicon tailored for clinician burnout surveillance, and five-topic latent Dirichlet allocation (LDA) with workload proxies. A provider-level logistic regression classifier achieves a precision of 0.80, a recall of 0.89, and an F1 score of 0.84 on a stratified hold-out set, surpassing metadata-only baselines by greater than or equal to 0.17 F1 score. Specialty-specific analysis indicates elevated burnout risk among providers in Radiology, Psychiatry, and Neurology. Our findings demonstrate that ICU clinical narratives contain actionable signals for proactive well-being monitoring.", "date": "2025-09-01", "categories": []}
{"id": "2509.04969", "title": "Classification of kinetic-related injury in hospital triage data using NLP", "abstract": "Triage notes, created at the start of a patient's hospital visit, contain a wealth of information that can help medical staff and researchers understand Emergency Department patient epidemiology and the degree of time-dependent illness or injury. Unfortunately, applying modern Natural Language Processing and Machine Learning techniques to analyse triage data faces some challenges: Firstly, hospital data contains highly sensitive information that is subject to privacy regulation thus need to be analysed on site; Secondly, most hospitals and medical facilities lack the necessary hardware to fine-tune a Large Language Model (LLM), much less training one from scratch; Lastly, to identify the records of interest, expert inputs are needed to manually label the datasets, which can be time-consuming and costly. We present in this paper a pipeline that enables the classification of triage data using LLM and limited compute resources. We first fine-tuned a pre-trained LLM with a classifier using a small (2k) open sourced dataset on a GPU; and then further fine-tuned the model with a hospital specific dataset of 1000 samples on a CPU. We demonstrated that by carefully curating the datasets and leveraging existing models and open sourced data, we can successfully classify triage data with limited compute resources.", "date": "2025-09-05", "categories": []}
{"id": "2501.14701", "title": "An Unsupervised Natural Language Processing Pipeline for Assessing Referral Appropriateness", "abstract": "Objective: Assessing the appropriateness of diagnostic referrals is critical for improving healthcare efficiency and reducing unnecessary procedures. However, this task becomes challenging when referral reasons are recorded only as free text rather than structured codes, like in the Italian NHS. To address this gap, we propose a fully unsupervised Natural Language Processing (NLP) pipeline capable of extracting and evaluating referral reasons without relying on labelled datasets.\n  Methods: Our pipeline leverages Transformer-based embeddings pre-trained on Italian medical texts to cluster referral reasons and assess their alignment with appropriateness guidelines. It operates in an unsupervised setting and is designed to generalize across different examination types. We analyzed two complete regional datasets from the Lombardy Region (Italy), covering all referrals between 2019 and 2021 for venous echocolordoppler of the lower limbs (ECD;n=496,971; development) and flexible endoscope colonoscopy (FEC; n=407,949; testing only). For both, a random sample of 1,000 referrals was manually annotated to measure performance.\n  Results: The pipeline achieved high performance in identifying referral reasons (Prec=92.43% (ECD), 93.59% (FEC); Rec=83.28% (ECD), 92.70% (FEC)) and appropriateness (Prec=93.58% (ECD), 94.66% (FEC); Rec=91.52% (ECD), 93.96% (FEC)). At the regional level, the analysis identified relevant inappropriate referral groups and variation across contexts, findings that informed a new Lombardy Region resolution to reinforce guideline adherence.\n  Conclusions: This study presents a robust, scalable, unsupervised NLP pipeline for assessing referral appropriateness in large, real-world datasets. It demonstrates how such data can be effectively leveraged, providing public health authorities with a deployable AI tool to monitor practices and support evidence-based policy.", "date": "2025-01-24", "categories": []}
{"id": "2509.03884", "title": "Peptidomic-Based Prediction Model for Coronary Heart Disease Using a Multilayer Perceptron Neural Network", "abstract": "Coronary heart disease (CHD) is a leading cause of death worldwide and contributes significantly to annual healthcare expenditures. To develop a non-invasive diagnostic approach, we designed a model based on a multilayer perceptron (MLP) neural network, trained on 50 key urinary peptide biomarkers selected via genetic algorithms. Treatment and control groups, each comprising 345 individuals, were balanced using the Synthetic Minority Over-sampling Technique (SMOTE). The neural network was trained using a stratified validation strategy. Using a network with three hidden layers of 60 neurons each and an output layer of two neurons, the model achieved a precision, sensitivity, and specificity of 95.67 percent, with an F1-score of 0.9565. The area under the ROC curve (AUC) reached 0.9748 for both classes, while the Matthews correlation coefficient (MCC) and Cohen's kappa coefficient were 0.9134 and 0.9131, respectively, demonstrating its reliability in detecting CHD. These results indicate that the model provides a highly accurate and robust non-invasive diagnostic tool for coronary heart disease.", "date": "2025-09-04", "categories": []}
{"id": "2509.04053", "title": "On Aligning Prediction Models with Clinical Experiential Learning: A Prostate Cancer Case Study", "abstract": "Over the past decade, the use of machine learning (ML) models in healthcare applications has rapidly increased. Despite high performance, modern ML models do not always capture patterns the end user requires. For example, a model may predict a non-monotonically decreasing relationship between cancer stage and survival, keeping all other features fixed. In this paper, we present a reproducible framework for investigating this misalignment between model behavior and clinical experiential learning, focusing on the effects of underspecification of modern ML pipelines. In a prostate cancer outcome prediction case study, we first identify and address these inconsistencies by incorporating clinical knowledge, collected by a survey, via constraints into the ML model, and subsequently analyze the impact on model performance and behavior across degrees of underspecification. The approach shows that aligning the ML model with clinical experiential learning is possible without compromising performance. Motivated by recent literature in generative AI, we further examine the feasibility of a feedback-driven alignment approach in non-generative AI clinical risk prediction models through a randomized experiment with clinicians. Our findings illustrate that, by eliciting clinicians' model preferences using our proposed methodology, the larger the difference in how the constrained and unconstrained models make predictions for a patient, the more apparent the difference is in clinical interpretation.", "date": "2025-09-04", "categories": []}
{"id": "2509.02746", "title": "Mentality: A Mamba-based Approach towards Foundation Models for EEG", "abstract": "This work explores the potential of foundation models, specifically a Mamba-based selective state space model, for enhancing EEG analysis in neurological disorder diagnosis. EEG, crucial for diagnosing conditions like epilepsy, presents significant challenges due to its noisy, high-dimensional, and nonlinear nature. Traditional machine learning methods have made advances in automating EEG analysis but often fail to capture its complex spatio-temporal dynamics. Recent advances in deep learning, particularly in sequence modeling, offer new avenues for creating more generalized and expressive models capable of handling such complexities. By training a Mamba-based model on a large dataset containing seizure and non-seizure EEG recordings through a self-supervised reconstruction task followed by a seizure detection task, we demonstrate the model's effectiveness, achieving an AUROC of 0.72 on a held-out test set. This approach marks a significant step toward developing large-scale, clinically applicable foundation models for EEG data analysis.", "date": "2025-09-02", "categories": []}
{"id": "2509.03393", "title": "Exploring a Graph-based Approach to Offline Reinforcement Learning for Sepsis Treatment", "abstract": "Sepsis is a serious, life-threatening condition. When treating sepsis, it is challenging to determine the correct amount of intravenous fluids and vasopressors for a given patient. While automated reinforcement learning (RL)-based methods have been used to support these decisions with promising results, previous studies have relied on relational data. Given the complexity of modern healthcare data, representing data as a graph may provide a more natural and effective approach. This study models patient data from the well-known MIMIC-III dataset as a heterogeneous graph that evolves over time. Subsequently, we explore two Graph Neural Network architectures - GraphSAGE and GATv2 - for learning patient state representations, adopting the approach of decoupling representation learning from policy learning. The encoders are trained to produce latent state representations, jointly with decoders that predict the next patient state. These representations are then used for policy learning with the dBCQ algorithm. The results of our experimental evaluation confirm the potential of a graph-based approach, while highlighting the complexity of representation learning in this domain.", "date": "2025-09-03", "categories": []}
{"id": "2508.18688", "title": "End to End Autoencoder MLP Framework for Sepsis Prediction", "abstract": "Sepsis is a life threatening condition that requires timely detection in intensive care settings. Traditional machine learning approaches, including Naive Bayes, Support Vector Machine (SVM), Random Forest, and XGBoost, often rely on manual feature engineering and struggle with irregular, incomplete time-series data commonly present in electronic health records. We introduce an end-to-end deep learning framework integrating an unsupervised autoencoder for automatic feature extraction with a multilayer perceptron classifier for binary sepsis risk prediction. To enhance clinical applicability, we implement a customized down sampling strategy that extracts high information density segments during training and a non-overlapping dynamic sliding window mechanism for real-time inference. Preprocessed time series data are represented as fixed dimension vectors with explicit missingness indicators, mitigating bias and noise. We validate our approach on three ICU cohorts. Our end-to-end model achieves accuracies of 74.6 percent, 80.6 percent, and 93.5 percent, respectively, consistently outperforming traditional machine learning baselines. These results demonstrate the framework's superior robustness, generalizability, and clinical utility for early sepsis detection across heterogeneous ICU environments.", "date": "2025-08-26", "categories": []}
{"id": "2509.00026", "title": "Diagnosing Psychiatric Patients: Can Large Language and Machine Learning Models Perform Effectively in Emergency Cases?", "abstract": "Mental disorders are clinically significant patterns of behavior that are associated with stress and/or impairment in social, occupational, or family activities. People suffering from such disorders are often misjudged and poorly diagnosed due to a lack of visible symptoms compared to other health complications. During emergency situations, identifying psychiatric issues is that's why challenging but highly required to save patients. In this paper, we have conducted research on how traditional machine learning and large language models (LLM) can assess these psychiatric patients based on their behavioral patterns to provide a diagnostic assessment. Data from emergency psychiatric patients were collected from a rescue station in Germany. Various machine learning models, including Llama 3.1, were used with rescue patient data to assess if the predictive capabilities of the models can serve as an efficient tool for identifying patients with unhealthy mental disorders, especially in rescue cases.", "date": "2025-08-20", "categories": []}
{"id": "2509.00900", "title": "Towards Early Detection: AI-Based Five-Year Forecasting of Breast Cancer Risk Using Digital Breast Tomosynthesis Imaging", "abstract": "As early detection of breast cancer strongly favors successful therapeutic outcomes, there is major commercial interest in optimizing breast cancer screening. However, current risk prediction models achieve modest performance and do not incorporate digital breast tomosynthesis (DBT) imaging, which was FDA-approved for breast cancer screening in 2011. To address this unmet need, we present a deep learning (DL)-based framework capable of forecasting an individual patient's 5-year breast cancer risk directly from screening DBT. Using an unparalleled dataset of 161,753 DBT examinations from 50,590 patients, we trained a risk predictor based on features extracted using the Meta AI DINOv2 image encoder, combined with a cumulative hazard layer, to assess a patient's likelihood of developing breast cancer over five years. On a held-out test set, our best-performing model achieved an AUROC of 0.80 on predictions within 5 years. These findings reveal the high potential of DBT-based DL approaches to complement traditional risk assessment tools, and serve as a promising basis for additional investigation to validate and enhance our work.", "date": "2025-08-31", "categories": []}
{"id": "2509.01885", "title": "Extracting OPQRST in Electronic Health Records using Large Language Models with Reasoning", "abstract": "The extraction of critical patient information from Electronic Health Records (EHRs) poses significant challenges due to the complexity and unstructured nature of the data. Traditional machine learning approaches often fail to capture pertinent details efficiently, making it difficult for clinicians to utilize these tools effectively in patient care. This paper introduces a novel approach to extracting the OPQRST assessment from EHRs by leveraging the capabilities of Large Language Models (LLMs). We propose to reframe the task from sequence labeling to text generation, enabling the models to provide reasoning steps that mimic a physician's cognitive processes. This approach enhances interpretability and adapts to the limited availability of labeled data in healthcare settings. Furthermore, we address the challenge of evaluating the accuracy of machine-generated text in clinical contexts by proposing a modification to traditional Named Entity Recognition (NER) metrics. This includes the integration of semantic similarity measures, such as the BERT Score, to assess the alignment between generated text and the clinical intent of the original records. Our contributions demonstrate a significant advancement in the use of AI in healthcare, offering a scalable solution that improves the accuracy and usability of information extraction from EHRs, thereby aiding clinicians in making more informed decisions and enhancing patient care outcomes.", "date": "2025-09-02", "categories": []}
{"id": "2508.21793", "title": "MoE-Health: A Mixture of Experts Framework for Robust Multimodal Healthcare Prediction", "abstract": "Healthcare systems generate diverse multimodal data, including Electronic Health Records (EHR), clinical notes, and medical images. Effectively leveraging this data for clinical prediction is challenging, particularly as real-world samples often present with varied or incomplete modalities. Existing approaches typically require complete modality data or rely on manual selection strategies, limiting their applicability in real-world clinical settings where data availability varies across patients and institutions. To address these limitations, we propose MoE-Health, a novel Mixture of Experts framework designed for robust multimodal fusion in healthcare prediction. MoE-Health architecture is specifically developed to handle samples with differing modalities and improve performance on critical clinical tasks. By leveraging specialized expert networks and a dynamic gating mechanism, our approach dynamically selects and combines relevant experts based on available data modalities, enabling flexible adaptation to varying data availability scenarios. We evaluate MoE-Health on the MIMIC-IV dataset across three critical clinical prediction tasks: in-hospital mortality prediction, long length of stay, and hospital readmission prediction. Experimental results demonstrate that MoE-Health achieves superior performance compared to existing multimodal fusion methods while maintaining robustness across different modality availability patterns. The framework effectively integrates multimodal information, offering improved predictive performance and robustness in handling heterogeneous and incomplete healthcare data, making it particularly suitable for deployment in diverse healthcare environments with heterogeneous data availability.", "date": "2025-08-29", "categories": []}
{"id": "2508.21803", "title": "Automated Clinical Problem Detection from SOAP Notes using a Collaborative Multi-Agent LLM Architecture", "abstract": "Accurate interpretation of clinical narratives is critical for patient care, but the complexity of these notes makes automation challenging. While Large Language Models (LLMs) show promise, single-model approaches can lack the robustness required for high-stakes clinical tasks. We introduce a collaborative multi-agent system (MAS) that models a clinical consultation team to address this gap. The system is tasked with identifying clinical problems by analyzing only the Subjective (S) and Objective (O) sections of SOAP notes, simulating the diagnostic reasoning process of synthesizing raw data into an assessment. A Manager agent orchestrates a dynamically assigned team of specialist agents who engage in a hierarchical, iterative debate to reach a consensus. We evaluated our MAS against a single-agent baseline on a curated dataset of 420 MIMIC-III notes. The dynamic multi-agent configuration demonstrated consistently improved performance in identifying congestive heart failure, acute kidney injury, and sepsis. Qualitative analysis of the agent debates reveals that this structure effectively surfaces and weighs conflicting evidence, though it can occasionally be susceptible to groupthink. By modeling a clinical team's reasoning process, our system offers a promising path toward more accurate, robust, and interpretable clinical decision support tools.", "date": "2025-08-29", "categories": []}
{"id": "2405.20825", "title": "Prediction of Local Failure after Stereotactic Radiotherapy in Melanoma Brain Metastases Using Ensemble Learning on Clinical, Dosimetric, and Radiomic Data", "abstract": "Background: This study aimed to predict lesion-specific outcomes after stereotactic radiotherapy (SRT) in patients with brain metastases from malignant melanoma (MBM), using clinical, dosimetric, and pretherapeutic MRI data. Methods: In this multicenter retrospective study, 517 MBM from 130 patients treated with single-fraction or hypofractionated SRT at three centers were analyzed. From contrast-enhanced T1-weighted MRI, 1576 radiomic features (RF) were extracted per lesion - 788 from the gross tumor volume (GTV) and 788 from a 3 mm peritumoral margin. Clinical, dosimetric and RF data from one center were used for feature selection and model development via nested cross-validation employing an ensemble learning approach; external validation used data from the other two centers. Results: Local failure occurred in 72/517 lesions (13.9%). Predictive models based on clinical data, RF, or a combination of both achieved c-indices of 0.60 +/- 0.15, 0.65 +/- 0.11, and 0.65 +/- 0.12, respectively. RF-based models outperformed the clinical models; dosimetric data alone were not predictive. Most predictive RF originated from the peritumoral margin (92%) versus GTV (76%). On the first external dataset, all models performed similarly (c-index: 0.60-0.63), but generalization was poor on the second (c-index < 0.50), likely due to differences in patient characteristics and imaging protocols. Conclusions: Pretherapeutic MRI features, particularly from the peritumoral region, show promise for predicting lesion-specific outcomes in MBM after SRT. Their consistent contribution suggests biologically relevant information that may support individualized treatment planning. Combined with clinical data, these markers offer prognostic insight, though generalizability remains limited by data heterogeneity.", "date": "2024-05-31", "categories": []}
{"id": "2508.01970", "title": "Improving Hospital Risk Prediction with Knowledge-Augmented Multimodal EHR Modeling", "abstract": "Accurate prediction of clinical outcomes using Electronic Health Records (EHRs) is critical for early intervention, efficient resource allocation, and improved patient care. EHRs contain multimodal data, including both structured data and unstructured clinical notes that provide rich, context-specific information. In this work, we introduce a unified framework that seamlessly integrates these diverse modalities, leveraging all relevant available information through a two-stage architecture for clinical risk prediction. In the first stage, a fine-tuned Large Language Model (LLM) extracts crucial, task-relevant information from clinical notes, which is enhanced by graph-based retrieval of external domain knowledge from sources such as a medical corpus like PubMed, grounding the LLM's understanding. The second stage combines both unstructured representations and features derived from the structured data to generate the final predictions. This approach supports a wide range of clinical tasks. Here, we demonstrate its effectiveness on 30-day readmission and in-hospital mortality prediction. Experimental results show that our framework achieves strong performance, with AUC scores of $0.84$ and $0.92$, respectively, despite these tasks involving severely imbalanced datasets, with positive rates ranging from approximately $4\\%$ to $13\\%$. Moreover, it outperforms all existing baselines and clinical practices, including established risk scoring systems. To the best of our knowledge, this is one of the first frameworks for healthcare prediction which enhances the power of an LLM-based graph-guided knowledge retrieval method by combining it with structured data for improved clinical outcome prediction.", "date": "2025-08-04", "categories": []}
{"id": "2508.20460", "title": "Prediction of mortality and resource utilization in critical care: a deep learning approach using multimodal electronic health records with natural language processing techniques", "abstract": "Background Predicting mortality and resource utilization from electronic health records (EHRs) is challenging yet crucial for optimizing patient outcomes and managing costs in intensive care unit (ICU). Existing approaches predominantly focus on structured EHRs, often ignoring the valuable clinical insights in free-text notes. Additionally, the potential of textual information within structured data is not fully leveraged. This study aimed to introduce and assess a deep learning framework using natural language processing techniques that integrates multimodal EHRs to predict mortality and resource utilization in critical care settings. Methods Utilizing two real-world EHR datasets, we developed and evaluated our model on three clinical tasks with leading existing methods. We also performed an ablation study on three key components in our framework: medical prompts, free-texts, and pre-trained sentence encoder. Furthermore, we assessed the model's robustness against the corruption in structured EHRs. Results Our experiments on two real-world datasets across three clinical tasks showed that our proposed model improved performance metrics by 1.6\\%/0.8\\% on BACC/AUROC for mortality prediction, 0.5%/2.2% on RMSE/MAE for LOS prediction, 10.9%/11.0% on RMSE/MAE for surgical duration estimation compared to the best existing methods. It consistently demonstrated superior performance compared to other baselines across three tasks at different corruption rates. Conclusions The proposed framework is an effective and accurate deep learning approach for predicting mortality and resource utilization in critical care. The study also highlights the success of using prompt learning with a transformer encoder in analyzing multimodal EHRs. Importantly, the model showed strong resilience to data corruption within structured data, especially at high corruption levels.", "date": "2025-08-28", "categories": []}
{"id": "2508.11063", "title": "Data-Driven Abdominal Phenotypes of Type 2 Diabetes in Lean, Overweight, and Obese Cohorts", "abstract": "Purpose: Although elevated BMI is a well-known risk factor for type 2 diabetes, the disease's presence in some lean adults and absence in others with obesity suggests that detailed body composition may uncover abdominal phenotypes of type 2 diabetes. With AI, we can now extract detailed measurements of size, shape, and fat content from abdominal structures in 3D clinical imaging at scale. This creates an opportunity to empirically define body composition signatures linked to type 2 diabetes risk and protection using large-scale clinical data. Approach: To uncover BMI-specific diabetic abdominal patterns from clinical CT, we applied our design four times: once on the full cohort (n = 1,728) and once on lean (n = 497), overweight (n = 611), and obese (n = 620) subgroups separately. Briefly, our experimental design transforms abdominal scans into collections of explainable measurements through segmentation, classifies type 2 diabetes through a cross-validated random forest, measures how features contribute to model-estimated risk or protection through SHAP analysis, groups scans by shared model decision patterns (clustering from SHAP) and links back to anatomical differences (classification). Results: The random-forests achieved mean AUCs of 0.72-0.74. There were shared type 2 diabetes signatures in each group; fatty skeletal muscle, older age, greater visceral and subcutaneous fat, and a smaller or fat-laden pancreas. Univariate logistic regression confirmed the direction of 14-18 of the top 20 predictors within each subgroup (p < 0.05). Conclusions: Our findings suggest that abdominal drivers of type 2 diabetes may be consistent across weight classes.", "date": "2025-08-14", "categories": []}
{"id": "2508.17631", "title": "ControlEchoSynth: Boosting Ejection Fraction Estimation Models via Controlled Video Diffusion", "abstract": "Synthetic data generation represents a significant advancement in boosting the performance of machine learning (ML) models, particularly in fields where data acquisition is challenging, such as echocardiography. The acquisition and labeling of echocardiograms (echo) for heart assessment, crucial in point-of-care ultrasound (POCUS) settings, often encounter limitations due to the restricted number of echo views available, typically captured by operators with varying levels of experience. This study proposes a novel approach for enhancing clinical diagnosis accuracy by synthetically generating echo views. These views are conditioned on existing, real views of the heart, focusing specifically on the estimation of ejection fraction (EF), a critical parameter traditionally measured from biplane apical views. By integrating a conditional generative model, we demonstrate an improvement in EF estimation accuracy, providing a comparative analysis with traditional methods. Preliminary results indicate that our synthetic echoes, when used to augment existing datasets, not only enhance EF estimation but also show potential in advancing the development of more robust, accurate, and clinically relevant ML models. This approach is anticipated to catalyze further research in synthetic data applications, paving the way for innovative solutions in medical imaging diagnostics.", "date": "2025-08-25", "categories": []}
{"id": "2508.19322", "title": "AT-CXR: Uncertainty-Aware Agentic Triage for Chest X-rays", "abstract": "Agentic AI is advancing rapidly, yet truly autonomous medical-imaging triage, where a system decides when to stop, escalate, or defer under real constraints, remains relatively underexplored. To address this gap, we introduce AT-CXR, an uncertainty-aware agent for chest X-rays. The system estimates per-case confidence and distributional fit, then follows a stepwise policy to issue an automated decision or abstain with a suggested label for human intervention. We evaluate two router designs that share the same inputs and actions: a deterministic rule-based router and an LLM-decided router. Across five-fold evaluation on a balanced subset of NIH ChestX-ray14 dataset, both variants outperform strong zero-shot vision-language models and state-of-the-art supervised classifiers, achieving higher full-coverage accuracy and superior selective-prediction performance, evidenced by a lower area under the risk-coverage curve (AURC) and a lower error rate at high coverage, while operating with lower latency that meets practical clinical constraints. The two routers provide complementary operating points, enabling deployments to prioritize maximal throughput or maximal accuracy. Our code is available at https://github.com/XLIAaron/uncertainty-aware-cxr-agent.", "date": "2025-08-26", "categories": []}
{"id": "2508.19349", "title": "EffNetViTLoRA: An Efficient Hybrid Deep Learning Approach for Alzheimer's Disease Diagnosis", "abstract": "Alzheimer's disease (AD) is one of the most prevalent neurodegenerative disorders worldwide. As it progresses, it leads to the deterioration of cognitive functions. Since AD is irreversible, early diagnosis is crucial for managing its progression. Mild Cognitive Impairment (MCI) represents an intermediate stage between Cognitively Normal (CN) individuals and those with AD, and is considered a transitional phase from normal cognition to Alzheimer's disease. Diagnosing MCI is particularly challenging due to the subtle differences between adjacent diagnostic categories. In this study, we propose EffNetViTLoRA, a generalized end-to-end model for AD diagnosis using the whole Alzheimer's Disease Neuroimaging Initiative (ADNI) Magnetic Resonance Imaging (MRI) dataset. Our model integrates a Convolutional Neural Network (CNN) with a Vision Transformer (ViT) to capture both local and global features from MRI images. Unlike previous studies that rely on limited subsets of data, our approach is trained on the full T1-weighted MRI dataset from ADNI, resulting in a more robust and unbiased model. This comprehensive methodology enhances the model's clinical reliability. Furthermore, fine-tuning large pretrained models often yields suboptimal results when source and target dataset domains differ. To address this, we incorporate Low-Rank Adaptation (LoRA) to effectively adapt the pretrained ViT model to our target domain. This method enables efficient knowledge transfer and reduces the risk of overfitting. Our model achieves a classification accuracy of 92.52% and an F1-score of 92.76% across three diagnostic categories: AD, MCI, and CN for full ADNI dataset.", "date": "2025-08-26", "categories": []}
{"id": "2508.19361", "title": "Atrial Fibrillation Prediction Using a Lightweight Temporal Convolutional and Selective State Space Architecture", "abstract": "Atrial fibrillation (AF) is the most common arrhythmia, increasing the risk of stroke, heart failure, and other cardiovascular complications. While AF detection algorithms perform well in identifying persistent AF, early-stage progression, such as paroxysmal AF (PAF), often goes undetected due to its sudden onset and short duration. However, undetected PAF can progress into sustained AF, increasing the risk of mortality and severe complications. Early prediction of AF offers an opportunity to reduce disease progression through preventive therapies, such as catecholamine-sparing agents or beta-blockers. In this study, we propose a lightweight deep learning model using only RR Intervals (RRIs), combining a Temporal Convolutional Network (TCN) for positional encoding with Mamba, a selective state space model, to enable early prediction of AF through efficient parallel sequence modeling. In subject-wise testing results, our model achieved a sensitivity of 0.908, specificity of 0.933, F1-score of 0.930, AUROC of 0.972, and AUPRC of 0.932. Additionally, our method demonstrates high computational efficiency, with only 73.5 thousand parameters and 38.3 MFLOPs, outperforming traditional Convolutional Neural Network-Recurrent Neural Network (CNN-RNN) approaches in both accuracy and model compactness. Notably, the model can predict AF up to two hours in advance using just 30 minutes of input data, providing enough lead time for preventive interventions.", "date": "2025-08-26", "categories": []}
{"id": "2410.03705", "title": "Gradient Boosting Decision Trees on Medical Diagnosis over Tabular Data", "abstract": "Medical diagnosis is a crucial task in the medical field, in terms of providing accurate classification and respective treatments. Having near-precise decisions based on correct diagnosis can affect a patient's life itself, and may extremely result in a catastrophe if not classified correctly. Several traditional machine learning (ML), such as support vector machines (SVMs) and logistic regression, and state-of-the-art tabular deep learning (DL) methods, including TabNet and TabTransformer, have been proposed and used over tabular medical datasets. Additionally, due to the superior performances, lower computational costs, and easier optimization over different tasks, ensemble methods have been used in the field more recently. They offer a powerful alternative in terms of providing successful medical decision-making processes in several diagnosis tasks. In this study, we investigated the benefits of ensemble methods, especially the Gradient Boosting Decision Tree (GBDT) algorithms in medical classification tasks over tabular data, focusing on XGBoost, CatBoost, and LightGBM. The experiments demonstrate that GBDT methods outperform traditional ML and deep neural network architectures and have the highest average rank over several benchmark tabular medical diagnosis datasets. Furthermore, they require much less computational power compared to DL models, creating the optimal methodology in terms of high performance and lower complexity.", "date": "2024-09-25", "categories": []}
{"id": "2507.16065", "title": "Handcrafted vs. Deep Radiomics vs. Fusion vs. Deep Learning: A Comprehensive Review of Machine Learning -Based Cancer Outcome Prediction in PET and SPECT Imaging", "abstract": "Machine learning (ML), including deep learning (DL) and radiomics-based methods, is increasingly used for cancer outcome prediction with PET and SPECT imaging. However, the comparative performance of handcrafted radiomics features (HRF), deep radiomics features (DRF), DL models, and hybrid fusion approaches remains inconsistent across clinical applications. This systematic review analyzed 226 studies published from 2020 to 2025 that applied ML to PET or SPECT imaging for outcome prediction. Each study was evaluated using a 59-item framework covering dataset construction, feature extraction, validation methods, interpretability, and risk of bias. We extracted key details including model type, cancer site, imaging modality, and performance metrics such as accuracy and area under the curve (AUC). PET-based studies (95%) generally outperformed those using SPECT, likely due to higher spatial resolution and sensitivity. DRF models achieved the highest mean accuracy (0.862), while fusion models yielded the highest AUC (0.861). ANOVA confirmed significant differences in performance (accuracy: p=0.0006, AUC: p=0.0027). Common limitations included inadequate handling of class imbalance (59%), missing data (29%), and low population diversity (19%). Only 48% of studies adhered to IBSI standards. These findings highlight the need for standardized pipelines, improved data quality, and explainable AI to support clinical integration.", "date": "2025-07-21", "categories": []}
{"id": "2508.14940", "title": "Cohort-Aware Agents for Individualized Lung Cancer Risk Prediction Using a Retrieval-Augmented Model Selection Framework", "abstract": "Accurate lung cancer risk prediction remains challenging due to substantial variability across patient populations and clinical settings -- no single model performs best for all cohorts. To address this, we propose a personalized lung cancer risk prediction agent that dynamically selects the most appropriate model for each patient by combining cohort-specific knowledge with modern retrieval and reasoning techniques. Given a patient's CT scan and structured metadata -- including demographic, clinical, and nodule-level features -- the agent first performs cohort retrieval using FAISS-based similarity search across nine diverse real-world cohorts to identify the most relevant patient population from a multi-institutional database. Second, a Large Language Model (LLM) is prompted with the retrieved cohort and its associated performance metrics to recommend the optimal prediction algorithm from a pool of eight representative models, including classical linear risk models (e.g., Mayo, Brock), temporally-aware models (e.g., TD-VIT, DLSTM), and multi-modal computer vision-based approaches (e.g., Liao, Sybil, DLS, DLI). This two-stage agent pipeline -- retrieval via FAISS and reasoning via LLM -- enables dynamic, cohort-aware risk prediction personalized to each patient's profile. Building on this architecture, the agent supports flexible and cohort-driven model selection across diverse clinical populations, offering a practical path toward individualized risk assessment in real-world lung cancer screening.", "date": "2025-08-20", "categories": []}
{"id": "2508.18301", "title": "A Fast and Minimal System to Identify Depression Using Smartphones: Explainable Machine Learning-Based Approach", "abstract": "Background: Existing robust, pervasive device-based systems developed in recent years to detect depression require data collected over a long period and may not be effective in cases where early detection is crucial.\n  Objective: Our main objective was to develop a minimalistic system to identify depression using data retrieved in the fastest possible time.\n  Methods: We developed a fast tool that retrieves the past 7 days' app usage data in 1 second (mean 0.31, SD 1.10 seconds). A total of 100 students from Bangladesh participated in our study, and our tool collected their app usage data. To identify depressed and nondepressed students, we developed a diverse set of ML models. We selected important features using the stable approach, along with 3 main types of feature selection (FS) approaches.\n  Results: Leveraging only the app usage data retrieved in 1 second, our light gradient boosting machine model used the important features selected by the stable FS approach and correctly identified 82.4% (n=42) of depressed students (precision=75%, F1-score=78.5%). Moreover, after comprehensive exploration, we presented a parsimonious stacking model where around 5 features selected by the all-relevant FS approach Boruta were used in each iteration of validation and showed a maximum precision of 77.4% (balanced accuracy=77.9%). A SHAP analysis of our best models presented behavioral markers that were related to depression.\n  Conclusions: Due to our system's fast and minimalistic nature, it may make a worthwhile contribution to identifying depression in underdeveloped and developing regions. In addition, our detailed discussion about the implication of our findings can facilitate the development of less resource-intensive systems to better understand students who are depressed.", "date": "2025-08-22", "categories": []}
{"id": "2508.18313", "title": "ProtoEHR: Hierarchical Prototype Learning for EHR-based Healthcare Predictions", "abstract": "Digital healthcare systems have enabled the collection of mass healthcare data in electronic healthcare records (EHRs), allowing artificial intelligence solutions for various healthcare prediction tasks. However, existing studies often focus on isolated components of EHR data, limiting their predictive performance and interpretability. To address this gap, we propose ProtoEHR, an interpretable hierarchical prototype learning framework that fully exploits the rich, multi-level structure of EHR data to enhance healthcare predictions. More specifically, ProtoEHR models relationships within and across three hierarchical levels of EHRs: medical codes, hospital visits, and patients. We first leverage large language models to extract semantic relationships among medical codes and construct a medical knowledge graph as the knowledge source. Building on this, we design a hierarchical representation learning framework that captures contextualized representations across three levels, while incorporating prototype information within each level to capture intrinsic similarities and improve generalization. To perform a comprehensive assessment, we evaluate ProtoEHR in two public datasets on five clinically significant tasks, including prediction of mortality, prediction of readmission, prediction of length of stay, drug recommendation, and prediction of phenotype. The results demonstrate the ability of ProtoEHR to make accurate, robust, and interpretable predictions compared to baselines in the literature. Furthermore, ProtoEHR offers interpretable insights on code, visit, and patient levels to aid in healthcare prediction.", "date": "2025-08-23", "categories": []}
{"id": "2508.18638", "title": "Biologically Disentangled Multi-Omic Modeling Reveals Mechanistic Insights into Pan-Cancer Immunotherapy Resistance", "abstract": "Immune checkpoint inhibitors (ICIs) have transformed cancer treatment, yet patient responses remain highly variable, and the biological mechanisms underlying resistance are poorly understood. While machine learning models hold promise for predicting responses to ICIs, most existing methods lack interpretability and do not effectively leverage the biological structure inherent to multi-omics data. Here, we introduce the Biologically Disentangled Variational Autoencoder (BDVAE), a deep generative model that integrates transcriptomic and genomic data through modality- and pathway-specific encoders. Unlike existing rigid, pathway-informed models, BDVAE employs a modular encoder architecture combined with variational inference to learn biologically meaningful latent features associated with immune, genomic, and metabolic processes. Applied to a pan-cancer cohort of 366 patients across four cancer types treated with ICIs, BDVAE accurately predicts treatment response (AUC-ROC = 0.94 on unseen test data) and uncovers critical resistance mechanisms, including immune suppression, metabolic shifts, and neuronal signaling. Importantly, BDVAE reveals that resistance spans a continuous biological spectrum rather than strictly binary states, reflecting gradations of tumor dysfunction. Several latent features correlate with survival outcomes and known clinical subtypes, demonstrating BDVAE's capability to generate interpretable, clinically relevant insights. These findings underscore the value of biologically structured machine learning in elucidating complex resistance patterns and guiding precision immunotherapy strategies.", "date": "2025-08-26", "categories": []}
{"id": "2508.19096", "title": "Trustworthy Agents for Electronic Health Records through Confidence Estimation", "abstract": "Large language models (LLMs) show promise for extracting information from Electronic Health Records (EHR) and supporting clinical decisions. However, deployment in clinical settings faces challenges due to hallucination risks. We propose Hallucination Controlled Accuracy at k% (HCAcc@k%), a novel metric quantifying the accuracy-reliability trade-off at varying confidence thresholds. We introduce TrustEHRAgent, a confidence-aware agent incorporating stepwise confidence estimation for clinical question answering. Experiments on MIMIC-III and eICU datasets show TrustEHRAgent outperforms baselines under strict reliability constraints, achieving improvements of 44.23%p and 25.34%p at HCAcc@70% while baseline methods fail at these thresholds. These results highlight limitations of traditional accuracy metrics in evaluating healthcare AI agents. Our work contributes to developing trustworthy clinical agents that deliver accurate information or transparently express uncertainty when confidence is low.", "date": "2025-08-26", "categories": []}
{"id": "2402.07949", "title": "Optimizing the Design of an Artificial Pancreas to Improve Diabetes Management", "abstract": "Diabetes, a chronic condition that impairs how the body turns food into energy, i.e. blood glucose, affects 38 million people in the US alone. The standard treatment is to supplement carbohydrate intake with an artificial pancreas, i.e. a continuous insulin pump (basal shots), as well as occasional insulin injections (bolus shots). The goal of the treatment is to keep blood glucose at the center of an acceptable range, as measured through a continuous glucose meter. A secondary goal is to minimize injections, which are unpleasant and difficult for some patients to implement. In this study, neuroevolution was used to discover an optimal strategy for the treatment. Based on a dataset of 30 days of treatment and measurements of a single patient, a random forest was first trained to predict future glucose levels. A neural network was then evolved to prescribe carbohydrates, basal pumping levels, and bolus injections. Evolution discovered a Pareto front that reduced deviation from the target and number of injections compared to the original data, thus improving patients' quality of life. To make the system easier to adopt, a language interface was developed with a large language model. Thus, these technologies not only improve patient care but also adoption in a broader population.", "date": "2024-02-10", "categories": []}
{"id": "2508.16655", "title": "A Laplace diffusion-based transformer model for heart rate forecasting within daily activity context", "abstract": "With the advent of wearable Internet of Things (IoT) devices, remote patient monitoring (RPM) emerged as a promising solution for managing heart failure. However, the heart rate can fluctuate significantly due to various factors, and without correlating it to the patient's actual physical activity, it becomes difficult to assess whether changes are significant. Although Artificial Intelligence (AI) models may enhance the accuracy and contextual understanding of remote heart rate monitoring, the integration of activity data is still rarely addressed. In this paper, we propose a Transformer model combined with a Laplace diffusion technique to model heart rate fluctuations driven by physical activity of the patient. Unlike prior models that treat activity as secondary, our approach conditions the entire modeling process on activity context using specialized embeddings and attention mechanisms to prioritize activity specific historical patents. The model captures both long-term patterns and activity-specific heart rate dynamics by incorporating contextualized embeddings and dedicated encoder. The Transformer model was validated on a real-world dataset collected from 29 patients over a 4-month period. Experimental results show that our model outperforms current state-of-the-art methods, achieving a 43% reduction in mean absolute error compared to the considered baseline models. Moreover, the coefficient of determination R2 is 0.97 indicating the model predicted heart rate is in strong agreement with actual heart rate values. These findings suggest that the proposed model is a practical and effective tool for supporting both healthcare providers and remote patient monitoring systems.", "date": "2025-08-20", "categories": []}
{"id": "2508.16670", "title": "COVID19 Prediction Based On CT Scans Of Lungs Using DenseNet Architecture", "abstract": "COVID19 took the world by storm since December 2019. A highly infectious communicable disease, COVID19 is caused by the SARSCoV2 virus. By March 2020, the World Health Organization (WHO) declared COVID19 as a global pandemic. A pandemic in the 21st century after almost 100 years was something the world was not prepared for, which resulted in the deaths of around 1.6 million people worldwide. The most common symptoms of COVID19 were associated with the respiratory system and resembled a cold, flu, or pneumonia. After extensive research, doctors and scientists concluded that the main reason for lives being lost due to COVID19 was failure of the respiratory system. Patients were dying gasping for breath. Top healthcare systems of the world were failing badly as there was an acute shortage of hospital beds, oxygen cylinders, and ventilators. Many were dying without receiving any treatment at all. The aim of this project is to help doctors decide the severity of COVID19 by reading the patient's Computed Tomography (CT) scans of the lungs. Computer models are less prone to human error, and Machine Learning or Neural Network models tend to give better accuracy as training improves over time. We have decided to use a Convolutional Neural Network model. Given that a patient tests positive, our model will analyze the severity of COVID19 infection within one month of the positive test result. The severity of the infection may be promising or unfavorable (if it leads to intubation or death), based entirely on the CT scans in the dataset.", "date": "2025-08-21", "categories": []}
{"id": "2508.17207", "title": "Explainable Counterfactual Reasoning in Depression Medication Selection at Multi-Levels (Personalized and Population)", "abstract": "Background: This study investigates how variations in Major Depressive Disorder (MDD) symptoms, quantified by the Hamilton Rating Scale for Depression (HAM-D), causally influence the prescription of SSRIs versus SNRIs. Methods: We applied explainable counterfactual reasoning with counterfactual explanations (CFs) to assess the impact of specific symptom changes on antidepressant choice. Results: Among 17 binary classifiers, Random Forest achieved highest performance (accuracy, F1, precision, recall, ROC-AUC near 0.85). Sample-based CFs revealed both local and global feature importance of individual symptoms in medication selection. Conclusions: Counterfactual reasoning elucidates which MDD symptoms most strongly drive SSRI versus SNRI selection, enhancing interpretability of AI-based clinical decision support systems. Future work should validate these findings on more diverse cohorts and refine algorithms for clinical deployment.", "date": "2025-08-24", "categories": []}
{"id": "2508.17613", "title": "A Weighted Vision Transformer-Based Multi-Task Learning Framework for Predicting ADAS-Cog Scores", "abstract": "Prognostic modeling is essential for forecasting future clinical scores and enabling early detection of Alzheimers disease (AD). While most existing methods focus on predicting the ADAS-Cog global score, they often overlook the predictive value of its 13 sub-scores, which reflect distinct cognitive domains. Some sub-scores may exert greater influence on determining global scores. Assigning higher loss weights to these clinically meaningful sub-scores can guide the model to focus on more relevant cognitive domains, enhancing both predictive accuracy and interpretability. In this study, we propose a weighted Vision Transformer (ViT)-based multi-task learning (MTL) framework to jointly predict the ADAS-Cog global score using baseline MRI scans and its 13 sub-scores at Month 24. Our framework integrates ViT as a feature extractor and systematically investigates the impact of sub-score-specific loss weighting on model performance. Results show that our proposed weighting strategies are group-dependent: strong weighting improves performance for MCI subjects with more heterogeneous MRI patterns, while moderate weighting is more effective for CN subjects with lower variability. Our findings suggest that uniform weighting underutilizes key sub-scores and limits generalization. The proposed framework offers a flexible, interpretable approach to AD prognosis using end-to-end MRI-based learning. (Github repo link will be provided after review)", "date": "2025-08-25", "categories": []}
{"id": "2508.18207", "title": "Clinical characteristics, complications and outcomes of critically ill patients with Dengue in Brazil, 2012-2024: a nationwide, multicentre cohort study", "abstract": "Background. Dengue outbreaks are a major public health issue, with Brazil reporting 71% of global cases in 2024. Purpose. This study aims to describe the profile of severe dengue patients admitted to Brazilian Intensive Care units (ICUs) (2012-2024), assess trends over time, describe new onset complications while in ICU and determine the risk factors at admission to develop complications during ICU stay. Methods. We performed a prospective study of dengue patients from 253 ICUs across 56 hospitals. We used descriptive statistics to describe the dengue ICU population, logistic regression to identify risk factors for complications during the ICU stay, and a machine learning framework to predict the risk of evolving to complications. Visualisations were generated using ISARIC VERTEX. Results. Of 11,047 admissions, 1,117 admissions (10.1%) evolved to complications, including non-invasive (437 admissions) and invasive ventilation (166), vasopressor (364), blood transfusion (353) and renal replacement therapy (103). Age>80 (OR: 3.10, 95% CI: 2.02-4.92), chronic kidney disease (OR: 2.94, 2.22-3.89), liver cirrhosis (OR: 3.65, 1.82-7.04), low platelets (<50,000 cells/mm3; OR: OR: 2.25, 1.89-2.68), and high leukocytes (>7,000 cells/mm3; OR: 2.47, 2.02-3.03) were significant risk factors for complications. A machine learning tool for predicting complications was proposed, showing accurate discrimination and calibration. Conclusion. We described a large cohort of dengue patients admitted to ICUs and identified key risk factors for severe dengue complications, such as advanced age, presence of comorbidities, higher level of leukocytes and lower level of platelets. The proposed prediction tool can be used for early identification and targeted interventions to improve outcomes in dengue-endemic regions.", "date": "2025-08-25", "categories": []}
{"id": "2508.16054", "title": "Generative Foundation Model for Structured and Unstructured Electronic Health Records", "abstract": "Electronic health records (EHRs) are rich clinical data sources but complex repositories of patient data, spanning structured elements (demographics, vitals, lab results, codes), unstructured clinical notes and other modalities of data. Harnessing this heterogeneity is critical for improving patient outcomes. Recent advances in large language models (LLMs) have enabled foundation models that can learn from multiple data modalities and support clinical tasks. However, most current approaches simply serialize numeric EHR data into text, which risks losing temporal and quantitative detail. We introduce Generative Deep Patient (GDP), a multimodal foundation model that natively encodes structured EHR time-series via a CNN-Transformer encoder and fuses it with unstructured EHRs through cross-modal attention into a LLaMA-based decoder. GDP is trained in two stages: (1) generative pretraining, where it learns to produce clinical narratives from raw patient timelines while also performing masked feature prediction (MFP) and next time-step prediction (NTP) to capture temporal dynamics; and (2) multi-task fine-tuning for clinically meaningful predictions (e.g., heart failure, type 2 diabetes, 30-day readmission). In clinical prediction, GDP demonstrated superior performance on MIMIC-IV: heart failure AUROC = 0.923, type 2 diabetes AUROC = 0.817, and 30-day readmission AUROC = 0.627. For narrative generation, GDP achieved ROUGE-L = 0.135 and BERTScore-F1 = 0.545. In a blinded human evaluation, GDP-Instruct scored highest on faithfulness, fluency, and overall clinical utility, suggesting reduced hospital documentation workload without sacrificing accuracy. Our results demonstrate that a single multimodal foundation model can both predict clinically actionable events and generate high-quality clinical narratives. Furthermore, GDP's flexible architecture can be extended to additional modalities.", "date": "2025-08-22", "categories": []}
{"id": "2508.16085", "title": "Ensemble learning of foundation models for precision oncology", "abstract": "Histopathology is essential for disease diagnosis and treatment decision-making. Recent advances in artificial intelligence (AI) have enabled the development of pathology foundation models that learn rich visual representations from large-scale whole-slide images (WSIs). However, existing models are often trained on disparate datasets using varying strategies, leading to inconsistent performance and limited generalizability. Here, we introduce ELF (Ensemble Learning of Foundation models), a novel framework that integrates five state-of-the-art pathology foundation models to generate unified slide-level representations. Trained on 53,699 WSIs spanning 20 anatomical sites, ELF leverages ensemble learning to capture complementary information from diverse models while maintaining high data efficiency. Unlike traditional tile-level models, ELF's slide-level architecture is particularly advantageous in clinical contexts where data are limited, such as therapeutic response prediction. We evaluated ELF across a wide range of clinical applications, including disease classification, biomarker detection, and response prediction to major anticancer therapies, cytotoxic chemotherapy, targeted therapy, and immunotherapy, across multiple cancer types. ELF consistently outperformed all constituent foundation models and existing slide-level models, demonstrating superior accuracy and robustness. Our results highlight the power of ensemble learning for pathology foundation models and suggest ELF as a scalable and generalizable solution for advancing AI-assisted precision oncology.", "date": "2025-08-22", "categories": []}
{"id": "2505.14477", "title": "Personalised Insulin Adjustment with Reinforcement Learning: An In-Silico Validation for People with Diabetes on Intensive Insulin Treatment", "abstract": "Despite recent advances in insulin preparations and technology, adjusting insulin remains an ongoing challenge for the majority of people with type 1 diabetes (T1D) and longstanding type 2 diabetes (T2D). In this study, we propose the Adaptive Basal-Bolus Advisor (ABBA), a personalised insulin treatment recommendation approach based on reinforcement learning for individuals with T1D and T2D, performing self-monitoring blood glucose measurements and multiple daily insulin injection therapy. We developed and evaluated the ability of ABBA to achieve better time-in-range (TIR) for individuals with T1D and T2D, compared to a standard basal-bolus advisor (BBA). The in-silico test was performed using an FDA-accepted population, including 101 simulated adults with T1D and 101 with T2D. An in-silico evaluation shows that ABBA significantly improved TIR and significantly reduced both times below- and above-range, compared to BBA. ABBA's performance continued to improve over two months, whereas BBA exhibited only modest changes. This personalised method for adjusting insulin has the potential to further optimise glycaemic control and support people with T1D and T2D in their daily self-management. Our results warrant ABBA to be trialed for the first time in humans.", "date": "2025-05-20", "categories": []}
{"id": "2508.14908", "title": "A Chinese Heart Failure Status Speech Database with Universal and Personalised Classification", "abstract": "Speech is a cost-effective and non-intrusive data source for identifying acute and chronic heart failure (HF). However, there is a lack of research on whether Chinese syllables contain HF-related information, as observed in other well-studied languages. This study presents the first Chinese speech database of HF patients, featuring paired recordings taken before and after hospitalisation. The findings confirm the effectiveness of the Chinese language in HF detection using both standard 'patient-wise' and personalised 'pair-wise' classification approaches, with the latter serving as an ideal speaker-decoupled baseline for future research. Statistical tests and classification results highlight individual differences as key contributors to inaccuracy. Additionally, an adaptive frequency filter (AFF) is proposed for frequency importance analysis. The data and demonstrations are published at https://github.com/panyue1998/Voice_HF.", "date": "2025-08-12", "categories": []}
{"id": "2508.14942", "title": "Structure-Aware Temporal Modeling for Chronic Disease Progression Prediction", "abstract": "This study addresses the challenges of symptom evolution complexity and insufficient temporal dependency modeling in Parkinson's disease progression prediction. It proposes a unified prediction framework that integrates structural perception and temporal modeling. The method leverages graph neural networks to model the structural relationships among multimodal clinical symptoms and introduces graph-based representations to capture semantic dependencies between symptoms. It also incorporates a Transformer architecture to model dynamic temporal features during disease progression. To fuse structural and temporal information, a structure-aware gating mechanism is designed to dynamically adjust the fusion weights between structural encodings and temporal features, enhancing the model's ability to identify key progression stages. To improve classification accuracy and stability, the framework includes a multi-component modeling pipeline, consisting of a graph construction module, a temporal encoding module, and a prediction output layer. The model is evaluated on real-world longitudinal Parkinson's disease data. The experiments involve comparisons with mainstream models, sensitivity analysis of hyperparameters, and graph connection density control. Results show that the proposed method outperforms existing approaches in AUC, RMSE, and IPW-F1 metrics. It effectively distinguishes progression stages and improves the model's ability to capture personalized symptom trajectories. The overall framework demonstrates strong generalization and structural scalability, providing reliable support for intelligent modeling of chronic progressive diseases such as Parkinson's disease.", "date": "2025-08-20", "categories": []}
{"id": "2405.07841", "title": "Sample Selection Bias in Machine Learning for Healthcare", "abstract": "While machine learning algorithms hold promise for personalised medicine, their clinical adoption remains limited, partly due to biases that can compromise the reliability of predictions. In this paper, we focus on sample selection bias (SSB), a specific type of bias where the study population is less representative of the target population, leading to biased and potentially harmful decisions. Despite being well-known in the literature, SSB remains scarcely studied in machine learning for healthcare. Moreover, the existing machine learning techniques try to correct the bias mostly by balancing distributions between the study and the target populations, which may result in a loss of predictive performance. To address these problems, our study illustrates the potential risks associated with SSB by examining SSB's impact on the performance of machine learning algorithms. Most importantly, we propose a new research direction for addressing SSB, based on the target population identification rather than the bias correction. Specifically, we propose two independent networks(T-Net) and a multitasking network (MT-Net) for addressing SSB, where one network/task identifies the target subpopulation which is representative of the study population and the second makes predictions for the identified subpopulation. Our empirical results with synthetic and semi-synthetic datasets highlight that SSB can lead to a large drop in the performance of an algorithm for the target population as compared with the study population, as well as a substantial difference in the performance for the target subpopulations that are representative of the selected and the non-selected patients from the study population. Furthermore, our proposed techniques demonstrate robustness across various settings, including different dataset sizes, event rates, and selection rates, outperforming the existing bias correction techniques.", "date": "2024-05-13", "categories": []}
{"id": "2508.14074", "title": "GEPD:GAN-Enhanced Generalizable Model for EEG-Based Detection of Parkinson's Disease", "abstract": "Electroencephalography has been established as an effective method for detecting Parkinson's disease, typically diagnosed early.Current Parkinson's disease detection methods have shown significant success within individual datasets, however, the variability in detection methods across different EEG datasets and the small size of each dataset pose challenges for training a generalizable model for cross-dataset scenarios. To address these issues, this paper proposes a GAN-enhanced generalizable model, named GEPD, specifically for EEG-based cross-dataset classification of Parkinson's disease.First, we design a generative network that creates fusion EEG data by controlling the distribution similarity between generated data and real data.In addition, an EEG signal quality assessment model is designed to ensure the quality of generated data great.Second, we design a classification network that utilizes a combination of multiple convolutional neural networks to effectively capture the time-frequency characteristics of EEG signals, while maintaining a generalizable structure and ensuring easy convergence.This work is dedicated to utilizing intelligent methods to study pathological manifestations, aiming to facilitate the diagnosis and monitoring of neurological diseases.The evaluation results demonstrate that our model performs comparably to state-of-the-art models in cross-dataset settings, achieving an accuracy of 84.3% and an F1-score of 84.0%, showcasing the generalizability of the proposed model.", "date": "2025-08-12", "categories": []}
{"id": "2401.07796", "title": "Fusing Echocardiography Images and Medical Records for Continuous Patient Stratification", "abstract": "Deep learning enables automatic and robust extraction of cardiac function descriptors from echocardiographic sequences, such as ejection fraction or strain. These descriptors provide fine-grained information that physicians consider, in conjunction with more global variables from the clinical record, to assess patients' condition. Drawing on novel Transformer models applied to tabular data, we propose a method that considers all descriptors extracted from medical records and echocardiograms to learn the representation of a cardiovascular pathology with a difficult-to-characterize continuum, namely hypertension. Our method first projects each variable into its own representation space using modality-specific approaches. These standardized representations of multimodal data are then fed to a Transformer encoder, which learns to merge them into a comprehensive representation of the patient through the task of predicting a clinical rating. This stratification task is formulated as an ordinal classification to enforce a pathological continuum in the representation space. We observe the major trends along this continuum on a cohort of 239 hypertensive patients, providing unprecedented details in the description of hypertension's impact on various cardiac function descriptors. Our analysis shows that i) the XTab foundation model's architecture allows to reach outstanding performance (96.8% AUROC) even with limited data (less than 200 training samples), ii) stratification across the population is reproducible between trainings (within 5.7% mean absolute error), and iii) patterns emerge in descriptors, some of which align with established physiological knowledge about hypertension, while others could pave the way for a more comprehensive understanding of this pathology. Code is available at https://github.com/creatis-myriad/didactic.", "date": "2024-01-15", "categories": []}
{"id": "2403.14926", "title": "Contrastive Learning on Multimodal Analysis of Electronic Health Records", "abstract": "Electronic health record (EHR) systems contain a wealth of multimodal clinical data including structured data like clinical codes and unstructured data such as clinical notes. However, many existing EHR-focused studies has traditionally either concentrated on an individual modality or merged different modalities in a rather rudimentary fashion. This approach often results in the perception of structured and unstructured data as separate entities, neglecting the inherent synergy between them. Specifically, the two important modalities contain clinically relevant, inextricably linked and complementary health information. A more complete picture of a patient's medical history is captured by the joint analysis of the two modalities of data. Despite the great success of multimodal contrastive learning on vision-language, its potential remains under-explored in the realm of multimodal EHR, particularly in terms of its theoretical understanding. To accommodate the statistical analysis of multimodal EHR data, in this paper, we propose a novel multimodal feature embedding generative model and design a multimodal contrastive loss to obtain the multimodal EHR feature representation. Our theoretical analysis demonstrates the effectiveness of multimodal learning compared to single-modality learning and connects the solution of the loss function to the singular value decomposition of a pointwise mutual information matrix. This connection paves the way for a privacy-preserving algorithm tailored for multimodal EHR feature representation learning. Simulation studies show that the proposed algorithm performs well under a variety of configurations. We further validate the clinical utility of the proposed algorithm in real-world EHR data.", "date": "2024-03-22", "categories": []}
{"id": "2508.06627", "title": "Early Detection of Pancreatic Cancer Using Multimodal Learning on Electronic Health Records", "abstract": "Pancreatic ductal adenocarcinoma (PDAC) is one of the deadliest cancers, and early detection remains a major clinical challenge due to the absence of specific symptoms and reliable biomarkers. In this work, we propose a new multimodal approach that integrates longitudinal diagnosis code histories and routinely collected laboratory measurements from electronic health records to detect PDAC up to one year prior to clinical diagnosis. Our method combines neural controlled differential equations to model irregular lab time series, pretrained language models and recurrent networks to learn diagnosis code trajectory representations, and cross-attention mechanisms to capture interactions between the two modalities. We develop and evaluate our approach on a real-world dataset of nearly 4,700 patients and achieve significant improvements in AUC ranging from 6.5% to 15.5% over state-of-the-art methods. Furthermore, our model identifies diagnosis codes and laboratory panels associated with elevated PDAC risk, including both established and new biomarkers. Our code is available at https://github.com/MosbahAouad/EarlyPDAC-MML.", "date": "2025-08-08", "categories": []}
{"id": "2508.11682", "title": "Age-Normalized HRV Features for Non-Invasive Glucose Prediction: A Pilot Sleep-Aware Machine Learning Study", "abstract": "Non-invasive glucose monitoring remains a critical challenge in the management of diabetes. HRV during sleep shows promise for glucose prediction however, age-related autonomic changes significantly confound traditional HRV analyses. We analyzed 43 subjects with multi-modal data including sleep-stage specific ECG, HRV features, and clinical measurements. A novel age-normalization technique was applied to the HRV features by, dividing the raw values by age-scaled factors. BayesianRidge regression with 5-fold cross-validation was employed for log-glucose prediction. Age-normalized HRV features achieved R2 = 0.161 (MAE = 0.182) for log-glucose prediction, representing a 25.6% improvement over non-normalized features (R2 = 0.132). The top predictive features were hrv rem mean rr age normalized (r = 0.443, p = 0.004), hrv ds mean rr age normalized (r = 0.438, p = 0.005), and diastolic blood pressure (r = 0.437, p = 0.005). Systematic ablation studies confirmed age-normalization as the critical component, with sleep-stage specific features providing additional predictive value. Age-normalized HRV features significantly enhance glucose prediction accuracy compared with traditional approaches. This sleep-aware methodology addresses fundamental limitations in autonomic function assessment and suggests a preliminary feasibility for non-invasive glucose monitoring applications. However, these results require validation in larger cohorts before clinical consideration.", "date": "2025-08-09", "categories": []}
{"id": "2508.13561", "title": "Prediction of Hospital Associated Infections During Continuous Hospital Stays", "abstract": "The US Centers for Disease Control and Prevention (CDC), in 2019, designated Methicillin-resistant Staphylococcus aureus (MRSA) as a serious antimicrobial resistance threat. The risk of acquiring MRSA and suffering life-threatening consequences due to it remains especially high for hospitalized patients due to a unique combination of factors, including: co-morbid conditions, immuno suppression, antibiotic use, and risk of contact with contaminated hospital workers and equipment. In this paper, we present a novel generative probabilistic model, GenHAI, for modeling sequences of MRSA test results outcomes for patients during a single hospitalization. This model can be used to answer many important questions from the perspectives of hospital administrators for mitigating the risk of MRSA infections. Our model is based on the probabilistic programming paradigm, and can be used to approximately answer a variety of predictive, causal, and counterfactual questions. We demonstrate the efficacy of our model by comparing it against discriminative and generative machine learning models using two real-world datasets.", "date": "2025-08-19", "categories": []}
{"id": "2508.13579", "title": "Toward Better EHR Reasoning in LLMs: Reinforcement Learning with Expert Attention Guidance", "abstract": "Improving large language models (LLMs) for electronic health record (EHR) reasoning is essential for enabling accurate and generalizable clinical predictions. While LLMs excel at medical text understanding, they underperform on EHR-based prediction tasks due to challenges in modeling temporally structured, high-dimensional data. Existing approaches often rely on hybrid paradigms, where LLMs serve merely as frozen prior retrievers while downstream deep learning (DL) models handle prediction, failing to improve the LLM's intrinsic reasoning capacity and inheriting the generalization limitations of DL models. To this end, we propose EAG-RL, a novel two-stage training framework designed to intrinsically enhance LLMs' EHR reasoning ability through expert attention guidance, where expert EHR models refer to task-specific DL models trained on EHR data. Concretely, EAG-RL first constructs high-quality, stepwise reasoning trajectories using expert-guided Monte Carlo Tree Search to effectively initialize the LLM's policy. Then, EAG-RL further optimizes the policy via reinforcement learning by aligning the LLM's attention with clinically salient features identified by expert EHR models. Extensive experiments on two real-world EHR datasets show that EAG-RL improves the intrinsic EHR reasoning ability of LLMs by an average of 14.62%, while also enhancing robustness to feature perturbations and generalization to unseen clinical domains. These results demonstrate the practical potential of EAG-RL for real-world deployment in clinical prediction tasks. Our code have been available at https://github.com/devilran6/EAG-RL.", "date": "2025-08-19", "categories": []}
{"id": "2411.02426", "title": "Diagnostic performance of deep learning for predicting glioma isocitrate dehydrogenase and 1p/19q co-deletion in MRI: a systematic review and meta-analysis", "abstract": "Objectives We aimed to evaluate the diagnostic performance of deep learning (DL)-based radiomics models for the noninvasive prediction of isocitrate dehydrogenase (IDH) mutation and 1p/19q co-deletion status in glioma patients using MRI sequences, and to identify methodological factors influencing accuracy and generalizability.\n  Materials and methods Following PRISMA guidelines, we systematically searched major databases (PubMed, Scopus, Embase, Web of Science, and Google Scholar) up to March 2025, screening studies that utilized DL to predict IDH and 1p/19q co-deletion status from MRI data. We assessed study quality and risk of bias using the Radiomics Quality Score and the QUADAS-2 tool. Our meta-analysis employed a bivariate model to compute pooled sensitivity and specificity, and meta-regression to assess interstudy heterogeneity.\n  Results Among the 1517 unique publications, 104 were included in the qualitative synthesis, and 72 underwent meta-analysis. Pooled estimates for IDH prediction in test cohorts yielded a sensitivity of 0.80 and specificity of 0.85. For 1p/19q co-deletion, sensitivity was 0.75 and specificity was 0.82. Meta-regression identified the tumor segmentation method and the extent of DL integration into the radiomics pipeline as significant contributors to interstudy variability.\n  Conclusion Although DL models demonstrate strong potential for noninvasive molecular classification of gliomas, clinical translation requires several critical steps: harmonization of multi-center MRI data using techniques such as histogram matching and DL-based style transfer; adoption of standardized and automated segmentation protocols; extensive multi-center external validation; and prospective clinical validation.", "date": "2024-10-28", "categories": []}
{"id": "2503.15578", "title": "MedSpaformer: a Transferable Transformer with Multi-granularity Token Sparsification for Medical Time Series Classification", "abstract": "Accurate medical time series (MedTS) classification is essential for effective clinical diagnosis, yet remains challenging due to complex multi-channel temporal dependencies, information redundancy, and label scarcity. While transformer-based models have shown promise in time series analysis, most are designed for forecasting tasks and fail to fully exploit the unique characteristics of MedTS. In this paper, we introduce MedSpaformer, a transformer-based framework tailored for MedTS classification. It incorporates a sparse token-based dual-attention mechanism that enables global context modeling and token sparsification, allowing dynamic feature refinement by focusing on informative tokens while reducing redundancy. This mechanism is integrated into a multi-granularity cross-channel encoding scheme to capture intra- and inter-granularity temporal dependencies and inter-channel correlations, enabling progressive refinement of task-relevant patterns in medical signals. The sparsification design allows our model to flexibly accommodate inputs with variable lengths and channel dimensions. We also introduce an adaptive label encoder to extract label semantics and address cross-dataset label space misalignment. Together, these components enhance the model's transferability across heterogeneous medical datasets, which helps alleviate the challenge of label scarcity. Our model outperforms 13 baselines across 7 medical datasets under supervised learning. It also excels in few-shot learning and demonstrates zero-shot capability in both in-domain and cross-domain diagnostics. These results highlight MedSpaformer's robustness and its potential as a unified solution for MedTS classification across diverse settings.", "date": "2025-03-19", "categories": []}
{"id": "2508.09809", "title": "A Comprehensive Review of Datasets for Clinical Mental Health AI Systems", "abstract": "Mental health disorders are rising worldwide. However, the availability of trained clinicians has not scaled proportionally, leaving many people without adequate or timely support. To bridge this gap, recent studies have shown the promise of Artificial Intelligence (AI) to assist mental health diagnosis, monitoring, and intervention. However, the development of efficient, reliable, and ethical AI to assist clinicians is heavily dependent on high-quality clinical training datasets. Despite growing interest in data curation for training clinical AI assistants, existing datasets largely remain scattered, under-documented, and often inaccessible, hindering the reproducibility, comparability, and generalizability of AI models developed for clinical mental health care. In this paper, we present the first comprehensive survey of clinical mental health datasets relevant to the training and development of AI-powered clinical assistants. We categorize these datasets by mental disorders (e.g., depression, schizophrenia), data modalities (e.g., text, speech, physiological signals), task types (e.g., diagnosis prediction, symptom severity estimation, intervention generation), accessibility (public, restricted or private), and sociocultural context (e.g., language and cultural background). Along with these, we also investigate synthetic clinical mental health datasets. Our survey identifies critical gaps such as a lack of longitudinal data, limited cultural and linguistic representation, inconsistent collection and annotation standards, and a lack of modalities in synthetic data. We conclude by outlining key challenges in curating and standardizing future datasets and provide actionable recommendations to facilitate the development of more robust, generalizable, and equitable mental health AI systems.", "date": "2025-08-13", "categories": []}
{"id": "2508.11656", "title": "Inductive transfer learning from regression to classification in ECG analysis", "abstract": "Cardiovascular diseases (CVDs) are the leading cause of mortality worldwide, accounting for over 30% of global deaths according to the World Health Organization (WHO). Importantly, one-third of these deaths are preventable with timely and accurate diagnosis. The electrocardiogram (ECG), a non-invasive method for recording the electrical activity of the heart, is crucial for diagnosing CVDs. However, privacy concerns surrounding the use of patient ECG data in research have spurred interest in synthetic data, which preserves the statistical properties of real data without compromising patient confidentiality. This study explores the potential of synthetic ECG data for training deep learning models from regression to classification tasks and evaluates the feasibility of transfer learning to enhance classification performance on real ECG data. We experimented with popular deep learning models to predict four key cardiac parameters, namely, Heart Rate (HR), PR interval, QT interval, and QRS complex-using separate regression models. Subsequently, we leveraged these regression models for transfer learning to perform 5-class ECG signal classification. Our experiments systematically investigate whether transfer learning from regression to classification is viable, enabling better utilization of diverse open-access and synthetic ECG datasets. Our findings demonstrate that transfer learning from regression to classification improves classification performance, highlighting its potential to maximize the utility of available data and advance deep learning applications in this domain.", "date": "2025-08-05", "categories": []}
{"id": "2508.12405", "title": "Extracting Post-Acute Sequelae of SARS-CoV-2 Infection Symptoms from Clinical Notes via Hybrid Natural Language Processing", "abstract": "Accurately and efficiently diagnosing Post-Acute Sequelae of COVID-19 (PASC) remains challenging due to its myriad symptoms that evolve over long- and variable-time intervals. To address this issue, we developed a hybrid natural language processing pipeline that integrates rule-based named entity recognition with BERT-based assertion detection modules for PASC-symptom extraction and assertion detection from clinical notes. We developed a comprehensive PASC lexicon with clinical specialists. From 11 health systems of the RECOVER initiative network across the U.S., we curated 160 intake progress notes for model development and evaluation, and collected 47,654 progress notes for a population-level prevalence study. We achieved an average F1 score of 0.82 in one-site internal validation and 0.76 in 10-site external validation for assertion detection. Our pipeline processed each note at $2.448\\pm 0.812$ seconds on average. Spearman correlation tests showed $\\rho >0.83$ for positive mentions and $\\rho >0.72$ for negative ones, both with $P <0.0001$. These demonstrate the effectiveness and efficiency of our models and their potential for improving PASC diagnosis.", "date": "2025-08-17", "categories": []}
{"id": "2508.12418", "title": "Bi-Axial Transformers: Addressing the Increasing Complexity of EHR Classification", "abstract": "Electronic Health Records (EHRs), the digital representation of a patient's medical history, are a valuable resource for epidemiological and clinical research. They are also becoming increasingly complex, with recent trends indicating larger datasets, longer time series, and multi-modal integrations. Transformers, which have rapidly gained popularity due to their success in natural language processing and other domains, are well-suited to address these challenges due to their ability to model long-range dependencies and process data in parallel. But their application to EHR classification remains limited by data representations, which can reduce performance or fail to capture informative missingness. In this paper, we present the Bi-Axial Transformer (BAT), which attends to both the clinical variable and time point axes of EHR data to learn richer data relationships and address the difficulties of data sparsity. BAT achieves state-of-the-art performance on sepsis prediction and is competitive to top methods for mortality classification. In comparison to other transformers, BAT demonstrates increased robustness to data missingness, and learns unique sensor embeddings which can be used in transfer learning. Baseline models, which were previously located across multiple repositories or utilized deprecated libraries, were re-implemented with PyTorch and made available for reproduction and future benchmarking.", "date": "2025-08-17", "categories": []}
{"id": "2508.12506", "title": "Design and Validation of a Responsible Artificial Intelligence-based System for the Referral of Diabetic Retinopathy Patients", "abstract": "Diabetic Retinopathy (DR) is a leading cause of vision loss in working-age individuals. Early detection of DR can reduce the risk of vision loss by up to 95%, but a shortage of retinologists and challenges in timely examination complicate detection. Artificial Intelligence (AI) models using retinal fundus photographs (RFPs) offer a promising solution. However, adoption in clinical settings is hindered by low-quality data and biases that may lead AI systems to learn unintended features. To address these challenges, we developed RAIS-DR, a Responsible AI System for DR screening that incorporates ethical principles across the AI lifecycle. RAIS-DR integrates efficient convolutional models for preprocessing, quality assessment, and three specialized DR classification models. We evaluated RAIS-DR against the FDA-approved EyeArt system on a local dataset of 1,046 patients, unseen by both systems. RAIS-DR demonstrated significant improvements, with F1 scores increasing by 5-12%, accuracy by 6-19%, and specificity by 10-20%. Additionally, fairness metrics such as Disparate Impact and Equal Opportunity Difference indicated equitable performance across demographic subgroups, underscoring RAIS-DR's potential to reduce healthcare disparities. These results highlight RAIS-DR as a robust and ethically aligned solution for DR screening in clinical settings. The code, weights of RAIS-DR are available at https://gitlab.com/inteligencia-gubernamental-jalisco/jalisco-retinopathy with RAIL.", "date": "2025-08-17", "categories": []}
{"id": "2508.11092", "title": "Predictive Multimodal Modeling of Diagnoses and Treatments in EHR", "abstract": "While the ICD code assignment problem has been widely studied, most works have focused on post-discharge document classification. Models for early forecasting of this information could be used for identifying health risks, suggesting effective treatments, or optimizing resource allocation. To address the challenge of predictive modeling using the limited information at the beginning of a patient stay, we propose a multimodal system to fuse clinical notes and tabular events captured in electronic health records. The model integrates pre-trained encoders, feature pooling, and cross-modal attention to learn optimal representations across modalities and balance their presence at every temporal point. Moreover, we present a weighted temporal loss that adjusts its contribution at each point in time. Experiments show that these strategies enhance the early prediction model, outperforming the current state-of-the-art systems.", "date": "2025-08-14", "categories": []}
{"id": "2508.11210", "title": "Borrowing From the Future: Enhancing Early Risk Assessment through Contrastive Learning", "abstract": "Risk assessments for a pediatric population are often conducted across multiple stages. For example, clinicians may evaluate risks prenatally, at birth, and during Well-Child visits. Although predictions made at later stages typically achieve higher precision, it is clinically desirable to make reliable risk assessments as early as possible. Therefore, this study focuses on improving prediction performance in early-stage risk assessments. Our solution, \\textbf{Borrowing From the Future (BFF)}, is a contrastive multi-modal framework that treats each time window as a distinct modality. In BFF, a model is trained on all available data throughout the time while performing a risk assessment using up-to-date information. This contrastive framework allows the model to ``borrow'' informative signals from later stages (e.g., Well-Child visits) to implicitly supervise the learning at earlier stages (e.g., prenatal/birth stages). We validate BFF on two real-world pediatric outcome prediction tasks, demonstrating consistent improvements in early risk assessments. The code is available at https://github.com/scotsun/bff.", "date": "2025-08-15", "categories": []}
{"id": "2504.08329", "title": "MedRep: Medical Concept Representation for General Electronic Health Record Foundation Models", "abstract": "Electronic health record (EHR) foundation models have been an area ripe for exploration with their improved performance in various medical tasks. Despite the rapid advances, there exists a fundamental limitation: Processing unseen medical codes out of vocabulary. This problem limits the generalizability of EHR foundation models and the integration of models trained with different vocabularies. To alleviate this problem, we propose a set of novel medical concept representations (MedRep) for EHR foundation models based on the observational medical outcome partnership (OMOP) common data model (CDM). For concept representation learning, we enrich the information of each concept with a minimal definition through large language model (LLM) prompts and complement the text-based representations through the graph ontology of OMOP vocabulary. Our approach outperforms the vanilla EHR foundation model and the model with a previously introduced medical code tokenizer in diverse prediction tasks. We also demonstrate the generalizability of MedRep through external validation.", "date": "2025-04-11", "categories": []}
{"id": "2506.14400", "title": "One Size Fits None: Rethinking Fairness in Medical AI", "abstract": "Machine learning (ML) models are increasingly used to support clinical decision-making. However, real-world medical datasets are often noisy, incomplete, and imbalanced, leading to performance disparities across patient subgroups. These differences raise fairness concerns, particularly when they reinforce existing disadvantages for marginalized groups. In this work, we analyze several medical prediction tasks and demonstrate how model performance varies with patient characteristics. While ML models may demonstrate good overall performance, we argue that subgroup-level evaluation is essential before integrating them into clinical workflows. By conducting a performance analysis at the subgroup level, differences can be clearly identified-allowing, on the one hand, for performance disparities to be considered in clinical practice, and on the other hand, for these insights to inform the responsible development of more effective models. Thereby, our work contributes to a practical discussion around the subgroup-sensitive development and deployment of medical ML models and the interconnectedness of fairness and transparency.", "date": "2025-06-17", "categories": []}
{"id": "2508.10025", "title": "Detecting and explaining postpartum depression in real-time with generative artificial intelligence", "abstract": "Among the many challenges mothers undergo after childbirth, postpartum depression (PPD) is a severe condition that significantly impacts their mental and physical well-being. Consequently, the rapid detection of ppd and their associated risk factors is critical for in-time assessment and intervention through specialized prevention procedures. Accordingly, this work addresses the need to help practitioners make decisions with the latest technological advancements to enable real-time screening and treatment recommendations. Mainly, our work contributes to an intelligent PPD screening system that combines Natural Language Processing, Machine Learning (ML), and Large Language Models (LLMs) towards an affordable, real-time, and non-invasive free speech analysis. Moreover, it addresses the black box problem since the predictions are described to the end users thanks to the combination of LLMs with interpretable ml models (i.e., tree-based algorithms) using feature importance and natural language. The results obtained are 90 % on ppd detection for all evaluation metrics, outperforming the competing solutions in the literature. Ultimately, our solution contributes to the rapid detection of PPD and their associated risk factors, critical for in-time and proper assessment and intervention.", "date": "2025-08-08", "categories": []}
{"id": "2508.10160", "title": "Pre-trained Transformer-models using chronic invasive electrophysiology for symptom decoding without patient-individual training", "abstract": "Neural decoding of pathological and physiological states can enable patient-individualized closed-loop neuromodulation therapy. Recent advances in pre-trained large-scale foundation models offer the potential for generalized state estimation without patient-individual training. Here we present a foundation model trained on chronic longitudinal deep brain stimulation recordings spanning over 24 days. Adhering to long time-scale symptom fluctuations, we highlight the extended context window of 30 minutes. We present an optimized pre-training loss function for neural electrophysiological data that corrects for the frequency bias of common masked auto-encoder loss functions due to the 1-over-f power law. We show in a downstream task the decoding of Parkinson's disease symptoms with leave-one-subject-out cross-validation without patient-individual training.", "date": "2025-08-13", "categories": []}
{"id": "2508.10226", "title": "Using Large Language Models to Measure Symptom Severity in Patients At Risk for Schizophrenia", "abstract": "Patients who are at clinical high risk (CHR) for schizophrenia need close monitoring of their symptoms to inform appropriate treatments. The Brief Psychiatric Rating Scale (BPRS) is a validated, commonly used research tool for measuring symptoms in patients with schizophrenia and other psychotic disorders; however, it is not commonly used in clinical practice as it requires a lengthy structured interview. Here, we utilize large language models (LLMs) to predict BPRS scores from clinical interview transcripts in 409 CHR patients from the Accelerating Medicines Partnership Schizophrenia (AMP-SCZ) cohort. Despite the interviews not being specifically structured to measure the BPRS, the zero-shot performance of the LLM predictions compared to the true assessment (median concordance: 0.84, ICC: 0.73) approaches human inter- and intra-rater reliability. We further demonstrate that LLMs have substantial potential to improve and standardize the assessment of CHR patients via their accuracy in assessing the BPRS in foreign languages (median concordance: 0.88, ICC: 0.70), and integrating longitudinal information in a one-shot or few-shot learning approach.", "date": "2025-08-13", "categories": []}
{"id": "2508.10233", "title": "Interpretable Machine Learning Model for Early Prediction of Acute Kidney Injury in Critically Ill Patients with Cirrhosis: A Retrospective Study", "abstract": "Background: Cirrhosis is a progressive liver disease with high mortality and frequent complications, notably acute kidney injury (AKI), which occurs in up to 50% of hospitalized patients and worsens outcomes. AKI stems from complex hemodynamic, inflammatory, and metabolic changes, making early detection essential. Many predictive tools lack accuracy, interpretability, and alignment with intensive care unit (ICU) workflows. This study developed an interpretable machine learning model for early AKI prediction in critically ill patients with cirrhosis.\n  Methods: We conducted a retrospective analysis of the MIMIC-IV v2.2 database, identifying 1240 adult ICU patients with cirrhosis and excluding those with ICU stays under 48 hours or missing key data. Laboratory and physiological variables from the first 48 hours were extracted. The pipeline included preprocessing, missingness filtering, LASSO feature selection, and SMOTE class balancing. Six algorithms-LightGBM, CatBoost, XGBoost, logistic regression, naive Bayes, and neural networks-were trained and evaluated using AUROC, accuracy, F1-score, sensitivity, specificity, and predictive values.\n  Results: LightGBM achieved the best performance (AUROC 0.808, 95% CI 0.741-0.856; accuracy 0.704; NPV 0.911). Key predictors included prolonged partial thromboplastin time, absence of outside-facility 20G placement, low pH, and altered pO2, consistent with known cirrhosis-AKI mechanisms and suggesting actionable targets.\n  Conclusion: The LightGBM-based model enables accurate early AKI risk stratification in ICU patients with cirrhosis using routine clinical variables. Its high negative predictive value supports safe de-escalation for low-risk patients, and interpretability fosters clinician trust and targeted prevention. External validation and integration into electronic health record systems are warranted.", "date": "2025-08-13", "categories": []}
{"id": "2508.10284", "title": "Uncertainty-Aware Prediction of Parkinson's Disease Medication Needs: A Two-Stage Conformal Prediction Approach", "abstract": "Parkinson's Disease (PD) medication management presents unique challenges due to heterogeneous disease progression and treatment response. Neurologists must balance symptom control with optimal dopaminergic dosing based on functional disability while minimizing side effects. This balance is crucial as inadequate or abrupt changes can cause levodopa-induced dyskinesia, wearing off, and neuropsychiatric effects, significantly reducing quality of life. Current approaches rely on trial-and-error decisions without systematic predictive methods. Despite machine learning advances, clinical adoption remains limited due to reliance on point predictions that do not account for prediction uncertainty, undermining clinical trust and utility. Clinicians require not only predictions of future medication needs but also reliable confidence measures. Without quantified uncertainty, adjustments risk premature escalation to maximum doses or prolonged inadequate symptom control. We developed a conformal prediction framework anticipating medication needs up to two years in advance with reliable prediction intervals and statistical guarantees. Our approach addresses zero-inflation in PD inpatient data, where patients maintain stable medication regimens between visits. Using electronic health records from 631 inpatient admissions at University of Florida Health (2011-2021), our two-stage approach identifies patients likely to need medication changes, then predicts required levodopa equivalent daily dose adjustments. Our framework achieved marginal coverage while reducing prediction interval lengths compared to traditional approaches, providing precise predictions for short-term planning and wider ranges for long-term forecasting. By quantifying uncertainty, our approach enables evidence-based decisions about levodopa dosing, optimizing symptom control while minimizing side effects and improving life quality.", "date": "2025-08-14", "categories": []}
{"id": "2508.10425", "title": "HiRef: Leveraging Hierarchical Ontology and Network Refinement for Robust Medication Recommendation", "abstract": "Medication recommendation is a crucial task for assisting physicians in making timely decisions from longitudinal patient medical records. However, real-world EHR data present significant challenges due to the presence of rarely observed medical entities and incomplete records that may not fully capture the clinical ground truth. While data-driven models trained on longitudinal Electronic Health Records often achieve strong empirical performance, they struggle to generalize under missing or novel conditions, largely due to their reliance on observed co-occurrence patterns. To address these issues, we propose Hierarchical Ontology and Network Refinement for Robust Medication Recommendation (HiRef), a unified framework that combines two complementary structures: (i) the hierarchical semantics encoded in curated medical ontologies, and (ii) refined co-occurrence patterns derived from real-world EHRs. We embed ontology entities in hyperbolic space, which naturally captures tree-like relationships and enables knowledge transfer through shared ancestors, thereby improving generalizability to unseen codes. To further improve robustness, we introduce a prior-guided sparse regularization scheme that refines the EHR co-occurrence graph by suppressing spurious edges while preserving clinically meaningful associations. Our model achieves strong performance on EHR benchmarks (MIMIC-III and MIMIC-IV) and maintains high accuracy under simulated unseen-code settings. Extensive experiments with comprehensive ablation studies demonstrate HiRef's resilience to unseen medical codes, supported by in-depth analyses of the learned sparsified graph structure and medical code embeddings.", "date": "2025-08-14", "categories": []}
{"id": "2409.01330", "title": "Pediatric brain tumor classification using digital histopathology and deep learning: evaluation of SOTA methods on a multi-center Swedish cohort", "abstract": "Brain tumors are the most common solid tumors in children and young adults, but the scarcity of large histopathology datasets has limited the application of computational pathology in this group. This study implements two weakly supervised multiple-instance learning (MIL) approaches on patch-features obtained from state-of-the-art histology-specific foundation models to classify pediatric brain tumors in hematoxylin and eosin whole slide images (WSIs) from a multi-center Swedish cohort. WSIs from 540 subjects (age 8.5$\\pm$4.9 years) diagnosed with brain tumor were gathered from the six Swedish university hospitals. Instance (patch)-level features were obtained from WSIs using three pre-trained feature extractors: ResNet50, UNI, and CONCH. Instances were aggregated using attention-based MIL (ABMIL) or clustering-constrained attention MIL (CLAM) for patient-level classification. Models were evaluated on three classification tasks based on the hierarchical classification of pediatric brain tumors: tumor category, family, and type. Model generalization was assessed by training on data from two of the centers and testing on data from four other centers. Model interpretability was evaluated through attention mapping. The highest classification performance was achieved using UNI features and ABMIL aggregation, with Matthew's correlation coefficient of 0.76$\\pm$0.04, 0.63$\\pm$0.04, and 0.60$\\pm$0.05 for tumor category, family, and type classification, respectively. When evaluating generalization, models utilizing UNI and CONCH features outperformed those using ResNet50. However, the drop in performance from the in-site to out-of-site testing was similar across feature extractors. These results show the potential of state-of-the-art computational pathology methods in diagnosing pediatric brain tumors at different hierarchical levels with fair generalizability on a multi-center national dataset.", "date": "2024-09-02", "categories": []}
{"id": "2505.00191", "title": "IP-CRR: Information Pursuit for Interpretable Classification of Chest Radiology Reports", "abstract": "The development of AI-based methods to analyze radiology reports could lead to significant advances in medical diagnosis, from improving diagnostic accuracy to enhancing efficiency and reducing workload. However, the lack of interpretability of AI-based methods could hinder their adoption in clinical settings. In this paper, we propose an interpretable-by-design framework for classifying chest radiology reports. First, we extract a set of representative facts from a large set of reports. Then, given a new report, we query whether a small subset of the representative facts is entailed by the report, and predict a diagnosis based on the selected subset of query-answer pairs. The explanation for a prediction is, by construction, the set of selected queries and answers. We use the Information Pursuit framework to select the most informative queries, a natural language inference model to determine if a fact is entailed by the report, and a classifier to predict the disease. Experiments on the MIMIC-CXR dataset demonstrate the effectiveness of the proposed method, highlighting its potential to enhance trust and usability in medical AI.", "date": "2025-04-30", "categories": []}
{"id": "2505.22964", "title": "Exploring Scaling Laws for EHR Foundation Models", "abstract": "The emergence of scaling laws has profoundly shaped the development of large language models (LLMs), enabling predictable performance gains through systematic increases in model size, dataset volume, and compute. Yet, these principles remain largely unexplored in the context of electronic health records (EHRs) -- a rich, sequential, and globally abundant data source that differs structurally from natural language. In this work, we present the first empirical investigation of scaling laws for EHR foundation models. By training transformer architectures on patient timeline data from the MIMIC-IV database across varying model sizes and compute budgets, we identify consistent scaling patterns, including parabolic IsoFLOPs curves and power-law relationships between compute, model parameters, data size, and clinical utility. These findings demonstrate that EHR models exhibit scaling behavior analogous to LLMs, offering predictive insights into resource-efficient training strategies. Our results lay the groundwork for developing powerful EHR foundation models capable of transforming clinical prediction tasks and advancing personalized healthcare.", "date": "2025-05-29", "categories": []}
{"id": "2506.16929", "title": "A deep learning and machine learning approach to predict neonatal death in the context of S\\~ao Paulo", "abstract": "Neonatal death is still a concerning reality for underdeveloped and even some developed countries. Worldwide data indicate that 26.693 babies out of 1,000 births die, according to Macro Trades. To reduce this number, early prediction of endangered babies is crucial. Such prediction enables the opportunity to take ample care of the child and mother so that early child death can be avoided. In this context, machine learning was used to determine whether a newborn baby is at risk. To train the predictive model, historical data of 1.4 million newborns was used. Machine learning and deep learning techniques such as logical regression, K-nearest neighbor, random forest classifier, extreme gradient boosting (XGBoost), convolutional neural network, and long short-term memory (LSTM) were implemented using the dataset to identify the most accurate model for predicting neonatal mortality. Among the machine learning algorithms, XGBoost and random forest classifier achieved the best accuracy with 94%, while among the deep learning models, LSTM delivered the highest accuracy with 99%. Therefore, using LSTM appears to be the most suitable approach to predict whether precautionary measures for a child are necessary.", "date": "2025-06-20", "categories": []}
{"id": "2508.09182", "title": "MedPatch: Confidence-Guided Multi-Stage Fusion for Multimodal Clinical Data", "abstract": "Clinical decision-making relies on the integration of information across various data modalities, such as clinical time-series, medical images and textual reports. Compared to other domains, real-world medical data is heterogeneous in nature, limited in size, and sparse due to missing modalities. This significantly limits model performance in clinical prediction tasks. Inspired by clinical workflows, we introduce MedPatch, a multi-stage multimodal fusion architecture, which seamlessly integrates multiple modalities via confidence-guided patching. MedPatch comprises three main components: (i) a multi-stage fusion strategy that leverages joint and late fusion simultaneously, (ii) a missingness-aware module that handles sparse samples with missing modalities, (iii) a joint fusion module that clusters latent token patches based on calibrated unimodal token-level confidence. We evaluated MedPatch using real-world data consisting of clinical time-series data, chest X-ray images, radiology reports, and discharge notes extracted from the MIMIC-IV, MIMIC-CXR, and MIMIC-Notes datasets on two benchmark tasks, namely in-hospital mortality prediction and clinical condition classification. Compared to existing baselines, MedPatch achieves state-of-the-art performance. Our work highlights the effectiveness of confidence-guided multi-stage fusion in addressing the heterogeneity of multimodal data, and establishes new state-of-the-art benchmark results for clinical prediction tasks.", "date": "2025-08-07", "categories": []}
{"id": "2508.09195", "title": "impuTMAE: Multi-modal Transformer with Masked Pre-training for Missing Modalities Imputation in Cancer Survival Prediction", "abstract": "The use of diverse modalities, such as omics, medical images, and clinical data can not only improve the performance of prognostic models but also deepen an understanding of disease mechanisms and facilitate the development of novel treatment approaches. However, medical data are complex, often incomplete, and contains missing modalities, making effective handling its crucial for training multimodal models. We introduce impuTMAE, a novel transformer-based end-to-end approach with an efficient multimodal pre-training strategy. It learns inter- and intra-modal interactions while simultaneously imputing missing modalities by reconstructing masked patches. Our model is pre-trained on heterogeneous, incomplete data and fine-tuned for glioma survival prediction using TCGA-GBM/LGG and BraTS datasets, integrating five modalities: genetic (DNAm, RNA-seq), imaging (MRI, WSI), and clinical data. By addressing missing data during pre-training and enabling efficient resource utilization, impuTMAE surpasses prior multimodal approaches, achieving state-of-the-art performance in glioma patient survival prediction. Our code is available at https://github.com/maryjis/mtcp", "date": "2025-08-08", "categories": []}
{"id": "2508.09747", "title": "A Machine Learning Approach to Predict Biological Age and its Longitudinal Drivers", "abstract": "Predicting an individual's aging trajectory is a central challenge in preventative medicine and bioinformatics. While machine learning models can predict chronological age from biomarkers, they often fail to capture the dynamic, longitudinal nature of the aging process. In this work, we developed and validated a machine learning pipeline to predict age using a longitudinal cohort with data from two distinct time periods (2019-2020 and 2021-2022). We demonstrate that a model using only static, cross-sectional biomarkers has limited predictive power when generalizing to future time points. However, by engineering novel features that explicitly capture the rate of change (slope) of key biomarkers over time, we significantly improved model performance. Our final LightGBM model, trained on the initial wave of data, successfully predicted age in the subsequent wave with high accuracy ($R^2 = 0.515$ for males, $R^2 = 0.498$ for females), significantly outperforming both traditional linear models and other tree-based ensembles. SHAP analysis of our successful model revealed that the engineered slope features were among the most important predictors, highlighting that an individual's health trajectory, not just their static health snapshot, is a key determinant of biological age. Our framework paves the way for clinical tools that dynamically track patient health trajectories, enabling early intervention and personalized prevention strategies for age-related diseases.", "date": "2025-08-13", "categories": []}
{"id": "2508.09059", "title": "Causal Machine Learning for Patient-Level Intraoperative Opioid Dose Prediction from Electronic Health Records", "abstract": "This paper introduces the OPIAID algorithm, a novel approach for predicting and recommending personalized opioid dosages for individual patients. The algorithm optimizes pain management while minimizing opioid related adverse events (ORADE) by employing machine learning models trained on observational electronic health records (EHR) data. It leverages a causal machine learning approach to understand the relationship between opioid dose, case specific patient and intraoperative characteristics, and pain versus ORADE outcomes. The OPIAID algorithm considers patient-specific characteristics and the influence of different opiates, enabling personalized dose recommendations. This paper outlines the algorithm's methodology and architecture, and discusses key assumptions, and approaches to evaluating its performance.", "date": "2025-08-12", "categories": []}
{"id": "2402.14701", "title": "COMPASS: Computational Mapping of Patient-Therapist Alliance Strategies with Language Modeling", "abstract": "The therapeutic working alliance is a critical predictor of psychotherapy success. Traditionally, working alliance assessment relies on questionnaires completed by both therapists and patients. In this paper, we present COMPASS, a novel framework to directly infer the therapeutic working alliance from the natural language used in psychotherapy sessions. Our approach leverages advanced large language models (LLMs) to analyze session transcripts and map them to distributed representations. These representations capture the semantic similarities between the dialogues and psychometric instruments, such as the Working Alliance Inventory. Analyzing a dataset of over 950 sessions spanning diverse psychiatric conditions -- including anxiety (N=498), depression (N=377), schizophrenia (N=71), and suicidal tendencies (N=12) -- collected between 1970 and 2012, we demonstrate the effectiveness of our method in providing fine-grained mapping of patient-therapist alignment trajectories, offering interpretable insights for clinical practice, and identifying emerging patterns related to the condition being treated. By employing various deep learning-based topic modeling techniques in combination with prompting generative language models, we analyze the topical characteristics of different psychiatric conditions and how these topics evolve during each turn of the conversation. This integrated framework enhances the understanding of therapeutic interactions, enables timely feedback for therapists on the quality of therapeutic relationships, and provides clear, actionable insights to improve the effectiveness of psychotherapy.", "date": "2024-02-22", "categories": []}
{"id": "2502.12204", "title": "Predicting Depression in Screening Interviews from Interactive Multi-Theme Collaboration", "abstract": "Automatic depression detection provides cues for early clinical intervention by clinicians. Clinical interviews for depression detection involve dialogues centered around multiple themes. Existing studies primarily design end-to-end neural network models to capture the hierarchical structure of clinical interview dialogues. However, these methods exhibit defects in modeling the thematic content of clinical interviews: 1) they fail to capture intra-theme and inter-theme correlation explicitly, and 2) they do not allow clinicians to intervene and focus on themes of interest. To address these issues, this paper introduces an interactive depression detection framework. This framework leverages in-context learning techniques to identify themes in clinical interviews and then models both intra-theme and inter-theme correlation. Additionally, it employs AI-driven feedback to simulate the interests of clinicians, enabling interactive adjustment of theme importance. PDIMC achieves absolute improvements of 35\\% and 12\\% compared to the state-of-the-art on the depression detection dataset DAIC-WOZ, which demonstrates the effectiveness of modeling theme correlation and incorporating interactive external feedback.", "date": "2025-02-16", "categories": []}
{"id": "2506.05386", "title": "Leaps Beyond the Seen: Reinforced Reasoning Augmented Generation for Clinical Notes", "abstract": "Clinical note generation aims to produce free-text summaries of a patient's condition and diagnostic process, with discharge instructions being a representative long-form example. While recent LLM-based methods pre-trained on general clinical corpora show promise in clinical text generation, they fall short in producing long-form notes from limited patient information. In this paper, we propose ReinRAG, a reinforced reasoning augmented generation (RAG) for long-form discharge instructions based on pre-admission information. ReinRAG retrieves reasoning paths from a medical knowledge graph to provide explicit semantic guidance to the LLM. To bridge the information gap, we propose group-based retriever optimization (GRO) which improves retrieval quality with group-normalized rewards, encouraging reasoning leaps for deeper inference by the LLM. Comprehensive experiments on the real-world dataset show that ReinRAG outperforms baselines in both clinical efficacy and natural language generation metrics. Further analysis reveals that ReinRAG fills semantic gaps in sparse input scenarios, and retrieved reasoning paths help LLMs avoid clinical misinterpretation by focusing on key evidence and following coherent reasoning.", "date": "2025-06-03", "categories": []}
{"id": "2507.22919", "title": "A novel language model for predicting serious adverse event results in clinical trials from their prospective registrations", "abstract": "Objectives: With accurate estimates of expected safety results, clinical trials could be better designed and monitored. We evaluated methods for predicting serious adverse event (SAE) results in clinical trials using information only from their registrations prior to the trial. Material and Methods: We analyzed 22,107 two-arm parallel interventional clinical trials from ClinicalTrials.gov with structured summary results. Two prediction models were developed: a classifier predicting whether a greater proportion of participants in an experimental arm would have SAEs (area under the receiver operating characteristic curve; AUC) compared to the control arm, and a regression model to predict the proportion of participants with SAEs in the control arms (root mean squared error; RMSE). A transfer learning approach using pretrained language models (e.g., ClinicalT5, BioBERT) was used for feature extraction, combined with a downstream model for prediction. To maintain semantic representation in long trial texts exceeding localized language model input limits, a sliding window method was developed for embedding extraction. Results: The best model (ClinicalT5+Transformer+MLP) had 77.6% AUC when predicting which trial arm had a higher proportion of SAEs. When predicting SAE proportion in the control arm, the same model achieved RMSE of 18.6%. The sliding window approach consistently outperformed direct comparisons. Across 12 classifiers, the average absolute AUC increase was 2.00%, and absolute RMSE reduction was 1.58% across 12 regressors. Discussion: Summary results data from ClinicalTrials.gov remains underutilized. Predicted results of publicly reported trials provides an opportunity to identify discrepancies between expected and reported safety results.", "date": "2025-07-21", "categories": []}
{"id": "2508.07127", "title": "How Effectively Can Large Language Models Connect SNP Variants and ECG Phenotypes for Cardiovascular Risk Prediction?", "abstract": "Cardiovascular disease (CVD) prediction remains a tremendous challenge due to its multifactorial etiology and global burden of morbidity and mortality. Despite the growing availability of genomic and electrophysiological data, extracting biologically meaningful insights from such high-dimensional, noisy, and sparsely annotated datasets remains a non-trivial task. Recently, LLMs has been applied effectively to predict structural variations in biological sequences. In this work, we explore the potential of fine-tuned LLMs to predict cardiac diseases and SNPs potentially leading to CVD risk using genetic markers derived from high-throughput genomic profiling. We investigate the effect of genetic patterns associated with cardiac conditions and evaluate how LLMs can learn latent biological relationships from structured and semi-structured genomic data obtained by mapping genetic aspects that are inherited from the family tree. By framing the problem as a Chain of Thought (CoT) reasoning task, the models are prompted to generate disease labels and articulate informed clinical deductions across diverse patient profiles and phenotypes. The findings highlight the promise of LLMs in contributing to early detection, risk assessment, and ultimately, the advancement of personalized medicine in cardiac care.", "date": "2025-08-10", "categories": []}
{"id": "2508.07681", "title": "MORE-CLEAR: Multimodal Offline Reinforcement learning for Clinical notes Leveraged Enhanced State Representation", "abstract": "Sepsis, a life-threatening inflammatory response to infection, causes organ dysfunction, making early detection and optimal management critical. Previous reinforcement learning (RL) approaches to sepsis management rely primarily on structured data, such as lab results or vital signs, and on a dearth of a comprehensive understanding of the patient's condition. In this work, we propose a Multimodal Offline REinforcement learning for Clinical notes Leveraged Enhanced stAte Representation (MORE-CLEAR) framework for sepsis control in intensive care units. MORE-CLEAR employs pre-trained large-scale language models (LLMs) to facilitate the extraction of rich semantic representations from clinical notes, preserving clinical context and improving patient state representation. Gated fusion and cross-modal attention allow dynamic weight adjustment in the context of time and the effective integration of multimodal data. Extensive cross-validation using two public (MIMIC-III and MIMIC-IV) and one private dataset demonstrates that MORE-CLEAR significantly improves estimated survival rate and policy performance compared to single-modal RL approaches. To our knowledge, this is the first to leverage LLM capabilities within a multimodal offline RL for better state representation in medical applications. This approach can potentially expedite the treatment and management of sepsis by enabling reinforcement learning models to propose enhanced actions based on a more comprehensive understanding of patient conditions.", "date": "2025-08-11", "categories": []}
{"id": "2508.06023", "title": "Stepwise Fine and Gray: Subject-Specific Variable Selection Shows When Hemodynamic Data Improves Prognostication of Comatose Post-Cardiac Arrest Patients", "abstract": "Prognostication for comatose post-cardiac arrest patients is a critical challenge that directly impacts clinical decision-making in the ICU. Clinical information that informs prognostication is collected serially over time. Shortly after cardiac arrest, various time-invariant baseline features are collected (e.g., demographics, cardiac arrest characteristics). After ICU admission, additional features are gathered, including time-varying hemodynamic data (e.g., blood pressure, doses of vasopressor medications). We view these as two phases in which we collect new features. In this study, we propose a novel stepwise dynamic competing risks model that improves the prediction of neurological outcomes by automatically determining when to take advantage of time-invariant features (first phase) and time-varying features (second phase). Notably, our model finds patients for whom this second phase (time-varying hemodynamic) information is beneficial for prognostication and also when this information is beneficial (as we collect more hemodynamic data for a patient over time, how important these data are for prognostication varies). Our approach extends the standard Fine and Gray model to explicitly model the two phases and to incorporate neural networks to flexibly capture complex nonlinear feature relationships. Evaluated on a retrospective cohort of 2,278 comatose post-arrest patients, our model demonstrates robust discriminative performance for the competing outcomes of awakening, withdrawal of life-sustaining therapy, and death despite maximal support. Our approach generalizes to more than two phases in which new features are collected and could be used in other dynamic prediction tasks, where it may be helpful to know when and for whom newly collected features significantly improve prediction.", "date": "2025-08-08", "categories": []}
{"id": "2508.06218", "title": "Interpretable Rheumatoid Arthritis Scoring via Anatomy-aware Multiple Instance Learning", "abstract": "The Sharp/van der Heijde (SvdH) score has been widely used in clinical trials to quantify radiographic damage in Rheumatoid Arthritis (RA), but its complexity has limited its adoption in routine clinical practice. To address the inefficiency of manual scoring, this work proposes a two-stage pipeline for interpretable image-level SvdH score prediction using dual-hand radiographs. Our approach extracts disease-relevant image regions and integrates them using attention-based multiple instance learning to generate image-level features for prediction. We propose two region extraction schemes: 1) sampling image tiles most likely to contain abnormalities, and 2) cropping patches containing disease-relevant joints. With Scheme 2, our best individual score prediction model achieved a Pearson's correlation coefficient (PCC) of 0.943 and a root mean squared error (RMSE) of 15.73. Ensemble learning further boosted prediction accuracy, yielding a PCC of 0.945 and RMSE of 15.57, achieving state-of-the-art performance that is comparable to that of experienced radiologists (PCC = 0.97, RMSE = 18.75). Finally, our pipeline effectively identified and made decisions based on anatomical structures which clinicians consider relevant to RA progression.", "date": "2025-08-08", "categories": []}
{"id": "2411.08882", "title": "A Novel Multimodal System to Predict Agitation in People with Dementia Within Clinical Settings: A Proof of Concept", "abstract": "Dementia is a neurodegenerative condition that combines several diseases and impacts millions around the world and those around them. Although cognitive impairment is profoundly disabling, it is the noncognitive features of dementia, referred to as Neuropsychiatric Symptoms (NPS), that are most closely associated with a diminished quality of life. Agitation and aggression (AA) in people living with dementia (PwD) contribute to distress and increased healthcare demands. Current assessment methods rely on caregiver intervention and reporting of incidents, introducing subjectivity and bias. Artificial Intelligence (AI) and predictive algorithms offer a potential solution for detecting AA episodes in PwD when utilized in real-time. We present a 5-year study system that integrates a multimodal approach, utilizing the EmbracePlus wristband and a video detection system to predict AA in severe dementia patients. We conducted a pilot study with three participants at the Ontario Shores Mental Health Institute to validate the functionality of the system. The system collects and processes raw and digital biomarkers from the EmbracePlus wristband to accurately predict AA. The system also detected pre-agitation patterns at least six minutes before the AA event, which was not previously discovered from the EmbracePlus wristband. Furthermore, the privacy-preserving video system uses a masking tool to hide the features of the people in frames and employs a deep learning model for AA detection. The video system also helps identify the actual start and end time of the agitation events for labeling. The promising results of the preliminary data analysis underscore the ability of the system to predict AA events. The ability of the proposed system to run autonomously in real-time and identify AA and pre-agitation symptoms without external assistance represents a significant milestone in this research field.", "date": "2024-10-26", "categories": []}
{"id": "2507.21197", "title": "An MLI-Guided Framework for Subgroup-Aware Modeling in Electronic Health Records (AdaptHetero)", "abstract": "Machine learning interpretation (MLI) has primarily been leveraged to foster clinician trust and extract insights from electronic health records (EHRs), rather than to guide subgroup-specific, operationalizable modeling strategies. To bridge this gap, we propose AdaptHetero, a novel MLI-driven framework that transforms interpretability insights into actionable guidance for tailoring model training and evaluation across subpopulations. Evaluated on three large-scale EHR datasets -- GOSSIS-1-eICU, WiDS, and MIMIC-IV -- AdaptHetero consistently uncovers heterogeneous model behaviors in predicting ICU mortality, in-hospital death, and hidden hypoxemia. Integrating SHAP-based interpretation with unsupervised clustering, AdaptHetero identifies clinically meaningful, subgroup-specific characteristics, improving predictive performance across many subpopulations (with gains up to 174.39 percent) while proactively flagging potential risks in others. These results highlight the framework's promise for more robust, equitable, and context-aware clinical deployment.", "date": "2025-07-28", "categories": []}
{"id": "2508.05222", "title": "ML-based Short Physical Performance Battery future score prediction based on questionnaire data", "abstract": "Effective slowing down of older adults\\' physical capacity deterioration requires intervention as soon as the first symptoms surface. In this paper, we analyze the possibility of predicting the Short Physical Performance Battery (SPPB) score at a four-year horizon based on questionnaire data. The ML algorithms tested included Random Forest, XGBoost, Linear Regression, dense and TabNet neural networks. The best results were achieved for the XGBoost (mean absolute error of 0.79 points). Based on the Shapley values analysis, we selected smaller subsets of features (from 10 to 20) and retrained the XGBoost regressor, achieving a mean absolute error of 0.82.", "date": "2025-08-07", "categories": []}
{"id": "2508.05391", "title": "Artificial Intelligence-Based Classification of Spitz Tumors", "abstract": "Spitz tumors are diagnostically challenging due to overlap in atypical histological features with conventional melanomas. We investigated to what extent AI models, using histological and/or clinical features, can: (1) distinguish Spitz tumors from conventional melanomas; (2) predict the underlying genetic aberration of Spitz tumors; and (3) predict the diagnostic category of Spitz tumors. The AI models were developed and validated using a dataset of 393 Spitz tumors and 379 conventional melanomas. Predictive performance was measured using the AUROC and the accuracy. The performance of the AI models was compared with that of four experienced pathologists in a reader study. Moreover, a simulation experiment was conducted to investigate the impact of implementing AI-based recommendations for ancillary diagnostic testing on the workflow of the pathology department. The best AI model based on UNI features reached an AUROC of 0.95 and an accuracy of 0.86 in differentiating Spitz tumors from conventional melanomas. The genetic aberration was predicted with an accuracy of 0.55 compared to 0.25 for randomly guessing. The diagnostic category was predicted with an accuracy of 0.51, where random chance-level accuracy equaled 0.33. On all three tasks, the AI models performed better than the four pathologists, although differences were not statistically significant for most individual comparisons. Based on the simulation experiment, implementing AI-based recommendations for ancillary diagnostic testing could reduce material costs, turnaround times, and examinations. In conclusion, the AI models achieved a strong predictive performance in distinguishing between Spitz tumors and conventional melanomas. On the more challenging tasks of predicting the genetic aberration and the diagnostic category of Spitz tumors, the AI models performed better than random chance.", "date": "2025-08-07", "categories": []}
{"id": "2508.05472", "title": "Prediction of Survival Outcomes under Clinical Presence Shift: A Joint Neural Network Architecture", "abstract": "Electronic health records arise from the complex interaction between patients and the healthcare system. This observation process of interactions, referred to as clinical presence, often impacts observed outcomes. When using electronic health records to develop clinical prediction models, it is standard practice to overlook clinical presence, impacting performance and limiting the transportability of models when this interaction evolves. We propose a multi-task recurrent neural network that jointly models the inter-observation time and the missingness processes characterising this interaction in parallel to the survival outcome of interest. Our work formalises the concept of clinical presence shift when the prediction model is deployed in new settings (e.g. different hospitals, regions or countries), and we theoretically justify why the proposed joint modelling can improve transportability under changes in clinical presence. We demonstrate, in a real-world mortality prediction task in the MIMIC-III dataset, how the proposed strategy improves performance and transportability compared to state-of-the-art prediction models that do not incorporate the observation process. These results emphasise the importance of leveraging clinical presence to improve performance and create more transportable clinical prediction models.", "date": "2025-08-07", "categories": []}
{"id": "2508.05492", "title": "MoMA: A Mixture-of-Multimodal-Agents Architecture for Enhancing Clinical Prediction Modelling", "abstract": "Multimodal electronic health record (EHR) data provide richer, complementary insights into patient health compared to single-modality data. However, effectively integrating diverse data modalities for clinical prediction modeling remains challenging due to the substantial data requirements. We introduce a novel architecture, Mixture-of-Multimodal-Agents (MoMA), designed to leverage multiple large language model (LLM) agents for clinical prediction tasks using multimodal EHR data. MoMA employs specialized LLM agents (\"specialist agents\") to convert non-textual modalities, such as medical images and laboratory results, into structured textual summaries. These summaries, together with clinical notes, are combined by another LLM (\"aggregator agent\") to generate a unified multimodal summary, which is then used by a third LLM (\"predictor agent\") to produce clinical predictions. Evaluating MoMA on three prediction tasks using real-world datasets with different modality combinations and prediction settings, MoMA outperforms current state-of-the-art methods, highlighting its enhanced accuracy and flexibility across various tasks.", "date": "2025-08-07", "categories": []}
{"id": "2502.06124", "title": "Foundation Model of Electronic Medical Records for Adaptive Risk Estimation", "abstract": "Hospitals struggle to predict critical outcomes. Traditional early warning systems, like NEWS and MEWS, rely on static variables and fixed thresholds, limiting their adaptability, accuracy, and personalization. We previously developed the Enhanced Transformer for Health Outcome Simulation (ETHOS), an AI model that tokenizes patient health timelines (PHTs) from EHRs and uses transformer-based architectures to predict future PHTs. ETHOS is a versatile framework for developing a wide range of applications. In this work, we develop the Adaptive Risk Estimation System (ARES) that leverages ETHOS to compute dynamic, personalized risk probabilities for clinician-defined critical events. ARES also features a personalized explainability module that highlights key clinical factors influencing risk estimates. We evaluated ARES using the MIMIC-IV v2.2 dataset together with its Emergency Department (ED) extension and benchmarked performance against both classical early warning systems and contemporary machine learning models. The entire dataset was tokenized resulting in 285,622 PHTs, comprising over 360 million tokens. ETHOS outperformed benchmark models in predicting hospital admissions, ICU admissions, and prolonged stays, achieving superior AUC scores. Its risk estimates were robust across demographic subgroups, with calibration curves confirming model reliability. The explainability module provided valuable insights into patient-specific risk factors. ARES, powered by ETHOS, advances predictive healthcare AI by delivering dynamic, real-time, personalized risk estimation with patient-specific explainability. Although our results are promising, the clinical impact remains uncertain. Demonstrating ARES's true utility in real-world settings will be the focus of our future work. We release the source code to facilitate future research.", "date": "2025-02-10", "categories": []}
{"id": "2508.03756", "title": "Predicting fall risk in older adults: A machine learning comparison of accelerometric and non-accelerometric factors", "abstract": "This study investigates fall risk prediction in older adults using various machine learning models trained on accelerometric, non-accelerometric, and combined data from 146 participants. Models combining both data types achieved superior performance, with Bayesian Ridge Regression showing the highest accuracy (MSE = 0.6746, R2 = 0.9941). Non-accelerometric variables, such as age and comorbidities, proved critical for prediction. Results support the use of integrated data and Bayesian approaches to enhance fall risk assessment and inform prevention strategies.", "date": "2025-08-04", "categories": []}
{"id": "2508.03635", "title": "Cross-patient Seizure Onset Zone Classification by Patient-Dependent Weight", "abstract": "Identifying the seizure onset zone (SOZ) in patients with focal epilepsy is essential for surgical treatment and remains challenging due to its dependence on visual judgment by clinical experts. The development of machine learning can assist in diagnosis and has made promising progress. However, unlike data in other fields, medical data is usually collected from individual patients, and each patient has different illnesses, physical conditions, and medical histories, which leads to differences in the distribution of each patient's data. This makes it difficult for a machine learning model to achieve consistently reliable performance in every new patient dataset, which we refer to as the \"cross-patient problem.\" In this paper, we propose a method to fine-tune a pretrained model using patient-specific weights for every new test patient to improve diagnostic performance. First, the supervised learning method is used to train a machine learning model. Next, using the intermediate features of the trained model obtained through the test patient data, the similarity between the test patient data and each training patient's data is defined to determine the weight of each training patient to be used in the following fine-tuning. Finally, we fine-tune all parameters in the pretrained model with training data and patient weights. In the experiment, the leave-one-patient-out method is used to evaluate the proposed method, and the results show improved classification accuracy for every test patient, with an average improvement of more than 10%.", "date": "2025-08-05", "categories": []}
{"id": "2410.04133", "title": "An Electrocardiogram Foundation Model Built on over 10 Million Recordings with External Evaluation across Multiple Domains", "abstract": "Artificial intelligence (AI) has demonstrated significant potential in ECG analysis and cardiovascular disease assessment. Recently, foundation models have played a remarkable role in advancing medical AI. The development of an ECG foundation model holds the promise of elevating AI-ECG research to new heights. However, building such a model faces several challenges, including insufficient database sample sizes and inadequate generalization across multiple domains. Additionally, there is a notable performance gap between single-lead and multi-lead ECG analyses. We introduced an ECG Foundation Model (ECGFounder), a general-purpose model that leverages real-world ECG annotations from cardiology experts to broaden the diagnostic capabilities of ECG analysis. ECGFounder was trained on over 10 million ECGs with 150 label categories from the Harvard-Emory ECG Database, enabling comprehensive cardiovascular disease diagnosis through ECG analysis. The model is designed to be both an effective out-of-the-box solution, and a to be fine-tunable for downstream tasks, maximizing usability. Importantly, we extended its application to lower rank ECGs, and arbitrary single-lead ECGs in particular. ECGFounder is applicable to supporting various downstream tasks in mobile monitoring scenarios. Experimental results demonstrate that ECGFounder achieves expert-level performance on internal validation sets, with AUROC exceeding 0.95 for eighty diagnoses. It also shows strong classification performance and generalization across various diagnoses on external validation sets. When fine-tuned, ECGFounder outperforms baseline models in demographic analysis, clinical event detection, and cross-modality cardiac rhythm diagnosis. The trained model and data will be publicly released upon publication through the bdsp.io. Our code is available at https://github.com/PKUDigitalHealth/ECGFounder", "date": "2024-10-05", "categories": []}
{"id": "2505.09805", "title": "Contextual Phenotyping of Pediatric Sepsis Cohort Using Large Language Models", "abstract": "Clustering patient subgroups is essential for personalized care and efficient resource use. Traditional clustering methods struggle with high-dimensional, heterogeneous healthcare data and lack contextual understanding. This study evaluates Large Language Model (LLM) based clustering against classical methods using a pediatric sepsis dataset from a low-income country (LIC), containing 2,686 records with 28 numerical and 119 categorical variables. Patient records were serialized into text with and without a clustering objective. Embeddings were generated using quantized LLAMA 3.1 8B, DeepSeek-R1-Distill-Llama-8B with low-rank adaptation(LoRA), and Stella-En-400M-V5 models. K-means clustering was applied to these embeddings. Classical comparisons included K-Medoids clustering on UMAP and FAMD-reduced mixed data. Silhouette scores and statistical tests evaluated cluster quality and distinctiveness. Stella-En-400M-V5 achieved the highest Silhouette Score (0.86). LLAMA 3.1 8B with the clustering objective performed better with higher number of clusters, identifying subgroups with distinct nutritional, clinical, and socioeconomic profiles. LLM-based methods outperformed classical techniques by capturing richer context and prioritizing key features. These results highlight potential of LLMs for contextual phenotyping and informed decision-making in resource-limited settings.", "date": "2025-05-14", "categories": []}
{"id": "2508.02307", "title": "Whole-body Representation Learning For Competing Preclinical Disease Risk Assessment", "abstract": "Reliable preclinical disease risk assessment is essential to move public healthcare from reactive treatment to proactive identification and prevention. However, image-based risk prediction algorithms often consider one condition at a time and depend on hand-crafted features obtained through segmentation tools. We propose a whole-body self-supervised representation learning method for the preclinical disease risk assessment under a competing risk modeling. This approach outperforms whole-body radiomics in multiple diseases, including cardiovascular disease (CVD), type 2 diabetes (T2D), chronic obstructive pulmonary disease (COPD), and chronic kidney disease (CKD). Simulating a preclinical screening scenario and subsequently combining with cardiac MRI, it sharpens further the prediction for CVD subgroups: ischemic heart disease (IHD), hypertensive diseases (HD), and stroke. The results indicate the translational potential of whole-body representations as a standalone screening modality and as part of a multi-modal framework within clinical workflows for early personalized risk stratification. The code is available at https://github.com/yayapa/WBRLforCR/", "date": "2025-08-04", "categories": []}
{"id": "2508.02354", "title": "Detecting COPD Through Speech Analysis: A Dataset of Danish Speech and Machine Learning Approach", "abstract": "Chronic Obstructive Pulmonary Disease (COPD) is a serious and debilitating disease affecting millions around the world. Its early detection using non-invasive means could enable preventive interventions that improve quality of life and patient outcomes, with speech recently shown to be a valuable biomarker. Yet, its validity across different linguistic groups remains to be seen. To that end, audio data were collected from 96 Danish participants conducting three speech tasks (reading, coughing, sustained vowels). Half of the participants were diagnosed with different levels of COPD and the other half formed a healthy control group. Subsequently, we investigated different baseline models using openSMILE features and learnt x-vector embeddings. We obtained a best accuracy of 67% using openSMILE features and logistic regression. Our findings support the potential of speech-based analysis as a non-invasive, remote, and scalable screening tool as part of future COPD healthcare solutions.", "date": "2025-08-04", "categories": []}
{"id": "2412.09727", "title": "A Large Sensor Foundation Model Pretrained on Continuous Glucose Monitor Data for Diabetes Management", "abstract": "Continuous glucose monitoring (CGM) combined with AI offers new opportunities for proactive diabetes management through real-time glucose forecasting. However, most existing models are task-specific and lack generalization across patient populations. Inspired by the autoregressive paradigm of large language models, we introduce CGM-LSM, a Transformer decoder-based Large Sensor Model (LSM) pretrained on 1.6 million CGM records from patients with different diabetes types, ages, and genders. We model patients as sequences of glucose time steps to learn latent knowledge embedded in CGM data and apply it to the prediction of glucose readings for a 2-hour horizon. Compared with prior methods, CGM-LSM significantly improves prediction accuracy and robustness: a 48.51% reduction in root mean square error in one-hour horizon forecasting and consistent zero-shot prediction performance across held-out patient groups. We analyze model performance variations across patient subgroups and prediction scenarios and outline key opportunities and challenges for advancing CGM foundation models.", "date": "2024-12-12", "categories": []}
{"id": "2508.00285", "title": "Integrating clinical reasoning into large language model-based diagnosis through etiology-aware attention steering", "abstract": "Objective: Large Language Models (LLMs) demonstrate significant capabilities in medical text understanding and generation. However, their diagnostic reliability in complex clinical scenarios remains limited. This study aims to enhance LLMs' diagnostic accuracy and clinical reasoning ability. Method: We propose an Etiology-Aware Attention Steering Framework to integrate structured clinical reasoning into LLM-based diagnosis. Specifically, we first construct Clinical Reasoning Scaffolding (CRS) based on authoritative clinical guidelines for three representative acute abdominal emergencies: acute appendicitis, acute pancreatitis, and acute cholecystitis. Next, we develop the Etiology-Aware Head Identification algorithm to pinpoint attention heads crucial for the model's etiology reasoning. To ensure reliable clinical reasoning alignment, we introduce the Reasoning-Guided Parameter-Efficient Fine-tuning that embeds etiological reasoning cues into input representations and steers the selected Etiology-Aware Heads toward critical information through a Reasoning-Guided Loss function. Result: On the Consistent Diagnosis Cohort, our framework improves average diagnostic accuracy by 15.65% and boosts the average Reasoning Focus Score by 31.6% over baselines. External validation on the Discrepant Diagnosis Cohort further confirms its effectiveness in enhancing diagnostic accuracy. Further assessments via Reasoning Attention Frequency indicate that our models exhibit enhanced reliability when faced with real-world complex scenarios. Conclusion: This study presents a practical and effective approach to enhance clinical reasoning in LLM-based diagnosis. By aligning model attention with structured CRS, the proposed framework offers a promising paradigm for building more interpretable and reliable AI diagnostic systems in complex clinical settings.", "date": "2025-08-01", "categories": []}
{"id": "2508.00581", "title": "From EMR Data to Clinical Insight: An LLM-Driven Framework for Automated Pre-Consultation Questionnaire Generation", "abstract": "Pre-consultation is a critical component of effective healthcare delivery. However, generating comprehensive pre-consultation questionnaires from complex, voluminous Electronic Medical Records (EMRs) is a challenging task. Direct Large Language Model (LLM) approaches face difficulties in this task, particularly regarding information completeness, logical order, and disease-level synthesis. To address this issue, we propose a novel multi-stage LLM-driven framework: Stage 1 extracts atomic assertions (key facts with timing) from EMRs; Stage 2 constructs personal causal networks and synthesizes disease knowledge by clustering representative networks from an EMR corpus; Stage 3 generates tailored personal and standardized disease-specific questionnaires based on these structured representations. This framework overcomes limitations of direct methods by building explicit clinical knowledge. Evaluated on a real-world EMR dataset and validated by clinical experts, our method demonstrates superior performance in information coverage, diagnostic relevance, understandability, and generation time, highlighting its practical potential to enhance patient information collection.", "date": "2025-08-01", "categories": []}
{"id": "2508.00657", "title": "TrajSurv: Learning Continuous Latent Trajectories from Electronic Health Records for Trustworthy Survival Prediction", "abstract": "Trustworthy survival prediction is essential for clinical decision making. Longitudinal electronic health records (EHRs) provide a uniquely powerful opportunity for the prediction. However, it is challenging to accurately model the continuous clinical progression of patients underlying the irregularly sampled clinical features and to transparently link the progression to survival outcomes. To address these challenges, we develop TrajSurv, a model that learns continuous latent trajectories from longitudinal EHR data for trustworthy survival prediction. TrajSurv employs a neural controlled differential equation (NCDE) to extract continuous-time latent states from the irregularly sampled data, forming continuous latent trajectories. To ensure the latent trajectories reflect the clinical progression, TrajSurv aligns the latent state space with patient state space through a time-aware contrastive learning approach. To transparently link clinical progression to the survival outcome, TrajSurv uses latent trajectories in a two-step divide-and-conquer interpretation process. First, it explains how the changes in clinical features translate into the latent trajectory's evolution using a learned vector field. Second, it clusters these latent trajectories to identify key clinical progression patterns associated with different survival outcomes. Evaluations on two real-world medical datasets, MIMIC-III and eICU, show TrajSurv's competitive accuracy and superior transparency over existing deep learning methods.", "date": "2025-08-01", "categories": []}
{"id": "2508.00695", "title": "Classification of Psychiatry Clinical Notes by Diagnosis: A Deep Learning and Machine Learning Approach", "abstract": "The classification of clinical notes into specific diagnostic categories is critical in healthcare, especially for mental health conditions like Anxiety and Adjustment Disorder. In this study, we compare the performance of various Artificial Intelligence models, including both traditional Machine Learning approaches (Random Forest, Support Vector Machine, K-nearest neighbors, Decision Tree, and eXtreme Gradient Boost) and Deep Learning models (DistilBERT and SciBERT), to classify clinical notes into these two diagnoses. Additionally, we implemented three oversampling strategies: No Oversampling, Random Oversampling, and Synthetic Minority Oversampling Technique (SMOTE), to assess their impact on model performance. Hyperparameter tuning was also applied to optimize model accuracy. Our results indicate that oversampling techniques had minimal impact on model performance overall. The only exception was SMOTE, which showed a positive effect specifically with BERT-based models. However, hyperparameter optimization significantly improved accuracy across the models, enhancing their ability to generalize and perform on the dataset. The Decision Tree and eXtreme Gradient Boost models achieved the highest accuracy among machine learning approaches, both reaching 96%, while the DistilBERT and SciBERT models also attained 96% accuracy in the deep learning category. These findings underscore the importance of hyperparameter tuning in maximizing model performance. This study contributes to the ongoing research on AI-assisted diagnostic tools in mental health by providing insights into the efficacy of different model architectures and data balancing methods.", "date": "2025-08-01", "categories": []}
{"id": "2408.08792", "title": "Assessing Generalization Capabilities of Malaria Diagnostic Models from Thin Blood Smears", "abstract": "Malaria remains a significant global health challenge, necessitating rapid and accurate diagnostic methods. While computer-aided diagnosis (CAD) tools utilizing deep learning have shown promise, their generalization to diverse clinical settings remains poorly assessed. This study evaluates the generalization capabilities of a CAD model for malaria diagnosis from thin blood smear images across four sites. We explore strategies to enhance generalization, including fine-tuning and incremental learning. Our results demonstrate that incorporating site-specific data significantly improves model performance, paving the way for broader clinical application.", "date": "2024-08-16", "categories": []}
{"id": "2507.22941", "title": "SigBERT: Combining Narrative Medical Reports and Rough Path Signature Theory for Survival Risk Estimation in Oncology", "abstract": "Electronic medical reports (EHR) contain a vast amount of information that can be leveraged for machine learning applications in healthcare. However, existing survival analysis methods often struggle to effectively handle the complexity of textual data, particularly in its sequential form. Here, we propose SigBERT, an innovative temporal survival analysis framework designed to efficiently process a large number of clinical reports per patient. SigBERT processes timestamped medical reports by extracting and averaging word embeddings into sentence embeddings. To capture temporal dynamics from the time series of sentence embedding coordinates, we apply signature extraction from rough path theory to derive geometric features for each patient, which significantly enhance survival model performance by capturing complex temporal dynamics. These features are then integrated into a LASSO-penalized Cox model to estimate patient-specific risk scores. The model was trained and evaluated on a real-world oncology dataset from the L\\'eon B\\'erard Center corpus, with a C-index score of 0.75 (sd 0.014) on the independent test cohort. SigBERT integrates sequential medical data to enhance risk estimation, advancing narrative-based survival analysis.", "date": "2025-07-25", "categories": []}
{"id": "2507.23043", "title": "Prediction of Significant Creatinine Elevation in First ICU Stays with Vancomycin Use: A retrospective study through Catboost", "abstract": "Background: Vancomycin, a key antibiotic for severe Gram-positive infections in ICUs, poses a high nephrotoxicity risk. Early prediction of kidney injury in critically ill patients is challenging. This study aimed to develop a machine learning model to predict vancomycin-related creatinine elevation using routine ICU data.\n  Methods: We analyzed 10,288 ICU patients (aged 18-80) from the MIMIC-IV database who received vancomycin. Kidney injury was defined by KDIGO criteria (creatinine rise >=0.3 mg/dL within 48h or >=50% within 7d). Features were selected via SelectKBest (top 30) and Random Forest ranking (final 15). Six algorithms were tested with 5-fold cross-validation. Interpretability was evaluated using SHAP, Accumulated Local Effects (ALE), and Bayesian posterior sampling.\n  Results: Of 10,288 patients, 2,903 (28.2%) developed creatinine elevation. CatBoost performed best (AUROC 0.818 [95% CI: 0.801-0.834], sensitivity 0.800, specificity 0.681, negative predictive value 0.900). Key predictors were phosphate, total bilirubin, magnesium, Charlson index, and APSIII. SHAP confirmed phosphate as a major risk factor. ALE showed dose-response patterns. Bayesian analysis estimated mean risk 60.5% (95% credible interval: 16.8-89.4%) in high-risk cases.\n  Conclusions: This machine learning model predicts vancomycin-associated creatinine elevation from routine ICU data with strong accuracy and interpretability, enabling early risk detection and supporting timely interventions in critical care.", "date": "2025-07-30", "categories": []}
{"id": "2507.22776", "title": "Label-free estimation of clinically relevant performance metrics under distribution shifts", "abstract": "Performance monitoring is essential for safe clinical deployment of image classification models. However, because ground-truth labels are typically unavailable in the target dataset, direct assessment of real-world model performance is infeasible. State-of-the-art performance estimation methods address this by leveraging confidence scores to estimate the target accuracy. Despite being a promising direction, the established methods mainly estimate the model's accuracy and are rarely evaluated in a clinical domain, where strong class imbalances and dataset shifts are common. Our contributions are twofold: First, we introduce generalisations of existing performance prediction methods that directly estimate the full confusion matrix. Then, we benchmark their performance on chest x-ray data in real-world distribution shifts as well as simulated covariate and prevalence shifts. The proposed confusion matrix estimation methods reliably predicted clinically relevant counting metrics on medical images under distribution shifts. However, our simulated shift scenarios exposed important failure modes of current performance estimation techniques, calling for a better understanding of real-world deployment contexts when implementing these performance monitoring techniques for postmarket surveillance of medical AI models.", "date": "2025-07-30", "categories": []}
{"id": "2507.22798", "title": "Quantifying surprise in clinical care: Detecting highly informative events in electronic health records with foundation models", "abstract": "We present a foundation model-derived method to identify highly informative tokens and events in electronic health records. Our approach considers incoming data in the entire context of a patient's hospitalization and so can flag anomalous events that rule-based approaches would consider within a normal range. We demonstrate that the events our model flags are significant for predicting downstream patient outcomes and that a fraction of events identified as carrying little information can safely be dropped. Additionally, we show how informativeness can help interpret the predictions of prognostic models trained on foundation model-derived representations.", "date": "2025-07-30", "categories": []}
{"id": "2507.21188", "title": "Embeddings to Diagnosis: Latent Fragility under Agentic Perturbations in Clinical LLMs", "abstract": "LLMs for clinical decision support often fail under small but clinically meaningful input shifts such as masking a symptom or negating a finding, despite high performance on static benchmarks. These reasoning failures frequently go undetected by standard NLP metrics, which are insensitive to latent representation shifts that drive diagnosis instability. We propose a geometry-aware evaluation framework, LAPD (Latent Agentic Perturbation Diagnostics), which systematically probes the latent robustness of clinical LLMs under structured adversarial edits. Within this framework, we introduce Latent Diagnosis Flip Rate (LDFR), a model-agnostic diagnostic signal that captures representational instability when embeddings cross decision boundaries in PCA-reduced latent space. Clinical notes are generated using a structured prompting pipeline grounded in diagnostic reasoning, then perturbed along four axes: masking, negation, synonym replacement, and numeric variation to simulate common ambiguities and omissions. We compute LDFR across both foundation and clinical LLMs, finding that latent fragility emerges even under minimal surface-level changes. Finally, we validate our findings on 90 real clinical notes from the DiReCT benchmark (MIMIC-IV), confirming the generalizability of LDFR beyond synthetic settings. Our results reveal a persistent gap between surface robustness and semantic stability, underscoring the importance of geometry-aware auditing in safety-critical clinical AI.", "date": "2025-07-27", "categories": []}
{"id": "2507.21302", "title": "Can human clinical rationales improve the performance and explainability of clinical text classification models?", "abstract": "AI-driven clinical text classification is vital for explainable automated retrieval of population-level health information. This work investigates whether human-based clinical rationales can serve as additional supervision to improve both performance and explainability of transformer-based models that automatically encode clinical documents. We analyzed 99,125 human-based clinical rationales that provide plausible explanations for primary cancer site diagnoses, using them as additional training samples alongside 128,649 electronic pathology reports to evaluate transformer-based models for extracting primary cancer sites. We also investigated sufficiency as a way to measure rationale quality for pre-selecting rationales. Our results showed that clinical rationales as additional training data can improve model performance in high-resource scenarios but produce inconsistent behavior when resources are limited. Using sufficiency as an automatic metric to preselect rationales also leads to inconsistent results. Importantly, models trained on rationales were consistently outperformed by models trained on additional reports instead. This suggests that clinical rationales don't consistently improve model performance and are outperformed by simply using more reports. Therefore, if the goal is optimizing accuracy, annotation efforts should focus on labeling more reports rather than creating rationales. However, if explainability is the priority, training models on rationale-supplemented data may help them better identify rationale-like features. We conclude that using clinical rationales as additional training data results in smaller performance improvements and only slightly better explainability (measured as average token-level rationale coverage) compared to training on additional reports.", "date": "2025-07-28", "categories": []}
{"id": "2507.21330", "title": "Predicting VBAC Outcomes from U.S. Natality Data using Deep and Classical Machine Learning Models", "abstract": "Accurately predicting the outcome of a trial of labor after cesarean (TOLAC) is essential for guiding prenatal counseling and minimizing delivery-related risks. This study presents supervised machine learning models for predicting vaginal birth after cesarean (VBAC) using 643,029 TOLAC cases from the CDC WONDER Natality dataset (2017-2023). After filtering for singleton births with one or two prior cesareans and complete data across 47 prenatal-period features, three classifiers were trained: logistic regression, XGBoost, and a multilayer perceptron (MLP). The MLP achieved the highest performance with an AUC of 0.7287, followed closely by XGBoost (AUC = 0.727), both surpassing the logistic regression baseline (AUC = 0.709). To address class imbalance, class weighting was applied to the MLP, and a custom loss function was implemented in XGBoost. Evaluation metrics included ROC curves, confusion matrices, and precision-recall analysis. Logistic regression coefficients highlighted maternal BMI, education, parity, comorbidities, and prenatal care indicators as key predictors. Overall, the results demonstrate that routinely collected, early-pregnancy variables can support scalable and moderately high-performing VBAC prediction models. These models offer potential utility in clinical decision support, particularly in settings lacking access to specialized intrapartum data.", "date": "2025-07-28", "categories": []}
{"id": "2507.21898", "title": "Cardiovascular Disease Prediction using Machine Learning: A Comparative Analysis", "abstract": "Cardiovascular diseases (CVDs) are a main cause of mortality globally, accounting for 31% of all deaths. This study involves a cardiovascular disease (CVD) dataset comprising 68,119 records to explore the influence of numerical (age, height, weight, blood pressure, BMI) and categorical gender, cholesterol, glucose, smoking, alcohol, activity) factors on CVD occurrence. We have performed statistical analyses, including t-tests, Chi-square tests, and ANOVA, to identify strong associations between CVD and elderly people, hypertension, higher weight, and abnormal cholesterol levels, while physical activity (a protective factor). A logistic regression model highlights age, blood pressure, and cholesterol as primary risk factors, with unexpected negative associations for smoking and alcohol, suggesting potential data issues. Model performance comparisons reveal CatBoost as the top performer with an accuracy of 0.734 and an ECE of 0.0064 and excels in probabilistic prediction (Brier score = 0.1824). Data challenges, including outliers and skewed distributions, indicate a need for improved preprocessing to enhance predictive reliability.", "date": "2025-07-29", "categories": []}
{"id": "2407.19299", "title": "The Impact of LoRA Adapters on LLMs for Clinical Text Classification Under Computational and Data Constraints", "abstract": "Fine-tuning Large Language Models (LLMs) for clinical Natural Language Processing (NLP) poses significant challenges due to domain gap, limited data, and stringent hardware constraints. In this study, we evaluate four adapter techniques-Adapter, Lightweight, TinyAttention, and Gated Residual Network (GRN) - equivalent to Low-Rank Adaptation (LoRA), for clinical note classification under real-world, resource-constrained conditions. All experiments were conducted on a single NVIDIA Quadro P620 GPU (2 GB VRAM, 512 CUDA cores, 1.386 TFLOPS FP32), limiting batch sizes to <8 sequences and maximum sequence length to 256 tokens. Our clinical corpus comprises only 580 000 tokens, several orders of magnitude smaller than standard LLM pre-training datasets. We fine-tuned three biomedical pre-trained LLMs (CamemBERT-bio, AliBERT, DrBERT) and two lightweight Transformer models trained from scratch. Results show that 1) adapter structures provide no consistent gains when fine-tuning biomedical LLMs under these constraints, and 2) simpler Transformers, with minimal parameter counts and training times under six hours, outperform adapter-augmented LLMs, which required over 1000 GPU-hours. Among adapters, GRN achieved the best metrics (accuracy, precision, recall, F1 = 0.88). These findings demonstrate that, in low-resource clinical settings with limited data and compute, lightweight Transformers trained from scratch offer a more practical and efficient solution than large LLMs, while GRN remains a viable adapter choice when minimal adaptation is needed.", "date": "2024-07-27", "categories": []}
{"id": "2503.08960", "title": "Are ECGs enough? Deep learning classification of pulmonary embolism using electrocardiograms", "abstract": "Pulmonary embolism is a leading cause of out of hospital cardiac arrest that requires fast diagnosis. While computed tomography pulmonary angiography is the standard diagnostic tool, it is not always accessible. Electrocardiography is an essential tool for diagnosing multiple cardiac anomalies, as it is affordable, fast and available in many settings. However, the availability of public ECG datasets, specially for PE, is limited and, in practice, these datasets tend to be small, making it essential to optimize learning strategies. In this study, we investigate the performance of multiple neural networks in order to assess the impact of various approaches. Moreover, we check whether these practices enhance model generalization when transfer learning is used to translate information learned in larger ECG datasets, such as PTB-XL, CPSC18 and MedalCare-XL, to a smaller, more challenging dataset for PE. By leveraging transfer learning, we analyze the extent to which we can improve learning efficiency and predictive performance on limited data. Code available at https://github.com/joaodsmarques/Are-ECGs-enough-Deep-Learning-Classifiers .", "date": "2025-03-11", "categories": []}
{"id": "2505.09619", "title": "Machine Learning Solutions Integrated in an IoT Healthcare Platform for Heart Failure Risk Stratification", "abstract": "The management of chronic Heart Failure (HF) presents significant challenges in modern healthcare, requiring continuous monitoring, early detection of exacerbations, and personalized treatment strategies. In this paper, we present a predictive model founded on Machine Learning (ML) techniques to identify patients at HF risk. This model is an ensemble learning approach, a modified stacking technique, that uses two specialized models leveraging clinical and echocardiographic features and then a meta-model to combine the predictions of these two models. We initially assess the model on a real dataset and the obtained results suggest that it performs well in the stratification of patients at HR risk. Specifically, we obtained high sensitivity (95\\%), ensuring that nearly all high-risk patients are identified. As for accuracy, we obtained 84\\%, which can be considered moderate in some ML contexts. However, it is acceptable given our priority of identifying patients at risk of HF because they will be asked to participate in the telemonitoring program of the PrediHealth research project on which some of the authors of this paper are working. The initial findings also suggest that ML-based risk stratification models can serve as valuable decision-support tools not only in the PrediHealth project but also for healthcare professionals, aiding in early intervention and personalized patient management. To have a better understanding of the value and of potentiality of our predictive model, we also contrasted its results with those obtained by using three baseline models. The preliminary results indicate that our predictive model outperforms these baselines that flatly consider features, \\ie not grouping them in clinical and echocardiographic features.", "date": "2025-04-07", "categories": []}
{"id": "2505.14730", "title": "Predicting Neoadjuvant Chemotherapy Response in Triple-Negative Breast Cancer Using Pre-Treatment Histopathologic Images", "abstract": "Triple-negative breast cancer (TNBC) remains a major clinical challenge due to its aggressive behavior and lack of targeted therapies. Accurate early prediction of response to neoadjuvant chemotherapy (NACT) is essential for guiding personalized treatment strategies and improving patient outcomes. In this study, we present an attention-based multiple instance learning (MIL) framework designed to predict pathologic complete response (pCR) directly from pre-treatment hematoxylin and eosin (H&E)-stained biopsy slides. The model was trained on a retrospective in-house cohort of 174 TNBC patients and externally validated on an independent cohort (n = 30). It achieved a mean area under the curve (AUC) of 0.85 during five-fold cross-validation and 0.78 on external testing, demonstrating robust predictive performance and generalizability. To enhance model interpretability, attention maps were spatially co-registered with multiplex immuno-histochemistry (mIHC) data stained for PD-L1, CD8+ T cells, and CD163+ macrophages. The attention regions exhibited moderate spatial overlap with immune-enriched areas, with mean Intersection over Union (IoU) scores of 0.47 for PD-L1, 0.45 for CD8+ T cells, and 0.46 for CD163+ macrophages. The presence of these biomarkers in high-attention regions supports their biological relevance to NACT response in TNBC. This not only improves model interpretability but may also inform future efforts to identify clinically actionable histological biomarkers directly from H&E-stained biopsy slides, further supporting the utility of this approach for accurate NACT response prediction and advancing precision oncology in TNBC.", "date": "2025-05-20", "categories": []}
{"id": "2506.11150", "title": "ADAgent: LLM Agent for Alzheimer's Disease Analysis with Collaborative Coordinator", "abstract": "Alzheimer's disease (AD) is a progressive and irreversible neurodegenerative disease. Early and precise diagnosis of AD is crucial for timely intervention and treatment planning to alleviate the progressive neurodegeneration. However, most existing methods rely on single-modality data, which contrasts with the multifaceted approach used by medical experts. While some deep learning approaches process multi-modal data, they are limited to specific tasks with a small set of input modalities and cannot handle arbitrary combinations. This highlights the need for a system that can address diverse AD-related tasks, process multi-modal or missing input, and integrate multiple advanced methods for improved performance. In this paper, we propose ADAgent, the first specialized AI agent for AD analysis, built on a large language model (LLM) to address user queries and support decision-making. ADAgent integrates a reasoning engine, specialized medical tools, and a collaborative outcome coordinator to facilitate multi-modal diagnosis and prognosis tasks in AD. Extensive experiments demonstrate that ADAgent outperforms SOTA methods, achieving significant improvements in accuracy, including a 2.7% increase in multi-modal diagnosis, a 0.7% improvement in multi-modal prognosis, and enhancements in MRI and PET diagnosis tasks.", "date": "2025-06-11", "categories": []}
{"id": "2507.19396", "title": "Detection of Adverse Drug Events in Dutch clinical free text documents using Transformer Models: benchmark study", "abstract": "In this study, we establish a benchmark for adverse drug event (ADE) detection in Dutch clinical free-text documents using several transformer models, clinical scenarios, and fit-for-purpose performance measures. We trained a Bidirectional Long Short-Term Memory (Bi-LSTM) model and four transformer-based Dutch and/or multilingual encoder models (BERTje, RobBERT, MedRoBERTa(.)nl, and NuNER) for the tasks of named entity recognition (NER) and relation classification (RC) using 102 richly annotated Dutch ICU clinical progress notes. Anonymized free-text clinical progress notes of patients admitted to the intensive care unit (ICU) of one academic hospital and discharge letters of patients admitted to Internal Medicine wards of two non-academic hospitals were reused. We evaluated our ADE RC models internally using the gold standard (two-step task) and predicted entities (end-to-end task). In addition, all models were externally validated for detecting ADEs at the document level. We report both micro- and macro-averaged F1 scores, given the dataset imbalance in ADEs. Although differences for the ADE RC task between the models were small, MedRoBERTa(.)nl was the best performing model with a macro-averaged F1 score of 0.63 using the gold standard and 0.62 using predicted entities. The MedRoBERTa(.)nl models also performed the best in our external validation and achieved a recall of between 0.67 to 0.74 using predicted entities, meaning between 67 to 74% of discharge letters with ADEs were detected. Our benchmark study presents a robust and clinically meaningful approach for evaluating language models for ADE detection in clinical free-text documents. Our study highlights the need to use appropriate performance measures fit for the task of ADE detection in clinical free-text documents and envisioned future clinical use.", "date": "2025-07-25", "categories": []}
{"id": "2507.19803", "title": "AI-Based Clinical Rule Discovery for NMIBC Recurrence through Tsetlin Machines", "abstract": "Bladder cancer claims one life every 3 minutes worldwide. Most patients are diagnosed with non-muscle-invasive bladder cancer (NMIBC), yet up to 70% recur after treatment, triggering a relentless cycle of surgeries, monitoring, and risk of progression. Clinical tools like the EORTC risk tables are outdated and unreliable - especially for intermediate-risk cases.\n  We propose an interpretable AI model using the Tsetlin Machine (TM), a symbolic learner that outputs transparent, human-readable logic. Tested on the PHOTO trial dataset (n=330), TM achieved an F1-score of 0.80, outperforming XGBoost (0.78), Logistic Regression (0.60), and EORTC (0.42). TM reveals the exact clauses behind each prediction, grounded in clinical features like tumour count, surgeon experience, and hospital stay - offering accuracy and full transparency. This makes TM a powerful, trustworthy decision-support tool ready for real-world adoption.", "date": "2025-07-26", "categories": []}
{"id": "2507.20017", "title": "VAMPIRE: Uncovering Vessel Directional and Morphological Information from OCTA Images for Cardiovascular Disease Risk Factor Prediction", "abstract": "Cardiovascular disease (CVD) remains the leading cause of death worldwide, requiring urgent development of effective risk assessment methods for timely intervention. While current research has introduced non-invasive and efficient approaches to predict CVD risk from retinal imaging with deep learning models, the commonly used fundus photographs and Optical Coherence Tomography (OCT) fail to capture detailed vascular features critical for CVD assessment compared with OCT angiography (OCTA) images. Moreover, existing methods typically classify CVD risk only as high or low, without providing a deeper analysis on CVD-related blood factor conditions, thus limiting prediction accuracy and clinical utility. As a result, we propose a novel multi-purpose paradigm of CVD risk assessment that jointly performs CVD risk and CVD-related condition prediction, aligning with clinical experiences. Based on this core idea, we introduce OCTA-CVD, the first OCTA dataset for CVD risk assessment, and a Vessel-Aware Mamba-based Prediction model with Informative Enhancement (VAMPIRE) based on OCTA enface images. Our proposed model aims to extract crucial vascular characteristics through two key components: (1) a Mamba-Based Directional (MBD) Module that captures fine-grained vascular trajectory features and (2) an Information-Enhanced Morphological (IEM) Module that incorporates comprehensive vessel morphology knowledge. Experimental results demonstrate that our method can surpass standard classification backbones, OCTA-based detection methods, and ophthalmologic foundation models. Our codes and the collected OCTA-CVD dataset are available at https://github.com/xmed-lab/VAMPIRE.", "date": "2025-07-26", "categories": []}
{"id": "2507.20872", "title": "Not Only Grey Matter: OmniBrain for Robust Multimodal Classification of Alzheimer's Disease", "abstract": "Alzheimer's disease affects over 55 million people worldwide and is projected to more than double by 2050, necessitating rapid, accurate, and scalable diagnostics. However, existing approaches are limited because they cannot achieve clinically acceptable accuracy, generalization across datasets, robustness to missing modalities, and explainability all at the same time. This inability to satisfy all these requirements simultaneously undermines their reliability in clinical settings. We propose OmniBrain, a multimodal framework that integrates brain MRI, radiomics, gene expression, and clinical data using a unified model with cross-attention and modality dropout. OmniBrain achieves $92.2 \\pm 2.4\\%$accuracy on the ANMerge dataset and generalizes to the MRI-only ADNI dataset with $70.4 \\pm 2.7\\%$ accuracy, outperforming unimodal and prior multimodal approaches. Explainability analyses highlight neuropathologically relevant brain regions and genes, enhancing clinical trust. OmniBrain offers a robust, interpretable, and practical solution for real-world Alzheimer's diagnosis.", "date": "2025-07-28", "categories": []}
{"id": "2507.18647", "title": "XAI-Guided Analysis of Residual Networks for Interpretable Pneumonia Detection in Paediatric Chest X-rays", "abstract": "Pneumonia remains one of the leading causes of death among children worldwide, underscoring a critical need for fast and accurate diagnostic tools. In this paper, we propose an interpretable deep learning model on Residual Networks (ResNets) for automatically diagnosing paediatric pneumonia on chest X-rays. We enhance interpretability through Bayesian Gradient-weighted Class Activation Mapping (BayesGrad-CAM), which quantifies uncertainty in visual explanations, and which offers spatial locations accountable for the decision-making process of the model. Our ResNet-50 model, trained on a large paediatric chest X-rays dataset, achieves high classification accuracy (95.94%), AUC-ROC (98.91%), and Cohen's Kappa (0.913), accompanied by clinically meaningful visual explanations. Our findings demonstrate that high performance and interpretability are not only achievable but critical for clinical AI deployment.", "date": "2025-07-18", "categories": []}
{"id": "2507.18866", "title": "Early Mortality Prediction in ICU Patients with Hypertensive Kidney Disease Using Interpretable Machine Learning", "abstract": "Background: Hypertensive kidney disease (HKD) patients in intensive care units (ICUs) face high short-term mortality, but tailored risk prediction tools are lacking. Early identification of high-risk individuals is crucial for clinical decision-making. Methods: We developed a machine learning framework to predict 30-day in-hospital mortality among ICU patients with HKD using early clinical data from the MIMIC-IV v2.2 database. A cohort of 1,366 adults was curated with strict criteria, excluding malignancy cases. Eighteen clinical features-including vital signs, labs, comorbidities, and therapies-were selected via random forest importance and mutual information filtering. Several models were trained and compared with stratified five-fold cross-validation; CatBoost demonstrated the best performance. Results: CatBoost achieved an AUROC of 0.88 on the independent test set, with sensitivity of 0.811 and specificity of 0.798. SHAP values and Accumulated Local Effects (ALE) plots showed the model relied on meaningful predictors such as altered consciousness, vasopressor use, and coagulation status. Additionally, the DREAM algorithm was integrated to estimate patient-specific posterior risk distributions, allowing clinicians to assess both predicted mortality and its uncertainty. Conclusions: We present an interpretable machine learning pipeline for early, real-time risk assessment in ICU patients with HKD. By combining high predictive performance with uncertainty quantification, our model supports individualized triage and transparent clinical decisions. This approach shows promise for clinical deployment and merits external validation in broader critical care populations.", "date": "2025-07-25", "categories": []}
{"id": "2507.18987", "title": "Differentiated Thyroid Cancer Recurrence Classification Using Machine Learning Models and Bayesian Neural Networks with Varying Priors: A SHAP-Based Interpretation of the Best Performing Model", "abstract": "Differentiated thyroid cancer DTC recurrence is a major public health concern, requiring classification and predictive models that are not only accurate but also interpretable and uncertainty aware. This study introduces a comprehensive framework for DTC recurrence classification using a dataset containing 383 patients and 16 clinical and pathological variables. Initially, 11 machine learning ML models were employed using the complete dataset, where the Support Vector Machines SVM model achieved the highest accuracy of 0.9481. To reduce complexity and redundancy, feature selection was carried out using the Boruta algorithm, and the same ML models were applied to the reduced dataset, where it was observed that the Logistic Regression LR model obtained the maximum accuracy of 0.9611. However, these ML models often lack uncertainty quantification, which is critical in clinical decision making. Therefore, to address this limitation, the Bayesian Neural Networks BNN with six varying prior distributions, including Normal 0,1, Normal 0,10, Laplace 0,1, Cauchy 0,1, Cauchy 0,2.5, and Horseshoe 1, were implemented on both the complete and reduced datasets. The BNN model with Normal 0,10 prior distribution exhibited maximum accuracies of 0.9740 and 0.9870 before and after feature selection, respectively.", "date": "2025-07-25", "categories": []}
{"id": "2507.19174", "title": "Automatic Cough Analysis for Non-Small Cell Lung Cancer Detection", "abstract": "Early detection of non-small cell lung cancer (NSCLC) is critical for improving patient outcomes, and novel approaches are needed to facilitate early diagnosis. In this study, we explore the use of automatic cough analysis as a pre-screening tool for distinguishing between NSCLC patients and healthy controls. Cough audio recordings were prospectively acquired from a total of 227 subjects, divided into NSCLC patients and healthy controls. The recordings were analyzed using machine learning techniques, such as support vector machine (SVM) and XGBoost, as well as deep learning approaches, specifically convolutional neural networks (CNN) and transfer learning with VGG16. To enhance the interpretability of the machine learning model, we utilized Shapley Additive Explanations (SHAP). The fairness of the models across demographic groups was assessed by comparing the performance of the best model across different age groups (less than or equal to 58y and higher than 58y) and gender using the equalized odds difference on the test set. The results demonstrate that CNN achieves the best performance, with an accuracy of 0.83 on the test set. Nevertheless, SVM achieves slightly lower performances (accuracy of 0.76 in validation and 0.78 in the test set), making it suitable in contexts with low computational power. The use of SHAP for SVM interpretation further enhances model transparency, making it more trustworthy for clinical applications. Fairness analysis shows slightly higher disparity across age (0.15) than gender (0.09) on the test set. Therefore, to strengthen our findings' reliability, a larger, more diverse, and unbiased dataset is needed -- particularly including individuals at risk of NSCLC and those in early disease stages.", "date": "2025-07-25", "categories": []}
{"id": "2409.15087", "title": "AI Workflow, External Validation, and Development in Eye Disease Diagnosis", "abstract": "Timely disease diagnosis is challenging due to increasing disease burdens and limited clinician availability. AI shows promise in diagnosis accuracy but faces real-world application issues due to insufficient validation in clinical workflows and diverse populations. This study addresses gaps in medical AI downstream accountability through a case study on age-related macular degeneration (AMD) diagnosis and severity classification. We designed and implemented an AI-assisted diagnostic workflow for AMD, comparing diagnostic performance with and without AI assistance among 24 clinicians from 12 institutions with real patient data sampled from the Age-Related Eye Disease Study (AREDS). Additionally, we demonstrated continual enhancement of an existing AI model by incorporating approximately 40,000 additional medical images (named AREDS2 dataset). The improved model was then systematically evaluated using both AREDS and AREDS2 test sets, as well as an external test set from Singapore. AI assistance markedly enhanced diagnostic accuracy and classification for 23 out of 24 clinicians, with the average F1-score increasing by 20% from 37.71 (Manual) to 45.52 (Manual + AI) (P-value < 0.0001), achieving an improvement of over 50% in some cases. In terms of efficiency, AI assistance reduced diagnostic times for 17 out of the 19 clinicians tracked, with time savings of up to 40%. Furthermore, a model equipped with continual learning showed robust performance across three independent datasets, recording a 29% increase in accuracy, and elevating the F1-score from 42 to 54 in the Singapore population.", "date": "2024-09-23", "categories": []}
{"id": "2504.03799", "title": "Experimental Study on Time Series Analysis of Lower Limb Rehabilitation Exercise Data Driven by Novel Model Architecture and Large Models", "abstract": "This study investigates the application of novel model architectures and large-scale foundational models in temporal series analysis of lower limb rehabilitation motion data, aiming to leverage advancements in machine learning and artificial intelligence to empower active rehabilitation guidance strategies for post-stroke patients in limb motor function recovery. Utilizing the SIAT-LLMD dataset of lower limb movement data proposed by the Shenzhen Institute of Advanced Technology, Chinese Academy of Sciences, we systematically elucidate the implementation and analytical outcomes of the innovative xLSTM architecture and the foundational model Lag-Llama in short-term temporal prediction tasks involving joint kinematics and dynamics parameters. The research provides novel insights for AI-enabled medical rehabilitation applications, demonstrating the potential of cutting-edge model architectures and large-scale models in rehabilitation medicine temporal prediction. These findings establish theoretical foundations for future applications of personalized rehabilitation regimens, offering significant implications for the development of customized therapeutic interventions in clinical practice.", "date": "2025-04-04", "categories": []}
{"id": "2507.16947", "title": "AI-based Clinical Decision Support for Primary Care: A Real-World Study", "abstract": "We evaluate the impact of large language model-based clinical decision support in live care. In partnership with Penda Health, a network of primary care clinics in Nairobi, Kenya, we studied AI Consult, a tool that serves as a safety net for clinicians by identifying potential documentation and clinical decision-making errors. AI Consult integrates into clinician workflows, activating only when needed and preserving clinician autonomy. We conducted a quality improvement study, comparing outcomes for 39,849 patient visits performed by clinicians with or without access to AI Consult across 15 clinics. Visits were rated by independent physicians to identify clinical errors. Clinicians with access to AI Consult made relatively fewer errors: 16% fewer diagnostic errors and 13% fewer treatment errors. In absolute terms, the introduction of AI Consult would avert diagnostic errors in 22,000 visits and treatment errors in 29,000 visits annually at Penda alone. In a survey of clinicians with AI Consult, all clinicians said that AI Consult improved the quality of care they delivered, with 75% saying the effect was \"substantial\". These results required a clinical workflow-aligned AI Consult implementation and active deployment to encourage clinician uptake. We hope this study demonstrates the potential for LLM-based clinical decision support tools to reduce errors in real-world settings and provides a practical framework for advancing responsible adoption.", "date": "2025-07-22", "categories": []}
{"id": "2507.17009", "title": "Multi-Label Classification with Generative AI Models in Healthcare: A Case Study of Suicidality and Risk Factors", "abstract": "Suicide remains a pressing global health crisis, with over 720,000 deaths annually and millions more affected by suicide ideation (SI) and suicide attempts (SA). Early identification of suicidality-related factors (SrFs), including SI, SA, exposure to suicide (ES), and non-suicidal self-injury (NSSI), is critical for timely intervention. While prior studies have applied AI to detect SrFs in clinical notes, most treat suicidality as a binary classification task, overlooking the complexity of cooccurring risk factors. This study explores the use of generative large language models (LLMs), specifically GPT-3.5 and GPT-4.5, for multi-label classification (MLC) of SrFs from psychiatric electronic health records (EHRs). We present a novel end to end generative MLC pipeline and introduce advanced evaluation methods, including label set level metrics and a multilabel confusion matrix for error analysis. Finetuned GPT-3.5 achieved top performance with 0.94 partial match accuracy and 0.91 F1 score, while GPT-4.5 with guided prompting showed superior performance across label sets, including rare or minority label sets, indicating a more balanced and robust performance. Our findings reveal systematic error patterns, such as the conflation of SI and SA, and highlight the models tendency toward cautious over labeling. This work not only demonstrates the feasibility of using generative AI for complex clinical classification tasks but also provides a blueprint for structuring unstructured EHR data to support large scale clinical research and evidence based medicine.", "date": "2025-07-22", "categories": []}
{"id": "2505.22116", "title": "Multimodal Forecasting of Sparse Intraoperative Hypotension Events Powered by Language Model", "abstract": "Intraoperative hypotension (IOH) frequently occurs under general anesthesia and is strongly linked to adverse outcomes such as myocardial injury and increased mortality. Despite its significance, IOH prediction is hindered by event sparsity and the challenge of integrating static and dynamic data across diverse patients. In this paper, we propose \\textbf{IOHFuseLM}, a multimodal language model framework. To accurately identify and differentiate sparse hypotensive events, we leverage a two-stage training strategy. The first stage involves domain adaptive pretraining on IOH physiological time series augmented through diffusion methods, thereby enhancing the model sensitivity to patterns associated with hypotension. Subsequently, task fine-tuning is performed on the original clinical dataset to further enhance the ability to distinguish normotensive from hypotensive states. To enable multimodal fusion for each patient, we align structured clinical descriptions with the corresponding physiological time series at the token level. Such alignment enables the model to capture individualized temporal patterns alongside their corresponding clinical semantics. In addition, we convert static patient attributes into structured text to enrich personalized information. Experimental evaluations on two intraoperative datasets demonstrate that IOHFuseLM outperforms established baselines in accurately identifying IOH events, highlighting its applicability in clinical decision support scenarios. Our code is publicly available to promote reproducibility at https://github.com/zjt-gpu/IOHFuseLM.", "date": "2025-05-28", "categories": []}
{"id": "2507.09759", "title": "AI-Enhanced Pediatric Pneumonia Detection: A CNN-Based Approach Using Data Augmentation and Generative Adversarial Networks (GANs)", "abstract": "Pneumonia is a leading cause of mortality in children under five, requiring accurate chest X-ray diagnosis. This study presents a machine learning-based Pediatric Chest Pneumonia Classification System to assist healthcare professionals in diagnosing pneumonia from chest X-ray images. The CNN-based model was trained on 5,863 labeled chest X-ray images from children aged 0-5 years from the Guangzhou Women and Children's Medical Center. To address limited data, we applied augmentation techniques (rotation, zooming, shear, horizontal flipping) and employed GANs to generate synthetic images, addressing class imbalance. The system achieved optimal performance using combined original, augmented, and GAN-generated data, evaluated through accuracy and F1 score metrics. The final model was deployed via a Flask web application, enabling real-time classification with probability estimates. Results demonstrate the potential of deep learning and GANs in improving diagnostic accuracy and efficiency for pediatric pneumonia classification, particularly valuable in resource-limited clinical settings https://github.com/AbdulManaf12/Pediatric-Chest-Pneumonia-Classification", "date": "2025-07-13", "categories": []}
{"id": "2507.16041", "title": "Radiological and Biological Dictionary of Radiomics Features: Addressing Understandable AI Issues in Personalized Breast Cancer; Dictionary Version BM1.0", "abstract": "Radiomics-based AI models show promise for breast cancer diagnosis but often lack interpretability, limiting clinical adoption. This study addresses the gap between radiomic features (RF) and the standardized BI-RADS lexicon by proposing a dual-dictionary framework. First, a Clinically-Informed Feature Interpretation Dictionary (CIFID) was created by mapping 56 RFs to BI-RADS descriptors (shape, margin, internal enhancement) through literature and expert review. The framework was applied to classify triple-negative breast cancer (TNBC) versus non-TNBC using dynamic contrast-enhanced MRI from a multi-institutional cohort of 1,549 patients. We trained 27 machine learning classifiers with 27 feature selection methods. SHapley Additive exPlanations (SHAP) were used to interpret predictions and generate a complementary Data-Driven Feature Interpretation Dictionary (DDFID) for 52 additional RFs. The best model, combining Variance Inflation Factor (VIF) selection with Extra Trees Classifier, achieved an average cross-validation accuracy of 0.83. Key predictive RFs aligned with clinical knowledge: higher Sphericity (round/oval shape) and lower Busyness (more homogeneous enhancement) were associated with TNBC. The framework confirmed known imaging biomarkers and uncovered novel, interpretable associations. This dual-dictionary approach (BM1.0) enhances AI model transparency and supports the integration of RFs into routine breast cancer diagnosis and personalized care.", "date": "2025-07-21", "categories": []}
{"id": "2507.16148", "title": "Learning Patient-Specific Spatial Biomarker Dynamics via Operator Learning for Alzheimer's Disease Progression", "abstract": "Alzheimer's disease (AD) is a complex, multifactorial neurodegenerative disorder with substantial heterogeneity in progression and treatment response. Despite recent therapeutic advances, predictive models capable of accurately forecasting individualized disease trajectories remain limited. Here, we present a machine learning-based operator learning framework for personalized modeling of AD progression, integrating longitudinal multimodal imaging, biomarker, and clinical data. Unlike conventional models with prespecified dynamics, our approach directly learns patient-specific disease operators governing the spatiotemporal evolution of amyloid, tau, and neurodegeneration biomarkers. Using Laplacian eigenfunction bases, we construct geometry-aware neural operators capable of capturing complex brain dynamics. Embedded within a digital twin paradigm, the framework enables individualized predictions, simulation of therapeutic interventions, and in silico clinical trials. Applied to AD clinical data, our method achieves high prediction accuracy exceeding 90% across multiple biomarkers, substantially outperforming existing approaches. This work offers a scalable, interpretable platform for precision modeling and personalized therapeutic optimization in neurodegenerative diseases.", "date": "2025-07-22", "categories": []}
{"id": "2507.16363", "title": "Bipartite Patient-Modality Graph Learning with Event-Conditional Modelling of Censoring for Cancer Survival Prediction", "abstract": "Accurately predicting the survival of cancer patients is crucial for personalized treatment. However, existing studies focus solely on the relationships between samples with known survival risks, without fully leveraging the value of censored samples. Furthermore, these studies may suffer performance degradation in modality-missing scenarios and even struggle during the inference process. In this study, we propose a bipartite patient-modality graph learning with event-conditional modelling of censoring for cancer survival prediction (CenSurv). Specifically, we first use graph structure to model multimodal data and obtain representation. Then, to alleviate performance degradation in modality-missing scenarios, we design a bipartite graph to simulate the patient-modality relationship in various modality-missing scenarios and leverage a complete-incomplete alignment strategy to explore modality-agnostic features. Finally, we design a plug-and-play event-conditional modeling of censoring (ECMC) that selects reliable censored data using dynamic momentum accumulation confidences, assigns more accurate survival times to these censored data, and incorporates them as uncensored data into training. Comprehensive evaluations on 5 publicly cancer datasets showcase the superiority of CenSurv over the best state-of-the-art by 3.1% in terms of the mean C-index, while also exhibiting excellent robustness under various modality-missing scenarios. In addition, using the plug-and-play ECMC module, the mean C-index of 8 baselines increased by 1.3% across 5 datasets. Code of CenSurv is available at https://github.com/yuehailin/CenSurv.", "date": "2025-07-22", "categories": []}
{"id": "2507.14766", "title": "CXR-TFT: Multi-Modal Temporal Fusion Transformer for Predicting Chest X-ray Trajectories", "abstract": "In intensive care units (ICUs), patients with complex clinical conditions require vigilant monitoring and prompt interventions. Chest X-rays (CXRs) are a vital diagnostic tool, providing insights into clinical trajectories, but their irregular acquisition limits their utility. Existing tools for CXR interpretation are constrained by cross-sectional analysis, failing to capture temporal dynamics. To address this, we introduce CXR-TFT, a novel multi-modal framework that integrates temporally sparse CXR imaging and radiology reports with high-frequency clinical data, such as vital signs, laboratory values, and respiratory flow sheets, to predict the trajectory of CXR findings in critically ill patients. CXR-TFT leverages latent embeddings from a vision encoder that are temporally aligned with hourly clinical data through interpolation. A transformer model is then trained to predict CXR embeddings at each hour, conditioned on previous embeddings and clinical measurements. In a retrospective study of 20,000 ICU patients, CXR-TFT demonstrated high accuracy in forecasting abnormal CXR findings up to 12 hours before they became radiographically evident. This predictive capability in clinical data holds significant potential for enhancing the management of time-sensitive conditions like acute respiratory distress syndrome, where early intervention is crucial and diagnoses are often delayed. By providing distinctive temporal resolution in prognostic CXR analysis, CXR-TFT offers actionable 'whole patient' insights that can directly improve clinical outcomes.", "date": "2025-07-19", "categories": []}
{"id": "2507.14824", "title": "Benchmarking Foundation Models with Multimodal Public Electronic Health Records", "abstract": "Foundation models have emerged as a powerful approach for processing electronic health records (EHRs), offering flexibility to handle diverse medical data modalities. In this study, we present a comprehensive benchmark that evaluates the performance, fairness, and interpretability of foundation models, both as unimodal encoders and as multimodal learners, using the publicly available MIMIC-IV database. To support consistent and reproducible evaluation, we developed a standardized data processing pipeline that harmonizes heterogeneous clinical records into an analysis-ready format. We systematically compared eight foundation models, encompassing both unimodal and multimodal models, as well as domain-specific and general-purpose variants. Our findings demonstrate that incorporating multiple data modalities leads to consistent improvements in predictive performance without introducing additional bias. Through this benchmark, we aim to support the development of effective and trustworthy multimodal artificial intelligence (AI) systems for real-world clinical applications. Our code is available at https://github.com/nliulab/MIMIC-Multimodal.", "date": "2025-07-20", "categories": []}
{"id": "2507.14847", "title": "Time-Aware Attention for Enhanced Electronic Health Records Modeling", "abstract": "Electronic Health Records (EHR) contain valuable clinical information for predicting patient outcomes and guiding healthcare decisions. However, effectively modeling Electronic Health Records (EHRs) requires addressing data heterogeneity and complex temporal patterns. Standard approaches often struggle with irregular time intervals between clinical events. We propose TALE-EHR, a Transformer-based framework featuring a novel time-aware attention mechanism that explicitly models continuous temporal gaps to capture fine-grained sequence dynamics. To complement this temporal modeling with robust semantics, TALE-EHR leverages embeddings derived from standardized code descriptions using a pre-trained Large Language Model (LLM), providing a strong foundation for understanding clinical concepts. Experiments on the MIMIC-IV and PIC dataset demonstrate that our approach outperforms state-of-the-art baselines on tasks such as disease progression forecasting. TALE-EHR underscores the benefit of integrating explicit, continuous temporal modeling with strong semantic representations provides a powerful solution for advancing EHR analysis.", "date": "2025-07-20", "categories": []}
{"id": "2507.15364", "title": "EEG-based Epileptic Prediction via a Two-stage Channel-aware Set Transformer Network", "abstract": "Epilepsy is a chronic, noncommunicable brain disorder, and sudden seizure onsets can significantly impact patients' quality of life and health. However, wearable seizure-predicting devices are still limited, partly due to the bulky size of EEG-collecting devices. To relieve the problem, we proposed a novel two-stage channel-aware Set Transformer Network that could perform seizure prediction with fewer EEG channel sensors. We also tested a seizure-independent division method which could prevent the adjacency of training and test data. Experiments were performed on the CHB-MIT dataset which includes 22 patients with 88 merged seizures. The mean sensitivity before channel selection was 76.4% with a false predicting rate (FPR) of 0.09/hour. After channel selection, dominant channels emerged in 20 out of 22 patients; the average number of channels was reduced to 2.8 from 18; and the mean sensitivity rose to 80.1% with an FPR of 0.11/hour. Furthermore, experimental results on the seizure-independent division supported our assertion that a more rigorous seizure-independent division should be used for patients with abundant EEG recordings.", "date": "2025-07-21", "categories": []}
{"id": "2507.15548", "title": "The added value for MRI radiomics and deep-learning for glioblastoma prognostication compared to clinical and molecular information", "abstract": "Background: Radiomics shows promise in characterizing glioblastoma, but its added value over clinical and molecular predictors has yet to be proven. This study assessed the added value of conventional radiomics (CR) and deep learning (DL) MRI radiomics for glioblastoma prognosis (<= 6 vs > 6 months survival) on a large multi-center dataset.\n  Methods: After patient selection, our curated dataset gathers 1152 glioblastoma (WHO 2016) patients from five Swiss centers and one public source. It included clinical (age, gender), molecular (MGMT, IDH), and baseline MRI data (T1, T1 contrast, FLAIR, T2) with tumor regions. CR and DL models were developed using standard methods and evaluated on internal and external cohorts. Sub-analyses assessed models with different feature sets (imaging-only, clinical/molecular-only, combined-features) and patient subsets (S-1: all patients, S-2: with molecular data, S-3: IDH wildtype).\n  Results: The best performance was observed in the full cohort (S-1). In external validation, the combined-feature CR model achieved an AUC of 0.75, slightly, but significantly outperforming clinical-only (0.74) and imaging-only (0.68) models. DL models showed similar trends, though without statistical significance. In S-2 and S-3, combined models did not outperform clinical-only models. Exploratory analysis of CR models for overall survival prediction suggested greater relevance of imaging data: across all subsets, combined-feature models significantly outperformed clinical-only models, though with a modest advantage of 2-4 C-index points.\n  Conclusions: While confirming the predictive value of anatomical MRI sequences for glioblastoma prognosis, this multi-center study found standard CR and DL radiomics approaches offer minimal added value over demographic predictors such as age and gender.", "date": "2025-07-21", "categories": []}
{"id": "2507.13542", "title": "Acoustic Index: A Novel AI-Driven Parameter for Cardiac Disease Risk Stratification Using Echocardiography", "abstract": "Traditional echocardiographic parameters such as ejection fraction (EF) and global longitudinal strain (GLS) have limitations in the early detection of cardiac dysfunction. EF often remains normal despite underlying pathology, and GLS is influenced by load conditions and vendor variability. There is a growing need for reproducible, interpretable, and operator-independent parameters that capture subtle and global cardiac functional alterations.\n  We introduce the Acoustic Index, a novel AI-derived echocardiographic parameter designed to quantify cardiac dysfunction from standard ultrasound views. The model combines Extended Dynamic Mode Decomposition (EDMD) based on Koopman operator theory with a hybrid neural network that incorporates clinical metadata. Spatiotemporal dynamics are extracted from echocardiographic sequences to identify coherent motion patterns. These are weighted via attention mechanisms and fused with clinical data using manifold learning, resulting in a continuous score from 0 (low risk) to 1 (high risk).\n  In a prospective cohort of 736 patients, encompassing various cardiac pathologies and normal controls, the Acoustic Index achieved an area under the curve (AUC) of 0.89 in an independent test set. Cross-validation across five folds confirmed the robustness of the model, showing that both sensitivity and specificity exceeded 0.8 when evaluated on independent data. Threshold-based analysis demonstrated stable trade-offs between sensitivity and specificity, with optimal discrimination near this threshold.\n  The Acoustic Index represents a physics-informed, interpretable AI biomarker for cardiac function. It shows promise as a scalable, vendor-independent tool for early detection, triage, and longitudinal monitoring. Future directions include external validation, longitudinal studies, and adaptation to disease-specific classifiers.", "date": "2025-07-17", "categories": []}
{"id": "2507.13655", "title": "CU-ICU: Customizing Unsupervised Instruction-Finetuned Language Models for ICU Datasets via Text-to-Text Transfer Transformer", "abstract": "Integrating large language models into specialized domains like healthcare presents unique challenges, including domain adaptation and limited labeled data. We introduce CU-ICU, a method for customizing unsupervised instruction-finetuned language models for ICU datasets by leveraging the Text-to-Text Transfer Transformer (T5) architecture. CU-ICU employs a sparse fine-tuning approach that combines few-shot prompting with selective parameter updates, enabling efficient adaptation with minimal supervision. Our evaluation across critical ICU tasks--early sepsis detection, mortality prediction, and clinical note generation--demonstrates that CU-ICU consistently improves predictive accuracy and interpretability over standard fine-tuning methods. Notably, CU-ICU achieves up to a 15% increase in sepsis detection accuracy and a 20% enhancement in generating clinically relevant explanations while updating fewer than 1% of model parameters in its most efficient configuration. These results establish CU-ICU as a scalable, low-overhead solution for delivering accurate and interpretable clinical decision support in real-world ICU environments.", "date": "2025-07-18", "categories": []}
{"id": "2507.14077", "title": "Glucose-ML: A collection of longitudinal diabetes datasets for development of robust AI solutions", "abstract": "Artificial intelligence (AI) algorithms are a critical part of state-of-the-art digital health technology for diabetes management. Yet, access to large high-quality datasets is creating barriers that impede development of robust AI solutions. To accelerate development of transparent, reproducible, and robust AI solutions, we present Glucose-ML, a collection of 10 publicly available diabetes datasets, released within the last 7 years (i.e., 2018 - 2025). The Glucose-ML collection comprises over 300,000 days of continuous glucose monitor (CGM) data with a total of 38 million glucose samples collected from 2500+ people across 4 countries. Participants include persons living with type 1 diabetes, type 2 diabetes, prediabetes, and no diabetes. To support researchers and innovators with using this rich collection of diabetes datasets, we present a comparative analysis to guide algorithm developers with data selection. Additionally, we conduct a case study for the task of blood glucose prediction - one of the most common AI tasks within the field. Through this case study, we provide a benchmark for short-term blood glucose prediction across all 10 publicly available diabetes datasets within the Glucose-ML collection. We show that the same algorithm can have significantly different prediction results when developed/evaluated with different datasets. Findings from this study are then used to inform recommendations for developing robust AI solutions within the diabetes or broader health domain. We provide direct links to each longitudinal diabetes dataset in the Glucose-ML collection and openly provide our code.", "date": "2025-07-18", "categories": []}
{"id": "2507.14093", "title": "Multi-Centre Validation of a Deep Learning Model for Scoliosis Assessment", "abstract": "Scoliosis affects roughly 2 to 4 percent of adolescents, and treatment decisions depend on precise Cobb angle measurement. Manual assessment is time consuming and subject to inter observer variation. We conducted a retrospective, multi centre evaluation of a fully automated deep learning software (Carebot AI Bones, Spine Measurement functionality; Carebot s.r.o.) on 103 standing anteroposterior whole spine radiographs collected from ten hospitals. Two musculoskeletal radiologists independently measured each study and served as reference readers. Agreement between the AI and each radiologist was assessed with Bland Altman analysis, mean absolute error (MAE), root mean squared error (RMSE), Pearson correlation coefficient, and Cohen kappa for four grade severity classification. Against Radiologist 1 the AI achieved an MAE of 3.89 degrees (RMSE 4.77 degrees) with a bias of 0.70 degrees and limits of agreement from minus 8.59 to plus 9.99 degrees. Against Radiologist 2 the AI achieved an MAE of 3.90 degrees (RMSE 5.68 degrees) with a bias of 2.14 degrees and limits from minus 8.23 to plus 12.50 degrees. Pearson correlations were r equals 0.906 and r equals 0.880 (inter reader r equals 0.928), while Cohen kappa for severity grading reached 0.51 and 0.64 (inter reader kappa 0.59). These results demonstrate that the proposed software reproduces expert level Cobb angle measurements and categorical grading across multiple centres, suggesting its utility for streamlining scoliosis reporting and triage in clinical workflows.", "date": "2025-07-18", "categories": []}
{"id": "2504.16394", "title": "ConTextual: Improving Clinical Text Summarization in LLMs with Context-preserving Token Filtering and Knowledge Graphs", "abstract": "Unstructured clinical data can serve as a unique and rich source of information that can meaningfully inform clinical practice. Extracting the most pertinent context from such data is critical for exploiting its true potential toward optimal and timely decision-making in patient care. While prior research has explored various methods for clinical text summarization, most prior studies either process all input tokens uniformly or rely on heuristic-based filters, which can overlook nuanced clinical cues and fail to prioritize information critical for decision-making. In this study, we propose Contextual, a novel framework that integrates a Context-Preserving Token Filtering method with a Domain-Specific Knowledge Graph (KG) for contextual augmentation. By preserving context-specific important tokens and enriching them with structured knowledge, ConTextual improves both linguistic coherence and clinical fidelity. Our extensive empirical evaluations on two public benchmark datasets demonstrate that ConTextual consistently outperforms other baselines. Our proposed approach highlights the complementary role of token-level filtering and structured retrieval in enhancing both linguistic and clinical integrity, as well as offering a scalable solution for improving precision in clinical text generation.", "date": "2025-04-23", "categories": []}
{"id": "2507.12485", "title": "Quantum Transfer Learning to Boost Dementia Detection", "abstract": "Dementia is a devastating condition with profound implications for individuals, families, and healthcare systems. Early and accurate detection of dementia is critical for timely intervention and improved patient outcomes. While classical machine learning and deep learning approaches have been explored extensively for dementia prediction, these solutions often struggle with high-dimensional biomedical data and large-scale datasets, quickly reaching computational and performance limitations. To address this challenge, quantum machine learning (QML) has emerged as a promising paradigm, offering faster training and advanced pattern recognition capabilities. This work aims to demonstrate the potential of quantum transfer learning (QTL) to enhance the performance of a weak classical deep learning model applied to a binary classification task for dementia detection. Besides, we show the effect of noise on the QTL-based approach, investigating the reliability and robustness of this method. Using the OASIS 2 dataset, we show how quantum techniques can transform a suboptimal classical model into a more effective solution for biomedical image classification, highlighting their potential impact on advancing healthcare technology.", "date": "2025-07-14", "categories": []}
{"id": "2507.12679", "title": "Improving Drug Identification in Overdose Death Surveillance using Large Language Models", "abstract": "The rising rate of drug-related deaths in the United States, largely driven by fentanyl, requires timely and accurate surveillance. However, critical overdose data are often buried in free-text coroner reports, leading to delays and information loss when coded into ICD (International Classification of Disease)-10 classifications. Natural language processing (NLP) models may automate and enhance overdose surveillance, but prior applications have been limited. A dataset of 35,433 death records from multiple U.S. jurisdictions in 2020 was used for model training and internal testing. External validation was conducted using a novel separate dataset of 3,335 records from 2023-2024. Multiple NLP approaches were evaluated for classifying specific drug involvement from unstructured death certificate text. These included traditional single- and multi-label classifiers, as well as fine-tuned encoder-only language models such as Bidirectional Encoder Representations from Transformers (BERT) and BioClinicalBERT, and contemporary decoder-only large language models such as Qwen 3 and Llama 3. Model performance was assessed using macro-averaged F1 scores, and 95% confidence intervals were calculated to quantify uncertainty. Fine-tuned BioClinicalBERT models achieved near-perfect performance, with macro F1 scores >=0.998 on the internal test set. External validation confirmed robustness (macro F1=0.966), outperforming conventional machine learning, general-domain BERT models, and various decoder-only large language models. NLP models, particularly fine-tuned clinical variants like BioClinicalBERT, offer a highly accurate and scalable solution for overdose death classification from free-text reports. These methods can significantly accelerate surveillance workflows, overcoming the limitations of manual ICD-10 coding and supporting near real-time detection of emerging substance use trends.", "date": "2025-07-16", "categories": []}
{"id": "2507.11381", "title": "From Observational Data to Clinical Recommendations: A Causal Framework for Estimating Patient-level Treatment Effects and Learning Policies", "abstract": "We propose a framework for building patient-specific treatment recommendation models, building on the large recent literature on learning patient-level causal models and inspired by the target trial paradigm of Hernan and Robins. We focus on safety and validity, including the crucial issue of causal identification when using observational data. We do not provide a specific model, but rather a way to integrate existing methods and know-how into a practical pipeline. We further provide a real world use-case of treatment optimization for patients with heart failure who develop acute kidney injury during hospitalization. The results suggest our pipeline can improve patient outcomes over the current treatment regime.", "date": "2025-07-15", "categories": []}
{"id": "2507.11570", "title": "SurgeryLSTM: A Time-Aware Neural Model for Accurate and Explainable Length of Stay Prediction After Spine Surgery", "abstract": "Objective: To develop and evaluate machine learning (ML) models for predicting length of stay (LOS) in elective spine surgery, with a focus on the benefits of temporal modeling and model interpretability. Materials and Methods: We compared traditional ML models (e.g., linear regression, random forest, support vector machine (SVM), and XGBoost) with our developed model, SurgeryLSTM, a masked bidirectional long short-term memory (BiLSTM) with an attention, using structured perioperative electronic health records (EHR) data. Performance was evaluated using the coefficient of determination (R2), and key predictors were identified using explainable AI. Results: SurgeryLSTM achieved the highest predictive accuracy (R2=0.86), outperforming XGBoost (R2 = 0.85) and baseline models. The attention mechanism improved interpretability by dynamically identifying influential temporal segments within preoperative clinical sequences, allowing clinicians to trace which events or features most contributed to each LOS prediction. Key predictors of LOS included bone disorder, chronic kidney disease, and lumbar fusion identified as the most impactful predictors of LOS. Discussion: Temporal modeling with attention mechanisms significantly improves LOS prediction by capturing the sequential nature of patient data. Unlike static models, SurgeryLSTM provides both higher accuracy and greater interpretability, which are critical for clinical adoption. These results highlight the potential of integrating attention-based temporal models into hospital planning workflows. Conclusion: SurgeryLSTM presents an effective and interpretable AI solution for LOS prediction in elective spine surgery. Our findings support the integration of temporal, explainable ML approaches into clinical decision support systems to enhance discharge readiness and individualized patient care.", "date": "2025-07-15", "categories": []}
{"id": "2507.11052", "title": "LLM-Augmented Symptom Analysis for Cardiovascular Disease Risk Prediction: A Clinical NLP", "abstract": "Timely identification and accurate risk stratification of cardiovascular disease (CVD) remain essential for reducing global mortality. While existing prediction models primarily leverage structured data, unstructured clinical notes contain valuable early indicators. This study introduces a novel LLM-augmented clinical NLP pipeline that employs domain-adapted large language models for symptom extraction, contextual reasoning, and correlation from free-text reports. Our approach integrates cardiovascular-specific fine-tuning, prompt-based inference, and entity-aware reasoning. Evaluations on MIMIC-III and CARDIO-NLP datasets demonstrate improved performance in precision, recall, F1-score, and AUROC, with high clinical relevance (kappa = 0.82) assessed by cardiologists. Challenges such as contextual hallucination, which occurs when plausible information contracts with provided source, and temporal ambiguity, which is related with models struggling with chronological ordering of events are addressed using prompt engineering and hybrid rule-based verification. This work underscores the potential of LLMs in clinical decision support systems (CDSS), advancing early warning systems and enhancing the translation of patient narratives into actionable risk assessments.", "date": "2025-07-15", "categories": []}
{"id": "2405.05204", "title": "CARE-SD: Classifier-based analysis for recognizing and eliminating stigmatizing and doubt marker labels in electronic health records: model development and validation", "abstract": "Objective: To detect and classify features of stigmatizing and biased language in intensive care electronic health records (EHRs) using natural language processing techniques. Materials and Methods: We first created a lexicon and regular expression lists from literature-driven stem words for linguistic features of stigmatizing patient labels, doubt markers, and scare quotes within EHRs. The lexicon was further extended using Word2Vec and GPT 3.5, and refined through human evaluation. These lexicons were used to search for matches across 18 million sentences from the de-identified Medical Information Mart for Intensive Care-III (MIMIC-III) dataset. For each linguistic bias feature, 1000 sentence matches were sampled, labeled by expert clinical and public health annotators, and used to supervised learning classifiers. Results: Lexicon development from expanded literature stem-word lists resulted in a doubt marker lexicon containing 58 expressions, and a stigmatizing labels lexicon containing 127 expressions. Classifiers for doubt markers and stigmatizing labels had the highest performance, with macro F1-scores of .84 and .79, positive-label recall and precision values ranging from .71 to .86, and accuracies aligning closely with human annotator agreement (.87). Discussion: This study demonstrated the feasibility of supervised classifiers in automatically identifying stigmatizing labels and doubt markers in medical text, and identified trends in stigmatizing language use in an EHR setting. Additional labeled data may help improve lower scare quote model performance. Conclusions: Classifiers developed in this study showed high model performance and can be applied to identify patterns and target interventions to reduce stigmatizing labels and doubt markers in healthcare systems.", "date": "2024-05-08", "categories": []}
{"id": "2406.00365", "title": "SynthBA: Reliable Brain Age Estimation Across Multiple MRI Sequences and Resolutions", "abstract": "Brain age is a critical measure that reflects the biological ageing process of the brain. The gap between brain age and chronological age, referred to as brain PAD (Predicted Age Difference), has been utilized to investigate neurodegenerative conditions. Brain age can be predicted using MRIs and machine learning techniques. However, existing methods are often sensitive to acquisition-related variabilities, such as differences in acquisition protocols, scanners, MRI sequences, and resolutions, significantly limiting their application in highly heterogeneous clinical settings. In this study, we introduce Synthetic Brain Age (SynthBA), a robust deep-learning model designed for predicting brain age. SynthBA utilizes an advanced domain randomization technique, ensuring effective operation across a wide array of acquisition-related variabilities. To assess the effectiveness and robustness of SynthBA, we evaluate its predictive capabilities on internal and external datasets, encompassing various MRI sequences and resolutions, and compare it with state-of-the-art techniques. Additionally, we calculate the brain PAD in a large cohort of subjects with Alzheimer's Disease (AD), demonstrating a significant correlation with AD-related measures of cognitive dysfunction. SynthBA holds the potential to facilitate the broader adoption of brain age prediction in clinical settings, where re-training or fine-tuning is often unfeasible. The SynthBA source code and pre-trained models are publicly available at https://github.com/LemuelPuglisi/SynthBA.", "date": "2024-06-01", "categories": []}
{"id": "2412.10454", "title": "An Interoperable Machine Learning Pipeline for Pediatric Obesity Risk Estimation", "abstract": "Reliable prediction of pediatric obesity can offer a valuable resource to providers, helping them engage in timely preventive interventions before the disease is established. Many efforts have been made to develop ML-based predictive models of obesity, and some studies have reported high predictive performances. However, no commonly used clinical decision support tool based on existing ML models currently exists. This study presents a novel end-to-end pipeline specifically designed for pediatric obesity prediction, which supports the entire process of data extraction, inference, and communication via an API or a user interface. While focusing only on routinely recorded data in pediatric electronic health records (EHRs), our pipeline uses a diverse expert-curated list of medical concepts to predict the 1-3 years risk of developing obesity. Furthermore, by using the Fast Healthcare Interoperability Resources (FHIR) standard in our design procedure, we specifically target facilitating low-effort integration of our pipeline with different EHR systems. In our experiments, we report the effectiveness of the predictive model as well as its alignment with the feedback from various stakeholders, including ML scientists, providers, health IT personnel, health administration representatives, and patient group representatives.", "date": "2024-12-12", "categories": []}
{"id": "2507.02342", "title": "DeltaSHAP: Explaining Prediction Evolutions in Online Patient Monitoring with Shapley Values", "abstract": "This study proposes DeltaSHAP, a novel explainable artificial intelligence (XAI) algorithm specifically designed for online patient monitoring systems. In clinical environments, discovering the causes driving patient risk evolution is critical for timely intervention, yet existing XAI methods fail to address the unique requirements of clinical time series explanation tasks. To this end, DeltaSHAP addresses three key clinical needs: explaining the changes in the consecutive predictions rather than isolated prediction scores, providing both magnitude and direction of feature attributions, and delivering these insights in real time. By adapting Shapley values to temporal settings, our approach accurately captures feature coalition effects. It further attributes prediction changes using only the actually observed feature combinations, making it efficient and practical for time-sensitive clinical applications. We also introduce new evaluation metrics to evaluate the faithfulness of the attributions for online time series, and demonstrate through experiments on online patient monitoring tasks that DeltaSHAP outperforms state-of-the-art XAI methods in both explanation quality as 62% and computational efficiency as 33% time reduction on the MIMIC-III decompensation benchmark. We release our code at https://github.com/AITRICS/DeltaSHAP.", "date": "2025-07-03", "categories": []}
{"id": "2507.09460", "title": "Enhancing ALS Progression Tracking with Semi-Supervised ALSFRS-R Scores Estimated from Ambient Home Health Monitoring", "abstract": "Clinical monitoring of functional decline in ALS relies on periodic assessments that may miss critical changes occurring between visits. To address this gap, semi-supervised regression models were developed to estimate rates of decline in a case series cohort by targeting ALSFRS- R scale trajectories with continuous in-home sensor monitoring data. Our analysis compared three model paradigms (individual batch learning and cohort-level batch versus incremental fine-tuned transfer learning) across linear slope, cubic polynomial, and ensembled self-attention pseudo-label interpolations. Results revealed cohort homogeneity across functional domains responding to learning methods, with transfer learning improving prediction error for ALSFRS-R subscales in 28 of 32 contrasts (mean RMSE=0.20(0.04)), and individual batch learning for predicting the composite scale (mean RMSE=3.15(1.25)) in 2 of 3. Self-attention interpolation achieved the lowest prediction error for subscale-level models (mean RMSE=0.19(0.06)), capturing complex nonlinear progression patterns, outperforming linear and cubic interpolations in 20 of 32 contrasts, though linear interpolation proved more stable in all ALSFRS-R composite scale models (mean RMSE=0.23(0.10)). We identified distinct homogeneity-heterogeneity profiles across functional domains with respiratory and speech exhibiting patient-specific patterns benefiting from personalized incremental adaptation, while swallowing and dressing functions followed cohort-level trajectories suitable for transfer models. These findings suggest that matching learning and pseudo-labeling techniques to functional domain-specific homogeneity-heterogeneity profiles enhances predictive accuracy in ALS progression tracking. Integrating adaptive model selection within sensor monitoring platforms could enable timely interventions and scalable deployment in future multi-center studies.", "date": "2025-07-13", "categories": []}
{"id": "2507.09470", "title": "Enhancing Clinical Text Classification via Fine-Tuned DRAGON Longformer Models", "abstract": "This study explores the optimization of the DRAGON Longformer base model for clinical text classification, specifically targeting the binary classification of medical case descriptions. A dataset of 500 clinical cases containing structured medical observations was used, with 400 cases for training and 100 for validation. Enhancements to the pre-trained joeranbosma/dragon-longformer-base-mixed-domain model included hyperparameter tuning, domain-specific preprocessing, and architectural adjustments. Key modifications involved increasing sequence length from 512 to 1024 tokens, adjusting learning rates from 1e-05 to 5e-06, extending training epochs from 5 to 8, and incorporating specialized medical terminology. The optimized model achieved notable performance gains: accuracy improved from 72.0% to 85.2%, precision from 68.0% to 84.1%, recall from 75.0% to 86.3%, and F1-score from 71.0% to 85.2%. Statistical analysis confirmed the significance of these improvements (p < .001). The model demonstrated enhanced capability in interpreting medical terminology, anatomical measurements, and clinical observations. These findings contribute to domain-specific language model research and offer practical implications for clinical natural language processing applications. The optimized model's strong performance across diverse medical conditions underscores its potential for broad use in healthcare settings.", "date": "2025-07-13", "categories": []}
{"id": "2507.09685", "title": "Symptom-Driven Personalized Proton Pump Inhibitors Therapy Using Bayesian Neural Networks and Model Predictive Control", "abstract": "Proton Pump Inhibitors (PPIs) are the standard of care for gastric acid disorders but carry significant risks when administered chronically at high doses. Precise long-term control of gastric acidity is challenged by the impracticality of invasive gastric acid monitoring beyond 72 hours and wide inter-patient variability. We propose a noninvasive, symptom-based framework that tailors PPI dosing solely on patient-reported reflux and digestive symptom patterns. A Bayesian Neural Network prediction model learns to predict patient symptoms and quantifies its uncertainty from historical symptom scores, meal, and PPIs intake data. These probabilistic forecasts feed a chance-constrained Model Predictive Control (MPC) algorithm that dynamically computes future PPI doses to minimize drug usage while enforcing acid suppression with high confidence - without any direct acid measurement. In silico studies over diverse dietary schedules and virtual patient profiles demonstrate that our learning-augmented MPC reduces total PPI consumption by 65 percent compared to standard fixed regimens, while maintaining acid suppression with at least 95 percent probability. The proposed approach offers a practical path to personalized PPI therapy, minimizing treatment burden and overdose risk without invasive sensors.", "date": "2025-07-13", "categories": []}
{"id": "2507.10008", "title": "Protective Factor-Aware Dynamic Influence Learning for Suicide Risk Prediction on Social Media", "abstract": "Suicide is a critical global health issue that requires urgent attention. Even though prior work has revealed valuable insights into detecting current suicide risk on social media, little attention has been paid to developing models that can predict subsequent suicide risk over time, limiting their ability to capture rapid fluctuations in individuals' mental state transitions. In addition, existing work ignores protective factors that play a crucial role in suicide risk prediction, focusing predominantly on risk factors alone. Protective factors such as social support and coping strategies can mitigate suicide risk by moderating the impact of risk factors. Therefore, this study proposes a novel framework for predicting subsequent suicide risk by jointly learning the dynamic influence of both risk factors and protective factors on users' suicide risk transitions. We propose a novel Protective Factor-Aware Dataset, which is built from 12 years of Reddit posts along with comprehensive annotations of suicide risk and both risk and protective factors. We also introduce a Dynamic Factors Influence Learning approach that captures the varying impact of risk and protective factors on suicide risk transitions, recognizing that suicide risk fluctuates over time according to established psychological theories. Our thorough experiments demonstrate that the proposed model significantly outperforms state-of-the-art models and large language models across three datasets. In addition, the proposed Dynamic Factors Influence Learning provides interpretable weights, helping clinicians better understand suicidal patterns and enabling more targeted intervention strategies.", "date": "2025-07-14", "categories": []}
{"id": "2507.10311", "title": "Recognizing Dementia from Neuropsychological Tests with State Space Models", "abstract": "Early detection of dementia is critical for timely medical intervention and improved patient outcomes. Neuropsychological tests are widely used for cognitive assessment but have traditionally relied on manual scoring. Automatic dementia classification (ADC) systems aim to infer cognitive decline directly from speech recordings of such tests. We propose Demenba, a novel ADC framework based on state space models, which scale linearly in memory and computation with sequence length. Trained on over 1,000 hours of cognitive assessments administered to Framingham Heart Study participants, some of whom were diagnosed with dementia through adjudicated review, our method outperforms prior approaches in fine-grained dementia classification by 21\\%, while using fewer parameters. We further analyze its scaling behavior and demonstrate that our model gains additional improvement when fused with large language models, paving the way for more transparent and scalable dementia assessment tools. Code: https://anonymous.4open.science/r/Demenba-0861", "date": "2025-07-14", "categories": []}
{"id": "2505.14765", "title": "Deep Learning-Based Forecasting of Boarding Patient Counts to Address ED Overcrowding", "abstract": "This study presents a deep learning-based framework for predicting emergency department (ED) boarding counts six hours in advance using only operational and contextual data, without patient-level information. Data from ED tracking systems, inpatient census, weather, holidays, and local events were aggregated hourly and processed with comprehensive feature engineering. The mean ED boarding count was 28.7 (standard deviation = 11.2). Multiple deep learning models, including ResNetPlus, TSTPlus, and TSiTPlus, were trained and optimized using Optuna, with TSTPlus achieving the best results (mean absolute error = 4.30, mean squared error = 29.47, R2 = 0.79). The framework accurately forecasted boarding counts, including during extreme periods, and demonstrated that broader input features improve predictive accuracy. This approach supports proactive hospital management and offers a practical method for mitigating ED overcrowding.", "date": "2025-05-20", "categories": []}
{"id": "2505.04631", "title": "Cryptogenic stroke and migraine: using probabilistic independence and machine learning to uncover latent sources of disease from the electronic health record", "abstract": "Migraine is a common but complex neurological disorder that doubles the lifetime risk of cryptogenic stroke (CS). However, this relationship remains poorly characterized, and few clinical guidelines exist to reduce this associated risk. We therefore propose a data-driven approach to extract probabilistically-independent sources from electronic health record (EHR) data and create a 10-year risk-predictive model for CS in migraine patients. These sources represent external latent variables acting on the causal graph constructed from the EHR data and approximate root causes of CS in our population. A random forest model trained on patient expressions of these sources demonstrated good accuracy (ROC 0.771) and identified the top 10 most predictive sources of CS in migraine patients. These sources revealed that pharmacologic interventions were the most important factor in minimizing CS risk in our population and identified a factor related to allergic rhinitis as a potential causative source of CS in migraine patients.", "date": "2025-04-22", "categories": []}
{"id": "2507.07589", "title": "Stress Monitoring in Healthcare: An Ensemble Machine Learning Framework Using Wearable Sensor Data", "abstract": "Healthcare professionals, particularly nurses, face elevated occupational stress, a concern amplified during the COVID-19 pandemic. While wearable sensors offer promising avenues for real-time stress monitoring, existing studies often lack comprehensive datasets and robust analytical frameworks. This study addresses these gaps by introducing a multimodal dataset comprising physiological signals, electrodermal activity, heart rate and skin temperature. A systematic literature review identified limitations in prior stress-detection methodologies, particularly in handling class imbalance and optimizing model generalizability. To overcome these challenges, the dataset underwent preprocessing with the Synthetic Minority Over sampling Technique (SMOTE), ensuring balanced representation of stress states. Advanced machine learning models including Random Forest, XGBoost and a Multi-Layer Perceptron (MLP) were evaluated and combined into a Stacking Classifier to leverage their collective predictive strengths. By using a publicly accessible dataset and a reproducible analytical pipeline, this work advances the development of deployable stress-monitoring systems, offering practical implications for safeguarding healthcare workers' mental health. Future research directions include expanding demographic diversity and exploring edge-computing implementations for low latency stress alerts.", "date": "2025-07-10", "categories": []}
{"id": "2507.07599", "title": "Enhancing Vaccine Safety Surveillance: Extracting Vaccine Mentions from Emergency Department Triage Notes Using Fine-Tuned Large Language Models", "abstract": "This study evaluates fine-tuned Llama 3.2 models for extracting vaccine-related information from emergency department triage notes to support near real-time vaccine safety surveillance. Prompt engineering was used to initially create a labeled dataset, which was then confirmed by human annotators. The performance of prompt-engineered models, fine-tuned models, and a rule-based approach was compared. The fine-tuned Llama 3 billion parameter model outperformed other models in its accuracy of extracting vaccine names. Model quantization enabled efficient deployment in resource-constrained environments. Findings demonstrate the potential of large language models in automating data extraction from emergency department notes, supporting efficient vaccine safety surveillance and early detection of emerging adverse events following immunization issues.", "date": "2025-07-10", "categories": []}
{"id": "2507.07804", "title": "Deep Survival Analysis in Multimodal Medical Data: A Parametric and Probabilistic Approach with Competing Risks", "abstract": "Accurate survival prediction is critical in oncology for prognosis and treatment planning. Traditional approaches often rely on a single data modality, limiting their ability to capture the complexity of tumor biology. To address this challenge, we introduce a multimodal deep learning framework for survival analysis capable of modeling both single and competing risks scenarios, evaluating the impact of integrating multiple medical data sources on survival predictions. We propose SAMVAE (Survival Analysis Multimodal Variational Autoencoder), a novel deep learning architecture designed for survival prediction that integrates six data modalities: clinical variables, four molecular profiles, and histopathological images. SAMVAE leverages modality specific encoders to project inputs into a shared latent space, enabling robust survival prediction while preserving modality specific information. Its parametric formulation enables the derivation of clinically meaningful statistics from the output distributions, providing patient-specific insights through interactive multimedia that contribute to more informed clinical decision-making and establish a foundation for interpretable, data-driven survival analysis in oncology. We evaluate SAMVAE on two cancer cohorts breast cancer and lower grade glioma applying tailored preprocessing, dimensionality reduction, and hyperparameter optimization. The results demonstrate the successful integration of multimodal data for both standard survival analysis and competing risks scenarios across different datasets. Our model achieves competitive performance compared to state-of-the-art multimodal survival models. Notably, this is the first parametric multimodal deep learning architecture to incorporate competing risks while modeling continuous time to a specific event, using both tabular and image data.", "date": "2025-07-10", "categories": []}
{"id": "2507.07811", "title": "Patient-specific vs Multi-Patient Vision Transformer for Markerless Tumor Motion Forecasting", "abstract": "Background: Accurate forecasting of lung tumor motion is essential for precise dose delivery in proton therapy. While current markerless methods mostly rely on deep learning, transformer-based architectures remain unexplored in this domain, despite their proven performance in trajectory forecasting.\n  Purpose: This work introduces a markerless forecasting approach for lung tumor motion using Vision Transformers (ViT). Two training strategies are evaluated under clinically realistic constraints: a patient-specific (PS) approach that learns individualized motion patterns, and a multi-patient (MP) model designed for generalization. The comparison explicitly accounts for the limited number of images that can be generated between planning and treatment sessions.\n  Methods: Digitally reconstructed radiographs (DRRs) derived from planning 4DCT scans of 31 patients were used to train the MP model; a 32nd patient was held out for evaluation. PS models were trained using only the target patient's planning data. Both models used 16 DRRs per input and predicted tumor motion over a 1-second horizon. Performance was assessed using Average Displacement Error (ADE) and Final Displacement Error (FDE), on both planning (T1) and treatment (T2) data.\n  Results: On T1 data, PS models outperformed MP models across all training set sizes, especially with larger datasets (up to 25,000 DRRs, p < 0.05). However, MP models demonstrated stronger robustness to inter-fractional anatomical variability and achieved comparable performance on T2 data without retraining.\n  Conclusions: This is the first study to apply ViT architectures to markerless tumor motion forecasting. While PS models achieve higher precision, MP models offer robust out-of-the-box performance, well-suited for time-constrained clinical settings.", "date": "2025-07-10", "categories": []}
{"id": "2507.06243", "title": "A Machine Learning Framework for Breast Cancer Treatment Classification Using a Novel Dataset", "abstract": "Breast cancer (BC) remains a significant global health challenge, with personalized treatment selection complicated by the disease's molecular and clinical heterogeneity. BC treatment decisions rely on various patient-specific clinical factors, and machine learning (ML) offers a powerful approach to predicting treatment outcomes. This study utilizes The Cancer Genome Atlas (TCGA) breast cancer clinical dataset to develop ML models for predicting the likelihood of undergoing chemotherapy or hormonal therapy. The models are trained using five-fold cross-validation and evaluated through performance metrics, including accuracy, precision, recall, specificity, sensitivity, F1-score, and area under the receiver operating characteristic curve (AUROC). Model uncertainty is assessed using bootstrap techniques, while SHAP values enhance interpretability by identifying key predictors. Among the tested models, the Gradient Boosting Machine (GBM) achieves the highest stable performance (accuracy = 0.7718, AUROC = 0.8252), followed by Extreme Gradient Boosting (XGBoost) (accuracy = 0.7557, AUROC = 0.8044) and Adaptive Boosting (AdaBoost) (accuracy = 0.7552, AUROC = 0.8016). These findings underscore the potential of ML in supporting personalized breast cancer treatment decisions through data-driven insights.", "date": "2025-06-23", "categories": []}
{"id": "2507.06432", "title": "Bridging Data Gaps of Rare Conditions in ICU: A Multi-Disease Adaptation Approach for Clinical Prediction", "abstract": "Artificial Intelligence has revolutionised critical care for common conditions. Yet, rare conditions in the intensive care unit (ICU), including recognised rare diseases and low-prevalence conditions in the ICU, remain underserved due to data scarcity and intra-condition heterogeneity. To bridge such gaps, we developed KnowRare, a domain adaptation-based deep learning framework for predicting clinical outcomes for rare conditions in the ICU. KnowRare mitigates data scarcity by initially learning condition-agnostic representations from diverse electronic health records through self-supervised pre-training. It addresses intra-condition heterogeneity by selectively adapting knowledge from clinically similar conditions with a developed condition knowledge graph. Evaluated on two ICU datasets across five clinical prediction tasks (90-day mortality, 30-day readmission, ICU mortality, remaining length of stay, and phenotyping), KnowRare consistently outperformed existing state-of-the-art models. Additionally, KnowRare demonstrated superior predictive performance compared to established ICU scoring systems, including APACHE IV and IV-a. Case studies further demonstrated KnowRare's flexibility in adapting its parameters to accommodate dataset-specific and task-specific characteristics, its generalisation to common conditions under limited data scenarios, and its rationality in selecting source conditions. These findings highlight KnowRare's potential as a robust and practical solution for supporting clinical decision-making and improving care for rare conditions in the ICU.", "date": "2025-07-08", "categories": []}
{"id": "2507.06715", "title": "CLI-RAG: A Retrieval-Augmented Framework for Clinically Structured and Context Aware Text Generation with LLMs", "abstract": "Large language models (LLMs), including zero-shot and few-shot paradigms, have shown promising capabilities in clinical text generation. However, real-world applications face two key challenges: (1) patient data is highly unstructured, heterogeneous, and scattered across multiple note types and (2) clinical notes are often long and semantically dense, making naive prompting infeasible due to context length constraints and the risk of omitting clinically relevant information.\n  We introduce CLI-RAG (Clinically Informed Retrieval-Augmented Generation), a domain-specific framework for structured and clinically grounded text generation using LLMs. It incorporates a novel hierarchical chunking strategy that respects clinical document structure and introduces a task-specific dual-stage retrieval mechanism. The global stage identifies relevant note types using evidence-based queries, while the local stage extracts high-value content within those notes creating relevance at both document and section levels.\n  We apply the system to generate structured progress notes for individual hospital visits using 15 clinical note types from the MIMIC-III dataset. Experiments show that it preserves temporal and semantic alignment across visits, achieving an average alignment score of 87.7%, surpassing the 80.7% baseline from real clinician-authored notes. The generated outputs also demonstrate high consistency across LLMs, reinforcing deterministic behavior essential for reproducibility, reliability, and clinical trust.", "date": "2025-07-09", "categories": []}
{"id": "2403.04945", "title": "MEIT: Multimodal Electrocardiogram Instruction Tuning on Large Language Models for Report Generation", "abstract": "Electrocardiogram (ECG) is the primary non-invasive diagnostic tool for monitoring cardiac conditions and is crucial in assisting clinicians. Recent studies have concentrated on classifying cardiac conditions using ECG data but have overlooked ECG report generation, which is time-consuming and requires clinical expertise. To automate ECG report generation and ensure its versatility, we propose the Multimodal ECG Instruction Tuning (MEIT) framework, the first attempt to tackle ECG report generation with LLMs and multimodal instructions. To facilitate future research, we establish a benchmark to evaluate MEIT with various LLMs backbones across two large-scale ECG datasets. Our approach uniquely aligns the representations of the ECG signal and the report, and we conduct extensive experiments to benchmark MEIT with nine open-source LLMs using more than 800,000 ECG reports. MEIT's results underscore the superior performance of instruction-tuned LLMs, showcasing their proficiency in quality report generation, zero-shot capabilities, resilience to signal perturbation, and alignment with human expert evaluation. These findings emphasize the efficacy of MEIT and its potential for real-world clinical application.", "date": "2024-03-07", "categories": []}
{"id": "2411.05983", "title": "Longitudinal Ensemble Integration for sequential classification with multimodal data", "abstract": "Effectively modeling multimodal longitudinal data is a pressing need in various application areas, especially biomedicine. Despite this, few approaches exist in the literature for this problem, with most not adequately taking into account the multimodality of the data. In this study, we developed multiple configurations of a novel multimodal and longitudinal learning framework, Longitudinal Ensemble Integration (LEI), for sequential classification. We evaluated LEI's performance, and compared it against existing approaches, for the early detection of dementia, which is among the most studied multimodal sequential classification tasks. LEI outperformed these approaches due to its use of intermediate base predictions arising from the individual data modalities, which enabled their better integration over time. LEI's design also enabled the identification of features that were consistently important across time for the effective prediction of dementia-related diagnoses. Overall, our work demonstrates the potential of LEI for sequential classification from longitudinal multimodal data.", "date": "2024-11-08", "categories": []}
{"id": "2507.05755", "title": "An autonomous agent for auditing and improving the reliability of clinical AI models", "abstract": "The deployment of AI models in clinical practice faces a critical challenge: models achieving expert-level performance on benchmarks can fail catastrophically when confronted with real-world variations in medical imaging. Minor shifts in scanner hardware, lighting or demographics can erode accuracy, but currently reliability auditing to identify such catastrophic failure cases before deployment is a bespoke and time-consuming process. Practitioners lack accessible and interpretable tools to expose and repair hidden failure modes. Here we introduce ModelAuditor, a self-reflective agent that converses with users, selects task-specific metrics, and simulates context-dependent, clinically relevant distribution shifts. ModelAuditor then generates interpretable reports explaining how much performance likely degrades during deployment, discussing specific likely failure modes and identifying root causes and mitigation strategies. Our comprehensive evaluation across three real-world clinical scenarios - inter-institutional variation in histopathology, demographic shifts in dermatology, and equipment heterogeneity in chest radiography - demonstrates that ModelAuditor is able correctly identify context-specific failure modes of state-of-the-art models such as the established SIIM-ISIC melanoma classifier. Its targeted recommendations recover 15-25% of performance lost under real-world distribution shift, substantially outperforming both baseline models and state-of-the-art augmentation methods. These improvements are achieved through a multi-agent architecture and execute on consumer hardware in under 10 minutes, costing less than US$0.50 per audit.", "date": "2025-07-08", "categories": []}
{"id": "2212.11892", "title": "An Adaptive Simulated Annealing-Based Machine Learning Approach for Developing an E-Triage Tool for Hospital Emergency Operations", "abstract": "Patient triage at emergency departments (EDs) is necessary to prioritize care for patients with critical and time-sensitive conditions. Different tools are used for patient triage and one of the most common ones is the emergency severity index (ESI), which has a scale of five levels, where level 1 is the most urgent and level 5 is the least urgent. This paper proposes a framework for utilizing machine learning to develop an e-triage tool that can be used at EDs. A large retrospective dataset of ED patient visits is obtained from the electronic health record of a healthcare provider in the Midwest of the US for three years. However, the main challenge of using machine learning algorithms is that most of them have many parameters and without optimizing these parameters, developing a high-performance model is not possible. This paper proposes an approach to optimize the hyperparameters of machine learning. The metaheuristic optimization algorithms simulated annealing (SA) and adaptive simulated annealing (ASA) are proposed to optimize the parameters of extreme gradient boosting (XGB) and categorical boosting (CaB). The newly proposed algorithms are SA-XGB, ASA-XGB, SA-CaB, ASA-CaB. Grid search (GS), which is a traditional approach used for machine learning fine-tunning is also used to fine-tune the parameters of XGB and CaB, which are named GS-XGB and GS-CaB. The six algorithms are trained and tested using eight data groups obtained from the feature selection phase. The results show ASA-CaB outperformed all the proposed algorithms with accuracy, precision, recall, and f1 of 83.3%, 83.2%, 83.3%, 83.2%, respectively.", "date": "2022-12-22", "categories": []}
{"id": "2402.00160", "title": "Emergency Department Decision Support using Clinical Pseudo-notes", "abstract": "In this work, we introduce the Multiple Embedding Model for EHR (MEME), an approach that serializes multimodal EHR tabular data into text using pseudo-notes, mimicking clinical text generation. This conversion not only preserves better representations of categorical data and learns contexts but also enables the effective employment of pretrained foundation models for rich feature representation. To address potential issues with context length, our framework encodes embeddings for each EHR modality separately. We demonstrate the effectiveness of MEME by applying it to several decision support tasks within the Emergency Department across multiple hospital systems. Our findings indicate that MEME outperforms traditional machine learning, EHR-specific foundation models, and general LLMs, highlighting its potential as a general and extendible EHR representation strategy.", "date": "2024-01-31", "categories": []}
{"id": "2502.14259", "title": "LabTOP: A Unified Model for Lab Test Outcome Prediction on Electronic Health Records", "abstract": "Lab tests are fundamental for diagnosing diseases and monitoring patient conditions. However, frequent testing can be burdensome for patients, and test results may not always be immediately available. To address these challenges, we propose LabTOP, a unified model that predicts lab test outcomes by leveraging a language modeling approach on EHR data. Unlike conventional methods that estimate only a subset of lab tests or classify discrete value ranges, LabTOP performs continuous numerical predictions for a diverse range of lab items. We evaluate LabTOP on three publicly available EHR datasets and demonstrate that it outperforms existing methods, including traditional machine learning models and state-of-the-art large language models. We also conduct extensive ablation studies to confirm the effectiveness of our design choices. We believe that LabTOP will serve as an accurate and generalizable framework for lab test outcome prediction, with potential applications in clinical decision support and early detection of critical conditions.", "date": "2025-02-20", "categories": []}
{"id": "2507.02773", "title": "KERAP: A Knowledge-Enhanced Reasoning Approach for Accurate Zero-shot Diagnosis Prediction Using Multi-agent LLMs", "abstract": "Medical diagnosis prediction plays a critical role in disease detection and personalized healthcare. While machine learning (ML) models have been widely adopted for this task, their reliance on supervised training limits their ability to generalize to unseen cases, particularly given the high cost of acquiring large, labeled datasets. Large language models (LLMs) have shown promise in leveraging language abilities and biomedical knowledge for diagnosis prediction. However, they often suffer from hallucinations, lack structured medical reasoning, and produce useless outputs. To address these challenges, we propose KERAP, a knowledge graph (KG)-enhanced reasoning approach that improves LLM-based diagnosis prediction through a multi-agent architecture. Our framework consists of a linkage agent for attribute mapping, a retrieval agent for structured knowledge extraction, and a prediction agent that iteratively refines diagnosis predictions. Experimental results demonstrate that KERAP enhances diagnostic reliability efficiently, offering a scalable and interpretable solution for zero-shot medical diagnosis prediction.", "date": "2025-07-03", "categories": []}
{"id": "2507.03046", "title": "Outcome prediction and individualized treatment effect estimation in patients with large vessel occlusion stroke", "abstract": "Mechanical thrombectomy has become the standard of care in patients with stroke due to large vessel occlusion (LVO). However, only 50% of successfully treated patients show a favorable outcome. We developed and evaluated interpretable deep learning models to predict functional outcomes in terms of the modified Rankin Scale score alongside individualized treatment effects (ITEs) using data of 449 LVO stroke patients from a randomized clinical trial. Besides clinical variables, we considered non-contrast CT (NCCT) and angiography (CTA) scans which were integrated using novel foundation models to make use of advanced imaging information. Clinical variables had a good predictive power for binary functional outcome prediction (AUC of 0.719 [0.666, 0.774]) which could slightly be improved when adding CTA imaging (AUC of 0.737 [0.687, 0.795]). Adding NCCT scans or a combination of NCCT and CTA scans to clinical features yielded no improvement. The most important clinical predictor for functional outcome was pre-stroke disability. While estimated ITEs were well calibrated to the average treatment effect, discriminatory ability was limited indicated by a C-for-Benefit statistic of around 0.55 in all models. In summary, the models allowed us to jointly integrate CT imaging and clinical features while achieving state-of-the-art prediction performance and ITE estimates. Yet, further research is needed to particularly improve ITE estimation.", "date": "2025-07-03", "categories": []}
{"id": "2507.03165", "title": "PiCME: Pipeline for Contrastive Modality Evaluation and Encoding in the MIMIC Dataset", "abstract": "Multimodal deep learning holds promise for improving clinical prediction by integrating diverse patient data, including text, imaging, time-series, and structured demographics. Contrastive learning facilitates this integration by producing a unified representation that can be reused across tasks, reducing the need for separate models or encoders. Although contrastive learning has seen success in vision-language domains, its use in clinical settings remains largely limited to image and text pairs. We propose the Pipeline for Contrastive Modality Evaluation and Encoding (PiCME), which systematically assesses five clinical data types from MIMIC: discharge summaries, radiology reports, chest X-rays, demographics, and time-series. We pre-train contrastive models on all 26 combinations of two to five modalities and evaluate their utility on in-hospital mortality and phenotype prediction. To address performance plateaus with more modalities, we introduce a Modality-Gated LSTM that weights each modality according to its contrastively learned importance. Our results show that contrastive models remain competitive with supervised baselines, particularly in three-modality settings. Performance declines beyond three modalities, which supervised models fail to recover. The Modality-Gated LSTM mitigates this drop, improving AUROC from 73.19% to 76.93% and AUPRC from 51.27% to 62.26% in the five-modality setting. We also compare contrastively learned modality importance scores with attribution scores and evaluate generalization across demographic subgroups, highlighting strengths in interpretability and fairness. PiCME is the first to scale contrastive learning across all modality combinations in MIMIC, offering guidance for modality selection, training strategies, and equitable clinical prediction.", "date": "2025-07-03", "categories": []}
{"id": "2507.03656", "title": "MLASDO: a software tool to detect and explain clinical and omics inconsistencies applied to the Parkinson's Progression Markers Initiative cohort", "abstract": "Inconsistencies between clinical and omics data may arise within medical cohorts. The identification, annotation and explanation of anomalous omics-based patients or individuals may become crucial to better reshape the disease, e.g., by detecting early onsets signaled by the omics and undetectable from observable symptoms. Here, we developed MLASDO (Machine Learning based Anomalous Sample Detection on Omics), a new method and software tool to identify, characterize and automatically describe anomalous samples based on omics data. Its workflow is based on three steps: (1) classification of healthy and cases individuals using a support vector machine algorithm; (2) detection of anomalous samples within groups; (3) explanation of anomalous individuals based on clinical data and expert knowledge. We showcase MLASDO using transcriptomics data of 317 healthy controls (HC) and 465 Parkinson's disease (PD) cases from the Parkinson's Progression Markers Initiative. In this cohort, MLASDO detected 15 anomalous HC with a PD-like transcriptomic signature and PD-like clinical features, including a lower proportion of CD4/CD8 naive T-cells and CD4 memory T-cells compared to HC (P<3.5*10^-3). MLASDO also identified 22 anomalous PD cases with a transcriptomic signature more similar to that of HC and some clinical features more similar to HC, including a lower proportion of mature neutrophils compared to PD cases (P<6*10^-3). In summary, MLASDO is a powerful tool that can help the clinician to detect and explain anomalous HC and cases of interest to be followed up. MLASDO is an open-source R package available at: https://github.com/JoseAdrian3/MLASDO.", "date": "2025-07-04", "categories": []}
{"id": "2507.03899", "title": "Transformer Model for Alzheimer's Disease Progression Prediction Using Longitudinal Visit Sequences", "abstract": "Alzheimer's disease (AD) is a neurodegenerative disorder with no known cure that affects tens of millions of people worldwide. Early detection of AD is critical for timely intervention to halt or slow the progression of the disease. In this study, we propose a Transformer model for predicting the stage of AD progression at a subject's next clinical visit using features from a sequence of visits extracted from the subject's visit history. We also rigorously compare our model to recurrent neural networks (RNNs) such as long short-term memory (LSTM), gated recurrent unit (GRU), and minimalRNN and assess their performances based on factors such as the length of prior visits and data imbalance. We test the importance of different feature categories and visit history, as well as compare the model to a newer Transformer-based model optimized for time series. Our model demonstrates strong predictive performance despite missing visits and missing features in available visits, particularly in identifying converter subjects -- individuals transitioning to more severe disease stages -- an area that has posed significant challenges in longitudinal prediction. The results highlight the model's potential in enhancing early diagnosis and patient outcomes.", "date": "2025-07-05", "categories": []}
{"id": "2507.04428", "title": "ARMR: Adaptively Responsive Network for Medication Recommendation", "abstract": "Medication recommendation is a crucial task in healthcare, especially for patients with complex medical conditions. However, existing methods often struggle to effectively balance the reuse of historical medications with the introduction of new drugs in response to the changing patient conditions. In order to address this challenge, we propose an Adaptively Responsive network for Medication Recommendation (ARMR), a new method which incorporates 1) a piecewise temporal learning component that distinguishes between recent and distant patient history, enabling more nuanced temporal understanding, and 2) an adaptively responsive mechanism that dynamically adjusts attention to new and existing drugs based on the patient's current health state and medication history. Experiments on the MIMIC-III and MIMIC-IV datasets indicate that ARMR has better performance compared with the state-of-the-art baselines in different evaluation metrics, which contributes to more personalized and accurate medication recommendations. The source code is publicly avaiable at: https://github.com/seucoin/armr2.", "date": "2025-07-06", "categories": []}
{"id": "2507.02807", "title": "In-Training Multicalibrated Survival Analysis for Healthcare via Constrained Optimization", "abstract": "Survival analysis is an important problem in healthcare because it models the relationship between an individual's covariates and the onset time of an event of interest (e.g., death). It is important for survival models to be well-calibrated (i.e., for their predicted probabilities to be close to ground-truth probabilities) because badly calibrated systems can result in erroneous clinical decisions. Existing survival models are typically calibrated at the population level only, and thus run the risk of being poorly calibrated for one or more minority subpopulations. We propose a model called GRADUATE that achieves multicalibration by ensuring that all subpopulations are well-calibrated too. GRADUATE frames multicalibration as a constrained optimization problem, and optimizes both calibration and discrimination in-training to achieve a good balance between them. We mathematically prove that the optimization method used yields a solution that is both near-optimal and feasible with high probability. Empirical comparisons against state-of-the-art baselines on real-world clinical datasets demonstrate GRADUATE's efficacy. In a detailed analysis, we elucidate the shortcomings of the baselines vis-a-vis GRADUATE's strengths.", "date": "2025-07-03", "categories": []}
{"id": "2410.09998", "title": "SlimSeiz: Efficient Channel-Adaptive Seizure Prediction Using a Mamba-Enhanced Network", "abstract": "Epileptic seizures cause abnormal brain activity, and their unpredictability can lead to accidents, underscoring the need for long-term seizure prediction. Although seizures can be predicted by analyzing electroencephalogram (EEG) signals, existing methods often require too many electrode channels or larger models, limiting mobile usability. This paper introduces a SlimSeiz framework that utilizes adaptive channel selection with a lightweight neural network model. SlimSeiz operates in two states: the first stage selects the optimal channel set for seizure prediction using machine learning algorithms, and the second stage employs a lightweight neural network based on convolution and Mamba for prediction. On the Children's Hospital Boston-MIT (CHB-MIT) EEG dataset, SlimSeiz can reduce channels from 22 to 8 while achieving a satisfactory result of 94.8% accuracy, 95.5% sensitivity, and 94.0% specificity with only 21.2K model parameters, matching or outperforming larger models' performance. We also validate SlimSeiz on a new EEG dataset, SRH-LEI, collected from Shanghai Renji Hospital, demonstrating its effectiveness across different patients. The code and SRH-LEI dataset are available at https://github.com/guoruilu/SlimSeiz.", "date": "2024-10-13", "categories": []}
{"id": "2506.22405", "title": "Sequential Diagnosis with Language Models", "abstract": "Artificial intelligence holds great promise for expanding access to expert medical knowledge and reasoning. However, most evaluations of language models rely on static vignettes and multiple-choice questions that fail to reflect the complexity and nuance of evidence-based medicine in real-world settings. In clinical practice, physicians iteratively formulate and revise diagnostic hypotheses, adapting each subsequent question and test to what they've just learned, and weigh the evolving evidence before committing to a final diagnosis. To emulate this iterative process, we introduce the Sequential Diagnosis Benchmark, which transforms 304 diagnostically challenging New England Journal of Medicine clinicopathological conference (NEJM-CPC) cases into stepwise diagnostic encounters. A physician or AI begins with a short case abstract and must iteratively request additional details from a gatekeeper model that reveals findings only when explicitly queried. Performance is assessed not just by diagnostic accuracy but also by the cost of physician visits and tests performed. We also present the MAI Diagnostic Orchestrator (MAI-DxO), a model-agnostic orchestrator that simulates a panel of physicians, proposes likely differential diagnoses and strategically selects high-value, cost-effective tests. When paired with OpenAI's o3 model, MAI-DxO achieves 80% diagnostic accuracy--four times higher than the 20% average of generalist physicians. MAI-DxO also reduces diagnostic costs by 20% compared to physicians, and 70% compared to off-the-shelf o3. When configured for maximum accuracy, MAI-DxO achieves 85.5% accuracy. These performance gains with MAI-DxO generalize across models from the OpenAI, Gemini, Claude, Grok, DeepSeek, and Llama families. We highlight how AI systems, when guided to think iteratively and act judiciously, can advance diagnostic precision and cost-effectiveness in clinical care.", "date": "2025-06-27", "categories": []}
{"id": "2507.01045", "title": "Sensing Cardiac Health Across Scenarios and Devices: A Multi-Modal Foundation Model Pretrained on Heterogeneous Data from 1.7 Million Individuals", "abstract": "Cardiac biosignals, such as electrocardiograms (ECG) and photoplethysmograms (PPG), are of paramount importance for the diagnosis, prevention, and management of cardiovascular diseases, and have been extensively used in a variety of clinical tasks. Conventional deep learning approaches for analyzing these signals typically rely on homogeneous datasets and static bespoke models, limiting their robustness and generalizability across diverse clinical settings and acquisition protocols. In this study, we present a cardiac sensing foundation model (CSFM) that leverages advanced transformer architectures and a generative, masked pretraining strategy to learn unified representations from vast, heterogeneous health records. Our model is pretrained on an innovative multi-modal integration of data from multiple large-scale datasets (including MIMIC-III-WDB, MIMIC-IV-ECG, and CODE), comprising cardiac signals and the corresponding clinical or machine-generated text reports from approximately 1.7 million individuals. We demonstrate that the embeddings derived from our CSFM not only serve as effective feature extractors across diverse cardiac sensing scenarios, but also enable seamless transfer learning across varying input configurations and sensor modalities. Extensive evaluations across diagnostic tasks, demographic information recognition, vital sign measurement, clinical outcome prediction, and ECG question answering reveal that CSFM consistently outperforms traditional one-modal-one-task approaches. Notably, CSFM exhibits robust performance across multiple ECG lead configurations from standard 12-lead systems to single-lead setups, and in scenarios where only ECG, only PPG, or a combination thereof is available. These findings highlight the potential of CSFM as a versatile and scalable solution, for comprehensive cardiac monitoring.", "date": "2025-06-23", "categories": []}
{"id": "2507.01437", "title": "Clinical NLP with Attention-Based Deep Learning for Multi-Disease Prediction", "abstract": "This paper addresses the challenges posed by the unstructured nature and high-dimensional semantic complexity of electronic health record texts. A deep learning method based on attention mechanisms is proposed to achieve unified modeling for information extraction and multi-label disease prediction. The study is conducted on the MIMIC-IV dataset. A Transformer-based architecture is used to perform representation learning over clinical text. Multi-layer self-attention mechanisms are employed to capture key medical entities and their contextual relationships. A Sigmoid-based multi-label classifier is then applied to predict multiple disease labels. The model incorporates a context-aware semantic alignment mechanism, enhancing its representational capacity in typical medical scenarios such as label co-occurrence and sparse information. To comprehensively evaluate model performance, a series of experiments were conducted, including baseline comparisons, hyperparameter sensitivity analysis, data perturbation studies, and noise injection tests. Results demonstrate that the proposed method consistently outperforms representative existing approaches across multiple performance metrics. The model maintains strong generalization under varying data scales, interference levels, and model depth configurations. The framework developed in this study offers an efficient algorithmic foundation for processing real-world clinical texts and presents practical significance for multi-label medical text modeling tasks.", "date": "2025-07-02", "categories": []}
{"id": "2507.00191", "title": "Beyond Sensor Data: Foundation Models of Behavioral Data from Wearables Improve Health Predictions", "abstract": "Wearable devices record physiological and behavioral signals that can improve health predictions. While foundation models are increasingly used for such predictions, they have been primarily applied to low-level sensor data, despite behavioral data often being more informative due to their alignment with physiologically relevant timescales and quantities. We develop foundation models of such behavioral signals using over 2.5B hours of wearable data from 162K individuals, systematically optimizing architectures and tokenization strategies for this unique dataset. Evaluated on 57 health-related tasks, our model shows strong performance across diverse real-world applications including individual-level classification and time-varying health state prediction. The model excels in behavior-driven tasks like sleep prediction, and improves further when combined with representations of raw sensor data. These results underscore the importance of tailoring foundation model design to wearables and demonstrate the potential to enable new health applications.", "date": "2025-06-30", "categories": []}
{"id": "2507.00205", "title": "Holistic Artificial Intelligence in Medicine; improved performance and explainability", "abstract": "With the increasing interest in deploying Artificial Intelligence in medicine, we previously introduced HAIM (Holistic AI in Medicine), a framework that fuses multimodal data to solve downstream clinical tasks. However, HAIM uses data in a task-agnostic manner and lacks explainability. To address these limitations, we introduce xHAIM (Explainable HAIM), a novel framework leveraging Generative AI to enhance both prediction and explainability through four structured steps: (1) automatically identifying task-relevant patient data across modalities, (2) generating comprehensive patient summaries, (3) using these summaries for improved predictive modeling, and (4) providing clinical explanations by linking predictions to patient-specific medical knowledge. Evaluated on the HAIM-MIMIC-MM dataset, xHAIM improves average AUC from 79.9% to 90.3% across chest pathology and operative tasks. Importantly, xHAIM transforms AI from a black-box predictor into an explainable decision support system, enabling clinicians to interactively trace predictions back to relevant patient data, bridging AI advancements with clinical utility.", "date": "2025-06-30", "categories": []}
{"id": "2507.00574", "title": "Foundation Models for Clinical Records at Health System Scale", "abstract": "Large-scale pretraining has transformed modeling of language and other data types, but its potential remains underexplored in healthcare with structured electronic health records (EHRs). We present a novel generative pretraining strategy for sequential EHR data using next-visit event prediction. Our model learns to autoregressively generate various tokenized clinical events for the next visit based on patient history and inherently handles the joint prediction of heterogeneous data types. Additionally, we introduce regularization on predicting repeated events and highlight a key pitfall in EHR-based foundation model evaluations: repeated event tokens can inflate performance metrics when new onsets are not distinguished from subsequent occurrences. Our model is evaluated via zero-shot prediction for forecasting dementia and knee osteoarthritis incidence within 2 and 5 years, and the model performance rivals a fully fine-tuned masked pretrained Transformer baseline, demonstrating that our approach captures complex clinical dependencies without requiring costly task-specific fine-tuning.", "date": "2025-07-01", "categories": []}
{"id": "2503.17222", "title": "Automating Adjudication of Cardiovascular Events Using Large Language Models", "abstract": "Cardiovascular events, such as heart attacks and strokes, remain a leading cause of mortality globally, necessitating meticulous monitoring and adjudication in clinical trials. This process, traditionally performed manually by clinical experts, is time-consuming, resource-intensive, and prone to inter-reviewer variability, potentially introducing bias and hindering trial progress. This study addresses these critical limitations by presenting a novel framework for automating the adjudication of cardiovascular events in clinical trials using Large Language Models (LLMs). We developed a two-stage approach: first, employing an LLM-based pipeline for event information extraction from unstructured clinical data and second, using an LLM-based adjudication process guided by a Tree of Thoughts approach and clinical endpoint committee (CEC) guidelines. Using cardiovascular event-specific clinical trial data, the framework achieved an F1-score of 0.82 for event extraction and an accuracy of 0.68 for adjudication. Furthermore, we introduce the CLEART score, a novel, automated metric specifically designed for evaluating the quality of AI-generated clinical reasoning in adjudicating cardiovascular events. This approach demonstrates significant potential for substantially reducing adjudication time and costs while maintaining high-quality, consistent, and auditable outcomes in clinical trials. The reduced variability and enhanced standardization also allow for faster identification and mitigation of risks associated with cardiovascular therapies.", "date": "2025-03-21", "categories": []}
{"id": "2503.17786", "title": "Assessing workflow impact and clinical utility of AI-assisted brain aneurysm detection: a multi-reader study", "abstract": "Despite the plethora of AI-based algorithms developed for anomaly detection in radiology, subsequent integration into clinical setting is rarely evaluated. In this work, we assess the applicability and utility of an AI-based model for brain aneurysm detection comparing the performance of two readers with different levels of experience (2 and 13 years). We aim to answer the following questions: 1) Do the readers improve their performance when assisted by the AI algorithm? 2) How much does the AI algorithm impact routine clinical workflow? We reuse and enlarge our open-access, Time-Of-Flight Magnetic Resonance Angiography dataset (N=460). We use 360 subjects for training/validating our algorithm and 100 as unseen test set for the reading session. Even though our model reaches state-of-the-art results on the test set (sensitivity=74%, false positive rate=1.6), we show that neither the junior nor the senior reader significantly increase their sensitivity (p=0.59, p=1, respectively). In addition, we find that reading time for both readers is significantly higher in the \"AI-assisted\" setting than in the \"Unassisted\" (+15 seconds, on average; p=3x10^(-4) junior, p=3x10^(-5) senior). The confidence reported by the readers is unchanged across the two settings, indicating that the AI assistance does not influence the certainty of the diagnosis. Our findings highlight the importance of clinical validation of AI algorithms in a clinical setting involving radiologists. This study should serve as a reminder to the community to always examine the real-word effectiveness and workflow impact of proposed algorithms.", "date": "2025-03-22", "categories": []}
{"id": "2506.14540", "title": "Aligning Evaluation with Clinical Priorities: Calibration, Label Shift, and Error Costs", "abstract": "Machine learning-based decision support systems are increasingly deployed in clinical settings, where probabilistic scoring functions are used to inform and prioritize patient management decisions. However, widely used scoring rules, such as accuracy and AUC-ROC, fail to adequately reflect key clinical priorities, including calibration, robustness to distributional shifts, and sensitivity to asymmetric error costs. In this work, we propose a principled yet practical evaluation framework for selecting calibrated thresholded classifiers that explicitly accounts for the uncertainty in class prevalences and domain-specific cost asymmetries often found in clinical settings. Building on the theory of proper scoring rules, particularly the Schervish representation, we derive an adjusted variant of cross-entropy (log score) that averages cost-weighted performance over clinically relevant ranges of class balance. The resulting evaluation is simple to apply, sensitive to clinical deployment conditions, and designed to prioritize models that are both calibrated and robust to real-world variations.", "date": "2025-06-17", "categories": []}
{"id": "2506.18915", "title": "Automatic Depression Assessment using Machine Learning: A Comprehensive Survey", "abstract": "Depression is a common mental illness across current human society. Traditional depression assessment relying on inventories and interviews with psychologists frequently suffer from subjective diagnosis results, slow and expensive diagnosis process as well as lack of human resources. Since there is a solid evidence that depression is reflected by various human internal brain activities and external expressive behaviours, early traditional machine learning (ML) and advanced deep learning (DL) models have been widely explored for human behaviour-based automatic depression assessment (ADA) since 2012. However, recent ADA surveys typically only focus on a limited number of human behaviour modalities. Despite being used as a theoretical basis for developing ADA approaches, existing ADA surveys lack a comprehensive review and summary of multi-modal depression-related human behaviours. To bridge this gap, this paper specifically summarises depression-related human behaviours across a range of modalities (e.g. the human brain, verbal language and non-verbal audio/facial/body behaviours). We focus on conducting an up-to-date and comprehensive survey of ML-based ADA approaches for learning depression cues from these behaviours as well as discussing and comparing their distinctive features and limitations. In addition, we also review existing ADA competitions and datasets, identify and discuss the main challenges and opportunities to provide further research directions for future ADA researchers.", "date": "2025-06-09", "categories": []}
{"id": "2506.22444", "title": "Active Learning for Forecasting Severity among Patients with Post Acute Sequelae of SARS-CoV-2", "abstract": "The long-term effects of Postacute Sequelae of SARS-CoV-2, known as PASC, pose a significant challenge to healthcare systems worldwide. Accurate identification of progression events, such as hospitalization and reinfection, is essential for effective patient management and resource allocation. However, traditional models trained on structured data struggle to capture the nuanced progression of PASC. In this study, we introduce the first publicly available cohort of 18 PASC patients, with text time series features based on Large Language Model Llama-3.1-70B-Instruct and clinical risk annotated by clinical expert. We propose an Active Attention Network to predict the clinical risk and identify progression events related to the risk. By integrating human expertise with active learning, we aim to enhance clinical risk prediction accuracy and enable progression events identification with fewer number of annotation. The ultimate goal is to improves patient care and decision-making for SARS-CoV-2 patient.", "date": "2025-06-11", "categories": []}
{"id": "2506.22454", "title": "Microelectrode Signal Dynamics as Biomarkers of Subthalamic Nucleus Entry on Deep Brain Stimulation: A Nonlinear Feature Approach", "abstract": "Accurate intraoperative localization of the subthalamic nucleus (STN) is essential for the efficacy of Deep Brain Stimulation (DBS) in patients with Parkinson's disease. While microelectrode recordings (MERs) provide rich electrophysiological information during DBS electrode implantation, current localization practices often rely on subjective interpretation of signal features. In this study, we propose a quantitative framework that leverages nonlinear dynamics and entropy-based metrics to classify neural activity recorded inside versus outside the STN. MER data from three patients were preprocessed using a robust artifact correction pipeline, segmented, and labelled based on surgical annotations. A comprehensive set of recurrence quantification analysis, nonlinear, and entropy features were extracted from each segment. Multiple supervised classifiers were trained on every combination of feature domains using stratified 10-fold cross-validation, followed by statistical comparison using paired Wilcoxon signed-rank tests with Holm-Bonferroni correction. The combination of entropy and nonlinear features yielded the highest discriminative power, and the Extra Trees classifier emerged as the best model with a cross-validated F1-score of 0.902+/-0.027 and ROC AUC of 0.887+/-0.055. Final evaluation on a 20% hold-out test set confirmed robust generalization (F1= 0.922, ROC AUC = 0.941). These results highlight the potential of nonlinear and entropy signal descriptors in supporting real-time, data-driven decision-making during DBS surgeries", "date": "2025-06-14", "categories": []}
{"id": "2506.23315", "title": "Ensemble BERT for Medication Event Classification on Electronic Health Records (EHRs)", "abstract": "Identification of key variables such as medications, diseases, relations from health records and clinical notes has a wide range of applications in the clinical domain. n2c2 2022 provided shared tasks on challenges in natural language processing for clinical data analytics on electronic health records (EHR), where it built a comprehensive annotated clinical data Contextualized Medication Event Dataset (CMED). This study focuses on subtask 2 in Track 1 of this challenge that is to detect and classify medication events from clinical notes through building a novel BERT-based ensemble model. It started with pretraining BERT models on different types of big data such as Wikipedia and MIMIC. Afterwards, these pretrained BERT models were fine-tuned on CMED training data. These fine-tuned BERT models were employed to accomplish medication event classification on CMED testing data with multiple predictions. These multiple predictions generated by these fine-tuned BERT models were integrated to build final prediction with voting strategies. Experimental results demonstrated that BERT-based ensemble models can effectively improve strict Micro-F score by about 5% and strict Macro-F score by about 6%, respectively.", "date": "2025-06-29", "categories": []}
{"id": "2210.09394", "title": "Review learning: Real world validation of privacy preserving continual learning across medical institutions", "abstract": "When a deep learning model is trained sequentially on different datasets, it often forgets the knowledge learned from previous data, a problem known as catastrophic forgetting. This damages the model's performance on diverse datasets, which is critical in privacy-preserving deep learning (PPDL) applications based on transfer learning (TL). To overcome this, we introduce \"review learning\" (RevL), a low cost continual learning algorithm for diagnosis prediction using electronic health records (EHR) within a PPDL framework. RevL generates data samples from the model which are used to review knowledge from previous datasets. Six simulated institutional experiments and one real-world experiment involving three medical institutions were conducted to validate RevL, using three binary classification EHR data. In the real-world experiment with data from 106,508 patients, the mean global area under the receiver operating curve was 0.710 for RevL and 0.655 for TL. These results demonstrate RevL's ability to retain previously learned knowledge and its effectiveness in real-world PPDL scenarios. Our work establishes a realistic pipeline for PPDL research based on model transfers across institutions and highlights the practicality of continual learning in real-world medical settings using private EHR data.", "date": "2022-10-17", "categories": []}
{"id": "2506.21500", "title": "Devising a solution to the problems of Cancer awareness in Telangana", "abstract": "According to the data, the percent of women who underwent screening for cervical cancer, breast and oral cancer in Telangana in the year 2020 was 3.3 percent, 0.3 percent and 2.3 percent respectively. Although early detection is the only way to reduce morbidity and mortality, people have very low awareness about cervical and breast cancer signs and symptoms and screening practices. We developed an ML classification model to predict if a person is susceptible to breast or cervical cancer based on demographic factors. We devised a system to provide suggestions for the nearest hospital or Cancer treatment centres based on the users location or address. In addition to this, we can integrate the health card to maintain medical records of all individuals and conduct awareness drives and campaigns. For ML classification models, we used decision tree classification and support vector classification algorithms for cervical cancer susceptibility and breast cancer susceptibility respectively. Thus, by devising this solution we come one step closer to our goal which is spreading cancer awareness, thereby, decreasing the cancer mortality and increasing cancer literacy among the people of Telangana.", "date": "2025-06-26", "categories": []}
{"id": "2406.04993", "title": "Development and Validation of a Deep-Learning Model for Differential Treatment Benefit Prediction for Adults with Major Depressive Disorder Deployed in the Artificial Intelligence in Depression Medication Enhancement (AIDME) Study", "abstract": "INTRODUCTION: The pharmacological treatment of Major Depressive Disorder (MDD) relies on a trial-and-error approach. We introduce an artificial intelligence (AI) model aiming to personalize treatment and improve outcomes, which was deployed in the Artificial Intelligence in Depression Medication Enhancement (AIDME) Study. OBJECTIVES: 1) Develop a model capable of predicting probabilities of remission across multiple pharmacological treatments for adults with at least moderate major depression. 2) Validate model predictions and examine them for amplification of harmful biases. METHODS: Data from previous clinical trials of antidepressant medications were standardized into a common framework and included 9,042 adults with moderate to severe major depression. Feature selection retained 25 clinical and demographic variables. Using Bayesian optimization, a deep learning model was trained on the training set, refined using the validation set, and tested once on the held-out test set. RESULTS: In the evaluation on the held-out test set, the model demonstrated achieved an AUC of 0.65. The model outperformed a null model on the test set (p = 0.01). The model demonstrated clinical utility, achieving an absolute improvement in population remission rate in hypothetical and actual improvement testing. While the model did identify one drug (escitalopram) as generally outperforming the other drugs (consistent with the input data), there was otherwise significant variation in drug rankings. On bias testing, the model did not amplify potentially harmful biases. CONCLUSIONS: We demonstrate the first model capable of predicting outcomes for 10 different treatment options for patients with MDD, intended to be used at or near the start of treatment to personalize treatment. The model was put into clinical practice during the AIDME randomized controlled trial whose results are reported separately.", "date": "2024-06-07", "categories": []}
{"id": "2506.20065", "title": "Supervised Coupled Matrix-Tensor Factorization (SCMTF) for Computational Phenotyping of Patient Reported Outcomes in Ulcerative Colitis", "abstract": "Phenotyping is the process of distinguishing groups of patients to identify different types of disease progression. A recent trend employs low-rank matrix and tensor factorization methods for their capability of dealing with multi-modal, heterogeneous, and missing data. Symptom quantification is crucial for understanding patient experiences in inflammatory bowel disease, especially in conditions such as ulcerative colitis (UC). However, patient-reported symptoms are typically noisy, subjective, and significantly more sparse than other data types. For this reason, they are usually not included in phenotyping and other machine learning methods. This paper explores the application of computational phenotyping to leverage Patient-Reported Outcomes (PROs) using a novel supervised coupled matrix-tensor factorization (SCMTF) method, which integrates temporal PROs and temporal labs with static features to predict medication persistence in ulcerative colitis. This is the first tensor-based method that is both supervised and coupled, it is the first application to the UC domain, and the first application to PROs. We use a deep learning framework that makes the model flexible and easy to train. The proposed method allows us to handle the large amount of missing data in the PROs. The best model predicts changes in medication 8 and 20 months in the future with AUCs of 0.853 and 0.803 on the test set respectively. We derive interpretable phenotypes consisting of static features and temporal features (including their temporal patterns). We show that low-rank matrix and tensor based phenotyping can be successfully applied to the UC domain and to highly missing PRO data. We identify phenotypes useful to predict medication persistence - these phenotypes include several symptom variables, showing that PROs contain relevant infromation that is usually discarded.", "date": "2025-06-24", "categories": []}
{"id": "2506.17412", "title": "VMRA-MaR: An Asymmetry-Aware Temporal Framework for Longitudinal Breast Cancer Risk Prediction", "abstract": "Breast cancer remains a leading cause of mortality worldwide and is typically detected via screening programs where healthy people are invited in regular intervals. Automated risk prediction approaches have the potential to improve this process by facilitating dynamically screening of high-risk groups. While most models focus solely on the most recent screening, there is growing interest in exploiting temporal information to capture evolving trends in breast tissue, as inspired by clinical practice. Early methods typically relied on two time steps, and although recent efforts have extended this to multiple time steps using Transformer architectures, challenges remain in fully harnessing the rich temporal dynamics inherent in longitudinal imaging data. In this work, we propose to instead leverage Vision Mamba RNN (VMRNN) with a state-space model (SSM) and LSTM-like memory mechanisms to effectively capture nuanced trends in breast tissue evolution. To further enhance our approach, we incorporate an asymmetry module that utilizes a Spatial Asymmetry Detector (SAD) and Longitudinal Asymmetry Tracker (LAT) to identify clinically relevant bilateral differences. This integrated framework demonstrates notable improvements in predicting cancer onset, especially for the more challenging high-density breast cases and achieves superior performance at extended time points (years four and five), highlighting its potential to advance early breast cancer recognition and enable more personalized screening strategies. Our code is available at https://github.com/Mortal-Suen/VMRA-MaR.git.", "date": "2025-06-20", "categories": []}
{"id": "2506.19174", "title": "MOSCARD -- Causal Reasoning and De-confounding for Multimodal Opportunistic Screening of Cardiovascular Adverse Events", "abstract": "Major Adverse Cardiovascular Events (MACE) remain the leading cause of mortality globally, as reported in the Global Disease Burden Study 2021. Opportunistic screening leverages data collected from routine health check-ups and multimodal data can play a key role to identify at-risk individuals. Chest X-rays (CXR) provide insights into chronic conditions contributing to major adverse cardiovascular events (MACE), while 12-lead electrocardiogram (ECG) directly assesses cardiac electrical activity and structural abnormalities. Integrating CXR and ECG could offer a more comprehensive risk assessment than conventional models, which rely on clinical scores, computed tomography (CT) measurements, or biomarkers, which may be limited by sampling bias and single modality constraints. We propose a novel predictive modeling framework - MOSCARD, multimodal causal reasoning with co-attention to align two distinct modalities and simultaneously mitigate bias and confounders in opportunistic risk estimation. Primary technical contributions are - (i) multimodal alignment of CXR with ECG guidance; (ii) integration of causal reasoning; (iii) dual back-propagation graph for de-confounding. Evaluated on internal, shift data from emergency department (ED) and external MIMIC datasets, our model outperformed single modality and state-of-the-art foundational models - AUC: 0.75, 0.83, 0.71 respectively. Proposed cost-effective opportunistic screening enables early intervention, improving patient outcomes and reducing disparities.", "date": "2025-06-23", "categories": []}
{"id": "2411.01418", "title": "Enhancing Glucose Level Prediction of ICU Patients through Hierarchical Modeling of Irregular Time-Series", "abstract": "Accurately predicting blood glucose (BG) levels of ICU patients is critical, as both hypoglycemia (BG < 70 mg/dL) and hyperglycemia (BG > 180 mg/dL) are associated with increased morbidity and mortality. This study presents a proof-of-concept machine learning framework, the Multi-source Irregular Time-Series Transformer (MITST), designed to predict BG levels in ICU patients. In contrast to existing methods that rely heavily on manual feature engineering or utilize limited Electronic Health Record (EHR) data sources, MITST integrates diverse clinical data--including laboratory results, medications, and vital signs without predefined aggregation. The model leverages a hierarchical Transformer architecture, designed to capture interactions among features within individual timestamps, temporal dependencies across different timestamps, and semantic relationships across multiple data sources. Evaluated using the extensive eICU database (200,859 ICU stays across 208 hospitals), MITST achieves a statistically significant ( p < 0.001 ) average improvement of 1.7 percentage points (pp) in AUROC and 1.8 pp in AUPRC over a state-of-the-art random forest baseline. Crucially, for hypoglycemia--a rare but life-threatening condition--MITST increases sensitivity by 7.2 pp, potentially enabling hundreds of earlier interventions across ICU populations. The flexible architecture of MITST allows seamless integration of new data sources without retraining the entire model, enhancing its adaptability for clinical decision support. While this study focuses on predicting BG levels, we also demonstrate MITST's ability to generalize to a distinct clinical task (in-hospital mortality prediction), highlighting its potential for broader applicability in ICU settings. MITST thus offers a robust and extensible solution for analyzing complex, multi-source, irregular time-series data.", "date": "2024-11-03", "categories": []}
{"id": "2412.05348", "title": "Accurate early detection of Parkinson's disease from SPECT imaging through Convolutional Neural Networks", "abstract": "Early and accurate detection of Parkinson's disease (PD) is a crucial diagnostic challenge carrying immense clinical significance, for effective treatment regimens and patient management. For instance, a group of subjects termed SWEDD who are clinically diagnosed as PD, but show normal Single Photon Emission Computed Tomography (SPECT) scans, change their diagnosis as non-PD after few years of follow up, and in the meantime, they are treated with PD medications which do more harm than good. In this work, machine learning models are developed using features from SPECT images to detect early PD and SWEDD subjects from normal. These models were observed to perform with high accuracy. It is inferred from the study that these diagnostic models carry potential to help PD clinicians in the diagnostic process", "date": "2024-12-06", "categories": []}
{"id": "2504.09027", "title": "Predicting Mild Cognitive Impairment Using Naturalistic Driving and Trip Destination Modeling", "abstract": "Understanding the relationship between mild cognitive impairment (MCI) and driving behavior is essential for enhancing road safety, particularly among older adults. This study introduces a novel approach by incorporating specific trip destinations-such as home, work, medical appointments, social activities, and errands-using geohashing to analyze the driving habits of older drivers in Nebraska. We employed a two-fold methodology that combines data visualization with advanced machine learning models, including C5.0, Random Forest, and Support Vector Machines, to assess the effectiveness of these location-based variables in predicting cognitive impairment. Notably, the C5.0 model showed a robust and stable performance, achieving a median recall of 0.68, which indicates that our methodology accurately identifies cognitive impairment in drivers 68\\% of the time. This emphasizes our model's capacity to reduce false negatives, a crucial factor given the profound implications of failing to identify impaired drivers. Our findings underscore the innovative use of life-space variables in understanding and predicting cognitive decline, offering avenues for early intervention and tailored support for affected individuals.", "date": "2025-04-12", "categories": []}
{"id": "2505.23075", "title": "Second Opinion Matters: Towards Adaptive Clinical AI via the Consensus of Expert Model Ensemble", "abstract": "Despite the growing clinical adoption of large language models (LLMs), current approaches heavily rely on single model architectures. To overcome risks of obsolescence and rigid dependence on single model systems, we present a novel framework, termed the Consensus Mechanism. Mimicking clinical triage and multidisciplinary clinical decision-making, the Consensus Mechanism implements an ensemble of specialized medical expert agents enabling improved clinical decision making while maintaining robust adaptability. This architecture enables the Consensus Mechanism to be optimized for cost, latency, or performance, purely based on its interior model configuration.\n  To rigorously evaluate the Consensus Mechanism, we employed three medical evaluation benchmarks: MedMCQA, MedQA, and MedXpertQA Text, and the differential diagnosis dataset, DDX+. On MedXpertQA, the Consensus Mechanism achieved an accuracy of 61.0% compared to 53.5% and 45.9% for OpenAI's O3 and Google's Gemini 2.5 Pro. Improvement was consistent across benchmarks with an increase in accuracy on MedQA ($\\Delta\\mathrm{Accuracy}_{\\mathrm{consensus\\text{-}O3}} = 3.4\\%$) and MedMCQA ($\\Delta\\mathrm{Accuracy}_{\\mathrm{consensus\\text{-}O3}} = 9.1\\%$). These accuracy gains extended to differential diagnosis generation, where our system demonstrated improved recall and precision (F1$_\\mathrm{consensus}$ = 0.326 vs. F1$_{\\mathrm{O3\\text{-}high}}$ = 0.2886) and a higher top-1 accuracy for DDX (Top1$_\\mathrm{consensus}$ = 52.0% vs. Top1$_{\\mathrm{O3\\text{-}high}}$ = 45.2%).", "date": "2025-05-29", "categories": []}
{"id": "2506.17620", "title": "Trustworthy Chronic Disease Risk Prediction For Self-Directed Preventive Care via Medical Literature Validation", "abstract": "Chronic diseases are long-term, manageable, yet typically incurable conditions, highlighting the need for effective preventive strategies. Machine learning has been widely used to assess individual risk for chronic diseases. However, many models rely on medical test data (e.g. blood results, glucose levels), which limits their utility for proactive self-assessment. Additionally, to gain public trust, machine learning models should be explainable and transparent. Although some research on self-assessment machine learning models includes explainability, their explanations are not validated against established medical literature, reducing confidence in their reliability. To address these issues, we develop deep learning models that predict the risk of developing 13 chronic diseases using only personal and lifestyle factors, enabling accessible, self-directed preventive care. Importantly, we use SHAP-based explainability to identify the most influential model features and validate them against established medical literature. Our results show a strong alignment between the models' most influential features and established medical literature, reinforcing the models' trustworthiness. Critically, we find that this observation holds across 13 distinct diseases, indicating that this machine learning approach can be broadly trusted for chronic disease prediction. This work lays the foundation for developing trustworthy machine learning tools for self-directed preventive care. Future research can explore other approaches for models' trustworthiness and discuss how the models can be used ethically and responsibly.", "date": "2025-06-21", "categories": []}
{"id": "2506.11996", "title": "Improving Surgical Risk Prediction Through Integrating Automated Body Composition Analysis: a Retrospective Trial on Colectomy Surgery", "abstract": "Objective: To evaluate whether preoperative body composition metrics automatically extracted from CT scans can predict postoperative outcomes after colectomy, either alone or combined with clinical variables or existing risk predictors. Main outcomes and measures: The primary outcome was the predictive performance for 1-year all-cause mortality following colectomy. A Cox proportional hazards model with 1-year follow-up was used, and performance was evaluated using the concordance index (C-index) and Integrated Brier Score (IBS). Secondary outcomes included postoperative complications, unplanned readmission, blood transfusion, and severe infection, assessed using AUC and Brier Score from logistic regression. Odds ratios (OR) described associations between individual CT-derived body composition metrics and outcomes. Over 300 features were extracted from preoperative CTs across multiple vertebral levels, including skeletal muscle area, density, fat areas, and inter-tissue metrics. NSQIP scores were available for all surgeries after 2012.", "date": "2025-06-13", "categories": []}
{"id": "2506.14782", "title": "Integrating Dynamical Systems Learning with Foundational Models: A Meta-Evolutionary AI Framework for Clinical Trials", "abstract": "Artificial intelligence (AI) has evolved into an ecosystem of specialized \"species,\" each with unique strengths. We analyze two: DeepSeek-V3, a 671-billion-parameter Mixture of Experts large language model (LLM) exemplifying scale-driven generality, and NetraAI, a dynamical system-based framework engineered for stability and interpretability on small clinical trial datasets. We formalize NetraAI's foundations, combining contraction mappings, information geometry, and evolutionary algorithms to identify predictive patient cohorts. Features are embedded in a metric space and iteratively contracted toward stable attractors that define latent subgroups. A pseudo-temporal embedding and long-range memory enable exploration of higher-order feature interactions, while an internal evolutionary loop selects compact, explainable 2-4-variable bundles (\"Personas\").\n  To guide discovery, we introduce an LLM Strategist as a meta-evolutionary layer that observes Persona outputs, prioritizes promising variables, injects domain knowledge, and assesses robustness. This two-tier architecture mirrors the human scientific process: NetraAI as experimentalist, the LLM as theorist, forming a self-improving loop.\n  In case studies (schizophrenia, depression, pancreatic cancer), NetraAI uncovered small, high-effect-size subpopulations that transformed weak baseline models (AUC ~0.50-0.68) into near-perfect classifiers using only a few features. We position NetraAI at the intersection of dynamical systems, information geometry, and evolutionary learning, aligned with emerging concept-level reasoning paradigms such as LeCun's Joint Embedding Predictive Architecture (JEPA). By prioritizing reliable, explainable knowledge, NetraAI offers a new generation of adaptive, self-reflective AI to accelerate clinical discovery.", "date": "2025-05-25", "categories": []}
{"id": "2506.15626", "title": "Federated Learning for MRI-based BrainAGE: a multicenter study on post-stroke functional outcome prediction", "abstract": "$\\textbf{Objective:}$ Brain-predicted age difference (BrainAGE) is a neuroimaging biomarker reflecting brain health. However, training robust BrainAGE models requires large datasets, often restricted by privacy concerns. This study evaluates the performance of federated learning (FL) for BrainAGE estimation in ischemic stroke patients treated with mechanical thrombectomy, and investigates its association with clinical phenotypes and functional outcomes.\n  $\\textbf{Methods:}$ We used FLAIR brain images from 1674 stroke patients across 16 hospital centers. We implemented standard machine learning and deep learning models for BrainAGE estimates under three data management strategies: centralized learning (pooled data), FL (local training at each site), and single-site learning. We reported prediction errors and examined associations between BrainAGE and vascular risk factors (e.g., diabetes mellitus, hypertension, smoking), as well as functional outcomes at three months post-stroke. Logistic regression evaluated BrainAGE's predictive value for these outcomes, adjusting for age, sex, vascular risk factors, stroke severity, time between MRI and arterial puncture, prior intravenous thrombolysis, and recanalisation outcome.\n  $\\textbf{Results:}$ While centralized learning yielded the most accurate predictions, FL consistently outperformed single-site models. BrainAGE was significantly higher in patients with diabetes mellitus across all models. Comparisons between patients with good and poor functional outcomes, and multivariate predictions of these outcomes showed the significance of the association between BrainAGE and post-stroke recovery.\n  $\\textbf{Conclusion:}$ FL enables accurate age predictions without data centralization. The strong association between BrainAGE, vascular risk factors, and post-stroke recovery highlights its potential for prognostic modeling in stroke care.", "date": "2025-06-18", "categories": []}
{"id": "2506.15696", "title": "CoC: Chain-of-Cancer based on Cross-Modal Autoregressive Traction for Survival Prediction", "abstract": "Survival prediction aims to evaluate the risk level of cancer patients. Existing methods primarily rely on pathology and genomics data, either individually or in combination. From the perspective of cancer pathogenesis, epigenetic changes, such as methylation data, could also be crucial for this task. Furthermore, no previous endeavors have utilized textual descriptions to guide the prediction. To this end, we are the first to explore the use of four modalities, including three clinical modalities and language, for conducting survival prediction. In detail, we are motivated by the Chain-of-Thought (CoT) to propose the Chain-of-Cancer (CoC) framework, focusing on intra-learning and inter-learning. We encode the clinical data as the raw features, which remain domain-specific knowledge for intra-learning. In terms of inter-learning, we use language to prompt the raw features and introduce an Autoregressive Mutual Traction module for synergistic representation. This tailored framework facilitates joint learning among multiple modalities. Our approach is evaluated across five public cancer datasets, and extensive experiments validate the effectiveness of our methods and proposed designs, leading to producing \\sota results. Codes will be released.", "date": "2025-05-28", "categories": []}
{"id": "2506.15761", "title": "Advancing Digital Precision Medicine for Chronic Fatigue Syndrome through Longitudinal Large-Scale Multi-Modal Biological Omics Modeling with Machine Learning and Artificial Intelligence", "abstract": "We studied a generalized question: chronic diseases like ME/CFS and long COVID exhibit high heterogeneity with multifactorial etiology and progression, complicating diagnosis and treatment. To address this, we developed BioMapAI, an explainable Deep Learning framework using the richest longitudinal multi-omics dataset for ME/CFS to date. This dataset includes gut metagenomics, plasma metabolome, immune profiling, blood labs, and clinical symptoms. By connecting multi-omics to a symptom matrix, BioMapAI identified both disease- and symptom-specific biomarkers, reconstructed symptoms, and achieved state-of-the-art precision in disease classification. We also created the first connectivity map of these omics in both healthy and disease states and revealed how microbiome-immune-metabolome crosstalk shifted from healthy to ME/CFS.", "date": "2025-06-18", "categories": []}
{"id": "2506.15823", "title": "AI-based modular warning machine for risk identification in proximity healthcare", "abstract": "\"DHEAL-COM - Digital Health Solutions in Community Medicine\" is a research and technology project funded by the Italian Department of Health for the development of digital solutions of interest in proximity healthcare. The activity within the DHEAL-COM framework allows scientists to gather a notable amount of multi-modal data whose interpretation can be performed by means of machine learning algorithms. The present study illustrates a general automated pipeline made of numerous unsupervised and supervised methods that can ingest such data, provide predictive results, and facilitate model interpretations via feature identification.", "date": "2025-06-13", "categories": []}
{"id": "2506.15901", "title": "Clinically Interpretable Mortality Prediction for ICU Patients with Diabetes and Atrial Fibrillation: A Machine Learning Approach", "abstract": "Background: Patients with both diabetes mellitus (DM) and atrial fibrillation (AF) face elevated mortality in intensive care units (ICUs), yet models targeting this high-risk group remain limited.\n  Objective: To develop an interpretable machine learning (ML) model predicting 28-day mortality in ICU patients with concurrent DM and AF using early-phase clinical data.\n  Methods: A retrospective cohort of 1,535 adult ICU patients with DM and AF was extracted from the MIMIC-IV database. Data preprocessing involved median/mode imputation, z-score normalization, and early temporal feature engineering. A two-step feature selection pipeline-univariate filtering (ANOVA F-test) and Random Forest-based multivariate ranking-yielded 19 interpretable features. Seven ML models were trained with stratified 5-fold cross-validation and SMOTE oversampling. Interpretability was assessed via ablation and Accumulated Local Effects (ALE) analysis.\n  Results: Logistic regression achieved the best performance (AUROC: 0.825; 95% CI: 0.779-0.867), surpassing more complex models. Key predictors included RAS, age, bilirubin, and extubation. ALE plots showed intuitive, non-linear effects such as age-related risk acceleration and bilirubin thresholds.\n  Conclusion: This interpretable ML model offers accurate risk prediction and clinical insights for early ICU triage in patients with DM and AF.", "date": "2025-06-18", "categories": []}
{"id": "2506.17035", "title": "Critical Appraisal of Fairness Metrics in Clinical Predictive AI", "abstract": "Predictive artificial intelligence (AI) offers an opportunity to improve clinical practice and patient outcomes, but risks perpetuating biases if fairness is inadequately addressed. However, the definition of \"fairness\" remains unclear. We conducted a scoping review to identify and critically appraise fairness metrics for clinical predictive AI. We defined a \"fairness metric\" as a measure quantifying whether a model discriminates (societally) against individuals or groups defined by sensitive attributes. We searched five databases (2014-2024), screening 820 records, to include 41 studies, and extracted 62 fairness metrics. Metrics were classified by performance-dependency, model output level, and base performance metric, revealing a fragmented landscape with limited clinical validation and overreliance on threshold-dependent measures. Eighteen metrics were explicitly developed for healthcare, including only one clinical utility metric. Our findings highlight conceptual challenges in defining and quantifying fairness and identify gaps in uncertainty quantification, intersectionality, and real-world applicability. Future work should prioritise clinically meaningful metrics.", "date": "2025-06-20", "categories": []}
{"id": "2403.10581", "title": "Large Language Model-informed ECG Dual Attention Network for Heart Failure Risk Prediction", "abstract": "Heart failure (HF) poses a significant public health challenge, with a rising global mortality rate. Early detection and prevention of HF could significantly reduce its impact. We introduce a novel methodology for predicting HF risk using 12-lead electrocardiograms (ECGs). We present a novel, lightweight dual-attention ECG network designed to capture complex ECG features essential for early HF risk prediction, despite the notable imbalance between low and high-risk groups. This network incorporates a cross-lead attention module and twelve lead-specific temporal attention modules, focusing on cross-lead interactions and each lead's local dynamics. To further alleviate model overfitting, we leverage a large language model (LLM) with a public ECG-Report dataset for pretraining on an ECG-report alignment task. The network is then fine-tuned for HF risk prediction using two specific cohorts from the UK Biobank study, focusing on patients with hypertension (UKB-HYP) and those who have had a myocardial infarction (UKB-MI).The results reveal that LLM-informed pre-training substantially enhances HF risk prediction in these cohorts. The dual-attention design not only improves interpretability but also predictive accuracy, outperforming existing competitive methods with C-index scores of 0.6349 for UKB-HYP and 0.5805 for UKB-MI. This demonstrates our method's potential in advancing HF risk assessment with clinical complex ECG data.", "date": "2024-03-15", "categories": []}
{"id": "2506.13584", "title": "From Data-Driven to Purpose-Driven Artificial Intelligence: Systems Thinking for Data-Analytic Automation of Patient Care", "abstract": "In this work, we reflect on the data-driven modeling paradigm that is gaining ground in AI-driven automation of patient care. We argue that the repurposing of existing real-world patient datasets for machine learning may not always represent an optimal approach to model development as it could lead to undesirable outcomes in patient care. We reflect on the history of data analysis to explain how the data-driven paradigm rose to popularity, and we envision ways in which systems thinking and clinical domain theory could complement the existing model development approaches in reaching human-centric outcomes. We call for a purpose-driven machine learning paradigm that is grounded in clinical theory and the sociotechnical realities of real-world operational contexts. We argue that understanding the utility of existing patient datasets requires looking in two directions: upstream towards the data generation, and downstream towards the automation objectives. This purpose-driven perspective to AI system development opens up new methodological opportunities and holds promise for AI automation of patient care.", "date": "2025-06-16", "categories": []}
{"id": "2506.14815", "title": "Predicting Anthropometric Body Composition Variables Using 3D Optical Imaging and Machine Learning", "abstract": "Accurate prediction of anthropometric body composition variables, such as Appendicular Lean Mass (ALM), Body Fat Percentage (BFP), and Bone Mineral Density (BMD), is essential for early diagnosis of several chronic diseases. Currently, researchers rely on Dual-Energy X-ray Absorptiometry (DXA) scans to measure these metrics; however, DXA scans are costly and time-consuming. This work proposes an alternative to DXA scans by applying statistical and machine learning models on biomarkers (height, volume, left calf circumference, etc) obtained from 3D optical images. The dataset consists of 847 patients and was sourced from Pennington Biomedical Research Center. Extracting patients' data in healthcare faces many technical challenges and legal restrictions. However, most supervised machine learning algorithms are inherently data-intensive, requiring a large amount of training data. To overcome these limitations, we implemented a semi-supervised model, the $p$-Laplacian regression model. This paper is the first to demonstrate the application of a $p$-Laplacian model for regression. Our $p$-Laplacian model yielded errors of $\\sim13\\%$ for ALM, $\\sim10\\%$ for BMD, and $\\sim20\\%$ for BFP when the training data accounted for 10 percent of all data. Among the supervised algorithms we implemented, Support Vector Regression (SVR) performed the best for ALM and BMD, yielding errors of $\\sim 8\\%$ for both, while Least Squares SVR performed the best for BFP with $\\sim 11\\%$ error when trained on 80 percent of the data. Our findings position the $p$-Laplacian model as a promising tool for healthcare applications, particularly in a data-constrained environment.", "date": "2025-06-08", "categories": []}
{"id": "2506.14843", "title": "CACTUS as a Reliable Tool for Early Classification of Age-related Macular Degeneration", "abstract": "Machine Learning (ML) is used to tackle various tasks, such as disease classification and prediction. The effectiveness of ML models relies heavily on having large amounts of complete data. However, healthcare data is often limited or incomplete, which can hinder model performance. Additionally, issues like the trustworthiness of solutions vary with the datasets used. The lack of transparency in some ML models further complicates their understanding and use. In healthcare, particularly in the case of Age-related Macular Degeneration (AMD), which affects millions of older adults, early diagnosis is crucial due to the absence of effective treatments for reversing progression. Diagnosing AMD involves assessing retinal images along with patients' symptom reports. There is a need for classification approaches that consider genetic, dietary, clinical, and demographic factors. Recently, we introduced the -Comprehensive Abstraction and Classification Tool for Uncovering Structures-(CACTUS), aimed at improving AMD stage classification. CACTUS offers explainability and flexibility, outperforming standard ML models. It enhances decision-making by identifying key factors and providing confidence in its results. The important features identified by CACTUS allow us to compare with existing medical knowledge. By eliminating less relevant or biased data, we created a clinical scenario for clinicians to offer feedback and address biases.", "date": "2025-06-16", "categories": []}
{"id": "2506.14986", "title": "Early Prediction of Multiple Sclerosis Disability Progression via Multimodal Foundation Model Benchmarks", "abstract": "Early multiple sclerosis (MS) disability progression prediction is challenging due to disease heterogeneity. This work predicts 48- and 72-week disability using sparse baseline clinical data and 12 weeks of daily digital Floodlight data from the CONSONANCE clinical trial. We employed state-of-the-art tabular and time-series foundation models (FMs), a custom multimodal attention-based transformer, and machine learning methods. Despite the difficulty of early prediction (AUROC 0.63), integrating digital data via advanced models improved performance over clinical data alone. A transformer model using unimodal embeddings from the Moment FM yielded the best result, but our multimodal transformer consistently outperformed its unimodal counterpart, confirming the advantages of combining clinical with digital data. Our findings demonstrate the promise of FMs and multimodal approaches to extract predictive signals from complex and diverse clinical and digital life sciences data (e.g., imaging, omics), enabling more accurate prognostics for MS and potentially other complex diseases.", "date": "2025-06-17", "categories": []}
{"id": "2506.15118", "title": "CKD-EHR:Clinical Knowledge Distillation for Electronic Health Records", "abstract": "Electronic Health Records (EHR)-based disease prediction models have demonstrated significant clinical value in promoting precision medicine and enabling early intervention. However, existing large language models face two major challenges: insufficient representation of medical knowledge and low efficiency in clinical deployment. To address these challenges, this study proposes the CKD-EHR (Clinical Knowledge Distillation for EHR) framework, which achieves efficient and accurate disease risk prediction through knowledge distillation techniques. Specifically, the large language model Qwen2.5-7B is first fine-tuned on medical knowledge-enhanced data to serve as the teacher model.It then generates interpretable soft labels through a multi-granularity attention distillation mechanism. Finally, the distilled knowledge is transferred to a lightweight BERT student model. Experimental results show that on the MIMIC-III dataset, CKD-EHR significantly outperforms the baseline model:diagnostic accuracy is increased by 9%, F1-score is improved by 27%, and a 22.2 times inference speedup is achieved. This innovative solution not only greatly improves resource utilization efficiency but also significantly enhances the accuracy and timeliness of diagnosis, providing a practical technical approach for resource optimization in clinical settings. The code and data for this research are available athttps://github.com/209506702/CKD_EHR.", "date": "2025-06-18", "categories": []}
{"id": "2506.15330", "title": "Universal Laboratory Model: prognosis of abnormal clinical outcomes based on routine tests", "abstract": "Clinical laboratory results are ubiquitous in any diagnosis making. Predicting abnormal values of not prescribed tests based on the results of performed tests looks intriguing, as it would be possible to make early diagnosis available to everyone. The special place is taken by the Common Blood Count (CBC) test, as it is the most widely used clinical procedure. Combining routine biochemical panels with CBC presents a set of test-value pairs that varies from patient to patient, or, in common settings, a table with missing values. Here we formulate a tabular modeling problem as a set translation problem where the source set comprises pairs of GPT-like label column embedding and its corresponding value while the target set consists of the same type embeddings only. The proposed approach can effectively deal with missing values without implicitly estimating them and bridges the world of LLM with the tabular domain. Applying this method to clinical laboratory data, we achieve an improvement up to 8% AUC for joint predictions of high uric acid, glucose, cholesterol, and low ferritin levels.", "date": "2025-06-18", "categories": []}
{"id": "2506.13786", "title": "Enhancing Bagging Ensemble Regression with Data Integration for Time Series-Based Diabetes Prediction", "abstract": "Diabetes is a chronic metabolic disease characterized by elevated blood glucose levels, leading to complications like heart disease, kidney failure, and nerve damage. Accurate state-level predictions are vital for effective healthcare planning and targeted interventions, but in many cases, data for necessary analyses are incomplete. This study begins with a data engineering process to integrate diabetes-related datasets from 2011 to 2021 to create a comprehensive feature set. We then introduce an enhanced bagging ensemble regression model (EBMBag+) for time series forecasting to predict diabetes prevalence across U.S. cities. Several baseline models, including SVMReg, BDTree, LSBoost, NN, LSTM, and ERMBag, were evaluated for comparison with our EBMBag+ algorithm. The experimental results demonstrate that EBMBag+ achieved the best performance, with an MAE of 0.41, RMSE of 0.53, MAPE of 4.01, and an R2 of 0.9.", "date": "2025-06-11", "categories": []}
{"id": "2506.13800", "title": "Enhancing Clinical Decision Support and EHR Insights through LLMs and the Model Context Protocol: An Open-Source MCP-FHIR Framework", "abstract": "Enhancing clinical decision support (CDS), reducing documentation burdens, and improving patient health literacy remain persistent challenges in digital health. This paper presents an open-source, agent-based framework that integrates Large Language Models (LLMs) with HL7 FHIR data via the Model Context Protocol (MCP) for dynamic extraction and reasoning over electronic health records (EHRs). Built on the established MCP-FHIR implementation, the framework enables declarative access to diverse FHIR resources through JSON-based configurations, supporting real-time summarization, interpretation, and personalized communication across multiple user personas, including clinicians, caregivers, and patients. To ensure privacy and reproducibility, the framework is evaluated using synthetic EHR data from the SMART Health IT sandbox (https://r4.smarthealthit.org/), which conforms to the FHIR R4 standard. Unlike traditional approaches that rely on hardcoded retrieval and static workflows, the proposed method delivers scalable, explainable, and interoperable AI-powered EHR applications. The agentic architecture further supports multiple FHIR formats, laying a robust foundation for advancing personalized digital health solutions.", "date": "2025-06-13", "categories": []}
{"id": "2506.14101", "title": "Abstract Meaning Representation for Hospital Discharge Summarization", "abstract": "The Achilles heel of Large Language Models (LLMs) is hallucination, which has drastic consequences for the clinical domain. This is particularly important with regards to automatically generating discharge summaries (a lengthy medical document that summarizes a hospital in-patient visit). Automatically generating these summaries would free physicians to care for patients and reduce documentation burden. The goal of this work is to discover new methods that combine language-based graphs and deep learning models to address provenance of content and trustworthiness in automatic summarization. Our method shows impressive reliability results on the publicly available Medical Information Mart for Intensive III (MIMIC-III) corpus and clinical notes written by physicians at Anonymous Hospital. rovide our method, generated discharge ary output examples, source code and trained models.", "date": "2025-06-17", "categories": []}
{"id": "2506.14560", "title": "Risk Estimation of Knee Osteoarthritis Progression via Predictive Multi-task Modelling from Efficient Diffusion Model using X-ray Images", "abstract": "Medical imaging plays a crucial role in assessing knee osteoarthritis (OA) risk by enabling early detection and disease monitoring. Recent machine learning methods have improved risk estimation (i.e., predicting the likelihood of disease progression) and predictive modelling (i.e., the forecasting of future outcomes based on current data) using medical images, but clinical adoption remains limited due to their lack of interpretability. Existing approaches that generate future images for risk estimation are complex and impractical. Additionally, previous methods fail to localize anatomical knee landmarks, limiting interpretability. We address these gaps with a new interpretable machine learning method to estimate the risk of knee OA progression via multi-task predictive modelling that classifies future knee OA severity and predicts anatomical knee landmarks from efficiently generated high-quality future images. Such image generation is achieved by leveraging a diffusion model in a class-conditioned latent space to forecast disease progression, offering a visual representation of how particular health conditions may evolve. Applied to the Osteoarthritis Initiative dataset, our approach improves the state-of-the-art (SOTA) by 2\\%, achieving an AUC of 0.71 in predicting knee OA progression while offering ~9% faster inference time.", "date": "2025-06-17", "categories": []}
{"id": "2407.00631", "title": "TrialBench: Multi-Modal Artificial Intelligence-Ready Clinical Trial Datasets", "abstract": "Clinical trials are pivotal for developing new medical treatments but typically carry risks such as patient mortality and enrollment failure that waste immense efforts spanning over a decade. Applying artificial intelligence (AI) to predict key events in clinical trials holds great potential for providing insights to guide trial designs. However, complex data collection and question definition requiring medical expertise have hindered the involvement of AI thus far. This paper tackles these challenges by presenting a comprehensive suite of 23 meticulously curated AI-ready datasets covering multi-modal input features and 8 crucial prediction challenges in clinical trial design, encompassing prediction of trial duration, patient dropout rate, serious adverse event, mortality rate, trial approval outcome, trial failure reason, drug dose finding, design of eligibility criteria. Furthermore, we provide basic validation methods for each task to ensure the datasets' usability and reliability. We anticipate that the availability of such open-access datasets will catalyze the development of advanced AI approaches for clinical trial design, ultimately advancing clinical trial research and accelerating medical solution development.", "date": "2024-06-30", "categories": []}
{"id": "2407.11034", "title": "Bridging Data Gaps in Healthcare: A Scoping Review of Transfer Learning in Biomedical Data Analysis", "abstract": "Clinical and biomedical research in low-resource settings often faces significant challenges due to the need for high-quality data with sufficient sample sizes to construct effective models. These constraints hinder robust model training and prompt researchers to seek methods for leveraging existing knowledge from related studies to support new research efforts. Transfer learning (TL), a machine learning technique, emerges as a powerful solution by utilizing knowledge from pre-trained models to enhance the performance of new models, offering promise across various healthcare domains. Despite its conceptual origins in the 1990s, the application of TL in medical research has remained limited, especially beyond image analysis. In our review of TL applications in structured clinical and biomedical data, we screened 3,515 papers, with 55 meeting the inclusion criteria. Among these, only 2% (one out of 55) utilized external studies, and 7% (four out of 55) addressed scenarios involving multi-site collaborations with privacy constraints. To achieve actionable TL with structured medical data while addressing regional disparities, inequality, and privacy constraints in healthcare research, we advocate for the careful identification of appropriate source data and models, the selection of suitable TL frameworks, and the validation of TL models with proper baselines.", "date": "2024-07-04", "categories": []}
{"id": "2505.16941", "title": "FoMoH: A clinically meaningful foundation model evaluation for structured electronic health records", "abstract": "Foundation models hold significant promise in healthcare, given their capacity to extract meaningful representations independent of downstream tasks. This property has enabled state-of-the-art performance across several clinical applications trained on structured electronic health record (EHR) data, even in settings with limited labeled data, a prevalent challenge in healthcare. However, there is little consensus on these models' potential for clinical utility due to the lack of desiderata of comprehensive and meaningful tasks and sufficiently diverse evaluations to characterize the benefit over conventional supervised learning. To address this gap, we propose a suite of clinically meaningful tasks spanning patient outcomes, early prediction of acute and chronic conditions, including desiderata for robust evaluations. We evaluate state-of-the-art foundation models on EHR data consisting of 5 million patients from Columbia University Irving Medical Center (CUMC), a large urban academic medical center in New York City, across 14 clinically relevant tasks. We measure overall accuracy, calibration, and subpopulation performance to surface tradeoffs based on the choice of pre-training, tokenization, and data representation strategies. Our study aims to advance the empirical evaluation of structured EHR foundation models and guide the development of future healthcare foundation models.", "date": "2025-05-22", "categories": []}
{"id": "2505.24101", "title": "A SHAP-based explainable multi-level stacking ensemble learning method for predicting the length of stay in acute stroke", "abstract": "Length of stay (LOS) prediction in acute stroke is critical for improving care planning. Existing machine learning models have shown suboptimal predictive performance, limited generalisability, and have overlooked system-level factors. We aimed to enhance model efficiency, performance, and interpretability by refining predictors and developing an interpretable multi-level stacking ensemble model. Data were accessed from the biennial Stroke Foundation Acute Audit (2015, 2017, 2019, 2021) in Australia. Models were developed for ischaemic and haemorrhagic stroke separately. The outcome was prolonged LOS (the LOS above the 75th percentile). Candidate predictors (ischaemic: n=89; haemorrhagic: n=83) were categorised into patient, clinical, and system domains. Feature selection with correlation-based approaches was used to refine key predictors. The evaluation of models included discrimination (AUC), calibration curves, and interpretability (SHAP plots). In ischaemic stroke (N=12,575), prolonged LOS was >=9 days, compared to >=11 days in haemorrhagic stroke (N=1,970). The ensemble model achieved superior performance [AUC: 0.824 (95% CI: 0.801-0.846)] and statistically outperformed logistic regression [AUC: 0.805 (95% CI: 0.782-0.829); P=0.0004] for ischaemic. However, the model [AUC: 0.843 (95% CI: 0.790-0.895)] did not statistically outperform logistic regression [AUC: 0.828 (95% CI: 0.774-0.882); P=0.136] for haemorrhagic. SHAP analysis identified shared predictors for both types of stroke: rehabilitation assessment, urinary incontinence, stroke unit care, inability to walk independently, physiotherapy, and stroke care coordinators involvement. An explainable ensemble model effectively predicted the prolonged LOS in ischaemic stroke. Further validation in larger cohorts is needed for haemorrhagic stroke.", "date": "2025-05-30", "categories": []}
{"id": "2506.06349", "title": "Heart Rate Classification in ECG Signals Using Machine Learning and Deep Learning", "abstract": "This study addresses the classification of heartbeats from ECG signals through two distinct approaches: traditional machine learning utilizing hand-crafted features and deep learning via transformed images of ECG beats. The dataset underwent preprocessing steps, including downsampling, filtering, and normalization, to ensure consistency and relevance for subsequent analysis. In the first approach, features such as heart rate variability (HRV), mean, variance, and RR intervals were extracted to train various classifiers, including SVM, Random Forest, AdaBoost, LSTM, Bi-directional LSTM, and LightGBM. The second approach involved transforming ECG signals into images using Gramian Angular Field (GAF), Markov Transition Field (MTF), and Recurrence Plots (RP), with these images subsequently classified using CNN architectures like VGG and Inception.\n  Experimental results demonstrate that the LightGBM model achieved the highest performance, with an accuracy of 99% and an F1 score of 0.94, outperforming the image-based CNN approach (F1 score of 0.85). Models such as SVM and AdaBoost yielded significantly lower scores, indicating limited suitability for this task. The findings underscore the superior ability of hand-crafted features to capture temporal and morphological variations in ECG signals compared to image-based representations of individual beats. Future investigations may benefit from incorporating multi-lead ECG signals and temporal dependencies across successive beats to enhance classification accuracy further.", "date": "2025-06-02", "categories": []}
{"id": "2506.12156", "title": "Explaining Recovery Trajectories of Older Adults Post Lower-Limb Fracture Using Modality-wise Multiview Clustering and Large Language Models", "abstract": "Interpreting large volumes of high-dimensional, unlabeled data in a manner that is comprehensible to humans remains a significant challenge across various domains. In unsupervised healthcare data analysis, interpreting clustered data can offer meaningful insights into patients' health outcomes, which hold direct implications for healthcare providers. This paper addresses the problem of interpreting clustered sensor data collected from older adult patients recovering from lower-limb fractures in the community. A total of 560 days of multimodal sensor data, including acceleration, step count, ambient motion, GPS location, heart rate, and sleep, alongside clinical scores, were remotely collected from patients at home. Clustering was first carried out separately for each data modality to assess the impact of feature sets extracted from each modality on patients' recovery trajectories. Then, using context-aware prompting, a large language model was employed to infer meaningful cluster labels for the clusters derived from each modality. The quality of these clusters and their corresponding labels was validated through rigorous statistical testing and visualization against clinical scores collected alongside the multimodal sensor data. The results demonstrated the statistical significance of most modality-specific cluster labels generated by the large language model with respect to clinical scores, confirming the efficacy of the proposed method for interpreting sensor data in an unsupervised manner. This unsupervised data analysis approach, relying solely on sensor data, enables clinicians to identify at-risk patients and take timely measures to improve health outcomes.", "date": "2025-06-13", "categories": []}
{"id": "2506.12808", "title": "Leveraging MIMIC Datasets for Better Digital Health: A Review on Open Problems, Progress Highlights, and Future Promises", "abstract": "The Medical Information Mart for Intensive Care (MIMIC) datasets have become the Kernel of Digital Health Research by providing freely accessible, deidentified records from tens of thousands of critical care admissions, enabling a broad spectrum of applications in clinical decision support, outcome prediction, and healthcare analytics. Although numerous studies and surveys have explored the predictive power and clinical utility of MIMIC based models, critical challenges in data integration, representation, and interoperability remain underexplored. This paper presents a comprehensive survey that focuses uniquely on open problems. We identify persistent issues such as data granularity, cardinality limitations, heterogeneous coding schemes, and ethical constraints that hinder the generalizability and real-time implementation of machine learning models. We highlight key progress in dimensionality reduction, temporal modelling, causal inference, and privacy preserving analytics, while also outlining promising directions including hybrid modelling, federated learning, and standardized preprocessing pipelines. By critically examining these structural limitations and their implications, this survey offers actionable insights to guide the next generation of MIMIC powered digital health innovations.", "date": "2025-06-15", "categories": []}
{"id": "2502.19175", "title": "MEDDxAgent: A Unified Modular Agent Framework for Explainable Automatic Differential Diagnosis", "abstract": "Differential Diagnosis (DDx) is a fundamental yet complex aspect of clinical decision-making, in which physicians iteratively refine a ranked list of possible diseases based on symptoms, antecedents, and medical knowledge. While recent advances in large language models (LLMs) have shown promise in supporting DDx, existing approaches face key limitations, including single-dataset evaluations, isolated optimization of components, unrealistic assumptions about complete patient profiles, and single-attempt diagnosis. We introduce a Modular Explainable DDx Agent (MEDDxAgent) framework designed for interactive DDx, where diagnostic reasoning evolves through iterative learning, rather than assuming a complete patient profile is accessible. MEDDxAgent integrates three modular components: (1) an orchestrator (DDxDriver), (2) a history taking simulator, and (3) two specialized agents for knowledge retrieval and diagnosis strategy. To ensure robust evaluation, we introduce a comprehensive DDx benchmark covering respiratory, skin, and rare diseases. We analyze single-turn diagnostic approaches and demonstrate the importance of iterative refinement when patient profiles are not available at the outset. Our broad evaluation demonstrates that MEDDxAgent achieves over 10% accuracy improvements in interactive DDx across both large and small LLMs, while offering critical explainability into its diagnostic reasoning process.", "date": "2025-02-26", "categories": []}
{"id": "2506.11132", "title": "Gender Fairness of Machine Learning Algorithms for Pain Detection", "abstract": "Automated pain detection through machine learning (ML) and deep learning (DL) algorithms holds significant potential in healthcare, particularly for patients unable to self-report pain levels. However, the accuracy and fairness of these algorithms across different demographic groups (e.g., gender) remain under-researched. This paper investigates the gender fairness of ML and DL models trained on the UNBC-McMaster Shoulder Pain Expression Archive Database, evaluating the performance of various models in detecting pain based solely on the visual modality of participants' facial expressions. We compare traditional ML algorithms, Linear Support Vector Machine (L SVM) and Radial Basis Function SVM (RBF SVM), with DL methods, Convolutional Neural Network (CNN) and Vision Transformer (ViT), using a range of performance and fairness metrics. While ViT achieved the highest accuracy and a selection of fairness metrics, all models exhibited gender-based biases. These findings highlight the persistent trade-off between accuracy and fairness, emphasising the need for fairness-aware techniques to mitigate biases in automated healthcare systems.", "date": "2025-06-10", "categories": []}
{"id": "2506.11410", "title": "Predicting Early-Onset Colorectal Cancer with Large Language Models", "abstract": "The incidence rate of early-onset colorectal cancer (EoCRC, age < 45) has increased every year, but this population is younger than the recommended age established by national guidelines for cancer screening. In this paper, we applied 10 different machine learning models to predict EoCRC, and compared their performance with advanced large language models (LLM), using patient conditions, lab results, and observations within 6 months of patient journey prior to the CRC diagnoses. We retrospectively identified 1,953 CRC patients from multiple health systems across the United States. The results demonstrated that the fine-tuned LLM achieved an average of 73% sensitivity and 91% specificity.", "date": "2025-06-13", "categories": []}
{"id": "2506.11501", "title": "Diabetes Prediction and Management Using Machine Learning Approaches", "abstract": "Diabetes has emerged as a significant global health issue, especially with the increasing number of cases in many countries. This trend Underlines the need for a greater emphasis on early detection and proactive management to avert or mitigate the severe health complications of this disease. Over recent years, machine learning algorithms have shown promising potential in predicting diabetes risk and are beneficial for practitioners. Objective: This study highlights the prediction capabilities of statistical and non-statistical machine learning methods over Diabetes risk classification in 768 samples from the Pima Indians Diabetes Database. It consists of the significant demographic and clinical features of age, body mass index (BMI) and blood glucose levels that greatly depend on the vulnerability against Diabetes. The experimentation assesses the various types of machine learning algorithms in terms of accuracy and effectiveness regarding diabetes prediction. These algorithms include Logistic Regression, Decision Tree, Random Forest, K-Nearest Neighbors, Naive Bayes, Support Vector Machine, Gradient Boosting and Neural Network Models. The results show that the Neural Network algorithm gained the highest predictive accuracy with 78,57 %, and then the Random Forest algorithm had the second position with 76,30 % accuracy. These findings show that machine learning techniques are not just highly effective. Still, they also can potentially act as early screening tools in predicting Diabetes within a data-driven fashion with valuable information on who is more likely to get affected. In addition, this study can help to realize the potential of machine learning for timely intervention over the longer term, which is a step towards reducing health outcomes and disease burden attributable to Diabetes on healthcare systems", "date": "2025-06-13", "categories": []}
{"id": "2506.11677", "title": "Predicting Patient Survival with Airway Biomarkers using nn-Unet/Radiomics", "abstract": "The primary objective of the AIIB 2023 competition is to evaluate the predictive significance of airway-related imaging biomarkers in determining the survival outcomes of patients with lung fibrosis.This study introduces a comprehensive three-stage approach. Initially, a segmentation network, namely nn-Unet, is employed to delineate the airway's structural boundaries. Subsequently, key features are extracted from the radiomic images centered around the trachea and an enclosing bounding box around the airway. This step is motivated by the potential presence of critical survival-related insights within the tracheal region as well as pertinent information encoded in the structure and dimensions of the airway. Lastly, radiomic features obtained from the segmented areas are integrated into an SVM classifier. We could obtain an overall-score of 0.8601 for the segmentation in Task 1 while 0.7346 for the classification in Task 2.", "date": "2025-06-13", "categories": []}
{"id": "2506.06574", "title": "The Optimization Paradox in Clinical AI Multi-Agent Systems", "abstract": "Multi-agent artificial intelligence systems are increasingly deployed in clinical settings, yet the relationship between component-level optimization and system-wide performance remains poorly understood. We evaluated this relationship using 2,400 real patient cases from the MIMIC-CDM dataset across four abdominal pathologies (appendicitis, pancreatitis, cholecystitis, diverticulitis), decomposing clinical diagnosis into information gathering, interpretation, and differential diagnosis. We evaluated single agent systems (one model performing all tasks) against multi-agent systems (specialized models for each task) using comprehensive metrics spanning diagnostic outcomes, process adherence, and cost efficiency. Our results reveal a paradox: while multi-agent systems generally outperformed single agents, the component-optimized or Best of Breed system with superior components and excellent process metrics (85.5% information accuracy) significantly underperformed in diagnostic accuracy (67.7% vs. 77.4% for a top multi-agent system). This finding underscores that successful integration of AI in healthcare requires not just component level optimization but also attention to information flow and compatibility between agents. Our findings highlight the need for end to end system validation rather than relying on component metrics alone.", "date": "2025-06-06", "categories": []}
{"id": "2506.10179", "title": "Correlation vs causation in Alzheimer's disease: an interpretability-driven study", "abstract": "Understanding the distinction between causation and correlation is critical in Alzheimer's disease (AD) research, as it impacts diagnosis, treatment, and the identification of true disease drivers. This experiment investigates the relationships among clinical, cognitive, genetic, and biomarker features using a combination of correlation analysis, machine learning classification, and model interpretability techniques. Employing the XGBoost algorithm, we identified key features influencing AD classification, including cognitive scores and genetic risk factors. Correlation matrices revealed clusters of interrelated variables, while SHAP (SHapley Additive exPlanations) values provided detailed insights into feature contributions across disease stages. Our results highlight that strong correlations do not necessarily imply causation, emphasizing the need for careful interpretation of associative data. By integrating feature importance and interpretability with classical statistical analysis, this work lays groundwork for future causal inference studies aimed at uncovering true pathological mechanisms. Ultimately, distinguishing causal factors from correlated markers can lead to improved early diagnosis and targeted interventions for Alzheimer's disease.", "date": "2025-06-11", "categories": []}
{"id": "2506.10180", "title": "A Comparative Study of Machine Learning Techniques for Early Prediction of Diabetes", "abstract": "In many nations, diabetes is becoming a significant health problem, and early identification and control are crucial. Using machine learning algorithms to predict diabetes has yielded encouraging results. Using the Pima Indians Diabetes dataset, this study attempts to evaluate the efficacy of several machine-learning methods for diabetes prediction. The collection includes information on 768 patients, such as their ages, BMIs, and glucose levels. The techniques assessed are Logistic Regression, Decision Tree, Random Forest, k-Nearest Neighbors, Naive Bayes, Support Vector Machine, Gradient Boosting, and Neural Network. The findings indicate that the Neural Network algorithm performed the best, with an accuracy of 78.57 percent, followed by the Random Forest method, with an accuracy of 76.30 percent. The study implies that machine learning algorithms can aid diabetes prediction and be an efficient early detection tool.", "date": "2025-06-11", "categories": []}
{"id": "2506.10189", "title": "Improving Oral Cancer Outcomes Through Machine Learning and Dimensionality Reduction", "abstract": "Oral cancer presents a formidable challenge in oncology, necessitating early diagnosis and accurate prognosis to enhance patient survival rates. Recent advancements in machine learning and data mining have revolutionized traditional diagnostic methodologies, providing sophisticated and automated tools for differentiating between benign and malignant oral lesions. This study presents a comprehensive review of cutting-edge data mining methodologies, including Neural Networks, K-Nearest Neighbors (KNN), Support Vector Machines (SVM), and ensemble learning techniques, specifically applied to the diagnosis and prognosis of oral cancer. Through a rigorous comparative analysis, our findings reveal that Neural Networks surpass other models, achieving an impressive classification accuracy of 93,6 % in predicting oral cancer. Furthermore, we underscore the potential benefits of integrating feature selection and dimensionality reduction techniques to enhance model performance. These insights underscore the significant promise of advanced data mining techniques in bolstering early detection, optimizing treatment strategies, and ultimately improving patient outcomes in the realm of oral oncology.", "date": "2025-06-11", "categories": []}
{"id": "2506.08574", "title": "SLEEPYLAND: trust begins with fair evaluation of automatic sleep staging models", "abstract": "Despite advances in deep learning for automatic sleep staging, clinical adoption remains limited due to challenges in fair model evaluation, generalization across diverse datasets, model bias, and variability in human annotations. We present SLEEPYLAND, an open-source sleep staging evaluation framework designed to address these barriers. It includes more than 220'000 hours in-domain (ID) sleep recordings, and more than 84'000 hours out-of-domain (OOD) sleep recordings, spanning a broad range of ages, sleep-wake disorders, and hardware setups. We release pre-trained models based on high-performing SoA architectures and evaluate them under standardized conditions across single- and multi-channel EEG/EOG configurations. We introduce SOMNUS, an ensemble combining models across architectures and channel setups via soft voting. SOMNUS achieves robust performance across twenty-four different datasets, with macro-F1 scores between 68.7% and 87.2%, outperforming individual models in 94.9% of cases. Notably, SOMNUS surpasses previous SoA methods, even including cases where compared models were trained ID while SOMNUS treated the same data as OOD. Using a subset of the BSWR (N=6'633), we quantify model biases linked to age, gender, AHI, and PLMI, showing that while ensemble improves robustness, no model architecture consistently minimizes bias in performance and clinical markers estimation. In evaluations on OOD multi-annotated datasets (DOD-H, DOD-O), SOMNUS exceeds the best human scorer, i.e., MF1 85.2% vs 80.8% on DOD-H, and 80.2% vs 75.9% on DOD-O, better reproducing the scorer consensus than any individual expert (k = 0.89/0.85 and ACS = 0.95/0.94 for healthy/OSA cohorts). Finally, we introduce ensemble disagreement metrics - entropy and inter-model divergence based - predicting regions of scorer disagreement with ROC AUCs up to 0.828, offering a data-driven proxy for human uncertainty.", "date": "2025-06-10", "categories": []}
{"id": "2405.07988", "title": "MedVersa: A Generalist Foundation Model for Medical Image Interpretation", "abstract": "Current medical AI systems are often limited to narrow applications, hindering widespread adoption. We present MedVersa, a generalist foundation model trained on tens of millions of compiled medical instances. MedVersa unlocks generalist learning from multimodal inputs and outputs, representing the first example of a generalist model reaching competitive performance with leading specialized solutions across a variety of medical imaging scenarios. MedVersa achieves state-of-the-art performance in nine tasks, sometimes outperforming counterparts by over 10%. Radiologist evaluation shows MedVersa-generated reports get superior performance in 95% of normal studies, while matching or exceeding human reports in 71% of cases overall. User studies showed notable reductions in report writing time and discrepancies with the use of MedVersa. Our findings underscore the value of flexible, multimodal AI systems in advancing medical image interpretation and supporting clinical expertise.", "date": "2024-05-13", "categories": []}
{"id": "2408.01214", "title": "High-Throughput Phenotyping of Clinical Text Using Large Language Models", "abstract": "High-throughput phenotyping automates the mapping of patient signs to standardized ontology concepts and is essential for precision medicine. This study evaluates the automation of phenotyping of clinical summaries from the Online Mendelian Inheritance in Man (OMIM) database using large language models. Due to their rich phenotype data, these summaries can be surrogates for physician notes. We conduct a performance comparison of GPT-4 and GPT-3.5-Turbo. Our results indicate that GPT-4 surpasses GPT-3.5-Turbo in identifying, categorizing, and normalizing signs, achieving concordance with manual annotators comparable to inter-rater agreement. Despite some limitations in sign normalization, the extensive pre-training of GPT-4 results in high performance and generalizability across several phenotyping tasks while obviating the need for manually annotated training data. Large language models are expected to be the dominant method for automating high-throughput phenotyping of clinical text.", "date": "2024-08-02", "categories": []}
{"id": "2503.08002", "title": "Predicting and Understanding College Student Mental Health with Interpretable Machine Learning", "abstract": "Mental health issues among college students have reached critical levels, significantly impacting academic performance and overall wellbeing. Predicting and understanding mental health status among college students is challenging due to three main factors: the necessity for large-scale longitudinal datasets, the prevalence of black-box machine learning models lacking transparency, and the tendency of existing approaches to provide aggregated insights at the population level rather than individualized understanding.\n  To tackle these challenges, this paper presents I-HOPE, the first Interpretable Hierarchical mOdel for Personalized mEntal health prediction. I-HOPE is a two-stage hierarchical model that connects raw behavioral features to mental health status through five defined behavioral categories as interaction labels. We evaluate I-HOPE on the College Experience Study, the longest longitudinal mobile sensing dataset. This dataset spans five years and captures data from both pre-pandemic periods and the COVID-19 pandemic. I-HOPE achieves a prediction accuracy of 91%, significantly surpassing the 60-70% accuracy of baseline methods. In addition, I-HOPE distills complex patterns into interpretable and individualized insights, enabling the future development of tailored interventions and improving mental health support. The code is available at https://github.com/roycmeghna/I-HOPE.", "date": "2025-03-11", "categories": []}
{"id": "2311.16593", "title": "Empowering COVID-19 Detection: Optimizing Performance Through Fine-Tuned EfficientNet Deep Learning Architecture", "abstract": "The worldwide COVID-19 pandemic has profoundly influenced the health and everyday experiences of individuals across the planet. It is a highly contagious respiratory disease requiring early and accurate detection to curb its rapid transmission. Initial testing methods primarily revolved around identifying the genetic composition of the coronavirus, exhibiting a relatively low detection rate and requiring a time-intensive procedure. To address this challenge, experts have suggested using radiological imagery, particularly chest X-rays, as a valuable approach within the diagnostic protocol. This study investigates the potential of leveraging radiographic imaging (X-rays) with deep learning algorithms to swiftly and precisely identify COVID-19 patients. The proposed approach elevates the detection accuracy by fine-tuning with appropriate layers on various established transfer learning models. The experimentation was conducted on a COVID-19 X-ray dataset containing 2000 images. The accuracy rates achieved were impressive of 100% for EfficientNetB4 model. The fine-tuned EfficientNetB4 achieved an excellent accuracy score, showcasing its potential as a robust COVID-19 detection model. Furthermore, EfficientNetB4 excelled in identifying Lung disease using Chest X-ray dataset containing 4,350 Images, achieving remarkable performance with an accuracy of 99.17%, precision of 99.13%, recall of 99.16%, and f1-score of 99.14%. These results highlight the promise of fine-tuned transfer learning for efficient lung detection through medical imaging, especially with X-ray images. This research offers radiologists an effective means of aiding rapid and precise COVID-19 diagnosis and contributes valuable assistance for healthcare professionals in accurately identifying affected patients.", "date": "2023-11-28", "categories": []}
{"id": "2506.06283", "title": "Facial Foundational Model Advances Early Warning of Coronary Artery Disease from Live Videos with DigitalShadow", "abstract": "Global population aging presents increasing challenges to healthcare systems, with coronary artery disease (CAD) responsible for approximately 17.8 million deaths annually, making it a leading cause of global mortality. As CAD is largely preventable, early detection and proactive management are essential. In this work, we introduce DigitalShadow, an advanced early warning system for CAD, powered by a fine-tuned facial foundation model. The system is pre-trained on 21 million facial images and subsequently fine-tuned into LiveCAD, a specialized CAD risk assessment model trained on 7,004 facial images from 1,751 subjects across four hospitals in China. DigitalShadow functions passively and contactlessly, extracting facial features from live video streams without requiring active user engagement. Integrated with a personalized database, it generates natural language risk reports and individualized health recommendations. With privacy as a core design principle, DigitalShadow supports local deployment to ensure secure handling of user data.", "date": "2025-04-23", "categories": []}
{"id": "2506.06306", "title": "Benchmarking Early Agitation Prediction in Community-Dwelling People with Dementia Using Multimodal Sensors and Machine Learning", "abstract": "Agitation is one of the most common responsive behaviors in people living with dementia, particularly among those residing in community settings without continuous clinical supervision. Timely prediction of agitation can enable early intervention, reduce caregiver burden, and improve the quality of life for both patients and caregivers. This study aimed to develop and benchmark machine learning approaches for the early prediction of agitation in community-dwelling older adults with dementia using multimodal sensor data. A new set of agitation-related contextual features derived from activity data was introduced and employed for agitation prediction. A wide range of machine learning and deep learning models was evaluated across multiple problem formulations, including binary classification for single-timestamp tabular sensor data and multi-timestamp sequential sensor data, as well as anomaly detection for single-timestamp tabular sensor data. The study utilized the Technology Integrated Health Management (TIHM) dataset, the largest publicly available dataset for remote monitoring of people living with dementia, comprising 2,803 days of in-home activity, physiology, and sleep data. The most effective setting involved binary classification of sensor data using the current 6-hour timestamp to predict agitation at the subsequent timestamp. Incorporating additional information, such as time of day and agitation history, further improved model performance, with the highest AUC-ROC of 0.9720 and AUC-PR of 0.4320 achieved by the light gradient boosting machine. This work presents the first comprehensive benchmarking of state-of-the-art techniques for agitation prediction in community-based dementia care using privacy-preserving sensor data. The approach enables accurate, explainable, and efficient agitation prediction, supporting proactive dementia care and aging in place.", "date": "2025-05-23", "categories": []}
{"id": "2506.06310", "title": "Enhancing Contrastive Learning-based Electrocardiogram Pretrained Model with Patient Memory Queue", "abstract": "In the field of automatic Electrocardiogram (ECG) diagnosis, due to the relatively limited amount of labeled data, how to build a robust ECG pretrained model based on unlabeled data is a key area of focus for researchers. Recent advancements in contrastive learning-based ECG pretrained models highlight the potential of exploiting the additional patient-level self-supervisory signals inherent in ECG. They are referred to as patient contrastive learning. Its rationale is that multiple physical recordings from the same patient may share commonalities, termed patient consistency, so redefining positive and negative pairs in contrastive learning as intrapatient and inter-patient samples provides more shared context to learn an effective representation. However, these methods still fail to efficiently exploit patient consistency due to the insufficient amount of intra-inter patient samples existing in a batch. Hence, we propose a contrastive learning-based ECG pretrained model enhanced by the Patient Memory Queue (PMQ), which incorporates a large patient memory queue to mitigate model degeneration that can arise from insufficient intra-inter patient samples. In order to further enhance the performance of the pretrained model, we introduce two extra data augmentation methods to provide more perspectives of positive and negative pairs for pretraining. Extensive experiments were conducted on three public datasets with three different data ratios. The experimental results show that the comprehensive performance of our method outperforms previous contrastive learning methods and exhibits greater robustness in scenarios with limited labeled data. The code is available at https://github.com/3hiuwoo/PMQ.", "date": "2025-05-26", "categories": []}
{"id": "2506.06340", "title": "Structured Semantics from Unstructured Notes: Language Model Approaches to EHR-Based Decision Support", "abstract": "The advent of large language models (LLMs) has opened new avenues for analyzing complex, unstructured data, particularly within the medical domain. Electronic Health Records (EHRs) contain a wealth of information in various formats, including free text clinical notes, structured lab results, and diagnostic codes. This paper explores the application of advanced language models to leverage these diverse data sources for improved clinical decision support. We will discuss how text-based features, often overlooked in traditional high dimensional EHR analysis, can provide semantically rich representations and aid in harmonizing data across different institutions. Furthermore, we delve into the challenges and opportunities of incorporating medical codes and ensuring the generalizability and fairness of AI models in healthcare.", "date": "2025-06-01", "categories": []}
{"id": "2506.06649", "title": "SAFER: A Calibrated Risk-Aware Multimodal Recommendation Model for Dynamic Treatment Regimes", "abstract": "Dynamic treatment regimes (DTRs) are critical to precision medicine, optimizing long-term outcomes through personalized, real-time decision-making in evolving clinical contexts, but require careful supervision for unsafe treatment risks. Existing efforts rely primarily on clinician-prescribed gold standards despite the absence of a known optimal strategy, and predominantly using structured EHR data without extracting valuable insights from clinical notes, limiting their reliability for treatment recommendations. In this work, we introduce SAFER, a calibrated risk-aware tabular-language recommendation framework for DTR that integrates both structured EHR and clinical notes, enabling them to learn from each other, and addresses inherent label uncertainty by assuming ambiguous optimal treatment solution for deceased patients. Moreover, SAFER employs conformal prediction to provide statistical guarantees, ensuring safe treatment recommendations while filtering out uncertain predictions. Experiments on two publicly available sepsis datasets demonstrate that SAFER outperforms state-of-the-art baselines across multiple recommendation metrics and counterfactual mortality rate, while offering robust formal assurances. These findings underscore SAFER potential as a trustworthy and theoretically grounded solution for high-stakes DTR applications.", "date": "2025-06-07", "categories": []}
{"id": "2506.07092", "title": "Patient Similarity Computation for Clinical Decision Support: An Efficient Use of Data Transformation, Combining Static and Time Series Data", "abstract": "Patient similarity computation (PSC) is a fundamental problem in healthcare informatics. The aim of the patient similarity computation is to measure the similarity among patients according to their historical clinical records, which helps to improve clinical decision support. This paper presents a novel distributed patient similarity computation (DPSC) technique based on data transformation (DT) methods, utilizing an effective combination of time series and static data. Time series data are sensor-collected patients' information, including metrics like heart rate, blood pressure, Oxygen saturation, respiration, etc. The static data are mainly patient background and demographic data, including age, weight, height, gender, etc. Static data has been used for clustering the patients. Before feeding the static data to the machine learning model adaptive Weight-of-Evidence (aWOE) and Z-score data transformation (DT) methods have been performed, which improve the prediction performances. In aWOE-based patient similarity models, sensitive patient information has been processed using aWOE which preserves the data privacy of the trained models. We used the Dynamic Time Warping (DTW) approach, which is robust and very popular, for time series similarity. However, DTW is not suitable for big data due to the significant computational run-time. To overcome this problem, distributed DTW computation is used in this study. For Coronary Artery Disease, our DT based approach boosts prediction performance by as much as 11.4%, 10.20%, and 12.6% in terms of AUC, accuracy, and F-measure, respectively. In the case of Congestive Heart Failure (CHF), our proposed method achieves performance enhancement up to 15.9%, 10.5%, and 21.9% for the same measures, respectively. The proposed method reduces the computation time by as high as 40%.", "date": "2025-06-08", "categories": []}
{"id": "2506.07234", "title": "A Comprehensive Analysis of COVID-19 Detection Using Bangladeshi Data and Explainable AI", "abstract": "COVID-19 is a rapidly spreading and highly infectious virus which has triggered a global pandemic, profoundly affecting millions across the world. The pandemic has introduced unprecedented challenges in public health, economic stability, and societal structures, necessitating the implementation of extensive and multifaceted health interventions globally. It had a tremendous impact on Bangladesh by April 2024, with around 29,495 fatalities and more than 2 million confirmed cases. This study focuses on improving COVID-19 detection in CXR images by utilizing a dataset of 4,350 images from Bangladesh categorized into four classes: Normal, Lung-Opacity, COVID-19 and Viral-Pneumonia. ML, DL and TL models are employed with the VGG19 model achieving an impressive 98% accuracy. LIME is used to explain model predictions, highlighting the regions and features influencing classification decisions. SMOTE is applied to address class imbalances. By providing insight into both correct and incorrect classifications, the study emphasizes the importance of XAI in enhancing the transparency and reliability of models, ultimately improving the effectiveness of detection from CXR images.", "date": "2025-06-08", "categories": []}
{"id": "2506.07984", "title": "CXR-LT 2024: A MICCAI challenge on long-tailed, multi-label, and zero-shot disease classification from chest X-ray", "abstract": "The CXR-LT series is a community-driven initiative designed to enhance lung disease classification using chest X-rays (CXR). It tackles challenges in open long-tailed lung disease classification and enhances the measurability of state-of-the-art techniques. The first event, CXR-LT 2023, aimed to achieve these goals by providing high-quality benchmark CXR data for model development and conducting comprehensive evaluations to identify ongoing issues impacting lung disease classification performance. Building on the success of CXR-LT 2023, the CXR-LT 2024 expands the dataset to 377,110 chest X-rays (CXRs) and 45 disease labels, including 19 new rare disease findings. It also introduces a new focus on zero-shot learning to address limitations identified in the previous event. Specifically, CXR-LT 2024 features three tasks: (i) long-tailed classification on a large, noisy test set, (ii) long-tailed classification on a manually annotated \"gold standard\" subset, and (iii) zero-shot generalization to five previously unseen disease findings. This paper provides an overview of CXR-LT 2024, detailing the data curation process and consolidating state-of-the-art solutions, including the use of multimodal models for rare disease detection, advanced generative approaches to handle noisy labels, and zero-shot learning strategies for unseen diseases. Additionally, the expanded dataset enhances disease coverage to better represent real-world clinical settings, offering a valuable resource for future research. By synthesizing the insights and innovations of participating teams, we aim to advance the development of clinically realistic and generalizable diagnostic models for chest radiography.", "date": "2025-06-09", "categories": []}
{"id": "2505.21824", "title": "Unsupervised Latent Pattern Analysis for Estimating Type 2 Diabetes Risk in Undiagnosed Populations", "abstract": "The global prevalence of diabetes, particularly type 2 diabetes mellitus (T2DM), is rapidly increasing, posing significant health and economic challenges. T2DM not only disrupts blood glucose regulation but also damages vital organs such as the heart, kidneys, eyes, nerves, and blood vessels, leading to substantial morbidity and mortality. In the US alone, the economic burden of diagnosed diabetes exceeded \\$400 billion in 2022. Early detection of individuals at risk is critical to mitigating these impacts. While machine learning approaches for T2DM prediction are increasingly adopted, many rely on supervised learning, which is often limited by the lack of confirmed negative cases. To address this limitation, we propose a novel unsupervised framework that integrates Non-negative Matrix Factorization (NMF) with statistical techniques to identify individuals at risk of developing T2DM. Our method identifies latent patterns of multimorbidity and polypharmacy among diagnosed T2DM patients and applies these patterns to estimate the T2DM risk in undiagnosed individuals. By leveraging data-driven insights from comorbidity and medication usage, our approach provides an interpretable and scalable solution that can assist healthcare providers in implementing timely interventions, ultimately improving patient outcomes and potentially reducing the future health and economic burden of T2DM.", "date": "2025-05-27", "categories": []}
{"id": "2506.04924", "title": "Predicting ICU In-Hospital Mortality Using Adaptive Transformer Layer Fusion", "abstract": "Early identification of high-risk ICU patients is crucial for directing limited medical resources. We introduce ALFIA (Adaptive Layer Fusion with Intelligent Attention), a modular, attention-based architecture that jointly trains LoRA (Low-Rank Adaptation) adapters and an adaptive layer-weighting mechanism to fuse multi-layer semantic features from a BERT backbone. Trained on our rigorous cw-24 (CriticalWindow-24) benchmark, ALFIA surpasses state-of-the-art tabular classifiers in AUPRC while preserving a balanced precision-recall profile. The embeddings produced by ALFIA's fusion module, capturing both fine-grained clinical cues and high-level concepts, enable seamless pairing with GBDTs (CatBoost/LightGBM) as ALFIA-boost, and deep neuro networks as ALFIA-nn, yielding additional performance gains. Our experiments confirm ALFIA's superior early-warning performance, by operating directly on routine clinical text, it furnishes clinicians with a convenient yet robust tool for risk stratification and timely intervention in critical-care settings.", "date": "2025-06-05", "categories": []}
{"id": "2506.06104", "title": "WoundAIssist: A Patient-Centered Mobile App for AI-Assisted Wound Care With Physicians in the Loop", "abstract": "The rising prevalence of chronic wounds, especially in aging populations, presents a significant healthcare challenge due to prolonged hospitalizations, elevated costs, and reduced patient quality of life. Traditional wound care is resource-intensive, requiring frequent in-person visits that strain both patients and healthcare professionals (HCPs). Therefore, we present WoundAIssist, a patient-centered, AI-driven mobile application designed to support telemedical wound care. WoundAIssist enables patients to regularly document wounds at home via photographs and questionnaires, while physicians remain actively engaged in the care process through remote monitoring and video consultations. A distinguishing feature is an integrated lightweight deep learning model for on-device wound segmentation, which, combined with patient-reported data, enables continuous monitoring of wound healing progression. Developed through an iterative, user-centered process involving both patients and domain experts, WoundAIssist prioritizes an user-friendly design, particularly for elderly patients. A conclusive usability study with patients and dermatologists reported excellent usability, good app quality, and favorable perceptions of the AI-driven wound recognition. Our main contribution is two-fold: (I) the implementation and (II) evaluation of WoundAIssist, an easy-to-use yet comprehensive telehealth solution designed to bridge the gap between patients and HCPs. Additionally, we synthesize design insights for remote patient monitoring apps, derived from over three years of interdisciplinary research, that may inform the development of similar digital health tools across clinical domains.", "date": "2025-06-06", "categories": []}
{"id": "2506.06192", "title": "ICU-TSB: A Benchmark for Temporal Patient Representation Learning for Unsupervised Stratification into Patient Cohorts", "abstract": "Patient stratification identifying clinically meaningful subgroups is essential for advancing personalized medicine through improved diagnostics and treatment strategies. Electronic health records (EHRs), particularly those from intensive care units (ICUs), contain rich temporal clinical data that can be leveraged for this purpose. In this work, we introduce ICU-TSB (Temporal Stratification Benchmark), the first comprehensive benchmark for evaluating patient stratification based on temporal patient representation learning using three publicly available ICU EHR datasets. A key contribution of our benchmark is a novel hierarchical evaluation framework utilizing disease taxonomies to measure the alignment of discovered clusters with clinically validated disease groupings. In our experiments with ICU-TSB, we compared statistical methods and several recurrent neural networks, including LSTM and GRU, for their ability to generate effective patient representations for subsequent clustering of patient trajectories. Our results demonstrate that temporal representation learning can rediscover clinically meaningful patient cohorts; nevertheless, it remains a challenging task, with v-measuring varying from up to 0.46 at the top level of the taxonomy to up to 0.40 at the lowest level. To further enhance the practical utility of our findings, we also evaluate multiple strategies for assigning interpretable labels to the identified clusters. The experiments and benchmark are fully reproducible and available at https://github.com/ds4dh/CBMS2025stratification.", "date": "2025-06-06", "categories": []}
{"id": "2506.04756", "title": "Ontology-based knowledge representation for bone disease diagnosis: a foundation for safe and sustainable medical artificial intelligence systems", "abstract": "Medical artificial intelligence (AI) systems frequently lack systematic domain expertise integration, potentially compromising diagnostic reliability. This study presents an ontology-based framework for bone disease diagnosis, developed in collaboration with Ho Chi Minh City Hospital for Traumatology and Orthopedics. The framework introduces three theoretical contributions: (1) a hierarchical neural network architecture guided by bone disease ontology for segmentation-classification tasks, incorporating Visual Language Models (VLMs) through prompts, (2) an ontology-enhanced Visual Question Answering (VQA) system for clinical reasoning, and (3) a multimodal deep learning model that integrates imaging, clinical, and laboratory data through ontological relationships. The methodology maintains clinical interpretability through systematic knowledge digitization, standardized medical terminology mapping, and modular architecture design. The framework demonstrates potential for extension beyond bone diseases through its standardized structure and reusable components. While theoretical foundations are established, experimental validation remains pending due to current dataset and computational resource limitations. Future work will focus on expanding the clinical dataset and conducting comprehensive system validation.", "date": "2025-06-05", "categories": []}
{"id": "2506.03209", "title": "Predicting Postoperative Stroke in Elderly SICU Patients: An Interpretable Machine Learning Model Using MIMIC Data", "abstract": "Postoperative stroke remains a critical complication in elderly surgical intensive care unit (SICU) patients, contributing to prolonged hospitalization, elevated healthcare costs, and increased mortality. Accurate early risk stratification is essential to enable timely intervention and improve clinical outcomes. We constructed a combined cohort of 19,085 elderly SICU admissions from the MIMIC-III and MIMIC-IV databases and developed an interpretable machine learning (ML) framework to predict in-hospital stroke using clinical data from the first 24 hours of Intensive Care Unit (ICU) stay. The preprocessing pipeline included removal of high-missingness features, iterative Singular Value Decomposition (SVD) imputation, z-score normalization, one-hot encoding, and class imbalance correction via the Adaptive Synthetic Sampling (ADASYN) algorithm. A two-stage feature selection process-combining Recursive Feature Elimination with Cross-Validation (RFECV) and SHapley Additive exPlanations (SHAP)-reduced the initial 80 variables to 20 clinically informative predictors. Among eight ML models evaluated, CatBoost achieved the best performance with an AUROC of 0.8868 (95% CI: 0.8802--0.8937). SHAP analysis and ablation studies identified prior cerebrovascular disease, serum creatinine, and systolic blood pressure as the most influential risk factors. Our results highlight the potential of interpretable ML approaches to support early detection of postoperative stroke and inform decision-making in perioperative critical care.", "date": "2025-06-02", "categories": []}
{"id": "2506.03698", "title": "Advancements in Artificial Intelligence Applications for Cardiovascular Disease Research", "abstract": "Recent advancements in artificial intelligence (AI) have revolutionized cardiovascular medicine, particularly through integration with computed tomography (CT), magnetic resonance imaging (MRI), electrocardiography (ECG) and ultrasound (US). Deep learning architectures, including convolutional neural networks and generative adversarial networks, enable automated analysis of medical imaging and physiological signals, surpassing human capabilities in diagnostic accuracy and workflow efficiency. However, critical challenges persist, including the inability to validate input data accuracy, which may propagate diagnostic errors. This review highlights AI's transformative potential in precision diagnostics while underscoring the need for robust validation protocols to ensure clinical reliability. Future directions emphasize hybrid models integrating multimodal data and adaptive algorithms to refine personalized cardiovascular care.", "date": "2025-06-04", "categories": []}
{"id": "2412.10849", "title": "Superhuman performance of a large language model on the reasoning tasks of a physician", "abstract": "A seminal paper published by Ledley and Lusted in 1959 introduced complex clinical diagnostic reasoning cases as the gold standard for the evaluation of expert medical computing systems, a standard that has held ever since. Here, we report the results of a physician evaluation of a large language model (LLM) on challenging clinical cases against a baseline of hundreds of physicians. We conduct five experiments to measure clinical reasoning across differential diagnosis generation, display of diagnostic reasoning, triage differential diagnosis, probabilistic reasoning, and management reasoning, all adjudicated by physician experts with validated psychometrics. We then report a real-world study comparing human expert and AI second opinions in randomly-selected patients in the emergency room of a major tertiary academic medical center in Boston, MA. We compared LLMs and board-certified physicians at three predefined diagnostic touchpoints: triage in the emergency room, initial evaluation by a physician, and admission to the hospital or intensive care unit. In all experiments--both vignettes and emergency room second opinions--the LLM displayed superhuman diagnostic and reasoning abilities, as well as continued improvement from prior generations of AI clinical decision support. Our study suggests that LLMs have achieved superhuman performance on general medical diagnostic and management reasoning, fulfilling the vision put forth by Ledley and Lusted, and motivating the urgent need for prospective trials.", "date": "2024-12-14", "categories": []}
{"id": "2501.16365", "title": "CAND: Cross-Domain Ambiguity Inference for Early Detecting Nuanced Illness Deterioration", "abstract": "Early detection of patient deterioration is essential for timely treatment, with vital signs like heart rates being key health indicators. Existing methods tend to solely analyze vital sign waveforms, ignoring transition relationships of waveforms within each vital sign and the correlation strengths among various vital signs. Such studies often overlook nuanced illness deterioration, which is the early sign of worsening health but is difficult to detect. In this paper, we introduce CAND, a novel method that organizes the transition relationships and the correlations within and among vital signs as domain-specific and cross-domain knowledge. CAND jointly models these knowledge in a unified representation space, considerably enhancing the early detection of nuanced illness deterioration. In addition, CAND integrates a Bayesian inference method that utilizes augmented knowledge from domain-specific and cross-domain knowledge to address the ambiguities in correlation strengths. With this architecture, the correlation strengths can be effectively inferred to guide joint modeling and enhance representations of vital signs. This allows a more holistic and accurate interpretation of patient health. Our experiments on a real-world ICU dataset demonstrate that CAND significantly outperforms existing methods in both effectiveness and earliness in detecting nuanced illness deterioration. Moreover, we conduct a case study for the interpretable detection process to showcase the practicality of CAND.", "date": "2025-01-22", "categories": []}
{"id": "2505.01305", "title": "Early Detection of Patient Deterioration from Real-Time Wearable Monitoring System", "abstract": "Early detection of patient deterioration is crucial for reducing mortality rates. Heart rate data has shown promise in assessing patient health, and wearable devices offer a cost-effective solution for real-time monitoring. However, extracting meaningful insights from diverse heart rate data and handling missing values in wearable device data remain key challenges. To address these challenges, we propose TARL, an innovative approach that models the structural relationships of representative subsequences, known as shapelets, in heart rate time series. TARL creates a shapelet-transition knowledge graph to model shapelet dynamics in heart rate time series, indicating illness progression and potential future changes. We further introduce a transition-aware knowledge embedding to reinforce relationships among shapelets and quantify the impact of missing values, enabling the formulation of comprehensive heart rate representations. These representations capture explanatory structures and predict future heart rate trends, aiding early illness detection. We collaborate with physicians and nurses to gather ICU patient heart rate data from wearables and diagnostic metrics assessing illness severity for evaluating deterioration. Experiments on real-world ICU data demonstrate that TARL achieves both high reliability and early detection. A case study further showcases TARL's explainable detection process, highlighting its potential as an AI-driven tool to assist clinicians in recognizing early signs of patient deterioration.", "date": "2025-05-02", "categories": []}
{"id": "2506.02327", "title": "Medical World Model: Generative Simulation of Tumor Evolution for Treatment Planning", "abstract": "Providing effective treatment and making informed clinical decisions are essential goals of modern medicine and clinical care. We are interested in simulating disease dynamics for clinical decision-making, leveraging recent advances in large generative models. To this end, we introduce the Medical World Model (MeWM), the first world model in medicine that visually predicts future disease states based on clinical decisions. MeWM comprises (i) vision-language models to serve as policy models, and (ii) tumor generative models as dynamics models. The policy model generates action plans, such as clinical treatments, while the dynamics model simulates tumor progression or regression under given treatment conditions. Building on this, we propose the inverse dynamics model that applies survival analysis to the simulated post-treatment tumor, enabling the evaluation of treatment efficacy and the selection of the optimal clinical action plan. As a result, the proposed MeWM simulates disease dynamics by synthesizing post-treatment tumors, with state-of-the-art specificity in Turing tests evaluated by radiologists. Simultaneously, its inverse dynamics model outperforms medical-specialized GPTs in optimizing individualized treatment protocols across all metrics. Notably, MeWM improves clinical decision-making for interventional physicians, boosting F1-score in selecting the optimal TACE protocol by 13%, paving the way for future integration of medical world models as the second readers.", "date": "2025-06-02", "categories": []}
{"id": "2506.03068", "title": "Causal Explainability of Machine Learning in Heart Failure Prediction from Electronic Health Records", "abstract": "The importance of clinical variables in the prognosis of the disease is explained using statistical correlation or machine learning (ML). However, the predictive importance of these variables may not represent their causal relationships with diseases. This paper uses clinical variables from a heart failure (HF) patient cohort to investigate the causal explainability of important variables obtained in statistical and ML contexts. Due to inherent regression modeling, popular causal discovery methods strictly assume that the cause and effect variables are numerical and continuous. This paper proposes a new computational framework to enable causal structure discovery (CSD) and score the causal strength of mixed-type (categorical, numerical, binary) clinical variables for binary disease outcomes. In HF classification, we investigate the association between the importance rank order of three feature types: correlated features, features important for ML predictions, and causal features. Our results demonstrate that CSD modeling for nonlinear causal relationships is more meaningful than its linear counterparts. Feature importance obtained from nonlinear classifiers (e.g., gradient-boosting trees) strongly correlates with the causal strength of variables without differentiating cause and effect variables. Correlated variables can be causal for HF, but they are rarely identified as effect variables. These results can be used to add the causal explanation of variables important for ML-based prediction modeling.", "date": "2025-06-03", "categories": []}
{"id": "2304.11265", "title": "Estimating Motor Symptom Presence and Severity in Parkinson's Disease from Wrist Accelerometer Time Series using ROCKET and InceptionTime", "abstract": "Parkinson's disease (PD) is a neurodegenerative condition characterized by frequently changing motor symptoms, necessitating continuous symptom monitoring for more targeted treatment. Classical time series classification and deep learning techniques have demonstrated limited efficacy in monitoring PD symptoms using wearable accelerometer data due to complex PD movement patterns and the small size of available datasets. We investigate InceptionTime and RandOm Convolutional KErnel Transform (ROCKET) as they are promising for PD symptom monitoring. InceptionTime's high learning capacity is well-suited to modeling complex movement patterns, while ROCKET is suited to small datasets. With random search methodology, we identify the highest-scoring InceptionTime architecture and compare its performance to ROCKET with a ridge classifier and a multi-layer perceptron (MLP) on wrist motion data from PD patients. Our findings indicate that all approaches can learn to estimate tremor severity and bradykinesia presence with moderate performance but encounter challenges in detecting dyskinesia. Among the presented approaches, ROCKET demonstrates higher scores in identifying dyskinesia, whereas InceptionTime exhibits slightly better performance in tremor and bradykinesia estimation. Notably, both methods outperform the multi-layer perceptron. In conclusion, InceptionTime can classify complex wrist motion time series and holds potential for continuous symptom monitoring in PD with further development.", "date": "2023-04-21", "categories": []}
{"id": "2506.00235", "title": "MedOrch: Medical Diagnosis with Tool-Augmented Reasoning Agents for Flexible Extensibility", "abstract": "Healthcare decision-making represents one of the most challenging domains for Artificial Intelligence (AI), requiring the integration of diverse knowledge sources, complex reasoning, and various external analytical tools. Current AI systems often rely on either task-specific models, which offer limited adaptability, or general language models without grounding with specialized external knowledge and tools. We introduce MedOrch, a novel framework that orchestrates multiple specialized tools and reasoning agents to provide comprehensive medical decision support. MedOrch employs a modular, agent-based architecture that facilitates the flexible integration of domain-specific tools without altering the core system. Furthermore, it ensures transparent and traceable reasoning processes, enabling clinicians to meticulously verify each intermediate step underlying the system's recommendations. We evaluate MedOrch across three distinct medical applications: Alzheimer's disease diagnosis, chest X-ray interpretation, and medical visual question answering, using authentic clinical datasets. The results demonstrate MedOrch's competitive performance across these diverse medical tasks. Notably, in Alzheimer's disease diagnosis, MedOrch achieves an accuracy of 93.26%, surpassing the state-of-the-art baseline by over four percentage points. For predicting Alzheimer's disease progression, it attains a 50.35% accuracy, marking a significant improvement. In chest X-ray analysis, MedOrch exhibits superior performance with a Macro AUC of 61.2% and a Macro F1-score of 25.5%. Moreover, in complex multimodal visual question answering (Image+Table), MedOrch achieves an accuracy of 54.47%. These findings underscore MedOrch's potential to advance healthcare AI by enabling reasoning-driven tool utilization for multimodal medical data processing and supporting intricate cognitive tasks in clinical decision-making.", "date": "2025-05-30", "categories": []}
{"id": "2506.01533", "title": "A Diffusion-Based Method for Learning the Multi-Outcome Distribution of Medical Treatments", "abstract": "In medicine, treatments often influence multiple, interdependent outcomes, such as primary endpoints, complications, adverse events, or other secondary endpoints. Hence, to make optimal treatment decisions, clinicians are interested in learning the distribution of multi-dimensional treatment outcomes. However, the vast majority of machine learning methods for predicting treatment effects focus on single-outcome settings, despite the fact that medical data often include multiple, interdependent outcomes. To address this limitation, we propose a novel diffusion-based method called DIME to learn the joint distribution of multiple outcomes of medical treatments. We addresses three challenges relevant in medical practice: (i)it is tailored to learn the joint interventional distribution of multiple medical outcomes, which enables reliable decision-making with uncertainty quantification rather than relying solely on point estimates; (ii)it explicitly captures the dependence structure between outcomes; (iii)it can handle outcomes of mixed type, including binary, categorical, and continuous variables. In DIME, we take into account the fundamental problem of causal inference through causal masking. For training, our method decomposes the joint distribution into a series of conditional distributions with a customized conditional masking to account for the dependence structure across outcomes. For inference, our method auto-regressively generates predictions. This allows our method to move beyond point estimates of causal quantities and thus learn the joint interventional distribution. To the best of our knowledge, DIME is the first neural method tailored to learn the joint, multi-outcome distribution of medical treatments. Across various experiments, we demonstrate that our method effectively learns the joint distribution and captures shared information among multiple outcomes.", "date": "2025-06-02", "categories": []}
{"id": "2505.23806", "title": "MedOrchestra: A Hybrid Cloud-Local LLM Approach for Clinical Data Interpretation", "abstract": "Deploying large language models (LLMs) in clinical settings faces critical trade-offs: cloud LLMs, with their extensive parameters and superior performance, pose risks to sensitive clinical data privacy, while local LLMs preserve privacy but often fail at complex clinical interpretation tasks. We propose MedOrchestra, a hybrid framework where a cloud LLM decomposes complex clinical tasks into manageable subtasks and prompt generation, while a local LLM executes these subtasks in a privacy-preserving manner. Without accessing clinical data, the cloud LLM generates and validates subtask prompts using clinical guidelines and synthetic test cases. The local LLM executes subtasks locally and synthesizes outputs generated by the cloud LLM. We evaluate MedOrchestra on pancreatic cancer staging using 100 radiology reports under NCCN guidelines. On free-text reports, MedOrchestra achieves 70.21% accuracy, outperforming local model baselines (without guideline: 48.94%, with guideline: 56.59%) and board-certified clinicians (gastroenterologists: 59.57%, surgeons: 65.96%, radiologists: 55.32%). On structured reports, MedOrchestra reaches 85.42% accuracy, showing clear superiority across all settings.", "date": "2025-05-27", "categories": []}
{"id": "2505.23834", "title": "Patient-Aware Feature Alignment for Robust Lung Sound Classification:Cohesion-Separation and Global Alignment Losses", "abstract": "Lung sound classification is vital for early diagnosis of respiratory diseases. However, biomedical signals often exhibit inter-patient variability even among patients with the same symptoms, requiring a learning approach that considers individual differences. We propose a Patient-Aware Feature Alignment (PAFA) framework with two novel losses, Patient Cohesion-Separation Loss (PCSL) and Global Patient Alignment Loss (GPAL). PCSL clusters features of the same patient while separating those from other patients to capture patient variability, whereas GPAL draws each patient's centroid toward a global center, preventing feature space fragmentation. Our method achieves outstanding results on the ICBHI dataset with a score of 64.84\\% for four-class and 72.08\\% for two-class classification. These findings highlight PAFA's ability to capture individualized patterns and demonstrate performance gains in distinct patient clusters, offering broader applications for patient-centered healthcare.", "date": "2025-05-28", "categories": []}
{"id": "2505.24609", "title": "Explainable Depression Detection using Masked Hard Instance Mining", "abstract": "This paper addresses the critical need for improved explainability in text-based depression detection. While offering predictive outcomes, current solutions often overlook the understanding of model predictions which can hinder trust in the system. We propose the use of Masked Hard Instance Mining (MHIM) to enhance the explainability in the depression detection task. MHIM strategically masks attention weights within the model, compelling it to distribute attention across a wider range of salient features. We evaluate MHIM on two datasets representing distinct languages: Thai (Thai-Maywe) and English (DAIC-WOZ). Our results demonstrate that MHIM significantly improves performance in terms of both prediction accuracy and explainability metrics.", "date": "2025-05-30", "categories": []}
{"id": "2505.24655", "title": "Adaptable Cardiovascular Disease Risk Prediction from Heterogeneous Data using Large Language Models", "abstract": "Cardiovascular disease (CVD) risk prediction models are essential for identifying high-risk individuals and guiding preventive actions. However, existing models struggle with the challenges of real-world clinical practice as they oversimplify patient profiles, rely on rigid input schemas, and are sensitive to distribution shifts. We developed AdaCVD, an adaptable CVD risk prediction framework built on large language models extensively fine-tuned on over half a million participants from the UK Biobank. In benchmark comparisons, AdaCVD surpasses established risk scores and standard machine learning approaches, achieving state-of-the-art performance. Crucially, for the first time, it addresses key clinical challenges across three dimensions: it flexibly incorporates comprehensive yet variable patient information; it seamlessly integrates both structured data and unstructured text; and it rapidly adapts to new patient populations using minimal additional data. In stratified analyses, it demonstrates robust performance across demographic, socioeconomic, and clinical subgroups, including underrepresented cohorts. AdaCVD offers a promising path toward more flexible, AI-driven clinical decision support tools suited to the realities of heterogeneous and dynamic healthcare environments.", "date": "2025-05-30", "categories": []}
{"id": "2505.22840", "title": "Development and Validation of SXI++ LNM Algorithm for Sepsis Prediction", "abstract": "Sepsis is a life-threatening condition affecting over 48.9 million people globally and causing 11 million deaths annually. Despite medical advancements, predicting sepsis remains a challenge due to non-specific symptoms and complex pathophysiology. The SXI++ LNM is a machine learning scoring system that refines sepsis prediction by leveraging multiple algorithms and deep neural networks. This study aims to improve robustness in clinical applications and evaluates the predictive performance of the SXI++ LNM for sepsis prediction. The model, utilizing a deep neural network, was trained and tested using multiple scenarios with different dataset distributions. The model's performance was assessed against unseen test data, and accuracy, precision, and area under the curve (AUC) were calculated. THE SXI++ LNM outperformed the state of the art in three use cases, achieving an AUC of 0.99 (95% CI: 0.98-1.00). The model demonstrated a precision of 99.9% (95% CI: 99.8-100.0) and an accuracy of 99.99% (95% CI: 99.98-100.0), maintaining high reliability.", "date": "2025-05-28", "categories": []}
{"id": "2505.23109", "title": "Identification of Patterns of Cognitive Impairment for Early Detection of Dementia", "abstract": "Early detection of dementia is crucial to devise effective interventions. Comprehensive cognitive tests, while being the most accurate means of diagnosis, are long and tedious, thus limiting their applicability to a large population, especially when periodic assessments are needed. The problem is compounded by the fact that people have differing patterns of cognitive impairment as they progress to different forms of dementia. This paper presents a novel scheme by which individual-specific patterns of impairment can be identified and used to devise personalized tests for periodic follow-up. Patterns of cognitive impairment are initially learned from a population cluster of combined normals and MCIs, using a set of standardized cognitive tests. Impairment patterns in the population are identified using a 2-step procedure involving an ensemble wrapper feature selection followed by cluster identification and analysis. These patterns have been shown to correspond to clinically accepted variants of MCI, a prodrome of dementia. The learned clusters of patterns can subsequently be used to identify the most likely route of cognitive impairment, even for pre-symptomatic and apparently normal people. Baseline data of 24,000 subjects from the NACC database was used for the study.", "date": "2025-05-29", "categories": []}
{"id": "2505.23528", "title": "Comparative assessment of fairness definitions and bias mitigation strategies in machine learning-based diagnosis of Alzheimer's disease from MR images", "abstract": "The present study performs a comprehensive fairness analysis of machine learning (ML) models for the diagnosis of Mild Cognitive Impairment (MCI) and Alzheimer's disease (AD) from MRI-derived neuroimaging features. Biases associated with age, race, and gender in a multi-cohort dataset, as well as the influence of proxy features encoding these sensitive attributes, are investigated. The reliability of various fairness definitions and metrics in the identification of such biases is also assessed. Based on the most appropriate fairness measures, a comparative analysis of widely used pre-processing, in-processing, and post-processing bias mitigation strategies is performed. Moreover, a novel composite measure is introduced to quantify the trade-off between fairness and performance by considering the F1-score and the equalized odds ratio, making it appropriate for medical diagnostic applications. The obtained results reveal the existence of biases related to age and race, while no significant gender bias is observed. The deployed mitigation strategies yield varying improvements in terms of fairness across the different sensitive attributes and studied subproblems. For race and gender, Reject Option Classification improves equalized odds by 46% and 57%, respectively, and achieves harmonic mean scores of 0.75 and 0.80 in the MCI versus AD subproblem, whereas for age, in the same subproblem, adversarial debiasing yields the highest equalized odds improvement of 40% with a harmonic mean score of 0.69. Insights are provided into how variations in AD neuropathology and risk factors, associated with demographic characteristics, influence model fairness.", "date": "2025-05-29", "categories": []}
{"id": "2503.17933", "title": "Experience Retrieval-Augmentation with Electronic Health Records Enables Accurate Discharge QA", "abstract": "To improve the reliability of Large Language Models (LLMs) in clinical applications, retrieval-augmented generation (RAG) is extensively applied to provide factual medical knowledge. However, beyond general medical knowledge from open-ended datasets, clinical case-based knowledge is also critical for effective medical reasoning, as it provides context grounded in real-world patient experiences.Motivated by this, we propose Experience Retrieval-Augmentation ExpRAG framework based on Electronic Health Record(EHR), aiming to offer the relevant context from other patients' discharge reports. ExpRAG performs retrieval through a coarse-to-fine process, utilizing an EHR-based report ranker to efficiently identify similar patients, followed by an experience retriever to extract task-relevant content for enhanced medical reasoning.To evaluate ExpRAG, we introduce DischargeQA, a clinical QA dataset with 1,280 discharge-related questions across diagnosis, medication, and instruction tasks. Each problem is generated using EHR data to ensure realistic and challenging scenarios. Experimental results demonstrate that ExpRAG consistently outperforms a text-based ranker, achieving an average relative improvement of 5.2%, highlighting the importance of case-based knowledge for medical reasoning.", "date": "2025-03-23", "categories": []}
{"id": "2504.05928", "title": "Evaluation of the impact of expert knowledge: How decision support scores impact the effectiveness of automatic knowledge-driven feature engineering (aKDFE)", "abstract": "Adverse Drug Events (ADEs), harmful medication effects, pose significant healthcare challenges, impacting patient safety and costs. This study evaluates automatic Knowledge-Driven Feature Engineering (aKDFE) for improved ADE prediction from Electronic Health Record (EHR) data, comparing it with automated event-based Knowledge Discovery in Databases (KDD). We investigated how incorporating domain-specific ADE risk scores for prolonged heart QT interval, extracted from the Janusmed Riskprofile (Janusmed) Clinical Decision Support System (CDSS), affects prediction performance using EHR data and medication handling events. Results indicate that, while aKDFE step 1 (event-based feature generation) alone did not significantly improve ADE prediction performance, aKDFE step 2 (patient-centric transformation) enhances the prediction performance. High Area Under the Receiver Operating Characteristic curve (AUROC) values suggest strong feature correlations to the outcome, aligning with the predictive power of patients' prior healthcare history for ADEs. Statistical analysis did not confirm that incorporating the Janusmed information (i) risk scores and (ii) medication route of administration into the model's feature set enhanced predictive performance. However, the patient-centric transformation applied by aKDFE proved to be a highly effective feature engineering approach. Limitations include a single-project focus, potential bias from machine learning pipeline methods, and reliance on AUROC. In conclusion, aKDFE, particularly with patient-centric transformation, improves ADE prediction from EHR data. Future work will explore attention-based models, event feature sequences, and automatic methods for incorporating domain knowledge into the aKDFE framework.", "date": "2025-04-08", "categories": []}
{"id": "2505.21507", "title": "Automatic detection of abnormal clinical EEG: comparison of a finetuned foundation model with two deep learning models", "abstract": "Electroencephalography (EEG) is commonly used by physicians for the diagnosis of numerous neurological disorders. Due to the large volume of EEGs requiring interpretation and the specific expertise involved, artificial intelligence-based tools are being developed to assist in their visual analysis. In this paper, we compare two deep learning models (CNN-LSTM and Transformer-based) with BioSerenity-E1, a recently proposed foundation model, in the task of classifying entire EEG recordings as normal or abnormal. The three models were trained or finetuned on 2,500 EEG recordings and their performances were evaluated on two private and one public datasets: a large multicenter dataset annotated by a single specialist (dataset A composed of n = 4,480 recordings), a small multicenter dataset annotated by three specialists (dataset B, n = 198), and the Temple University Abnormal (TUAB) EEG corpus evaluation dataset (n = 276). On dataset A, the three models achieved at least 86% balanced accuracy, with BioSerenity-E1 finetuned achieving the highest balanced accuracy (89.19% [88.36-90.41]). BioSerenity-E1 finetuned also achieved the best performance on dataset B, with 94.63% [92.32-98.12] balanced accuracy. The models were then validated on TUAB evaluation dataset, whose corresponding training set was not used during training, where they achieved at least 76% accuracy. Specifically, BioSerenity-E1 finetuned outperformed the other two models, reaching an accuracy of 82.25% [78.27-87.48]. Our results highlight the usefulness of leveraging pre-trained models for automatic EEG classification: enabling robust and efficient interpretation of EEG data with fewer resources and broader applicability.", "date": "2025-05-13", "categories": []}
{"id": "2505.22496", "title": "Risk-Sensitive Conformal Prediction for Catheter Placement Detection in Chest X-rays", "abstract": "This paper presents a novel approach to catheter and line position detection in chest X-rays, combining multi-task learning with risk-sensitive conformal prediction to address critical clinical requirements. Our model simultaneously performs classification, segmentation, and landmark detection, leveraging the synergistic relationship between these tasks to improve overall performance. We further enhance clinical reliability through risk-sensitive conformal prediction, which provides statistically guaranteed prediction sets with higher reliability for clinically critical findings. Experimental results demonstrate excellent performance with 90.68\\% overall empirical coverage and 99.29\\% coverage for critical conditions, while maintaining remarkable precision in prediction sets. Most importantly, our risk-sensitive approach achieves zero high-risk mispredictions (cases where the system dangerously declares problematic tubes as confidently normal), making the system particularly suitable for clinical deployment. This work offers both accurate predictions and reliably quantified uncertainty -- essential features for life-critical medical applications.", "date": "2025-05-28", "categories": []}
{"id": "2505.22592", "title": "Comparative Analysis of Machine Learning Models for Lung Cancer Mutation Detection and Staging Using 3D CT Scans", "abstract": "Lung cancer is the leading cause of cancer mortality worldwide, and non-invasive methods for detecting key mutations and staging are essential for improving patient outcomes. Here, we compare the performance of two machine learning models - FMCIB+XGBoost, a supervised model with domain-specific pretraining, and Dinov2+ABMIL, a self-supervised model with attention-based multiple-instance learning - on 3D lung nodule data from the Stanford Radiogenomics and Lung-CT-PT-Dx cohorts. In the task of KRAS and EGFR mutation detection, FMCIB+XGBoost consistently outperformed Dinov2+ABMIL, achieving accuracies of 0.846 and 0.883 for KRAS and EGFR mutations, respectively. In cancer staging, Dinov2+ABMIL demonstrated competitive generalization, achieving an accuracy of 0.797 for T-stage prediction in the Lung-CT-PT-Dx cohort, suggesting SSL's adaptability across diverse datasets. Our results emphasize the clinical utility of supervised models in mutation detection and highlight the potential of SSL to improve staging generalization, while identifying areas for enhancement in mutation sensitivity.", "date": "2025-05-28", "categories": []}
{"id": "2405.00715", "title": "Towards Adapting Open-Source Large Language Models for Expert-Level Clinical Note Generation", "abstract": "Proprietary Large Language Models (LLMs) such as GPT-4 and Gemini have demonstrated promising capabilities in clinical text summarization tasks. However, due to patient data privacy concerns and computational costs, many healthcare providers prefer using small, locally-hosted models over external generic LLMs. This study presents a comprehensive domain- and task-specific adaptation process for the open-source LLaMA-2 13 billion parameter model, enabling it to generate high-quality clinical notes from outpatient patient-doctor dialogues. Our process incorporates continued pretraining, supervised fine-tuning, and reinforcement learning from both AI and human feedback. We introduced a new approach, DistillDirect, for performing on-policy reinforcement learning with Gemini 1.0 Pro as the teacher model. Our resulting model, LLaMA-Clinic, can generate clinical notes comparable in quality to those authored by physicians. In a blinded physician reader study, the majority (92.8%) of individual evaluations rated the notes generated by LLaMA-Clinic as \"acceptable\" or higher across three criteria: real-world readiness, completeness, and accuracy. In the more challenging \"Assessment and Plan\" section, LLaMA-Clinic matched physician-authored notes in real-world readiness score. We highlight key considerations for future clinical note-generation tasks, emphasizing the importance of pre-defining a \"best practice\" note format, rather than relying on LLMs to determine this for clinical practice.", "date": "2024-04-25", "categories": []}
{"id": "2505.15974", "title": "Real-Time Stress Monitoring, Detection, and Management in College Students: A Wearable Technology and Machine-Learning Approach", "abstract": "College students are increasingly affected by stress, anxiety, and depression, yet face barriers to traditional mental health care. This study evaluated the efficacy of a mobile health (mHealth) intervention, Mental Health Evaluation and Lookout Program (mHELP), which integrates a smartwatch sensor and machine learning (ML) algorithms for real-time stress detection and self-management. In a 12-week randomized controlled trial (n = 117), participants were assigned to a treatment group using mHELP's full suite of interventions or a control group using the app solely for real-time stress logging and weekly psychological assessments. The primary outcome, \"Moments of Stress\" (MS), was assessed via physiological and self-reported indicators and analyzed using Generalized Linear Mixed Models (GLMM) approaches. Similarly, secondary outcomes of psychological assessments, including the Generalized Anxiety Disorder-7 (GAD-7) for anxiety, the Patient Health Questionnaire (PHQ-8) for depression, and the Perceived Stress Scale (PSS), were also analyzed via GLMM. The finding of the objective measure, MS, indicates a substantial decrease in MS among the treatment group compared to the control group, while no notable between-group differences were observed in subjective scores of anxiety (GAD-7), depression (PHQ-8), or stress (PSS). However, the treatment group exhibited a clinically meaningful decline in GAD-7 and PSS scores. These findings underscore the potential of wearable-enabled mHealth tools to reduce acute stress in college populations and highlight the need for extended interventions and tailored features to address chronic symptoms like depression.", "date": "2025-05-21", "categories": []}
{"id": "2505.20609", "title": "Comparisons between a Large Language Model-based Real-Time Compound Diagnostic Medical AI Interface and Physicians for Common Internal Medicine Cases using Simulated Patients", "abstract": "Objective To develop an LLM based realtime compound diagnostic medical AI interface and performed a clinical trial comparing this interface and physicians for common internal medicine cases based on the United States Medical License Exam (USMLE) Step 2 Clinical Skill (CS) style exams. Methods A nonrandomized clinical trial was conducted on August 20, 2024. We recruited one general physician, two internal medicine residents (2nd and 3rd year), and five simulated patients. The clinical vignettes were adapted from the USMLE Step 2 CS style exams. We developed 10 representative internal medicine cases based on actual patients and included information available on initial diagnostic evaluation. Primary outcome was the accuracy of the first differential diagnosis. Repeatability was evaluated based on the proportion of agreement. Results The accuracy of the physicians' first differential diagnosis ranged from 50% to 70%, whereas the realtime compound diagnostic medical AI interface achieved an accuracy of 80%. The proportion of agreement for the first differential diagnosis was 0.7. The accuracy of the first and second differential diagnoses ranged from 70% to 90% for physicians, whereas the AI interface achieved an accuracy rate of 100%. The average time for the AI interface (557 sec) was 44.6% shorter than that of the physicians (1006 sec). The AI interface ($0.08) also reduced costs by 98.1% compared to the physicians' average ($4.2). Patient satisfaction scores ranged from 4.2 to 4.3 for care by physicians and were 3.9 for the AI interface Conclusion An LLM based realtime compound diagnostic medical AI interface demonstrated diagnostic accuracy and patient satisfaction comparable to those of a physician, while requiring less time and lower costs. These findings suggest that AI interfaces may have the potential to assist primary care consultations for common internal medicine cases.", "date": "2025-05-27", "categories": []}
{"id": "2505.21139", "title": "Identifying Heart Attack Risk in Vulnerable Population: A Machine Learning Approach", "abstract": "The COVID-19 pandemic has significantly increased the incidence of post-infection cardiovascular events, particularly myocardial infarction, in individuals over 40. While the underlying mechanisms remain elusive, this study employs a hybrid machine learning approach to analyze epidemiological data in assessing 13 key heart attack risk factors and their susceptibility. Based on a unique dataset that combines demographic, biochemical, ECG, and thallium stress-tests, this study categorizes distinct subpopulations against varying risk profiles and then divides the population into 'at-risk' (AR) and 'not-at-risk' (NAR) groups using clustering algorithms. The study reveals strong association between the likelihood of experiencing a heart attack on the 13 risk factors studied. The aggravated risk for postmenopausal patients indicates compromised individual risk factors due to estrogen depletion that may be, further compromised by extraneous stress impacts, like anxiety and fear, aspects that have traditionally eluded data modeling predictions.", "date": "2025-05-27", "categories": []}
{"id": "2505.21204", "title": "Developing hybrid mechanistic and data-driven personalized prediction models for platelet dynamics", "abstract": "Hematotoxicity, drug-induced damage to the blood-forming system, is a frequent side effect of cytotoxic chemotherapy and poses a significant challenge in clinical practice due to its high inter-patient variability and limited predictability. Current mechanistic models often struggle to accurately forecast outcomes for patients with irregular or atypical trajectories. In this study, we develop and compare hybrid mechanistic and data-driven approaches for individualized time series modeling of platelet counts during chemotherapy. We consider hybrid models that combine mechanistic models with neural networks, known as universal differential equations. As a purely data-driven alternative, we utilize a nonlinear autoregressive exogenous model using gated recurrent units as the underlying architecture. These models are evaluated across a range of real patient scenarios, varying in data availability and sparsity, to assess predictive performance. Our findings demonstrate that data-driven methods, when provided with sufficient data, significantly improve prediction accuracy, particularly for high-risk patients with irregular platelet dynamics. This highlights the potential of data-driven approaches in enhancing clinical decision-making. In contrast, hybrid and mechanistic models are superior in scenarios with limited or sparse data. The proposed modeling and comparison framework is generalizable and could be extended to predict other treatment-related toxicities, offering broad applicability in personalized medicine.", "date": "2025-05-27", "categories": []}
{"id": "2310.10187", "title": "An Interpretable Deep-Learning Framework for Predicting Hospital Readmissions From Electronic Health Records", "abstract": "With the increasing availability of patient data, modern medicine is shifting towards prospective healthcare. Electronic health records offer a variety of information useful for clinical patient characterization and the development of predictive models, given that similar medical histories often lead to analogous health progressions. One application is the prediction of unplanned hospital readmissions, an essential task for reducing healthcare costs and improving patient outcomes. While predictive models demonstrate strong performances especially with deep learning approaches, they are often criticized for their lack of interpretability, a critical requirement in the medical domain where incorrect predictions may have severe consequences for patient safety. In this paper, we propose a novel and interpretable deep learning framework for predicting unplanned hospital readmissions, supported by NLP findings on word embeddings and by ConvLSTM neural networks for better handling temporal data. We validate the framework on two predictive tasks for hospital readmission within 30 and 180 days, using real-world data. Additionally, we introduce and evaluate a model-dependent technique designed to enhance result interpretability for medical professionals. Our solution outperforms traditional machine learning models in prediction accuracy while simultaneously providing more interpretable results.", "date": "2023-10-16", "categories": []}
{"id": "2405.19864", "title": "Out-of-distribution Reject Option Method for Dataset Shift Problem in Early Disease Onset Prediction", "abstract": "Machine learning is increasingly used to predict lifestyle-related disease onset using health and medical data. However, its predictive accuracy for use is often hindered by dataset shift, which refers to discrepancies in data distribution between the training and testing datasets. This issue leads to the misclassification of out-of-distribution (OOD) data. To diminish dataset shift in real-world settings, this paper proposes the out-of-distribution reject option for prediction (ODROP). This method integrates an OOD detection model to preclude OOD data from the prediction phase. We used two real-world health checkup datasets (Hirosaki and Wakayama) with dataset shift, across three disease onset prediction tasks: diabetes, dyslipidemia, and hypertension. Both components of ODROP method -- the OOD detection model and the prediction model -- were trained on the Hirosaki dataset. We assessed the effectiveness of ODROP on the Wakayama dataset using AUROC-rejection rate curve plot. In the five OOD detection approaches (the variational autoencoder, neural network ensemble std, neural network ensemble epistemic, neural network energy, and neural network gaussian mixture based energy measurement), the variational autoencoder method demonstrated notably higher stability and a greater improvement in AUROC. For example, in the Wakayama dataset, the AUROC for diabetes onset increased from 0.80 without ODROP to 0.90 at a 31.1% rejection rate, and for dyslipidemia, it improved from 0.70 without ODROP to 0.76 at a 34% rejection rate. In addition, we categorized dataset shifts into two types using SHAP clustering -- those that considerably affect predictions and those that do not. This study is the first to apply OOD detection to actual health and medical data, demonstrating its potential to substantially improve the accuracy and reliability of disease prediction models amidst dataset shift.", "date": "2024-05-30", "categories": []}
{"id": "2505.18421", "title": "Development of Interactive Nomograms for Predicting Short-Term Survival in ICU Patients with Aplastic Anemia", "abstract": "Aplastic anemia is a rare, life-threatening hematologic disorder characterized by pancytopenia and bone marrow failure. ICU admission in these patients often signals critical complications or disease progression, making early risk assessment crucial for clinical decision-making and resource allocation. In this study, we used the MIMIC-IV database to identify ICU patients diagnosed with aplastic anemia and extracted clinical features from five domains: demographics, synthetic indicators, laboratory results, comorbidities, and medications. Over 400 variables were reduced to seven key predictors through machine learning-based feature selection. Logistic regression and Cox regression models were constructed to predict 7-, 14-, and 28-day mortality, and their performance was evaluated using AUROC. External validation was conducted using the eICU Collaborative Research Database to assess model generalizability. Among 1,662 included patients, the logistic regression model demonstrated superior performance, with AUROC values of 0.8227, 0.8311, and 0.8298 for 7-, 14-, and 28-day mortality, respectively, compared to the Cox model. External validation yielded AUROCs of 0.7391, 0.7119, and 0.7093. Interactive nomograms were developed based on the logistic regression model to visually estimate individual patient risk. In conclusion, we identified a concise set of seven predictors, led by APS III, to build validated and generalizable nomograms that accurately estimate short-term mortality in ICU patients with aplastic anemia. These tools may aid clinicians in personalized risk stratification and decision-making at the point of care.", "date": "2025-05-23", "categories": []}
{"id": "2505.18527", "title": "CLaDMoP: Learning Transferrable Models from Successful Clinical Trials via LLMs", "abstract": "Many existing models for clinical trial outcome prediction are optimized using task-specific loss functions on trial phase-specific data. While this scheme may boost prediction for common diseases and drugs, it can hinder learning of generalizable representations, leading to more false positives/negatives. To address this limitation, we introduce CLaDMoP, a new pre-training approach for clinical trial outcome prediction, alongside the Successful Clinical Trials dataset(SCT), specifically designed for this task. CLaDMoP leverages a Large Language Model-to encode trials' eligibility criteria-linked to a lightweight Drug-Molecule branch through a novel multi-level fusion technique. To efficiently fuse long embeddings across levels, we incorporate a grouping block, drastically reducing computational overhead. CLaDMoP avoids reliance on task-specific objectives by pre-training on a \"pair matching\" proxy task. Compared to established zero-shot and few-shot baselines, our method significantly improves both PR-AUC and ROC-AUC, especially for phase I and phase II trials. We further evaluate and perform ablation on CLaDMoP after Parameter-Efficient Fine-Tuning, comparing it to state-of-the-art supervised baselines, including MEXA-CTP, on the Trial Outcome Prediction(TOP) benchmark. CLaDMoP achieves up to 10.5% improvement in PR-AUC and 3.6% in ROC-AUC, while attaining comparable F1 score to MEXA-CTP, highlighting its potential for clinical trial outcome prediction. Code and SCT dataset can be downloaded from https://github.com/murai-lab/CLaDMoP.", "date": "2025-05-24", "categories": []}
{"id": "2505.19023", "title": "A Smart Healthcare System for Monkeypox Skin Lesion Detection and Tracking", "abstract": "Monkeypox is a viral disease characterized by distinctive skin lesions and has been reported in many countries. The recent global outbreak has emphasized the urgent need for scalable, accessible, and accurate diagnostic solutions to support public health responses.\n  In this study, we developed ITMAINN, an intelligent, AI-driven healthcare system specifically designed to detect Monkeypox from skin lesion images using advanced deep learning techniques. Our system consists of three main components. First, we trained and evaluated several pretrained models using transfer learning on publicly available skin lesion datasets to identify the most effective models. For binary classification (Monkeypox vs. non-Monkeypox), the Vision Transformer, MobileViT, Transformer-in-Transformer, and VGG16 achieved the highest performance, each with an accuracy and F1-score of 97.8%. For multiclass classification, which contains images of patients with Monkeypox and five other classes (chickenpox, measles, hand-foot-mouth disease, cowpox, and healthy), ResNetViT and ViT Hybrid models achieved 92% accuracy, with F1 scores of 92.24% and 92.19%, respectively. The best-performing and most lightweight model, MobileViT, was deployed within the mobile application. The second component is a cross-platform smartphone application that enables users to detect Monkeypox through image analysis, track symptoms, and receive recommendations for nearby healthcare centers based on their location. The third component is a real-time monitoring dashboard designed for health authorities to support them in tracking cases, analyzing symptom trends, guiding public health interventions, and taking proactive measures.\n  This system is fundamental in developing responsive healthcare infrastructure within smart cities. Our solution, ITMAINN, is part of revolutionizing public health management.", "date": "2025-05-25", "categories": []}
{"id": "2505.19195", "title": "CardioCoT: Hierarchical Reasoning for Multimodal Survival Analysis", "abstract": "Accurate prediction of major adverse cardiovascular events recurrence risk in acute myocardial infarction patients based on postoperative cardiac MRI and associated clinical notes is crucial for precision treatment and personalized intervention. Existing methods primarily focus on risk stratification capability while overlooking the need for intermediate robust reasoning and model interpretability in clinical practice. Moreover, end-to-end risk prediction using LLM/VLM faces significant challenges due to data limitations and modeling complexity. To bridge this gap, we propose CardioCoT, a novel two-stage hierarchical reasoning-enhanced survival analysis framework designed to enhance both model interpretability and predictive performance. In the first stage, we employ an evidence-augmented self-refinement mechanism to guide LLM/VLMs in generating robust hierarchical reasoning trajectories based on associated radiological findings. In the second stage, we integrate the reasoning trajectories with imaging data for risk model training and prediction. CardioCoT demonstrates superior performance in MACE recurrence risk prediction while providing interpretable reasoning processes, offering valuable insights for clinical decision-making.", "date": "2025-05-25", "categories": []}
{"id": "2505.20109", "title": "Language-Agnostic Suicidal Risk Detection Using Large Language Models", "abstract": "Suicidal risk detection in adolescents is a critical challenge, yet existing methods rely on language-specific models, limiting scalability and generalization. This study introduces a novel language-agnostic framework for suicidal risk assessment with large language models (LLMs). We generate Chinese transcripts from speech using an ASR model and then employ LLMs with prompt-based queries to extract suicidal risk-related features from these transcripts. The extracted features are retained in both Chinese and English to enable cross-linguistic analysis and then used to fine-tune corresponding pretrained language models independently. Experimental results show that our method achieves performance comparable to direct fine-tuning with ASR results or to models trained solely on Chinese suicidal risk-related features, demonstrating its potential to overcome language constraints and improve the robustness of suicidal risk assessment.", "date": "2025-05-26", "categories": []}
{"id": "2505.17054", "title": "METHOD: Modular Efficient Transformer for Health Outcome Discovery", "abstract": "Recent advances in transformer architectures have revolutionised natural language processing, but their application to healthcare domains presents unique challenges. Patient timelines are characterised by irregular sampling, variable temporal dependencies, and complex contextual relationships that differ substantially from traditional language tasks. This paper introduces \\METHOD~(Modular Efficient Transformer for Health Outcome Discovery), a novel transformer architecture specifically designed to address the challenges of clinical sequence modelling in electronic health records. \\METHOD~integrates three key innovations: (1) a patient-aware attention mechanism that prevents information leakage whilst enabling efficient batch processing; (2) an adaptive sliding window attention scheme that captures multi-scale temporal dependencies; and (3) a U-Net inspired architecture with dynamic skip connections for effective long sequence processing. Evaluations on the MIMIC-IV database demonstrate that \\METHOD~consistently outperforms the state-of-the-art \\ETHOS~model, particularly in predicting high-severity cases that require urgent clinical intervention. \\METHOD~exhibits stable performance across varying inference lengths, a crucial feature for clinical deployment where patient histories vary significantly in length. Analysis of learned embeddings reveals that \\METHOD~better preserves clinical hierarchies and relationships between medical concepts. These results suggest that \\METHOD~represents a significant advancement in transformer architectures optimised for healthcare applications, providing more accurate and clinically relevant predictions whilst maintaining computational efficiency.", "date": "2025-05-16", "categories": []}
{"id": "2505.17643", "title": "Bridging Electronic Health Records and Clinical Texts: Contrastive Learning for Enhanced Clinical Tasks", "abstract": "Conventional machine learning models, particularly tree-based approaches, have demonstrated promising performance across various clinical prediction tasks using electronic health record (EHR) data. Despite their strengths, these models struggle with tasks that require deeper contextual understanding, such as predicting 30-day hospital readmission. This can be primarily due to the limited semantic information available in structured EHR data. To address this limitation, we propose a deep multimodal contrastive learning (CL) framework that aligns the latent representations of structured EHR data with unstructured discharge summary notes. It works by pulling together paired EHR and text embeddings while pushing apart unpaired ones. Fine-tuning the pretrained EHR encoder extracted from this framework significantly boosts downstream task performance, e.g., a 4.1% AUROC enhancement over XGBoost for 30-day readmission prediction. Such results demonstrate the effect of integrating domain knowledge from clinical notes into EHR-based pipelines, enabling more accurate and context-aware clinical decision support systems.", "date": "2025-05-23", "categories": []}
{"id": "2505.17929", "title": "Predicting Length of Stay in Neurological ICU Patients Using Classical Machine Learning and Neural Network Models: A Benchmark Study on MIMIC-IV", "abstract": "Intensive care unit (ICU) is a crucial hospital department that handles life-threatening cases. Nowadays machine learning (ML) is being leveraged in healthcare ubiquitously. In recent years, management of ICU became one of the most significant parts of the hospital functionality (largely but not only due to the worldwide COVID-19 pandemic). This study explores multiple ML approaches for predicting LOS in ICU specifically for the patients with neurological diseases based on the MIMIC-IV dataset. The evaluated models include classic ML algorithms (K-Nearest Neighbors, Random Forest, XGBoost and CatBoost) and Neural Networks (LSTM, BERT and Temporal Fusion Transformer). Given that LOS prediction is often framed as a classification task, this study categorizes LOS into three groups: less than two days, less than a week, and a week or more. As the first ML-based approach targeting LOS prediction for neurological disorder patients, this study does not aim to outperform existing methods but rather to assess their effectiveness in this specific context. The findings provide insights into the applicability of ML techniques for improving ICU resource management and patient care. According to the results, Random Forest model proved to outperform others on static, achieving an accuracy of 0.68, a precision of 0.68, a recall of 0.68, and F1-score of 0.67. While BERT model outperformed LSTM model on time-series data with an accuracy of 0.80, a precision of 0.80, a recall of 0.80 and F1-score 0.80.", "date": "2025-05-23", "categories": []}
{"id": "2407.14960", "title": "Addressing Data Heterogeneity in Federated Learning of Cox Proportional Hazards Models", "abstract": "The diversity in disease profiles and therapeutic approaches between hospitals and health professionals underscores the need for patient-centric personalized strategies in healthcare. Alongside this, similarities in disease progression across patients can be utilized to improve prediction models in survival analysis. The need for patient privacy and the utility of prediction models can be simultaneously addressed in the framework of Federated Learning (FL). This paper outlines an approach in the domain of federated survival analysis, specifically the Cox Proportional Hazards (CoxPH) model, with a specific focus on mitigating data heterogeneity and elevating model performance. We present an FL approach that employs feature-based clustering to enhance model accuracy across synthetic datasets and real-world applications, including the Surveillance, Epidemiology, and End Results (SEER) database. Furthermore, we consider an event-based reporting strategy that provides a dynamic approach to model adaptation by responding to local data changes. Our experiments show the efficacy of our approach and discuss future directions for a practical application of FL in healthcare.", "date": "2024-07-20", "categories": []}
{"id": "2411.08701", "title": "TRACE: Transformer-based Risk Assessment for Clinical Evaluation", "abstract": "We present TRACE (Transformer-based Risk Assessment for Clinical Evaluation), a novel method for clinical risk assessment based on clinical data, leveraging the self-attention mechanism for enhanced feature interaction and result interpretation. Our approach is able to handle different data modalities, including continuous, categorical and multiple-choice (checkbox) attributes. The proposed architecture features a shared representation of the clinical data obtained by integrating specialized embeddings of each data modality, enabling the detection of high-risk individuals using Transformer encoder layers. To assess the effectiveness of the proposed method, a strong baseline based on non-negative multi-layer perceptrons (MLPs) is introduced. The proposed method outperforms various baselines widely used in the domain of clinical risk assessment, while effectively handling missing values. In terms of explainability, our Transformer-based method offers easily interpretable results via attention weights, further enhancing the clinicians' decision-making process.", "date": "2024-11-13", "categories": []}
{"id": "2502.18225", "title": "Liver Cirrhosis Stage Estimation from MRI with Deep Learning", "abstract": "We present an end-to-end deep learning framework for automated liver cirrhosis stage estimation from multi-sequence MRI. Cirrhosis is the severe scarring (fibrosis) of the liver and a common endpoint of various chronic liver diseases. Early diagnosis is vital to prevent complications such as decompensation and cancer, which significantly decreases life expectancy. However, diagnosing cirrhosis in its early stages is challenging, and patients often present with life-threatening complications. Our approach integrates multi-scale feature learning with sequence-specific attention mechanisms to capture subtle tissue variations across cirrhosis progression stages. Using CirrMRI600+, a large-scale publicly available dataset of 628 high-resolution MRI scans from 339 patients, we demonstrate state-of-the-art performance in three-stage cirrhosis classification. Our best model achieves 72.8% accuracy on T1W and 63.8% on T2W sequences, significantly outperforming traditional radiomics-based approaches. Through extensive ablation studies, we show that our architecture effectively learns stage-specific imaging biomarkers. We establish new benchmarks for automated cirrhosis staging and provide insights for developing clinically applicable deep learning systems. The source code will be available at https://github.com/JunZengz/CirrhosisStage.", "date": "2025-02-23", "categories": []}
{"id": "2504.00485", "title": "Stroke Disease Classification Using Machine Learning with Feature Selection Techniques", "abstract": "Heart disease remains a leading cause of mortality and morbidity worldwide, necessitating the development of accurate and reliable predictive models to facilitate early detection and intervention. While state of the art work has focused on various machine learning approaches for predicting heart disease, but they could not able to achieve remarkable accuracy. In response to this need, we applied nine machine learning algorithms XGBoost, logistic regression, decision tree, random forest, k-nearest neighbors (KNN), support vector machine (SVM), gaussian na\\\"ive bayes (NB gaussian), adaptive boosting, and linear regression to predict heart disease based on a range of physiological indicators. Our approach involved feature selection techniques to identify the most relevant predictors, aimed at refining the models to enhance both performance and interpretability. The models were trained, incorporating processes such as grid search hyperparameter tuning, and cross-validation to minimize overfitting. Additionally, we have developed a novel voting system with feature selection techniques to advance heart disease classification. Furthermore, we have evaluated the models using key performance metrics including accuracy, precision, recall, F1-score, and the area under the receiver operating characteristic curve (ROC AUC). Among the models, XGBoost demonstrated exceptional performance, achieving 99% accuracy, precision, F1-Score, 98% recall, and 100% ROC AUC. This study offers a promising approach to early heart disease diagnosis and preventive healthcare.", "date": "2025-04-01", "categories": []}
{"id": "2505.16027", "title": "Benchmarking Chest X-ray Diagnosis Models Across Multinational Datasets", "abstract": "Foundation models leveraging vision-language pretraining have shown promise in chest X-ray (CXR) interpretation, yet their real-world performance across diverse populations and diagnostic tasks remains insufficiently evaluated. This study benchmarks the diagnostic performance and generalizability of foundation models versus traditional convolutional neural networks (CNNs) on multinational CXR datasets. We evaluated eight CXR diagnostic models - five vision-language foundation models and three CNN-based architectures - across 37 standardized classification tasks using six public datasets from the USA, Spain, India, and Vietnam, and three private datasets from hospitals in China. Performance was assessed using AUROC, AUPRC, and other metrics across both shared and dataset-specific tasks. Foundation models outperformed CNNs in both accuracy and task coverage. MAVL, a model incorporating knowledge-enhanced prompts and structured supervision, achieved the highest performance on public (mean AUROC: 0.82; AUPRC: 0.32) and private (mean AUROC: 0.95; AUPRC: 0.89) datasets, ranking first in 14 of 37 public and 3 of 4 private tasks. All models showed reduced performance on pediatric cases, with average AUROC dropping from 0.88 +/- 0.18 in adults to 0.57 +/- 0.29 in children (p = 0.0202). These findings highlight the value of structured supervision and prompt design in radiologic AI and suggest future directions including geographic expansion and ensemble modeling for clinical deployment. Code for all evaluated models is available at https://drive.google.com/drive/folders/1B99yMQm7bB4h1sVMIBja0RfUu8gLktCE", "date": "2025-05-21", "categories": []}
{"id": "2408.06930", "title": "Diagnosis extraction from unstructured Dutch echocardiogram reports using span- and document-level characteristic classification", "abstract": "Clinical machine learning research and AI driven clinical decision support models rely on clinically accurate labels. Manually extracting these labels with the help of clinical specialists is often time-consuming and expensive. This study tests the feasibility of automatic span- and document-level diagnosis extraction from unstructured Dutch echocardiogram reports. We included 115,692 unstructured echocardiogram reports from the UMCU a large university hospital in the Netherlands. A randomly selected subset was manually annotated for the occurrence and severity of eleven commonly described cardiac characteristics. We developed and tested several automatic labelling techniques at both span and document levels, using weighted and macro F1-score, precision, and recall for performance evaluation. We compared the performance of span labelling against document labelling methods, which included both direct document classifiers and indirect document classifiers that rely on span classification results. The SpanCategorizer and MedRoBERTa$.$nl models outperformed all other span and document classifiers, respectively. The weighted F1-score varied between characteristics, ranging from 0.60 to 0.93 in SpanCategorizer and 0.96 to 0.98 in MedRoBERTa$.$nl. Direct document classification was superior to indirect document classification using span classifiers. SetFit achieved competitive document classification performance using only 10% of the training data. Utilizing a reduced label set yielded near-perfect document classification results. We recommend using our published SpanCategorizer and MedRoBERTa$.$nl models for span- and document-level diagnosis extraction from Dutch echocardiography reports. For settings with limited training data, SetFit may be a promising alternative for document classification.", "date": "2024-08-13", "categories": []}
{"id": "2412.19511", "title": "Uncertainty quantification for improving radiomic-based models in radiation pneumonitis prediction", "abstract": "Background: Radiation pneumonitis is a side effect of thoracic radiation therapy. Recently, machine learning models with radiomic features have improved radiation pneumonitis prediction by capturing spatial information. To further support clinical decision-making, this study explores the role of post hoc uncertainty quantification methods in enhancing model uncertainty estimate. Methods: We retrospectively analyzed a cohort of 101 esophageal cancer patients. This study evaluated four machine learning models: logistic regression, support vector machines, extreme gradient boosting, and random forest, using 15 dosimetric, 79 dosiomic, and 237 radiomic features to predict radiation pneumonitis. We applied uncertainty quantification methods, including Platt scaling, isotonic regression, Venn-ABERS predictor, and conformal prediction, to quantify uncertainty. Model performance was assessed through an area under the receiver operating characteristic curve (AUROC), area under the precision-recall curve (AUPRC), and adaptive calibration error using leave-one-out cross-validation. Results: Highest AUROC is achieved by the logistic regression model with the conformal prediction method (AUROC 0.75+-0.01, AUPRC 0.74+-0.01) at a certainty cut point of 0.8. Highest AUPRC of 0.82+-0.02 (with AUROC of 0.67+-0.04) achieved by The extreme gradient boosting model with conformal prediction at the 0.9 certainty threshold. Radiomic and dosiomic features improve both discriminative and calibration performance. Conclusions: Integrating uncertainty quantification into machine learning models with radiomic and dosiomic features may improve both predictive accuracy and calibration, supporting more reliable clinical decision-making. The findings emphasize the value of uncertainty quantification methods in enhancing applicability of predictive models for radiation pneumonitis in healthcare settings.", "date": "2024-12-27", "categories": []}
{"id": "2502.06835", "title": "Reinforcement Learning on Dyads to Enhance Medication Adherence", "abstract": "Medication adherence is critical for the recovery of adolescents and young adults (AYAs) who have undergone hematopoietic cell transplantation (HCT). However, maintaining adherence is challenging for AYAs after hospital discharge, who experience both individual (e.g. physical and emotional symptoms) and interpersonal barriers (e.g., relational difficulties with their care partner, who is often involved in medication management). To optimize the effectiveness of a three-component digital intervention targeting both members of the dyad as well as their relationship, we propose a novel Multi-Agent Reinforcement Learning (MARL) approach to personalize the delivery of interventions. By incorporating the domain knowledge, the MARL framework, where each agent is responsible for the delivery of one intervention component, allows for faster learning compared with a flattened agent. Evaluation using a dyadic simulator environment, based on real clinical data, shows a significant improvement in medication adherence (approximately 3%) compared to purely random intervention delivery. The effectiveness of this approach will be further evaluated in an upcoming trial.", "date": "2025-02-06", "categories": []}
{"id": "2404.05762", "title": "Evaluating the Effectiveness of Artificial Intelligence in Predicting Adverse Drug Reactions among Cancer Patients: A Systematic Review and Meta-Analysis", "abstract": "Adverse drug reactions considerably impact patient outcomes and healthcare costs in cancer therapy. Using artificial intelligence to predict adverse drug reactions in real time could revolutionize oncology treatment. This study aims to assess the performance of artificial intelligence models in predicting adverse drug reactions in patients with cancer. This is the first systematic review and meta-analysis. Scopus, PubMed, IEEE Xplore, and ACM Digital Library databases were searched for studies in English, French, and Arabic from January 1, 2018, to August 20, 2023. The inclusion criteria were: (1) peer-reviewed research articles; (2) use of artificial intelligence algorithms (machine learning, deep learning, knowledge graphs); (3) study aimed to predict adverse drug reactions (cardiotoxicity, neutropenia, nephrotoxicity, hepatotoxicity); (4) study was on cancer patients. The data were extracted and evaluated by three reviewers for study quality. Of the 332 screened articles, 17 studies (5%) involving 93,248 oncology patients from 17 countries were included in the systematic review, of which ten studies synthesized the meta-analysis. A random-effects model was created to pool the sensitivity, specificity, and AUC of the included studies. The pooled results were 0.82 (95% CI:0.69, 0.9), 0.84 (95% CI:0.75, 0.9), and 0.83 (95% CI:0.77, 0.87) for sensitivity, specificity, and AUC, respectively, of ADR predictive models. Biomarkers proved their effectiveness in predicting ADRs, yet they were adopted by only half of the reviewed studies. The use of AI in cancer treatment shows great potential, with models demonstrating high specificity and sensitivity in predicting ADRs. However, standardized research and multicenter studies are needed to improve the quality of evidence. AI can enhance cancer patient care by bridging the gap between data-driven insights and clinical expertise.", "date": "2024-04-06", "categories": []}
{"id": "2412.03717", "title": "Electrocardiogram-based diagnosis of liver diseases: an externally validated and explainable machine learning approach", "abstract": "Background: Liver diseases present a significant global health challenge and often require costly, invasive diagnostics. Electrocardiography (ECG), a widely available and non-invasive tool, can enable the detection of liver disease by capturing cardiovascular-hepatic interactions.\n  Methods: We trained tree-based machine learning models on ECG features to detect liver diseases using two large datasets: MIMIC-IV-ECG (467,729 patients, 2008-2019) and ECG-View II (775,535 patients, 1994-2013). The task was framed as binary classification, with performance evaluated via the area under the receiver operating characteristic curve (AUROC). To improve interpretability, we applied explainability methods to identify key predictive features.\n  Findings: The models showed strong predictive performance with good generalizability. For example, AUROCs for alcoholic liver disease (K70) were 0.8025 (95% confidence interval (CI), 0.8020-0.8035) internally and 0.7644 (95% CI, 0.7641-0.7649) externally; for hepatic failure (K72), scores were 0.7404 (95% CI, 0.7389-0.7415) and 0.7498 (95% CI, 0.7494-0.7509), respectively. The explainability analysis consistently identified age and prolonged QTc intervals (corrected QT, reflecting ventricular repolarization) as key predictors. Features linked to autonomic regulation and electrical conduction abnormalities were also prominent, supporting known cardiovascular-liver connections and suggesting QTc as a potential biomarker.\n  Interpretation: ECG-based machine learning offers a promising, interpretable approach for liver disease detection, particularly in resource-limited settings. By revealing clinically relevant biomarkers, this method supports non-invasive diagnostics, early detection, and risk stratification prior to targeted clinical assessments.", "date": "2024-12-04", "categories": []}
{"id": "2502.07158", "title": "Early Risk Prediction of Pediatric Cardiac Arrest from Electronic Health Records via Multimodal Fused Transformer", "abstract": "Early prediction of pediatric cardiac arrest (CA) is critical for timely intervention in high-risk intensive care settings. We introduce PedCA-FT, a novel transformer-based framework that fuses tabular view of EHR with the derived textual view of EHR to fully unleash the interactions of high-dimensional risk factors and their dynamics. By employing dedicated transformer modules for each modality view, PedCA-FT captures complex temporal and contextual patterns to produce robust CA risk estimates. Evaluated on a curated pediatric cohort from the CHOA-CICU database, our approach outperforms ten other artificial intelligence models across five key performance metrics and identifies clinically meaningful risk factors. These findings underscore the potential of multimodal fusion techniques to enhance early CA detection and improve patient care.", "date": "2025-02-11", "categories": []}
{"id": "2505.13480", "title": "Evaluating Reasoning LLMs for Suicide Screening with the Columbia-Suicide Severity Rating Scale", "abstract": "Suicide prevention remains a critical public health challenge. While online platforms such as Reddit's r/SuicideWatch have historically provided spaces for individuals to express suicidal thoughts and seek community support, the advent of large language models (LLMs) introduces a new paradigm-where individuals may begin disclosing ideation to AI systems instead of humans. This study evaluates the capability of LLMs to perform automated suicide risk assessment using the Columbia-Suicide Severity Rating Scale (C-SSRS). We assess the zero-shot performance of six models-including Claude, GPT, Mistral, and LLaMA-in classifying posts across a 7-point severity scale (Levels 0-6). Results indicate that Claude and GPT closely align with human annotations, while Mistral achieves the lowest ordinal prediction error. Most models exhibit ordinal sensitivity, with misclassifications typically occurring between adjacent severity levels. We further analyze confusion patterns, misclassification sources, and ethical considerations, underscoring the importance of human oversight, transparency, and cautious deployment. Full code and supplementary materials are available at https://github.com/av9ash/llm_cssrs_code.", "date": "2025-05-11", "categories": []}
{"id": "2505.14217", "title": "Federated learning in low-resource settings: A chest imaging study in Africa -- Challenges and lessons learned", "abstract": "This study explores the use of Federated Learning (FL) for tuberculosis (TB) diagnosis using chest X-rays in low-resource settings across Africa. FL allows hospitals to collaboratively train AI models without sharing raw patient data, addressing privacy concerns and data scarcity that hinder traditional centralized models. The research involved hospitals and research centers in eight African countries. Most sites used local datasets, while Ghana and The Gambia used public ones. The study compared locally trained models with a federated model built across all institutions to evaluate FL's real-world feasibility. Despite its promise, implementing FL in sub-Saharan Africa faces challenges such as poor infrastructure, unreliable internet, limited digital literacy, and weak AI regulations. Some institutions were also reluctant to share model updates due to data control concerns. In conclusion, FL shows strong potential for enabling AI-driven healthcare in underserved regions, but broader adoption will require improvements in infrastructure, education, and regulatory support.", "date": "2025-05-20", "categories": []}
{"id": "2505.14643", "title": "Early Diagnosis of Atrial Fibrillation Recurrence: A Large Tabular Model Approach with Structured and Unstructured Clinical Data", "abstract": "BACKGROUND: Atrial fibrillation (AF), the most common arrhythmia, is linked to high morbidity and mortality. In a fast-evolving AF rhythm control treatment era, predicting AF recurrence after its onset may be crucial to achieve the optimal therapeutic approach, yet traditional scores like CHADS2-VASc, HATCH, and APPLE show limited predictive accuracy. Moreover, early diagnosis studies often rely on codified electronic health record (EHR) data, which may contain errors and missing information.\n  OBJECTIVE: This study aims to predict AF recurrence between one month and two years after onset by evaluating traditional clinical scores, ML models, and our LTM approach. Moreover, another objective is to develop a methodology for integrating structured and unstructured data to enhance tabular dataset quality.\n  METHODS: A tabular dataset was generated by combining structured clinical data with free-text discharge reports processed through natural language processing techniques, reducing errors and annotation effort. A total of 1,508 patients with documented AF onset were identified, and models were evaluated on a manually annotated test set. The proposed approach includes a LTM compared against traditional clinical scores and ML models.\n  RESULTS: The proposed LTM approach achieved the highest predictive performance, surpassing both traditional clinical scores and ML models. Additionally, the gender and age bias analyses revealed demographic disparities.\n  CONCLUSION: The integration of structured data and free-text sources resulted in a high-quality dataset. The findings emphasize the limitations of traditional clinical scores in predicting AF recurrence and highlight the potential of ML-based approaches, particularly our LTM model.", "date": "2025-05-20", "categories": []}
{"id": "2410.03794", "title": "Repurposing Foundation Model for Generalizable Medical Time Series Classification", "abstract": "Medical time series (MedTS) classification suffers from poor generalizability in real-world deployment due to inter- and intra-dataset heterogeneity, such as varying numbers of channels, signal lengths, task definitions, and patient characteristics. To address this, we propose FORMED, a novel framework for repurposing a backbone foundation model, pre-trained on generic time series, to enable highly generalizable MedTS classification on unseen datasets. FORMED combines the backbone with a novel classifier comprising two components: (1) task-specific channel embeddings and label queries, dynamically sized to match any number of channels and target classes, and (2) a shared decoding attention layer, jointly trained across datasets to capture medical domain knowledge through task-agnostic feature-query interactions. After repurposing, FORMED achieves seamless adaptation to unseen MedTS datasets through lightweight label query training (0.1% of parameters), eliminating the need for full fine-tuning or architectural redesign. We evaluate FORMED on 5 diverse MedTS datasets, benchmarking against 11 Task-Specific Models (TSM) and 4 Task-Specific Adaptation (TSA) methods. Our results demonstrate FORMED's dominant performance, achieving up to 35% absolute improvement in F1-score (on ADFTD dataset) over specialized baselines. Further analysis reveals consistent generalization across varying channel configurations, time series lengths, and clinical tasks, which are key challenges in real-world deployment. By decoupling domain-invariant representation learning from task-specific adaptation, FORMED establishes a scalable and resource-efficient paradigm for foundation model repurposing in healthcare. This approach prioritizes clinical adaptability over rigid task-centric design, offering a practical pathway for real-world implementation.", "date": "2024-10-03", "categories": []}
{"id": "2502.17978", "title": "Machine Learning-Based Prediction of ICU Mortality in Sepsis-Associated Acute Kidney Injury Patients Using MIMIC-IV Database with Validation from eICU Database", "abstract": "Background: Sepsis-Associated Acute Kidney Injury (SA-AKI) leads to high mortality in intensive care. This study develops machine learning models using the Medical Information Mart for Intensive Care IV (MIMIC-IV) database to predict Intensive Care Unit (ICU) mortality in SA-AKI patients. External validation is conducted using the eICU Collaborative Research Database.\n  Methods: For 9,474 identified SA-AKI patients in MIMIC-IV, key features like lab results, vital signs, and comorbidities were selected using Variance Inflation Factor (VIF), Recursive Feature Elimination (RFE), and expert input, narrowing to 24 predictive variables. An Extreme Gradient Boosting (XGBoost) model was built for in-hospital mortality prediction, with hyperparameters optimized using GridSearch. Model interpretability was enhanced with SHapley Additive exPlanations (SHAP) and Local Interpretable Model-agnostic Explanations (LIME). External validation was conducted using the eICU database.\n  Results: The proposed XGBoost model achieved an internal Area Under the Receiver Operating Characteristic curve (AUROC) of 0.878 (95% Confidence Interval: 0.859-0.897). SHAP identified Sequential Organ Failure Assessment (SOFA), serum lactate, and respiratory rate as key mortality predictors. LIME highlighted serum lactate, Acute Physiology and Chronic Health Evaluation II (APACHE II) score, total urine output, and serum calcium as critical features.\n  Conclusions: The integration of advanced techniques with the XGBoost algorithm yielded a highly accurate and interpretable model for predicting SA-AKI mortality across diverse populations. It supports early identification of high-risk patients, enhancing clinical decision-making in intensive care. Future work needs to focus on enhancing adaptability, versatility, and real-world applications.", "date": "2025-02-25", "categories": []}
{"id": "2505.11612", "title": "Heart2Mind: Human-Centered Contestable Psychiatric Disorder Diagnosis System using Wearable ECG Monitors", "abstract": "Psychiatric disorders affect millions globally, yet their diagnosis faces significant challenges in clinical practice due to subjective assessments and accessibility concerns, leading to potential delays in treatment. To help address this issue, we present Heart2Mind, a human-centered contestable psychiatric disorder diagnosis system using wearable electrocardiogram (ECG) monitors. Our approach leverages cardiac biomarkers, particularly heart rate variability (HRV) and R-R intervals (RRI) time series, as objective indicators of autonomic dysfunction in psychiatric conditions. The system comprises three key components: (1) a Cardiac Monitoring Interface (CMI) for real-time data acquisition from Polar H9/H10 devices; (2) a Multi-Scale Temporal-Frequency Transformer (MSTFT) that processes RRI time series through integrated time-frequency domain analysis; (3) a Contestable Diagnosis Interface (CDI) combining Self-Adversarial Explanations (SAEs) with contestable Large Language Models (LLMs). Our MSTFT achieves 91.7% accuracy on the HRV-ACC dataset using leave-one-out cross-validation, outperforming state-of-the-art methods. SAEs successfully detect inconsistencies in model predictions by comparing attention-based and gradient-based explanations, while LLMs enable clinicians to validate correct predictions and contest erroneous ones. This work demonstrates the feasibility of combining wearable technology with Explainable Artificial Intelligence (XAI) and contestable LLMs to create a transparent, contestable system for psychiatric diagnosis that maintains clinical oversight while leveraging advanced AI capabilities. Our implementation is publicly available at: https://github.com/Analytics-Everywhere-Lab/heart2mind.", "date": "2025-05-16", "categories": []}
{"id": "2505.11802", "title": "Diffmv: A Unified Diffusion Framework for Healthcare Predictions with Random Missing Views and View Laziness", "abstract": "Advanced healthcare predictions offer significant improvements in patient outcomes by leveraging predictive analytics. Existing works primarily utilize various views of Electronic Health Record (EHR) data, such as diagnoses, lab tests, or clinical notes, for model training. These methods typically assume the availability of complete EHR views and that the designed model could fully leverage the potential of each view. However, in practice, random missing views and view laziness present two significant challenges that hinder further improvements in multi-view utilization. To address these challenges, we introduce Diffmv, an innovative diffusion-based generative framework designed to advance the exploitation of multiple views of EHR data. Specifically, to address random missing views, we integrate various views of EHR data into a unified diffusion-denoising framework, enriched with diverse contextual conditions to facilitate progressive alignment and view transformation. To mitigate view laziness, we propose a novel reweighting strategy that assesses the relative advantages of each view, promoting a balanced utilization of various data views within the model. Our proposed strategy achieves superior performance across multiple health prediction tasks derived from three popular datasets, including multi-view and multi-modality scenarios.", "date": "2025-05-17", "categories": []}
{"id": "2505.12220", "title": "Machine Learning Applications Related to Suicide in Military and Veterans: A Scoping Literature Review", "abstract": "Suicide remains one of the main preventable causes of death among active service members and veterans. Early detection and prediction are crucial in suicide prevention. Machine learning techniques have yielded promising results in this area recently. This study aims to assess and summarize current research and provides a comprehensive review regarding the application of machine learning techniques in assessing and predicting suicidal ideation, attempts, and mortality among members of military and veteran populations.\n  A keyword search using PubMed, IEEE, ACM, and Google Scholar was conducted, and the PRISMA protocol was adopted for relevant study selection. Thirty-two articles met the inclusion criteria. These studies consistently identified risk factors relevant to mental health issues such as depression, post-traumatic stress disorder (PTSD), suicidal ideation, prior attempts, physical health problems, and demographic characteristics.\n  Machine learning models applied in this area have demonstrated reasonable predictive accuracy. However, additional research gaps still exist. First, many studies have overlooked metrics that distinguish between false positives and negatives, such as positive predictive value and negative predictive value, which are crucial in the context of suicide prevention policies. Second, more dedicated approaches to handling survival and longitudinal data should be explored. Lastly, most studies focused on machine learning methods, with limited discussion of their connection to clinical rationales.\n  In summary, machine learning analyses have identified a wide range of risk factors associated with suicide in military populations. The diversity and complexity of these factors also demonstrates that effective prevention strategies must be comprehensive and flexible.", "date": "2025-05-18", "categories": []}
{"id": "2505.10691", "title": "Predicting Risk of Pulmonary Fibrosis Formation in PASC Patients", "abstract": "While the acute phase of the COVID-19 pandemic has subsided, its long-term effects persist through Post-Acute Sequelae of COVID-19 (PASC), commonly known as Long COVID. There remains substantial uncertainty regarding both its duration and optimal management strategies. PASC manifests as a diverse array of persistent or newly emerging symptoms--ranging from fatigue, dyspnea, and neurologic impairments (e.g., brain fog), to cardiovascular, pulmonary, and musculoskeletal abnormalities--that extend beyond the acute infection phase. This heterogeneous presentation poses substantial challenges for clinical assessment, diagnosis, and treatment planning. In this paper, we focus on imaging findings that may suggest fibrotic damage in the lungs, a critical manifestation characterized by scarring of lung tissue, which can potentially affect long-term respiratory function in patients with PASC. This study introduces a novel multi-center chest CT analysis framework that combines deep learning and radiomics for fibrosis prediction. Our approach leverages convolutional neural networks (CNNs) and interpretable feature extraction, achieving 82.2% accuracy and 85.5% AUC in classification tasks. We demonstrate the effectiveness of Grad-CAM visualization and radiomics-based feature analysis in providing clinically relevant insights for PASC-related lung fibrosis prediction. Our findings highlight the potential of deep learning-driven computational methods for early detection and risk assessment of PASC-related lung fibrosis--presented for the first time in the literature.", "date": "2025-05-15", "categories": []}
{"id": "2311.04229", "title": "Exploring Best Practices for ECG Pre-Processing in Machine Learning", "abstract": "In this work we search for best practices in pre-processing of Electrocardiogram (ECG) signals in order to train better classifiers for the diagnosis of heart conditions. State of the art machine learning algorithms have achieved remarkable results in classification of some heart conditions using ECG data, yet there appears to be no consensus on pre-processing best practices. Is this lack of consensus due to different conditions and architectures requiring different processing steps for optimal performance? Is it possible that state of the art deep-learning models have rendered pre-processing unnecessary? In this work we apply down-sampling, normalization, and filtering functions to 3 different multi-label ECG datasets and measure their effects on 3 different high-performing time-series classifiers. We find that sampling rates as low as 50Hz can yield comparable results to the commonly used 500Hz. This is significant as smaller sampling rates will result in smaller datasets and models, which require less time and resources to train. Additionally, despite their common usage, we found min-max normalization to be slightly detrimental overall, and band-passing to make no measurable difference. We found the blind approach to pre-processing of ECGs for multi-label classification to be ineffective, with the exception of sample rate reduction which reliably reduces computational resources, but does not increase accuracy.", "date": "2023-11-02", "categories": []}
{"id": "2505.09812", "title": "Comparative Analysis of Stroke Prediction Models Using Machine Learning", "abstract": "Stroke remains one of the most critical global health challenges, ranking as the second leading cause of death and the third leading cause of disability worldwide. This study explores the effectiveness of machine learning algorithms in predicting stroke risk using demographic, clinical, and lifestyle data from the Stroke Prediction Dataset. By addressing key methodological challenges such as class imbalance and missing data, we evaluated the performance of multiple models, including Logistic Regression, Random Forest, and XGBoost. Our results demonstrate that while these models achieve high accuracy, sensitivity remains a limiting factor for real-world clinical applications. In addition, we identify the most influential predictive features and propose strategies to improve machine learning-based stroke prediction. These findings contribute to the development of more reliable and interpretable models for the early assessment of stroke risk.", "date": "2025-05-14", "categories": []}
{"id": "2505.09969", "title": "A Comprehensive Machine Learning Framework for Heart Disease Prediction: Performance Evaluation and Future Perspectives", "abstract": "This study presents a machine learning-based framework for heart disease prediction using the heart-disease dataset, comprising 303 samples with 14 features. The methodology involves data preprocessing, model training, and evaluation using three classifiers: Logistic Regression, K-Nearest Neighbors (KNN), and Random Forest. Hyperparameter tuning with GridSearchCV and RandomizedSearchCV was employed to enhance model performance. The Random Forest classifier outperformed other models, achieving an accuracy of 91% and an F1-score of 0.89. Evaluation metrics, including precision, recall, and confusion matrix, revealed balanced performance across classes. The proposed model demonstrates strong potential for aiding clinical decision-making by effectively predicting heart disease. Limitations such as dataset size and generalizability underscore the need for future studies using larger and more diverse datasets. This work highlights the utility of machine learning in healthcare, offering insights for further advancements in predictive diagnostics.", "date": "2025-05-15", "categories": []}
{"id": "2505.08821", "title": "A Comparative Study of Transformer-Based Models for Multi-Horizon Blood Glucose Prediction", "abstract": "Accurate blood glucose prediction can enable novel interventions for type 1 diabetes treatment, including personalized insulin and dietary adjustments. Although recent advances in transformer-based architectures have demonstrated the power of attention mechanisms in complex multivariate time series prediction, their potential for blood glucose (BG) prediction remains underexplored. We present a comparative analysis of transformer models for multi-horizon BG prediction, examining forecasts up to 4 hours and input history up to 1 week. The publicly available DCLP3 dataset (n=112) was split (80%-10%-10%) for training, validation, and testing, and the OhioT1DM dataset (n=12) served as an external test set. We trained networks with point-wise, patch-wise, series-wise, and hybrid embeddings, using CGM, insulin, and meal data. For short-term blood glucose prediction, Crossformer, a patch-wise transformer architecture, achieved a superior 30-minute prediction of RMSE (15.6 mg / dL on OhioT1DM). For longer-term predictions (1h, 2h, and 4h), PatchTST, another path-wise transformer, prevailed with the lowest RMSE (24.6 mg/dL, 36.1 mg/dL, and 46.5 mg/dL on OhioT1DM). In general, models that used tokenization through patches demonstrated improved accuracy with larger input sizes, with the best results obtained with a one-week history. These findings highlight the promise of transformer-based architectures for BG prediction by capturing and leveraging seasonal patterns in multivariate time-series data to improve accuracy.", "date": "2025-05-12", "categories": []}
{"id": "2504.04120", "title": "Transformer representation learning is necessary for dynamic multi-modal physiological data on small-cohort patients", "abstract": "Postoperative delirium (POD), a severe neuropsychiatric complication affecting nearly 50% of high-risk surgical patients, is defined as an acute disorder of attention and cognition, It remains significantly underdiagnosed in the intensive care units (ICUs) due to subjective monitoring methods. Early and accurate diagnosis of POD is critical and achievable. Here, we propose a POD prediction framework comprising a Transformer representation model followed by traditional machine learning algorithms. Our approaches utilizes multi-modal physiological data, including amplitude-integrated electroencephalography (aEEG), vital signs, electrocardiographic monitor data as well as hemodynamic parameters. We curated the first multi-modal POD dataset encompassing two patient types and evaluated the various Transformer architectures for representation learning. Empirical results indicate a consistent improvements of sensitivity and Youden index in patient TYPE I using Transformer representations, particularly our fusion adaptation of Pathformer. By enabling effective delirium diagnosis from postoperative day 1 to 3, our extensive experimental findings emphasize the potential of multi-modal physiological data and highlight the necessity of representation learning via multi-modal Transformer architecture in clinical diagnosis.", "date": "2025-04-05", "categories": []}
{"id": "2505.08086", "title": "Multi-modal wound classification using wound image and location by Xception and Gaussian Mixture Recurrent Neural Network (GMRNN)", "abstract": "The effective diagnosis of acute and hard-to-heal wounds is crucial for wound care practitioners to provide effective patient care. Poor clinical outcomes are often linked to infection, peripheral vascular disease, and increasing wound depth, which collectively exacerbate these comorbidities. However, diagnostic tools based on Artificial Intelligence (AI) speed up the interpretation of medical images and improve early detection of disease. In this article, we propose a multi-modal AI model based on transfer learning (TL), which combines two state-of-the-art architectures, Xception and GMRNN, for wound classification. The multi-modal network is developed by concatenating the features extracted by a transfer learning algorithm and location features to classify the wound types of diabetic, pressure, surgical, and venous ulcers. The proposed method is comprehensively compared with deep neural networks (DNN) for medical image analysis. The experimental results demonstrate a notable wound-class classifications (containing only diabetic, pressure, surgical, and venous) vary from 78.77 to 100\\% in various experiments. The results presented in this study showcase the exceptional accuracy of the proposed methodology in accurately classifying the most commonly occurring wound types using wound images and their corresponding locations.", "date": "2025-05-12", "categories": []}
{"id": "2505.08111", "title": "Sleep Position Classification using Transfer Learning for Bed-based Pressure Sensors", "abstract": "Bed-based pressure-sensitive mats (PSMs) offer a non-intrusive way of monitoring patients during sleep. We focus on four-way sleep position classification using data collected from a PSM placed under a mattress in a sleep clinic. Sleep positions can affect sleep quality and the prevalence of sleep disorders, such as apnea. Measurements were performed on patients with suspected sleep disorders referred for assessments at a sleep clinic. Training deep learning models can be challenging in clinical settings due to the need for large amounts of labeled data. To overcome the shortage of labeled training data, we utilize transfer learning to adapt pre-trained deep learning models to accurately estimate sleep positions from a low-resolution PSM dataset collected in a polysomnography sleep lab. Our approach leverages Vision Transformer models pre-trained on ImageNet using masked autoencoding (ViTMAE) and a pre-trained model for human pose estimation (ViTPose). These approaches outperform previous work from PSM-based sleep pose classification using deep learning (TCN) as well as traditional machine learning models (SVM, XGBoost, Random Forest) that use engineered features. We evaluate the performance of sleep position classification from 112 nights of patient recordings and validate it on a higher resolution 13-patient dataset. Despite the challenges of differentiating between sleep positions from low-resolution PSM data, our approach shows promise for real-world deployment in clinical settings", "date": "2025-05-12", "categories": []}
{"id": "2505.08242", "title": "Congenital Heart Disease recognition using Deep Learning/Transformer models", "abstract": "Congenital Heart Disease (CHD) remains a leading cause of infant morbidity and mortality, yet non-invasive screening methods often yield false negatives. Deep learning models, with their ability to automatically extract features, can assist doctors in detecting CHD more effectively. In this work, we investigate the use of dual-modality (sound and image) deep learning methods for CHD diagnosis. We achieve 73.9% accuracy on the ZCHSound dataset and 80.72% accuracy on the DICOM Chest X-ray dataset.", "date": "2025-05-13", "categories": []}
{"id": "2505.08508", "title": "TrialMatchAI: An End-to-End AI-powered Clinical Trial Recommendation System to Streamline Patient-to-Trial Matching", "abstract": "Patient recruitment remains a major bottleneck in clinical trials, calling for scalable and automated solutions. We present TrialMatchAI, an AI-powered recommendation system that automates patient-to-trial matching by processing heterogeneous clinical data, including structured records and unstructured physician notes. Built on fine-tuned, open-source large language models (LLMs) within a retrieval-augmented generation framework, TrialMatchAI ensures transparency and reproducibility and maintains a lightweight deployment footprint suitable for clinical environments. The system normalizes biomedical entities, retrieves relevant trials using a hybrid search strategy combining lexical and semantic similarity, re-ranks results, and performs criterion-level eligibility assessments using medical Chain-of-Thought reasoning. This pipeline delivers explainable outputs with traceable decision rationales. In real-world validation, 92 percent of oncology patients had at least one relevant trial retrieved within the top 20 recommendations. Evaluation across synthetic and real clinical datasets confirmed state-of-the-art performance, with expert assessment validating over 90 percent accuracy in criterion-level eligibility classification, particularly excelling in biomarker-driven matches. Designed for modularity and privacy, TrialMatchAI supports Phenopackets-standardized data, enables secure local deployment, and allows seamless replacement of LLM components as more advanced models emerge. By enhancing efficiency and interpretability and offering lightweight, open-source deployment, TrialMatchAI provides a scalable solution for AI-driven clinical trial matching in precision medicine.", "date": "2025-05-13", "categories": []}
{"id": "2505.08628", "title": "Integrating Natural Language Processing and Exercise Monitoring for Early Diagnosis of Metabolic Syndrome: A Deep Learning Approach", "abstract": "Metabolic syndrome (MetS) is a medication condition characterized by abdominal obesity, insulin resistance, hypertension and hyperlipidemia. It increases the risk of majority of chronic diseases, including type 2 diabetes mellitus, and affects about one quarter of the global population. Therefore, early detection and timely intervention for MetS are crucial. Standard diagnosis for MetS components requires blood tests conducted within medical institutions. However, it is frequently underestimated, leading to unmet need for care for MetS population. This study aims to use the least physiological data and free texts about exercises related activities, which are obtained easily in daily life, to diagnosis MetS. We collected the data from 40 volunteers in a nursing home and used data augmentation to reduce the imbalance. We propose a deep learning framework for classifying MetS that integrates natural language processing (NLP) and exercise monitoring. The results showed that the best model reported a high positive result (AUROC=0.806 and REC=76.3%) through 3-fold cross-validation. Feature importance analysis revealed that text and minimum heart rate on a daily basis contribute the most in the classification of MetS. This study demonstrates the potential application of data that are easily measurable in daily life for the early diagnosis of MetS, which could contribute to reducing the cost of screening and management for MetS population.", "date": "2025-05-13", "categories": []}
{"id": "2407.18227", "title": "Automated Ensemble Multimodal Machine Learning for Healthcare", "abstract": "The application of machine learning in medicine and healthcare has led to the creation of numerous diagnostic and prognostic models. However, despite their success, current approaches generally issue predictions using data from a single modality. This stands in stark contrast with clinician decision-making which employs diverse information from multiple sources. While several multimodal machine learning approaches exist, significant challenges in developing multimodal systems remain that are hindering clinical adoption. In this paper, we introduce a multimodal framework, AutoPrognosis-M, that enables the integration of structured clinical (tabular) data and medical imaging using automated machine learning. AutoPrognosis-M incorporates 17 imaging models, including convolutional neural networks and vision transformers, and three distinct multimodal fusion strategies. In an illustrative application using a multimodal skin lesion dataset, we highlight the importance of multimodal machine learning and the power of combining multiple fusion strategies using ensemble learning. We have open-sourced our framework as a tool for the community and hope it will accelerate the uptake of multimodal machine learning in healthcare and spur further innovation.", "date": "2024-07-25", "categories": []}
{"id": "2505.06264", "title": "Prediction of Delirium Risk in Mild Cognitive Impairment Using Time-Series data, Machine Learning and Comorbidity Patterns -- A Retrospective Study", "abstract": "Delirium represents a significant clinical concern characterized by high morbidity and mortality rates, particularly in patients with mild cognitive impairment (MCI). This study investigates the associated risk factors for delirium by analyzing the comorbidity patterns relevant to MCI and developing a longitudinal predictive model leveraging machine learning methodologies. A retrospective analysis utilizing the MIMIC-IV v2.2 database was performed to evaluate comorbid conditions, survival probabilities, and predictive modeling outcomes. The examination of comorbidity patterns identified distinct risk profiles for the MCI population. Kaplan-Meier survival analysis demonstrated that individuals with MCI exhibit markedly reduced survival probabilities when developing delirium compared to their non-MCI counterparts, underscoring the heightened vulnerability within this cohort. For predictive modeling, a Long Short-Term Memory (LSTM) ML network was implemented utilizing time-series data, demographic variables, Charlson Comorbidity Index (CCI) scores, and an array of comorbid conditions. The model demonstrated robust predictive capabilities with an AUROC of 0.93 and an AUPRC of 0.92. This study underscores the critical role of comorbidities in evaluating delirium risk and highlights the efficacy of time-series predictive modeling in pinpointing patients at elevated risk for delirium development.", "date": "2025-05-05", "categories": []}
{"id": "2505.06367", "title": "CAST: Time-Varying Treatment Effects with Application to Chemotherapy and Radiotherapy on Head and Neck Squamous Cell Carcinoma", "abstract": "Causal machine learning (CML) enables individualized estimation of treatment effects, offering critical advantages over traditional correlation-based methods. However, existing approaches for medical survival data with censoring such as causal survival forests estimate effects at fixed time points, limiting their ability to capture dynamic changes over time. We introduce Causal Analysis for Survival Trajectories (CAST), a novel framework that models treatment effects as continuous functions of time following treatment. By combining parametric and non-parametric methods, CAST overcomes the limitations of discrete time-point analysis to estimate continuous effect trajectories. Using the RADCURE dataset [1] of 2,651 patients with head and neck squamous cell carcinoma (HNSCC) as a clinically relevant example, CAST models how chemotherapy and radiotherapy effects evolve over time at the population and individual levels. By capturing the temporal dynamics of treatment response, CAST reveals how treatment effects rise, peak, and decline over the follow-up period, helping clinicians determine when and for whom treatment benefits are maximized. This framework advances the application of CML to personalized care in HNSCC and other life-threatening medical conditions. Source code/data available at: https://github.com/CAST-FW/HNSCC", "date": "2025-05-09", "categories": []}
{"id": "2505.06898", "title": "Multi-Modal Explainable Medical AI Assistant for Trustworthy Human-AI Collaboration", "abstract": "Generalist Medical AI (GMAI) systems have demonstrated expert-level performance in biomedical perception tasks, yet their clinical utility remains limited by inadequate multi-modal explainability and suboptimal prognostic capabilities. Here, we present XMedGPT, a clinician-centric, multi-modal AI assistant that integrates textual and visual interpretability to support transparent and trustworthy medical decision-making. XMedGPT not only produces accurate diagnostic and descriptive outputs, but also grounds referenced anatomical sites within medical images, bridging critical gaps in interpretability and enhancing clinician usability. To support real-world deployment, we introduce a reliability indexing mechanism that quantifies uncertainty through consistency-based assessment via interactive question-answering. We validate XMedGPT across four pillars: multi-modal interpretability, uncertainty quantification, and prognostic modeling, and rigorous benchmarking. The model achieves an IoU of 0.703 across 141 anatomical regions, and a Kendall's tau-b of 0.479, demonstrating strong alignment between visual rationales and clinical outcomes. For uncertainty estimation, it attains an AUC of 0.862 on visual question answering and 0.764 on radiology report generation. In survival and recurrence prediction for lung and glioma cancers, it surpasses prior leading models by 26.9%, and outperforms GPT-4o by 25.0%. Rigorous benchmarking across 347 datasets covers 40 imaging modalities and external validation spans 4 anatomical systems confirming exceptional generalizability, with performance gains surpassing existing GMAI by 20.7% for in-domain evaluation and 16.7% on 11,530 in-house data evaluation. Together, XMedGPT represents a significant leap forward in clinician-centric AI integration, offering trustworthy and scalable support for diverse healthcare applications.", "date": "2025-05-11", "categories": []}
{"id": "2505.07036", "title": "Predicting Diabetes Using Machine Learning: A Comparative Study of Classifiers", "abstract": "Diabetes remains a significant health challenge globally, contributing to severe complications like kidney disease, vision loss, and heart issues. The application of machine learning (ML) in healthcare enables efficient and accurate disease prediction, offering avenues for early intervention and patient support. Our study introduces an innovative diabetes prediction framework, leveraging both traditional ML techniques such as Logistic Regression, SVM, Na\\\"ive Bayes, and Random Forest and advanced ensemble methods like AdaBoost, Gradient Boosting, Extra Trees, and XGBoost. Central to our approach is the development of a novel model, DNet, a hybrid architecture combining Convolutional Neural Network (CNN) and Long Short-Term Memory (LSTM) layers for effective feature extraction and sequential learning. The DNet model comprises an initial convolutional block for capturing essential features, followed by a residual block with skip connections to facilitate efficient information flow. Batch Normalization and Dropout are employed for robust regularization, and an LSTM layer captures temporal dependencies within the data. Using a Kaggle-sourced real-world diabetes dataset, our model evaluation spans cross-validation accuracy, precision, recall, F1 score, and ROC-AUC. Among the models, DNet demonstrates the highest efficacy with an accuracy of 99.79% and an AUC-ROC of 99.98%, establishing its potential for superior diabetes prediction. This robust hybrid architecture showcases the value of combining CNN and LSTM layers, emphasizing its applicability in medical diagnostics and disease prediction tasks.", "date": "2025-05-11", "categories": []}
{"id": "2505.07347", "title": "AI-Enabled Accurate Non-Invasive Assessment of Pulmonary Hypertension Progression via Multi-Modal Echocardiography", "abstract": "Echocardiographers can detect pulmonary hypertension using Doppler echocardiography; however, accurately assessing its progression often proves challenging. Right heart catheterization (RHC), the gold standard for precise evaluation, is invasive and unsuitable for routine use, limiting its practicality for timely diagnosis and monitoring of pulmonary hypertension progression. Here, we propose MePH, a multi-view, multi-modal vision-language model to accurately assess pulmonary hypertension progression using non-invasive echocardiography. We constructed a large dataset comprising paired standardized echocardiogram videos, spectral images and RHC data, covering 1,237 patient cases from 12 medical centers. For the first time, MePH precisely models the correlation between non-invasive multi-view, multi-modal echocardiography and the pressure and resistance obtained via RHC. We show that MePH significantly outperforms echocardiographers' assessments using echocardiography, reducing the mean absolute error in estimating mean pulmonary arterial pressure (mPAP) and pulmonary vascular resistance (PVR) by 49.73% and 43.81%, respectively. In eight independent external hospitals, MePH achieved a mean absolute error of 3.147 for PVR assessment. Furthermore, MePH achieved an area under the curve of 0.921, surpassing echocardiographers (area under the curve of 0.842) in accurately predicting the severity of pulmonary hypertension, whether mild or severe. A prospective study demonstrated that MePH can predict treatment efficacy for patients. Our work provides pulmonary hypertension patients with a non-invasive and timely method for monitoring disease progression, improving the accuracy and efficiency of pulmonary hypertension management while enabling earlier interventions and more personalized treatment decisions.", "date": "2025-05-12", "categories": []}
{"id": "2505.07533", "title": "IKrNet: A Neural Network for Detecting Specific Drug-Induced Patterns in Electrocardiograms Amidst Physiological Variability", "abstract": "Monitoring and analyzing electrocardiogram (ECG) signals, even under varying physiological conditions, including those influenced by physical activity, drugs and stress, is crucial to accurately assess cardiac health. However, current AI-based methods often fail to account for how these factors interact and alter ECG patterns, ultimately limiting their applicability in real-world settings. This study introduces IKrNet, a novel neural network model, which identifies drug-specific patterns in ECGs amidst certain physiological conditions. IKrNet's architecture incorporates spatial and temporal dynamics by using a convolutional backbone with varying receptive field size to capture spatial features. A bi-directional Long Short-Term Memory module is also employed to model temporal dependencies. By treating heart rate variability as a surrogate for physiological fluctuations, we evaluated IKrNet's performance across diverse scenarios, including conditions with physical stress, drug intake alone, and a baseline without drug presence. Our assessment follows a clinical protocol in which 990 healthy volunteers were administered 80mg of Sotalol, a drug which is known to be a precursor to Torsades-de-Pointes, a life-threatening arrhythmia. We show that IKrNet outperforms state-of-the-art models' accuracy and stability in varying physiological conditions, underscoring its clinical viability.", "date": "2025-05-12", "categories": []}
{"id": "2505.07702", "title": "4TaStiC: Time and trend traveling time series clustering for classifying long-term type 2 diabetes patients", "abstract": "Diabetes is one of the most prevalent diseases worldwide, characterized by persistently high blood sugar levels, capable of damaging various internal organs and systems. Diabetes patients require routine check-ups, resulting in a time series of laboratory records, such as hemoglobin A1c, which reflects each patient's health behavior over time and informs their doctor's recommendations. Clustering patients into groups based on their entire time series data assists doctors in making recommendations and choosing treatments without the need to review all records. However, time series clustering of this type of dataset introduces some challenges; patients visit their doctors at different time points, making it difficult to capture and match trends, peaks, and patterns. Additionally, two aspects must be considered: differences in the levels of laboratory results and differences in trends and patterns. To address these challenges, we introduce a new clustering algorithm called Time and Trend Traveling Time Series Clustering (4TaStiC), using a base dissimilarity measure combined with Euclidean and Pearson correlation metrics. We evaluated this algorithm on artificial datasets, comparing its performance with that of seven existing methods. The results show that 4TaStiC outperformed the other methods on the targeted datasets. Finally, we applied 4TaStiC to cluster a cohort of 1,989 type 2 diabetes patients at Siriraj Hospital. Each group of patients exhibits clear characteristics that will benefit doctors in making efficient clinical decisions. Furthermore, the proposed algorithm can be applied to contexts outside the medical field.", "date": "2025-05-12", "categories": []}
{"id": "2504.16098", "title": "SeizureFormer: A Transformer Model for IEA-Based Seizure Risk Forecasting", "abstract": "We present SeizureFormer, a Transformer-based model for long-term seizure risk forecasting using interictal epileptiform activity (IEA) surrogate biomarkers and long episode (LE) biomarkers from responsive neurostimulation (RNS) systems. Unlike raw scalp EEG-based models, SeizureFormer leverages structured, clinically relevant features and integrates CNN-based patch embedding, multi-head self-attention, and squeeze-and-excitation blocks to model both short-term dynamics and long-term seizure cycles. Tested across five patients and multiple prediction windows (1 to 14 days), SeizureFormer achieved state-of-the-art performance with mean ROC AUC of 79.44 percent and mean PR AUC of 76.29 percent. Compared to statistical, machine learning, and deep learning baselines, it demonstrates enhanced generalizability and seizure risk forecasting performance under class imbalance. This work supports future clinical integration of interpretable and robust seizure forecasting tools for personalized epilepsy management.", "date": "2025-04-13", "categories": []}
{"id": "2505.05683", "title": "Interactive Diabetes Risk Prediction Using Explainable Machine Learning: A Dash-Based Approach with SHAP, LIME, and Comorbidity Insights", "abstract": "This study presents a web-based interactive health risk prediction tool designed to assess diabetes risk using machine learning models. Built on the 2015 CDC BRFSS dataset, the study evaluates models including Logistic Regression, Random Forest, XGBoost, LightGBM, KNN, and Neural Networks under original, SMOTE, and undersampling strategies. LightGBM with undersampling achieved the best recall, making it ideal for risk detection. The tool integrates SHAP and LIME to explain predictions and highlights comorbidity correlations using Pearson analysis. A Dash-based UI enables user-friendly interaction with model predictions, personalized suggestions, and feature insights, supporting data-driven health awareness.", "date": "2025-05-08", "categories": []}
{"id": "2505.05768", "title": "Predicting Diabetic Macular Edema Treatment Responses Using OCT: Dataset and Methods of APTOS Competition", "abstract": "Diabetic macular edema (DME) significantly contributes to visual impairment in diabetic patients. Treatment responses to intravitreal therapies vary, highlighting the need for patient stratification to predict therapeutic benefits and enable personalized strategies. To our knowledge, this study is the first to explore pre-treatment stratification for predicting DME treatment responses. To advance this research, we organized the 2nd Asia-Pacific Tele-Ophthalmology Society (APTOS) Big Data Competition in 2021. The competition focused on improving predictive accuracy for anti-VEGF therapy responses using ophthalmic OCT images. We provided a dataset containing tens of thousands of OCT images from 2,000 patients with labels across four sub-tasks. This paper details the competition's structure, dataset, leading methods, and evaluation metrics. The competition attracted strong scientific community participation, with 170 teams initially registering and 41 reaching the final round. The top-performing team achieved an AUC of 80.06%, highlighting the potential of AI in personalized DME treatment and clinical decision-making.", "date": "2025-05-09", "categories": []}
{"id": "2209.04793", "title": "Temporal Pattern Mining for Analysis of Longitudinal Clinical Data:\n  Identifying Risk Factors for Alzheimer's Disease", "abstract": "  A novel framework is proposed for handling the complex task of modelling and\nanalysis of longitudinal, multivariate, heterogeneous clinical data. This\nmethod uses temporal abstraction to convert the data into a more appropriate\nform for modelling, temporal pattern mining, to discover patterns in the\ncomplex, longitudinal data and machine learning models of survival analysis to\nselect the discovered patterns. The method is applied to a real-world study of\nAlzheimer's disease (AD), a progressive neurodegenerative disease that has no\ncure. The patterns discovered were predictive of AD in survival analysis models\nwith a Concordance index of up to 0.8. This is the first work that performs\nsurvival analysis of AD data using temporal data collections for AD. A\nvisualisation module also provides a clear picture of the discovered patterns\nfor ease of interpretability.\n", "date": "2022-09-11", "categories": []}
{"id": "2505.03787", "title": "ArrhythmiaVision: Resource-Conscious Deep Learning Models with Visual\n  Explanations for ECG Arrhythmia Classification", "abstract": "  Cardiac arrhythmias are a leading cause of life-threatening cardiac events,\nhighlighting the urgent need for accurate and timely detection.\nElectrocardiography (ECG) remains the clinical gold standard for arrhythmia\ndiagnosis; however, manual interpretation is time-consuming, dependent on\nclinical expertise, and prone to human error. Although deep learning has\nadvanced automated ECG analysis, many existing models abstract away the\nsignal's intrinsic temporal and morphological features, lack interpretability,\nand are computationally intensive-hindering their deployment on\nresource-constrained platforms. In this work, we propose two novel lightweight\n1D convolutional neural networks, ArrhythmiNet V1 and V2, optimized for\nefficient, real-time arrhythmia classification on edge devices. Inspired by\nMobileNet's depthwise separable convolutional design, these models maintain\nmemory footprints of just 302.18 KB and 157.76 KB, respectively, while\nachieving classification accuracies of 0.99 (V1) and 0.98 (V2) on the MIT-BIH\nArrhythmia Dataset across five classes: Normal Sinus Rhythm, Left Bundle Branch\nBlock, Right Bundle Branch Block, Atrial Premature Contraction, and Premature\nVentricular Contraction. In order to ensure clinical transparency and\nrelevance, we integrate Shapley Additive Explanations and Gradient-weighted\nClass Activation Mapping, enabling both local and global interpretability.\nThese techniques highlight physiologically meaningful patterns such as the QRS\ncomplex and T-wave that contribute to the model's predictions. We also discuss\nperformance-efficiency trade-offs and address current limitations related to\ndataset diversity and generalizability. Overall, our findings demonstrate the\nfeasibility of combining interpretability, predictive accuracy, and\ncomputational efficiency in practical, wearable, and embedded ECG monitoring\nsystems.\n", "date": "2025-04-30", "categories": []}
{"id": "2505.04655", "title": "Integration of Large Language Models and Traditional Deep Learning for\n  Social Determinants of Health Prediction", "abstract": "  Social Determinants of Health (SDoH) are economic, social and personal\ncircumstances that affect or influence an individual's health status. SDoHs\nhave shown to be correlated to wellness outcomes, and therefore, are useful to\nphysicians in diagnosing diseases and in decision-making. In this work, we\nautomatically extract SDoHs from clinical text using traditional deep learning\nand Large Language Models (LLMs) to find the advantages and disadvantages of\neach on an existing publicly available dataset. Our models outperform a\nprevious reference point on a multilabel SDoH classification by 10 points, and\nwe present a method and model to drastically speed up classification (12X\nexecution time) by eliminating expensive LLM processing. The method we present\ncombines a more nimble and efficient solution that leverages the power of the\nLLM for precision and traditional deep learning methods for efficiency. We also\nshow highly performant results on a dataset supplemented with synthetic data\nand several traditional deep learning models that outperform LLMs. Our models\nand methods offer the next iteration of automatic prediction of SDoHs that\nimpact at-risk patients.\n", "date": "2025-05-06", "categories": []}
{"id": "2505.03845", "title": "A Deep Learning approach for Depressive Symptoms assessment in\n  Parkinson's disease patients using facial videos", "abstract": "  Parkinson's disease (PD) is a neurodegenerative disorder, manifesting with\nmotor and non-motor symptoms. Depressive symptoms are prevalent in PD,\naffecting up to 45% of patients. They are often underdiagnosed due to\noverlapping motor features, such as hypomimia. This study explores deep\nlearning (DL) models-ViViT, Video Swin Tiny, and 3D CNN-LSTM with attention\nlayers-to assess the presence and severity of depressive symptoms, as detected\nby the Geriatric Depression Scale (GDS), in PD patients through facial video\nanalysis. The same parameters were assessed in a secondary analysis taking into\naccount whether patients were one hour after (ON-medication state) or 12 hours\nwithout (OFF-medication state) dopaminergic medication. Using a dataset of\n1,875 videos from 178 patients, the Video Swin Tiny model achieved the highest\nperformance, with up to 94% accuracy and 93.7% F1-score in binary\nclassification (presence of absence of depressive symptoms), and 87.1% accuracy\nwith an 85.4% F1-score in multiclass tasks (absence or mild or severe\ndepressive symptoms).\n", "date": "2025-05-05", "categories": []}
{"id": "2407.19894", "title": "CardioSyntax: end-to-end SYNTAX score prediction -- dataset, benchmark\n  and method", "abstract": "  The SYNTAX score has become a widely used measure of coronary disease\nseverity, crucial in selecting the optimal mode of the revascularization\nprocedure. This paper introduces a new medical regression and classification\nproblem - automatically estimating SYNTAX score from coronary angiography. Our\nstudy presents a comprehensive CardioSYNTAX dataset of 3,018 patients for the\nSYNTAX score estimation and coronary dominance classification. The dataset\nfeatures a balanced distribution of individuals with zero and non-zero scores.\nThis dataset includes a first-of-its-kind, complete coronary angiography\nsamples captured through a multi-view X-ray video, allowing one to observe\ncoronary arteries from multiple perspectives. Furthermore, we present a novel,\nfully automatic end-to-end method for estimating the SYNTAX. For such a\ndifficult task, we have achieved a solid coefficient of determination R2 of\n0.51 in score value prediction and 77.3% accuracy for zero score\nclassification.\n", "date": "2024-07-29", "categories": []}
{"id": "2505.02874", "title": "Uncertainty Quantification for Machine Learning in Healthcare: A Survey", "abstract": "  Uncertainty Quantification (UQ) is pivotal in enhancing the robustness,\nreliability, and interpretability of Machine Learning (ML) systems for\nhealthcare, optimizing resources and improving patient care. Despite the\nemergence of ML-based clinical decision support tools, the lack of principled\nquantification of uncertainty in ML models remains a major challenge. Current\nreviews have a narrow focus on analyzing the state-of-the-art UQ in specific\nhealthcare domains without systematically evaluating method efficacy across\ndifferent stages of model development, and despite a growing body of research,\nits implementation in healthcare applications remains limited. Therefore, in\nthis survey, we provide a comprehensive analysis of current UQ in healthcare,\noffering an informed framework that highlights how different methods can be\nintegrated into each stage of the ML pipeline including data processing,\ntraining and evaluation. We also highlight the most popular methods used in\nhealthcare and novel approaches from other domains that hold potential for\nfuture adoption in the medical context. We expect this study will provide a\nclear overview of the challenges and opportunities of implementing UQ in the ML\npipeline for healthcare, guiding researchers and practitioners in selecting\nsuitable techniques to enhance the reliability, safety and trust from patients\nand clinicians on ML-driven healthcare solutions.\n", "date": "2025-05-04", "categories": []}
{"id": "2505.02889", "title": "Early Prediction of Sepsis: Feature-Aligned Transfer Learning", "abstract": "  Sepsis is a life threatening medical condition that occurs when the body has\nan extreme response to infection, leading to widespread inflammation, organ\nfailure, and potentially death. Because sepsis can worsen rapidly, early\ndetection is critical to saving lives. However, current diagnostic methods\noften identify sepsis only after significant damage has already occurred. Our\nproject aims to address this challenge by developing a machine learning based\nsystem to predict sepsis in its early stages, giving healthcare providers more\ntime to intervene.\n  A major problem with existing models is the wide variability in the patient\ninformation or features they use, such as heart rate, temperature, and lab\nresults. This inconsistency makes models difficult to compare and limits their\nability to work across different hospitals and settings. To solve this, we\npropose a method called Feature Aligned Transfer Learning (FATL), which\nidentifies and focuses on the most important and commonly reported features\nacross multiple studies, ensuring the model remains consistent and clinically\nrelevant.\n  Most existing models are trained on narrow patient groups, leading to\npopulation bias. FATL addresses this by combining knowledge from models trained\non diverse populations, using a weighted approach that reflects each models\ncontribution. This makes the system more generalizable and effective across\ndifferent patient demographics and clinical environments. FATL offers a\npractical and scalable solution for early sepsis detection, particularly in\nhospitals with limited resources, and has the potential to improve patient\noutcomes, reduce healthcare costs, and support more equitable healthcare\ndelivery.\n", "date": "2025-05-05", "categories": []}
{"id": "2410.01859", "title": "Enhancing End Stage Renal Disease Outcome Prediction: A Multi-Sourced\n  Data-Driven Approach", "abstract": "  Objective: To improve prediction of Chronic Kidney Disease (CKD) progression\nto End Stage Renal Disease (ESRD) using machine learning (ML) and deep learning\n(DL) models applied to an integrated clinical and claims dataset of varying\nobservation windows, supported by explainable AI (XAI) to enhance\ninterpretability and reduce bias.\n  Materials and Methods: We utilized data about 10,326 CKD patients, combining\ntheir clinical and claims information from 2009 to 2018. Following data\npreprocessing, cohort identification, and feature engineering, we evaluated\nmultiple statistical, ML and DL models using data extracted from five distinct\nobservation windows. Feature importance and Shapley value analysis were\nemployed to understand key predictors. Models were tested for robustness,\nclinical relevance, misclassification errors and bias issues.\n  Results: Integrated data models outperformed those using single data sources,\nwith the Long Short-Term Memory (LSTM) model achieving the highest AUC (0.93)\nand F1 score (0.65). A 24-month observation window was identified as optimal\nfor balancing early detection and prediction accuracy. The 2021 eGFR equation\nimproved prediction accuracy and reduced racial bias, notably for African\nAmerican patients. Discussion: Improved ESRD prediction accuracy, results\ninterpretability and bias mitigation strategies presented in this study have\nthe potential to significantly enhance CKD and ESRD management, support\ntargeted early interventions and reduce healthcare disparities.\n  Conclusion: This study presents a robust framework for predicting ESRD\noutcomes in CKD patients, improving clinical decision-making and patient care\nthrough multi-sourced, integrated data and AI/ML methods. Future research will\nexpand data integration and explore the application of this framework to other\nchronic diseases.\n", "date": "2024-10-02", "categories": []}
{"id": "2410.10907", "title": "An Explainable AI Model for Predicting the Recurrence of Differentiated\n  Thyroid Cancer", "abstract": "  Thyroid carcinoma, a significant yet often controllable cancer, has seen a\nrise in cases, largely due to advancements in diagnostic methods.\nDifferentiated thyroid cancer (DTC), which includes papillary and follicular\nvarieties, is typically associated with a positive prognosis in academic\ncircles. Nevertheless, there are still some individuals who may experience a\nrecurrence. This study employs machine learning, particularly deep learning\nmodels, to predict the recurrence of DTC, with the goal of improving patient\ncare through personalized treatment approaches. By analysing a dataset\ncontaining clinicopathological features of patients, the model achieved\nremarkable accuracy rates of 98% during training and 96% during testing. To\nimprove the model's interpretability, we used techniques like LIME and Morris\nSensitivity Analysis. These methods gave us valuable insights into how the\nmodel makes decisions. The results suggest that combining deep learning models\nwith interpretability techniques can be extremely useful in quickly identifying\nthe recurrence of thyroid cancer in patients. This can help in making informed\ntherapeutic choices and customizing treatment approaches for individual\npatients.\n", "date": "2024-10-13", "categories": []}
{"id": "2410.21512", "title": "Diagnosis of Knee Osteoarthritis Using Bioimpedance and Deep Learning", "abstract": "  Diagnosing knee osteoarthritis (OA) early is crucial for managing symptoms\nand preventing further joint damage, ultimately improving patient outcomes and\nquality of life. In this paper, a bioimpedance-based diagnostic tool that\ncombines precise hardware and deep learning for effective non-invasive\ndiagnosis is proposed. system features a relay-based circuit and strategically\nplaced electrodes to capture comprehensive bioimpedance data. The data is\nprocessed by a neural network model, which has been optimized using\nconvolutional layers, dropout regularization, and the Adam optimizer. This\napproach achieves a 98% test accuracy, making it a promising tool for detecting\nknee osteoarthritis musculoskeletal disorders.\n", "date": "2024-10-28", "categories": []}
{"id": "2505.01429", "title": "Explainable AI-Driven Detection of Human Monkeypox Using Deep Learning\n  and Vision Transformers: A Comprehensive Analysis", "abstract": "  Since mpox can spread from person to person, it is a zoonotic viral illness\nthat poses a significant public health concern. It is difficult to make an\nearly clinical diagnosis because of how closely its symptoms match those of\nmeasles and chickenpox. Medical imaging combined with deep learning (DL)\ntechniques has shown promise in improving disease detection by analyzing\naffected skin areas. Our study explore the feasibility to train deep learning\nand vision transformer-based models from scratch with publicly available skin\nlesion image dataset. Our experimental results show dataset limitation as a\nmajor drawback to build better classifier models trained from scratch. We used\ntransfer learning with the help of pre-trained models to get a better\nclassifier. The MobileNet-v2 outperformed other state of the art pre-trained\nmodels with 93.15% accuracy and 93.09% weighted average F1 score. ViT B16 and\nResNet-50 also achieved satisfactory performance compared to already available\nstudies with accuracy 92.12% and 86.21% respectively. To further validate the\nperformance of the models, we applied explainable AI techniques.\n", "date": "2025-04-03", "categories": []}
{"id": "2505.02396", "title": "Diagnostic Uncertainty in Pneumonia Detection using CNN MobileNetV2 and\n  CNN from Scratch", "abstract": "  Pneumonia Diagnosis, though it is crucial for an effective treatment, it can\nbe hampered by uncertainty. This uncertainty starts to arise due to some\nfactors like atypical presentations, limitations of diagnostic tools such as\nchest X-rays, and the presence of co-existing respiratory conditions. This\nresearch proposes one of the supervised learning methods, CNN. Using\nMobileNetV2 as the pre-trained one with ResNet101V2 architecture and using\nKeras API as the built from scratch model, for identifying lung diseases\nespecially pneumonia. The datasets used in this research were obtained from the\nwebsite through Kaggle. The result shows that by implementing CNN MobileNetV2\nand CNN from scratch the result is promising. While validating data,\nMobileNetV2 performs with stability and minimal overfitting, while the training\naccuracy increased to 84.87% later it slightly decreased to 78.95%, with\nincreasing validation loss from 0.499 to 0.6345. Nonetheless, MobileNetV2 is\nmore stable. Although it takes more time to train each epoch. Meanwhile, after\nthe 10th epoch, the Scratch model displayed more instability and overfitting\ndespite having higher validation accuracy, training accuracy decreased\nsignificantly to 78.12% and the validation loss increased from 0.5698 to\n1.1809. With these results, ResNet101V2 offers stability, and the Scratch model\noffers high accuracy.\n", "date": "2025-05-05", "categories": []}
{"id": "2505.02722", "title": "Enhancing LLMs' Clinical Reasoning with Real-World Data from a\n  Nationwide Sepsis Registry", "abstract": "  Although large language models (LLMs) have demonstrated impressive reasoning\ncapabilities across general domains, their effectiveness in real-world clinical\npractice remains limited. This is likely due to their insufficient exposure to\nreal-world clinical data during training, as such data is typically not\nincluded due to privacy concerns. To address this, we propose enhancing the\nclinical reasoning capabilities of LLMs by leveraging real-world clinical data.\nWe constructed reasoning-intensive questions from a nationwide sepsis registry\nand fine-tuned Phi-4 on these questions using reinforcement learning, resulting\nin C-Reason. C-Reason exhibited strong clinical reasoning capabilities on the\nin-domain test set, as evidenced by both quantitative metrics and expert\nevaluations. Furthermore, its enhanced reasoning capabilities generalized to a\nsepsis dataset involving different tasks and patient cohorts, an open-ended\nconsultations on antibiotics use task, and other diseases. Future research\nshould focus on training LLMs with large-scale, multi-disease clinical datasets\nto develop more powerful, general-purpose clinical reasoning models.\n", "date": "2025-05-05", "categories": []}
{"id": "2405.11178", "title": "Automating PTSD Diagnostics in Clinical Interviews: Leveraging Large\n  Language Models for Trauma Assessments", "abstract": "  The shortage of clinical workforce presents significant challenges in mental\nhealthcare, limiting access to formal diagnostics and services. We aim to\ntackle this shortage by integrating a customized large language model (LLM)\ninto the workflow, thus promoting equity in mental healthcare for the general\npopulation. Although LLMs have showcased their capability in clinical\ndecision-making, their adaptation to severe conditions like Post-traumatic\nStress Disorder (PTSD) remains largely unexplored. Therefore, we collect 411\nclinician-administered diagnostic interviews and devise a novel approach to\nobtain high-quality data. Moreover, we build a comprehensive framework to\nautomate PTSD diagnostic assessments based on interview contents by leveraging\ntwo state-of-the-art LLMs, GPT-4 and Llama-2, with potential for broader\nclinical diagnoses. Our results illustrate strong promise for LLMs, tested on\nour dataset, to aid clinicians in diagnostic validation. To the best of our\nknowledge, this is the first AI system that fully automates assessments for\nmental illness based on clinician-administered interviews.\n", "date": "2024-05-18", "categories": []}
{"id": "2407.17856", "title": "Enhancing clinical decision support with physiological waveforms -- a\n  multimodal benchmark in emergency care", "abstract": "  Background: AI-driven prediction algorithms have the potential to enhance\nemergency medicine by enabling rapid and accurate decision-making regarding\npatient status and potential deterioration. However, the integration of\nmultimodal data, including raw waveform signals, remains underexplored in\nclinical decision support. Methods: We present a dataset and benchmarking\nprotocol designed to advance multimodal decision support in emergency care. Our\nmodels utilize demographics, biometrics, vital signs, laboratory values, and\nelectrocardiogram (ECG) waveforms as inputs to predict both discharge diagnoses\nand patient deterioration. Results: The diagnostic model achieves area under\nthe receiver operating curve (AUROC) scores above 0.8 for 609 out of 1,428\nconditions, covering both cardiac (e.g., myocardial infarction) and non-cardiac\n(e.g., renal disease, diabetes) diagnoses. The deterioration model attains\nAUROC scores above 0.8 for 14 out of 15 targets, accurately predicting critical\nevents such as cardiac arrest, mechanical ventilation, ICU admission, and\nmortality. Conclusions: Our study highlights the positive impact of\nincorporating raw waveform data into decision support models, improving\npredictive performance. By introducing a unique, publicly available dataset and\nbaseline models, we provide a foundation for measurable progress in AI-driven\ndecision support for emergency care.\n", "date": "2024-07-25", "categories": []}
{"id": "2505.00032", "title": "MDD-LLM: Towards Accuracy Large Language Models for Major Depressive\n  Disorder Diagnosis", "abstract": "  Major depressive disorder (MDD) impacts more than 300 million people\nworldwide, highlighting a significant public health issue. However, the uneven\ndistribution of medical resources and the complexity of diagnostic methods have\nresulted in inadequate attention to this disorder in numerous countries and\nregions. This paper introduces a high-performance MDD diagnosis tool named\nMDD-LLM, an AI-driven framework that utilizes fine-tuned large language models\n(LLMs) and extensive real-world samples to tackle challenges in MDD diagnosis.\nTherefore, we select 274,348 individual information from the UK Biobank cohort\nto train and evaluate the proposed method. Specifically, we select 274,348\nindividual records from the UK Biobank cohort and design a tabular data\ntransformation method to create a large corpus for training and evaluating the\nproposed approach. To illustrate the advantages of MDD-LLM, we perform\ncomprehensive experiments and provide several comparative analyses against\nexisting model-based solutions across multiple evaluation metrics. Experimental\nresults show that MDD-LLM (70B) achieves an accuracy of 0.8378 and an AUC of\n0.8919 (95% CI: 0.8799 - 0.9040), significantly outperforming existing machine\nlearning and deep learning frameworks for MDD diagnosis. Given the limited\nexploration of LLMs in MDD diagnosis, we examine numerous factors that may\ninfluence the performance of our proposed method, such as tabular data\ntransformation techniques and different fine-tuning strategies.\n", "date": "2025-04-28", "categories": []}
{"id": "2505.00525", "title": "A Methodological and Structural Review of Parkinsons Disease Detection\n  Across Diverse Data Modalities", "abstract": "  Parkinsons Disease (PD) is a progressive neurological disorder that primarily\naffects motor functions and can lead to mild cognitive impairment (MCI) and\ndementia in its advanced stages. With approximately 10 million people diagnosed\nglobally 1 to 1.8 per 1,000 individuals, according to reports by the Japan\nTimes and the Parkinson Foundation early and accurate diagnosis of PD is\ncrucial for improving patient outcomes. While numerous studies have utilized\nmachine learning (ML) and deep learning (DL) techniques for PD recognition,\nexisting surveys are limited in scope, often focusing on single data modalities\nand failing to capture the potential of multimodal approaches. To address these\ngaps, this study presents a comprehensive review of PD recognition systems\nacross diverse data modalities, including Magnetic Resonance Imaging (MRI),\ngait-based pose analysis, gait sensory data, handwriting analysis, speech test\ndata, Electroencephalography (EEG), and multimodal fusion techniques. Based on\nover 347 articles from leading scientific databases, this review examines key\naspects such as data collection methods, settings, feature representations, and\nsystem performance, with a focus on recognition accuracy and robustness. This\nsurvey aims to serve as a comprehensive resource for researchers, providing\nactionable guidance for the development of next generation PD recognition\nsystems. By leveraging diverse data modalities and cutting-edge machine\nlearning paradigms, this work contributes to advancing the state of PD\ndiagnostics and improving patient care through innovative, multimodal\napproaches.\n", "date": "2025-05-01", "categories": []}
{"id": "2412.16406", "title": "Learning Disease Progression Models That Capture Health Disparities", "abstract": "  Disease progression models are widely used to inform the diagnosis and\ntreatment of many progressive diseases. However, a significant limitation of\nexisting models is that they do not account for health disparities that can\nbias the observed data. To address this, we develop an interpretable Bayesian\ndisease progression model that captures three key health disparities: certain\npatient populations may (1) start receiving care only when their disease is\nmore severe, (2) experience faster disease progression even while receiving\ncare, or (3) receive follow-up care less frequently conditional on disease\nseverity. We show theoretically and empirically that failing to account for any\nof these disparities can result in biased estimates of severity (e.g.,\nunderestimating severity for disadvantaged groups). On a dataset of heart\nfailure patients, we show that our model can identify groups that face each\ntype of health disparity, and that accounting for these disparities while\ninferring disease severity meaningfully shifts which patients are considered\nhigh-risk.\n", "date": "2024-12-20", "categories": []}
{"id": "2502.21138", "title": "Predicting clinical outcomes from patient care pathways represented with\n  temporal knowledge graphs", "abstract": "  Background: With the increasing availability of healthcare data, predictive\nmodeling finds many applications in the biomedical domain, such as the\nevaluation of the level of risk for various conditions, which in turn can guide\nclinical decision making. However, it is unclear how knowledge graph data\nrepresentations and their embedding, which are competitive in some settings,\ncould be of interest in biomedical predictive modeling. Method: We simulated\nsynthetic but realistic data of patients with intracranial aneurysm and\nexperimented on the task of predicting their clinical outcome. We compared the\nperformance of various classification approaches on tabular data versus a\ngraph-based representation of the same data. Next, we investigated how the\nadopted schema for representing first individual data and second temporal data\nimpacts predictive performances. Results: Our study illustrates that in our\ncase, a graph representation and Graph Convolutional Network (GCN) embeddings\nreach the best performance for a predictive task from observational data. We\nemphasize the importance of the adopted schema and of the consideration of\nliteral values in the representation of individual data. Our study also\nmoderates the relative impact of various time encoding on GCN performance.\n", "date": "2025-02-28", "categories": []}
{"id": "2504.21189", "title": "Artificial Intelligence for Personalized Prediction of Alzheimer's\n  Disease Progression: A Survey of Methods, Data Challenges, and Future\n  Directions", "abstract": "  Alzheimer's Disease (AD) is marked by significant inter-individual\nvariability in its progression, complicating accurate prognosis and\npersonalized care planning. This heterogeneity underscores the critical need\nfor predictive models capable of forecasting patient-specific disease\ntrajectories. Artificial Intelligence (AI) offers powerful tools to address\nthis challenge by analyzing complex, multi-modal, and longitudinal patient\ndata. This paper provides a comprehensive survey of AI methodologies applied to\npersonalized AD progression prediction. We review key approaches including\nstate-space models for capturing temporal dynamics, deep learning techniques\nlike Recurrent Neural Networks for sequence modeling, Graph Neural Networks\n(GNNs) for leveraging network structures, and the emerging concept of AI-driven\ndigital twins for individualized simulation. Recognizing that data limitations\noften impede progress, we examine common challenges such as high\ndimensionality, missing data, and dataset imbalance. We further discuss\nAI-driven mitigation strategies, with a specific focus on synthetic data\ngeneration using Variational Autoencoders (VAEs) and Generative Adversarial\nNetworks (GANs) to augment and balance datasets. The survey synthesizes the\nstrengths and limitations of current approaches, emphasizing the trend towards\nmultimodal integration and the persistent need for model interpretability and\ngeneralizability. Finally, we identify critical open challenges, including\nrobust external validation, clinical integration, and ethical considerations,\nand outline promising future research directions such as hybrid models, causal\ninference, and federated learning. This review aims to consolidate current\nknowledge and guide future efforts in developing clinically relevant AI tools\nfor personalized AD prognostication.\n", "date": "2025-04-29", "categories": []}
{"id": "2407.03821", "title": "Seamless Monitoring of Stress Levels Leveraging a Universal Model for\n  Time Sequences", "abstract": "  Monitoring the stress level in patients with neurodegenerative diseases can\nhelp manage symptoms, improve patient's quality of life, and provide insight\ninto disease progression. In the literature, ECG, actigraphy, speech, voice,\nand facial analysis have proven effective at detecting patients' emotions. On\nthe other hand, these tools are invasive and do not integrate smoothly into the\npatient's daily life. HRV has also been proven to effectively indicate stress\nconditions, especially in combination with other signals. However, when HRV is\nderived from less invasive devices than the ECG, like wristbands and\nsmartwatches, the quality of measurements significantly degrades. This paper\npresents a methodology for stress detection from a wristband based on a\nuniversal model for time series, UniTS, which we finetuned for the task and\nequipped with explainability features. We cast the problem as anomaly detection\nrather than classification to favor model adaptation to individual patients and\nallow the clinician to maintain greater control over the system's predictions.\nWe demonstrate that our proposed model considerably surpasses 12 top-performing\nmethods on three benchmark datasets. Furthermore, unlike other state-of-the-art\nsystems, UniTS enables seamless monitoring, as it shows comparable performance\nwhen using signals from invasive or lightweight devices.\n", "date": "2024-07-04", "categories": []}
{"id": "2504.20368", "title": "AKIBoards: A Structure-Following Multiagent System for Predicting Acute\n  Kidney Injury", "abstract": "  Diagnostic reasoning entails a physician's local (mental) model based on an\nassumed or known shared perspective (global model) to explain patient\nobservations with evidence assigned towards a clinical assessment. But in\nseveral (complex) medical situations, multiple experts work together as a team\nto optimize health evaluation and decision-making by leveraging different\nperspectives. Such consensus-driven reasoning reflects individual knowledge\ncontributing toward a broader perspective on the patient. In this light, we\nintroduce STRUCture-following for Multiagent Systems (STRUC-MAS), a framework\nautomating the learning of these global models and their incorporation as prior\nbeliefs for agents in multiagent systems (MAS) to follow. We demonstrate proof\nof concept with a prosocial MAS application for predicting acute kidney\ninjuries (AKIs). In this case, we found that incorporating a global structure\nenabled multiple agents to achieve better performance (average precision, AP)\nin predicting AKI 48 hours before onset (structure-following-fine-tuned, SF-FT,\nAP=0.195; SF-FT-retrieval-augmented generation, SF-FT-RAG, AP=0.194) vs.\nbaseline (non-structure-following-FT, NSF-FT, AP=0.141; NSF-FT-RAG, AP=0.180)\nfor balanced precision-weighted-recall-weighted voting. Markedly, SF-FT agents\nwith higher recall scores reported lower confidence levels in the initial round\non true positive and false negative cases. But after explicit interactions,\ntheir confidence in their decisions increased (suggesting reinforced belief).\nIn contrast, the SF-FT agent with the lowest recall decreased its confidence in\ntrue positive and false negative cases (suggesting a new belief). This approach\nsuggests that learning and leveraging global structures in MAS is necessary\nprior to achieving competitive classification and diagnostic reasoning\nperformance.\n", "date": "2025-04-29", "categories": []}
{"id": "2504.20547", "title": "Revisiting the MIMIC-IV Benchmark: Experiments Using Language Models for\n  Electronic Health Records", "abstract": "  The lack of standardized evaluation benchmarks in the medical domain for text\ninputs can be a barrier to widely adopting and leveraging the potential of\nnatural language models for health-related downstream tasks. This paper\nrevisited an openly available MIMIC-IV benchmark for electronic health records\n(EHRs) to address this issue. First, we integrate the MIMIC-IV data within the\nHugging Face datasets library to allow an easy share and use of this\ncollection. Second, we investigate the application of templates to convert EHR\ntabular data to text. Experiments using fine-tuned and zero-shot LLMs on the\nmortality of patients task show that fine-tuned text-based models are\ncompetitive against robust tabular classifiers. In contrast, zero-shot LLMs\nstruggle to leverage EHR representations. This study underlines the potential\nof text-based approaches in the medical field and highlights areas for further\nimprovement.\n", "date": "2025-04-29", "categories": []}
{"id": "2504.20635", "title": "Bridging the Generalisation Gap: Synthetic Data Generation for\n  Multi-Site Clinical Model Validation", "abstract": "  Ensuring the generalisability of clinical machine learning (ML) models across\ndiverse healthcare settings remains a significant challenge due to variability\nin patient demographics, disease prevalence, and institutional practices.\nExisting model evaluation approaches often rely on real-world datasets, which\nare limited in availability, embed confounding biases, and lack the flexibility\nneeded for systematic experimentation. Furthermore, while generative models aim\nfor statistical realism, they often lack transparency and explicit control over\nfactors driving distributional shifts. In this work, we propose a novel\nstructured synthetic data framework designed for the controlled benchmarking of\nmodel robustness, fairness, and generalisability. Unlike approaches focused\nsolely on mimicking observed data, our framework provides explicit control over\nthe data generating process, including site-specific prevalence variations,\nhierarchical subgroup effects, and structured feature interactions. This\nenables targeted investigation into how models respond to specific\ndistributional shifts and potential biases. Through controlled experiments, we\ndemonstrate the framework's ability to isolate the impact of site variations,\nsupport fairness-aware audits, and reveal generalisation failures, particularly\nhighlighting how model complexity interacts with site-specific effects. This\nwork contributes a reproducible, interpretable, and configurable tool designed\nto advance the reliable deployment of ML in clinical settings.\n", "date": "2025-04-29", "categories": []}
{"id": "2504.20915", "title": "Statistical and Predictive Analysis to Identify Risk Factors and Effects\n  of Post COVID-19 Syndrome", "abstract": "  Based on recent studies, some COVID-19 symptoms can persist for months after\ninfection, leading to what is termed long COVID. Factors such as vaccination\ntiming, patient characteristics, and symptoms during the acute phase of\ninfection may contribute to the prolonged effects and intensity of long COVID.\nEach patient, based on their unique combination of factors, develops a specific\nrisk or intensity of long COVID. In this work, we aim to achieve two\nobjectives: (1) conduct a statistical analysis to identify relationships\nbetween various factors and long COVID, and (2) perform predictive analysis of\nlong COVID intensity using these factors. We benchmark and interpret various\ndata-driven approaches, including linear models, random forests, gradient\nboosting, and neural networks, using data from the Lifelines COVID-19 cohort.\nOur results show that Neural Networks (NN) achieve the best performance in\nterms of MAPE, with predictions averaging 19\\% error. Additionally,\ninterpretability analysis reveals key factors such as loss of smell, headache,\nmuscle pain, and vaccination timing as significant predictors, while chronic\ndisease and gender are critical risk factors. These insights provide valuable\nguidance for understanding long COVID and developing targeted interventions.\n", "date": "2025-04-29", "categories": []}
{"id": "2307.12369", "title": "Early Prediction of Alzheimers Disease Leveraging Symptom Occurrences\n  from Longitudinal Electronic Health Records of US Military Veterans", "abstract": "  Early prediction of Alzheimer's disease (AD) is crucial for timely\nintervention and treatment. This study aims to use machine learning approaches\nto analyze longitudinal electronic health records (EHRs) of patients with AD\nand identify signs and symptoms that can predict AD onset earlier. We used a\ncase-control design with longitudinal EHRs from the U.S. Department of Veterans\nAffairs Veterans Health Administration (VHA) from 2004 to 2021. Cases were VHA\npatients with AD diagnosed after 1/1/2016 based on ICD-10-CM codes, matched 1:9\nwith controls by age, sex and clinical utilization with replacement. We used a\npanel of AD-related keywords and their occurrences over time in a patient's\nlongitudinal EHRs as predictors for AD prediction with four machine learning\nmodels. We performed subgroup analyses by age, sex, and race/ethnicity, and\nvalidated the model in a hold-out and \"unseen\" VHA stations group. Model\ndiscrimination, calibration, and other relevant metrics were reported for\npredictions up to ten years before ICD-based diagnosis. The study population\nincluded 16,701 cases and 39,097 matched controls. The average number of\nAD-related keywords (e.g., \"concentration\", \"speaking\") per year increased\nrapidly for cases as diagnosis approached, from around 10 to over 40, while\nremaining flat at 10 for controls. The best model achieved high discriminative\naccuracy (ROCAUC 0.997) for predictions using data from at least ten years\nbefore ICD-based diagnoses. The model was well-calibrated (Hosmer-Lemeshow\ngoodness-of-fit p-value = 0.99) and consistent across subgroups of age, sex and\nrace/ethnicity, except for patients younger than 65 (ROCAUC 0.746). Machine\nlearning models using AD-related keywords identified from EHR notes can predict\nfuture AD diagnoses, suggesting its potential use for identifying AD risk using\nEHR notes, offering an affordable way for early screening on large population.\n", "date": "2023-07-23", "categories": []}
{"id": "2408.08784", "title": "Multi-task Learning Approach for Intracranial Hemorrhage Prognosis", "abstract": "  Prognosis after intracranial hemorrhage (ICH) is influenced by a complex\ninterplay between imaging and tabular data. Rapid and reliable prognosis are\ncrucial for effective patient stratification and informed treatment\ndecision-making. In this study, we aim to enhance image-based prognosis by\nlearning a robust feature representation shared between prognosis and the\nclinical and demographic variables most highly correlated with it. Our approach\nmimics clinical decision-making by reinforcing the model to learn valuable\nprognostic data embedded in the image. We propose a 3D multi-task image model\nto predict prognosis, Glasgow Coma Scale and age, improving accuracy and\ninterpretability. Our method outperforms current state-of-the-art baseline\nimage models, and demonstrates superior performance in ICH prognosis compared\nto four board-certified neuroradiologists using only CT scans as input. We\nfurther validate our model with interpretability saliency maps. Code is\navailable at https://github.com/MiriamCobo/MultitaskLearning_ICH_Prognosis.git.\n", "date": "2024-08-16", "categories": []}
{"id": "2502.17049", "title": "TabulaTime: A Novel Multimodal Deep Learning Framework for Advancing\n  Acute Coronary Syndrome Prediction through Environmental and Clinical Data\n  Integration", "abstract": "  Acute Coronary Syndromes (ACS), including ST-segment elevation myocardial\ninfarctions (STEMI) and non-ST-segment elevation myocardial infarctions\n(NSTEMI), remain a leading cause of mortality worldwide. Traditional\ncardiovascular risk scores rely primarily on clinical data, often overlooking\nenvironmental influences like air pollution that significantly impact heart\nhealth. Moreover, integrating complex time-series environmental data with\nclinical records is challenging.\n  We introduce TabulaTime, a multimodal deep learning framework that enhances\nACS risk prediction by combining clinical risk factors with air pollution data.\nTabulaTime features three key innovations: First, it integrates time-series air\npollution data with clinical tabular data to improve prediction accuracy.\nSecond, its PatchRWKV module automatically extracts complex temporal patterns,\novercoming limitations of traditional feature engineering while maintaining\nlinear computational complexity. Third, attention mechanisms enhance\ninterpretability by revealing interactions between clinical and environmental\nfactors.\n  Experimental results show that TabulaTime improves prediction accuracy by\nover 20% compared to conventional models such as CatBoost, Random Forest, and\nLightGBM, with air pollution data alone contributing over a 10% improvement.\nFeature importance analysis identifies critical predictors including previous\nangina, systolic blood pressure, PM10, and NO2. Overall, TabulaTime bridges\nclinical and environmental insights, supporting personalized prevention\nstrategies and informing public health policies to mitigate ACS risk.\n", "date": "2025-02-24", "categories": []}
{"id": "2504.18593", "title": "Severity Classification of Chronic Obstructive Pulmonary Disease in\n  Intensive Care Units: A Semi-Supervised Approach Using MIMIC-III Dataset", "abstract": "  Chronic obstructive pulmonary disease (COPD) represents a significant global\nhealth burden, where precise severity assessment is particularly critical for\neffective clinical management in intensive care unit (ICU) settings. This study\nintroduces an innovative machine learning framework for COPD severity\nclassification utilizing the MIMIC-III critical care database, thereby\nexpanding the applications of artificial intelligence in critical care\nmedicine. Our research developed a robust classification model incorporating\nkey ICU parameters such as blood gas measurements and vital signs, while\nimplementing semi-supervised learning techniques to effectively utilize\nunlabeled data and enhance model performance. The random forest classifier\nemerged as particularly effective, demonstrating exceptional discriminative\ncapability with 92.51% accuracy and 0.98 ROC AUC in differentiating between\nmild-to-moderate and severe COPD cases. This machine learning approach provides\nclinicians with a practical, accurate, and efficient tool for rapid COPD\nseverity evaluation in ICU environments, with significant potential to improve\nboth clinical decision-making processes and patient outcomes. Future research\ndirections should prioritize external validation across diverse patient\npopulations and integration with clinical decision support systems to optimize\nCOPD management in critical care settings.\n", "date": "2025-04-24", "categories": []}
{"id": "2504.19357", "title": "MERA: Multimodal and Multiscale Self-Explanatory Model with Considerably\n  Reduced Annotation for Lung Nodule Diagnosis", "abstract": "  Lung cancer, a leading cause of cancer-related deaths globally, emphasises\nthe importance of early detection for better patient outcomes. Pulmonary\nnodules, often early indicators of lung cancer, necessitate accurate, timely\ndiagnosis. Despite Explainable Artificial Intelligence (XAI) advances, many\nexisting systems struggle providing clear, comprehensive explanations,\nespecially with limited labelled data. This study introduces MERA, a Multimodal\nand Multiscale self-Explanatory model designed for lung nodule diagnosis with\nconsiderably Reduced Annotation requirements. MERA integrates unsupervised and\nweakly supervised learning strategies (self-supervised learning techniques and\nVision Transformer architecture for unsupervised feature extraction) and a\nhierarchical prediction mechanism leveraging sparse annotations via\nsemi-supervised active learning in the learned latent space. MERA explains its\ndecisions on multiple levels: model-level global explanations via semantic\nlatent space clustering, instance-level case-based explanations showing similar\ninstances, local visual explanations via attention maps, and concept\nexplanations using critical nodule attributes. Evaluations on the public LIDC\ndataset show MERA's superior diagnostic accuracy and self-explainability. With\nonly 1% annotated samples, MERA achieves diagnostic accuracy comparable to or\nexceeding state-of-the-art methods requiring full annotation. The model's\ninherent design delivers comprehensive, robust, multilevel explanations aligned\nclosely with clinical practice, enhancing trustworthiness and transparency.\nDemonstrated viability of unsupervised and weakly supervised learning lowers\nthe barrier to deploying diagnostic AI in broader medical domains. Our complete\ncode is open-source available: https://github.com/diku-dk/credanno.\n", "date": "2025-04-27", "categories": []}
{"id": "2504.19755", "title": "Hybrid Approach Combining Ultrasound and Blood Test Analysis with a\n  Voting Classifier for Accurate Liver Fibrosis and Cirrhosis Assessment", "abstract": "  Liver cirrhosis is an insidious condition involving the substitution of\nnormal liver tissue with fibrous scar tissue and causing major health\ncomplications. The conventional method of diagnosis using liver biopsy is\ninvasive and, therefore, inconvenient for use in regular screening. In this\npaper,we present a hybrid model that combines machine learning techniques with\nclinical data and ultrasoundscans to improve liver fibrosis and cirrhosis\ndetection accuracy is presented. The model integrates fixed blood test\nprobabilities with deep learning model predictions (DenseNet-201) for\nultrasonic images. The combined hybrid model achieved an accuracy of 92.5%. The\nfindings establish the viability of the combined model in enhancing diagnosis\naccuracy and supporting early intervention in liver disease care.\n", "date": "2025-04-28", "categories": []}
{"id": "2504.17908", "title": "The use of Multi-domain Electroencephalogram Representations in the\n  building of Models based on Convolutional and Recurrent Neural Networks for\n  Epilepsy Detection", "abstract": "  Epilepsy, affecting approximately 50 million people globally, is\ncharacterized by abnormal brain activity and remains challenging to treat. The\ndiagnosis of epilepsy relies heavily on electroencephalogram (EEG) data, where\nspecialists manually analyze epileptiform patterns across pre-ictal, ictal,\npost-ictal, and interictal periods. However, the manual analysis of EEG signals\nis prone to variability between experts, emphasizing the need for automated\nsolutions. Although previous studies have explored preprocessing techniques and\nmachine learning approaches for seizure detection, there is a gap in\nunderstanding how the representation of EEG data (time, frequency, or\ntime-frequency domains) impacts the predictive performance of deep learning\nmodels. This work addresses this gap by systematically comparing deep neural\nnetworks trained on EEG data in these three domains. Through the use of\nstatistical tests, we identify the optimal data representation and model\narchitecture for epileptic seizure detection. The results demonstrate that\nfrequency-domain data achieves detection metrics exceeding 97\\%, providing a\nrobust foundation for more accurate and reliable seizure detection systems.\n", "date": "2025-04-24", "categories": []}
{"id": "2504.18128", "title": "Temporal Entailment Pretraining for Clinical Language Models over EHR\n  Data", "abstract": "  Clinical language models have achieved strong performance on downstream tasks\nby pretraining on domain specific corpora such as discharge summaries and\nmedical notes. However, most approaches treat the electronic health record as a\nstatic document, neglecting the temporally-evolving and causally entwined\nnature of patient trajectories. In this paper, we introduce a novel temporal\nentailment pretraining objective for language models in the clinical domain.\nOur method formulates EHR segments as temporally ordered sentence pairs and\ntrains the model to determine whether a later state is entailed by,\ncontradictory to, or neutral with respect to an earlier state. Through this\ntemporally structured pretraining task, models learn to perform latent clinical\nreasoning over time, improving their ability to generalize across forecasting\nand diagnosis tasks. We pretrain on a large corpus derived from MIMIC IV and\ndemonstrate state of the art results on temporal clinical QA, early warning\nprediction, and disease progression modeling.\n", "date": "2025-04-25", "categories": []}
{"id": "2504.18393", "title": "Machine Learning and Statistical Insights into Hospital Stay Durations:\n  The Italian EHR Case", "abstract": "  Length of hospital stay is a critical metric for assessing healthcare quality\nand optimizing hospital resource management. This study aims to identify\nfactors influencing LoS within the Italian healthcare context, using a dataset\nof hospitalization records from over 60 healthcare facilities in the Piedmont\nregion, spanning from 2020 to 2023. We explored a variety of features,\nincluding patient characteristics, comorbidities, admission details, and\nhospital-specific factors. Significant correlations were found between LoS and\nfeatures such as age group, comorbidity score, admission type, and the month of\nadmission. Machine learning models, specifically CatBoost and Random Forest,\nwere used to predict LoS. The highest R2 score, 0.49, was achieved with\nCatBoost, demonstrating good predictive performance.\n", "date": "2025-04-25", "categories": []}
{"id": "2409.01444", "title": "A causal viewpoint on prediction model performance under changes in\n  case-mix: discrimination and calibration respond differently for prognosis\n  and diagnosis predictions", "abstract": "  Prediction models need reliable predictive performance as they inform\nclinical decisions, aiding in diagnosis, prognosis, and treatment planning. The\npredictive performance of these models is typically assessed through\ndiscrimination and calibration. Changes in the distribution of the data impact\nmodel performance and there may be important changes between a model's current\napplication and when and where its performance was last evaluated. In\nhealth-care, a typical change is a shift in case-mix. For example, for\ncardiovascular risk management, a general practitioner sees a different mix of\npatients than a specialist in a tertiary hospital.\n  This work introduces a novel framework that differentiates the effects of\ncase-mix shifts on discrimination and calibration based on the causal direction\nof the prediction task. When prediction is in the causal direction (often the\ncase for prognosis predictions), calibration remains stable under case-mix\nshifts, while discrimination does not. Conversely, when predicting in the\nanti-causal direction (often with diagnosis predictions), discrimination\nremains stable, but calibration does not.\n  A simulation study and empirical validation using cardiovascular disease\nprediction models demonstrate the implications of this framework. The causal\ncase-mix framework provides insights for developing, evaluating and deploying\nprediction models across different clinical settings, emphasizing the\nimportance of understanding the causal structure of the prediction task.\n", "date": "2024-09-02", "categories": []}
{"id": "2504.08824", "title": "ColonScopeX: Leveraging Explainable Expert Systems with Multimodal Data\n  for Improved Early Diagnosis of Colorectal Cancer", "abstract": "  Colorectal cancer (CRC) ranks as the second leading cause of cancer-related\ndeaths and the third most prevalent malignant tumour worldwide. Early detection\nof CRC remains problematic due to its non-specific and often embarrassing\nsymptoms, which patients frequently overlook or hesitate to report to\nclinicians. Crucially, the stage at which CRC is diagnosed significantly\nimpacts survivability, with a survival rate of 80-95\\% for Stage I and a stark\ndecline to 10\\% for Stage IV. Unfortunately, in the UK, only 14.4\\% of cases\nare diagnosed at the earliest stage (Stage I).\n  In this study, we propose ColonScopeX, a machine learning framework utilizing\nexplainable AI (XAI) methodologies to enhance the early detection of CRC and\npre-cancerous lesions. Our approach employs a multimodal model that integrates\nsignals from blood sample measurements, processed using the Savitzky-Golay\nalgorithm for fingerprint smoothing, alongside comprehensive patient metadata,\nincluding medication history, comorbidities, age, weight, and BMI. By\nleveraging XAI techniques, we aim to render the model's decision-making process\ntransparent and interpretable, thereby fostering greater trust and\nunderstanding in its predictions. The proposed framework could be utilised as a\ntriage tool or a screening tool of the general population.\n  This research highlights the potential of combining diverse patient data\nsources and explainable machine learning to tackle critical challenges in\nmedical diagnostics.\n", "date": "2025-04-09", "categories": []}
{"id": "2504.17739", "title": "Interpretable Early Detection of Parkinson's Disease through Speech\n  Analysis", "abstract": "  Parkinson's disease is a progressive neurodegenerative disorder affecting\nmotor and non-motor functions, with speech impairments among its earliest\nsymptoms. Speech impairments offer a valuable diagnostic opportunity, with\nmachine learning advances providing promising tools for timely detection. In\nthis research, we propose a deep learning approach for early Parkinson's\ndisease detection from speech recordings, which also highlights the vocal\nsegments driving predictions to enhance interpretability. This approach seeks\nto associate predictive speech patterns with articulatory features, providing a\nbasis for interpreting underlying neuromuscular impairments. We evaluated our\napproach using the Italian Parkinson's Voice and Speech Database, containing\n831 audio recordings from 65 participants, including both healthy individuals\nand patients. Our approach showed competitive classification performance\ncompared to state-of-the-art methods, while providing enhanced interpretability\nby identifying key speech features influencing predictions.\n", "date": "2025-04-24", "categories": []}
{"id": "2502.13108", "title": "Clinical QA 2.0: Multi-Task Learning for Answer Extraction and\n  Categorization", "abstract": "  Clinical Question Answering (CQA) plays a crucial role in medical\ndecision-making, enabling physicians to extract relevant information from\nElectronic Medical Records (EMRs). While transformer-based models such as BERT,\nBioBERT, and ClinicalBERT have demonstrated state-of-the-art performance in\nCQA, existing models lack the ability to categorize extracted answers, which is\ncritical for structured retrieval, content filtering, and medical decision\nsupport.\n  To address this limitation, we introduce a Multi-Task Learning (MTL)\nframework that jointly trains CQA models for both answer extraction and medical\ncategorization. In addition to predicting answer spans, our model classifies\nresponses into five standardized medical categories: Diagnosis, Medication,\nSymptoms, Procedure, and Lab Reports. This categorization enables more\nstructured and interpretable outputs, making clinical QA models more useful in\nreal-world healthcare settings.\n  We evaluate our approach on emrQA, a large-scale dataset for medical question\nanswering. Results show that MTL improves F1-score by 2.2% compared to standard\nfine-tuning, while achieving 90.7% accuracy in answer categorization. These\nfindings suggest that MTL not only enhances CQA performance but also introduces\nan effective mechanism for categorization and structured medical information\nretrieval.\n", "date": "2025-02-18", "categories": []}
{"id": "2504.16101", "title": "xLSTM-ECG: Multi-label ECG Classification via Feature Fusion with xLSTM", "abstract": "  Cardiovascular diseases (CVDs) remain the leading cause of mortality\nworldwide, highlighting the critical need for efficient and accurate diagnostic\ntools. Electrocardiograms (ECGs) are indispensable in diagnosing various heart\nconditions; however, their manual interpretation is time-consuming and\nerror-prone. In this paper, we propose xLSTM-ECG, a novel approach that\nleverages an extended Long Short-Term Memory (xLSTM) network for multi-label\nclassification of ECG signals, using the PTB-XL dataset. To the best of our\nknowledge, this work represents the first design and application of xLSTM\nmodules specifically adapted for multi-label ECG classification. Our method\nemploys a Short-Time Fourier Transform (STFT) to convert time-series ECG\nwaveforms into the frequency domain, thereby enhancing feature extraction. The\nxLSTM architecture is specifically tailored to address the complexities of\n12-lead ECG recordings by capturing both local and global signal features.\nComprehensive experiments on the PTB-XL dataset reveal that our model achieves\nstrong multi-label classification performance, while additional tests on the\nGeorgia 12-Lead dataset underscore its robustness and efficiency. This approach\nsignificantly improves ECG classification accuracy, thereby advancing clinical\ndiagnostics and patient care. The code will be publicly available upon\nacceptance.\n", "date": "2025-04-14", "categories": []}
{"id": "2504.15312", "title": "M-TabNet: A Multi-Encoder Transformer Model for Predicting Neonatal\n  Birth Weight from Multimodal Data", "abstract": "  Birth weight (BW) is a key indicator of neonatal health, with low birth\nweight (LBW) linked to increased mortality and morbidity. Early prediction of\nBW enables timely interventions; however, current methods like ultrasonography\nhave limitations, including reduced accuracy before 20 weeks and operator\ndependent variability. Existing models often neglect nutritional and genetic\ninfluences, focusing mainly on physiological and lifestyle factors. This study\npresents an attention-based transformer model with a multi-encoder architecture\nfor early (less than 12 weeks of gestation) BW prediction. Our model\neffectively integrates diverse maternal data such as physiological, lifestyle,\nnutritional, and genetic, addressing limitations seen in prior attention-based\nmodels such as TabNet. The model achieves a Mean Absolute Error (MAE) of 122\ngrams and an R-squared value of 0.94, demonstrating high predictive accuracy\nand interoperability with our in-house private dataset. Independent validation\nconfirms generalizability (MAE: 105 grams, R-squared: 0.95) with the IEEE\nchildren dataset. To enhance clinical utility, predicted BW is classified into\nlow and normal categories, achieving a sensitivity of 97.55% and a specificity\nof 94.48%, facilitating early risk stratification. Model interpretability is\nreinforced through feature importance and SHAP analyses, highlighting\nsignificant influences of maternal age, tobacco exposure, and vitamin B12\nstatus, with genetic factors playing a secondary role. Our results emphasize\nthe potential of advanced deep-learning models to improve early BW prediction,\noffering clinicians a robust, interpretable, and personalized tool for\nidentifying pregnancies at risk and optimizing neonatal outcomes.\n", "date": "2025-04-20", "categories": []}
{"id": "2308.10781", "title": "Improving Clinical Decision Support through Interpretable Machine\n  Learning and Error Handling in Electronic Health Records", "abstract": "  The objective of this work is to develop an Electronic Medical Record (EMR)\ndata processing tool that confers clinical context to Machine Learning (ML)\nalgorithms for error handling, bias mitigation and interpretability. We present\nTrust-MAPS, an algorithm that translates clinical domain knowledge into\nhigh-dimensional, mixed-integer programming models that capture physiological\nand biological constraints on clinical measurements. EMR data is projected onto\nthis constrained space, effectively bringing outliers to fall within a\nphysiologically feasible range. We then compute the distance of each data point\nfrom the constrained space modeling healthy physiology to quantify deviation\nfrom the norm. These distances, termed \"trust-scores,\" are integrated into the\nfeature space for downstream ML applications. We demonstrate the utility of\nTrust-MAPS by training a binary classifier for early sepsis prediction on data\nfrom the 2019 PhysioNet Computing in Cardiology Challenge, using the XGBoost\nalgorithm and applying SMOTE for overcoming class-imbalance. The Trust-MAPS\nframework shows desirable behavior in handling potential errors and boosting\npredictive performance. We achieve an AUROC of 0.91 (0.89, 0.92 : 95% CI) for\npredicting sepsis 6 hours before onset - a marked 15% improvement over a\nbaseline model trained without Trust-MAPS. Trust-scores emerge as clinically\nmeaningful features that not only boost predictive performance for clinical\ndecision support tasks, but also lend interpretability to ML models. This work\nis the first to translate clinical domain knowledge into mathematical\nconstraints, model cross-vital dependencies, and identify aberrations in\nhigh-dimensional medical data. Our method allows for error handling in EMR, and\nconfers interpretability and superior predictive power to models trained for\nclinical decision support.\n", "date": "2023-08-21", "categories": []}
{"id": "2501.05970", "title": "A Brain Age Residual Biomarker (BARB): Leveraging MRI-Based Models to\n  Detect Latent Health Conditions in U.S. Veterans", "abstract": "  Age prediction using brain imaging, such as MRIs, has achieved promising\nresults, with several studies identifying the model's residual as a potential\nbiomarker for chronic disease states. In this study, we developed a brain age\npredictive model using a dataset of 1,220 U.S. veterans (18--80 years) and\nconvolutional neural networks (CNNs) trained on two-dimensional slices of axial\nT2-weighted fast spin-echo and T2-weighted fluid attenuated inversion recovery\nMRI images. The model, incorporating a degree-3 polynomial ensemble, achieved\nan $R^{2}$ of 0.816 on the testing set. Images were acquired at the level of\nthe anterior commissure and the frontal horns of the lateral ventricles.\nResidual analysis was performed to assess its potential as a biomarker for five\nICD-coded conditions: hypertension (HTN), diabetes mellitus (DM), mild\ntraumatic brain injury (mTBI), illicit substance abuse/dependence (SAD), and\nalcohol abuse/dependence (AAD). Residuals grouped by the number of ICD-coded\nconditions demonstrated different trends that were statistically significant\n($p = 0.002$), suggesting a relationship between disease states and predicted\nbrain age. This association was particularly pronounced in patients over 49\nyears, where negative residuals (indicating advanced brain aging) correlated\nwith the presence of multiple ICD codes. These findings support the potential\nof residuals as biomarkers for detecting latent health conditions.\n", "date": "2025-01-10", "categories": []}
{"id": "2501.15293", "title": "Deep Learning in Early Alzheimer's disease's Detection: A Comprehensive\n  Survey of Classification, Segmentation, and Feature Extraction Methods", "abstract": "  Alzheimers disease is a deadly neurological condition, impairing important\nmemory and brain functions. Alzheimers disease promotes brain shrinkage,\nultimately leading to dementia. Dementia diagnosis typically takes 2.8 to 4.4\nyears after the first clinical indication. Advancements in computing and\ninformation technology have led to many techniques of studying Alzheimers\ndisease. Early identification and therapy are crucial for preventing Alzheimers\ndisease, as early-onset dementia hits people before the age of 65, while\nlate-onset dementia occurs after this age. According to the 2015 World\nAlzheimers disease Report, there are 46.8 million individuals worldwide\nsuffering from dementia, with an anticipated 74.7 million more by 2030 and\n131.5 million by 2050. Deep Learning has outperformed conventional Machine\nLearning techniques by identifying intricate structures in high-dimensional\ndata. Convolutional Neural Network (CNN) and Recurrent Neural Network (RNN),\nhave achieved an accuracy of up to 96.0% for Alzheimers disease classification,\nand 84.2% for mild cognitive impairment (MCI) conversion prediction. There have\nbeen few literature surveys available on applying ML to predict dementia,\nlacking in congenital observations. However, this survey has focused on a\nspecific data channel for dementia detection. This study evaluated Deep\nLearning algorithms for early Alzheimers disease detection, using openly\naccessible datasets, feature segmentation, and classification methods. This\narticle also has identified research gaps and limits in detecting Alzheimers\ndisease, which can inform future research.\n", "date": "2025-01-25", "categories": []}
{"id": "2502.19625", "title": "Revealing Treatment Non-Adherence Bias in Clinical Machine Learning\n  Using Large Language Models", "abstract": "  Machine learning systems trained on electronic health records (EHRs)\nincreasingly guide treatment decisions, but their reliability depends on the\ncritical assumption that patients follow the prescribed treatments recorded in\nEHRs. Using EHR data from 3,623 hypertension patients, we investigate how\ntreatment non-adherence introduces implicit bias that can fundamentally distort\nboth causal inference and predictive modeling. By extracting patient adherence\ninformation from clinical notes using a large language model (LLM), we identify\n786 patients (21.7%) with medication non-adherence. We further uncover key\ndemographic and clinical factors associated with non-adherence, as well as\npatient-reported reasons including side effects and difficulties obtaining\nrefills. Our findings demonstrate that this implicit bias can not only reverse\nestimated treatment effects, but also degrade model performance by up to 5%\nwhile disproportionately affecting vulnerable populations by exacerbating\ndisparities in decision outcomes and model error rates. This highlights the\nimportance of accounting for treatment non-adherence in developing responsible\nand equitable clinical machine learning systems.\n", "date": "2025-02-26", "categories": []}
{"id": "2504.13974", "title": "Enhancing Stroke Diagnosis in the Brain Using a Weighted Deep Learning\n  Approach", "abstract": "  A brain stroke occurs when blood flow to a part of the brain is disrupted,\nleading to cell death. Traditional stroke diagnosis methods, such as CT scans\nand MRIs, are costly and time-consuming. This study proposes a weighted voting\nensemble (WVE) machine learning model that combines predictions from\nclassifiers like random forest, Deep Learning, and histogram-based gradient\nboosting to predict strokes more effectively. The model achieved 94.91%\naccuracy on a private dataset, enabling early risk assessment and prevention.\nFuture research could explore optimization techniques to further enhance\naccuracy.\n", "date": "2025-04-17", "categories": []}
{"id": "2504.13978", "title": "Association between nutritional factors, inflammatory biomarkers and\n  cancer types: an analysis of NHANES data using machine learning", "abstract": "  Background. Diet and inflammation are critical factors influencing cancer\nrisk. However, the combined impact of nutritional status and inflammatory\nbiomarkers on cancer status and type, using machine learning (ML), remains\nunderexplored.\n  Objectives. This study investigates the association between nutritional\nfactors, inflammatory biomarkers, and cancer status, and whether these\nrelationships differ across cancer types using National Health and Nutrition\nExamination Survey (NHANES) data.\n  Methods. We analyzed 24 macro- and micronutrients, C-reactive protein (CRP),\nand the advanced lung cancer inflammation index (ALI) in 26,409 NHANES\nparticipants (2,120 with cancer). Multivariable logistic regression assessed\nassociations with cancer prevalence. We also examined whether these features\ndiffered across the five most common cancer types. To evaluate predictive\nvalue, we applied three ML models - Logistic Regression, Random Forest, and\nXGBoost - on the full feature set.\n  Results. The cohort's mean age was 49.1 years; 34.7% were obese.\nComorbidities such as anemia and liver conditions, along with nutritional\nfactors like protein and several vitamins, were key predictors of cancer\nstatus. Among the models, Random Forest performed best, achieving an accuracy\nof 0.72.\n  Conclusions. Higher-quality nutritional intake and lower levels of\ninflammation may offer protective effects against cancer. These findings\nhighlight the potential of combining nutritional and inflammatory markers with\nML to inform cancer prevention strategies.\n", "date": "2025-04-18", "categories": []}
{"id": "2504.14469", "title": "A computational framework for longitudinal medication adherence\n  prediction in breast cancer survivors: A social cognitive theory based\n  approach", "abstract": "  Non-adherence to medications is a critical concern since nearly half of\npatients with chronic illnesses do not follow their prescribed medication\nregimens, leading to increased mortality, costs, and preventable human\ndistress. Amongst stage 0-3 breast cancer survivors, adherence to long-term\nadjuvant endocrine therapy (i.e., Tamoxifen and aromatase inhibitors) is\nassociated with a significant increase in recurrence-free survival. This work\naims to develop multi-scale models of medication adherence to understand the\nsignificance of different factors influencing adherence across varying time\nframes. We introduce a computational framework guided by Social Cognitive\nTheory for multi-scale (daily and weekly) modeling of longitudinal medication\nadherence. Our models employ both dynamic medication-taking patterns in the\nrecent past (dynamic factors) as well as less frequently changing factors\n(static factors) for adherence prediction. Additionally, we assess the\nsignificance of various factors in influencing adherence behavior across\ndifferent time scales. Our models outperform traditional machine learning\ncounterparts in both daily and weekly tasks in terms of both accuracy and\nspecificity. Daily models achieved an accuracy of 87.25%, and weekly models, an\naccuracy of 76.04%. Notably, dynamic past medication-taking patterns prove most\nvaluable for predicting daily adherence, while a combination of dynamic and\nstatic factors is significant for macro-level weekly adherence patterns.\n", "date": "2025-04-20", "categories": []}
{"id": "2504.13331", "title": "Wearable-Derived Behavioral and Physiological Biomarkers for Classifying\n  Unipolar and Bipolar Depression Severity", "abstract": "  Depression is a complex mental disorder characterized by a diverse range of\nobservable and measurable indicators that go beyond traditional subjective\nassessments. Recent research has increasingly focused on objective, passive,\nand continuous monitoring using wearable devices to gain more precise insights\ninto the physiological and behavioral aspects of depression. However, most\nexisting studies primarily distinguish between healthy and depressed\nindividuals, adopting a binary classification that fails to capture the\nheterogeneity of depressive disorders. In this study, we leverage wearable\ndevices to predict depression subtypes-specifically unipolar and bipolar\ndepression-aiming to identify distinctive biomarkers that could enhance\ndiagnostic precision and support personalized treatment strategies. To this\nend, we introduce the CALYPSO dataset, designed for non-invasive detection of\ndepression subtypes and symptomatology through physiological and behavioral\nsignals, including blood volume pulse, electrodermal activity, body\ntemperature, and three-axis acceleration. Additionally, we establish a\nbenchmark on the dataset using well-known features and standard machine\nlearning methods. Preliminary results indicate that features related to\nphysical activity, extracted from accelerometer data, are the most effective in\ndistinguishing between unipolar and bipolar depression, achieving an accuracy\nof $96.77\\%$. Temperature-based features also showed high discriminative power,\nreaching an accuracy of $93.55\\%$. These findings highlight the potential of\nphysiological and behavioral monitoring for improving the classification of\ndepressive subtypes, paving the way for more tailored clinical interventions.\n", "date": "2025-04-17", "categories": []}
{"id": "2504.12338", "title": "Paging Dr. GPT: Extracting Information from Clinical Notes to Enhance\n  Patient Predictions", "abstract": "  There is a long history of building predictive models in healthcare using\ntabular data from electronic medical records. However, these models fail to\nextract the information found in unstructured clinical notes, which document\ndiagnosis, treatment, progress, medications, and care plans. In this study, we\ninvestigate how answers generated by GPT-4o-mini (ChatGPT) to simple clinical\nquestions about patients, when given access to the patient's discharge summary,\ncan support patient-level mortality prediction. Using data from 14,011\nfirst-time admissions to the Coronary Care or Cardiovascular Intensive Care\nUnits in the MIMIC-IV Note dataset, we implement a transparent framework that\nuses GPT responses as input features in logistic regression models. Our\nfindings demonstrate that GPT-based models alone can outperform models trained\non standard tabular data, and that combining both sources of information yields\neven greater predictive power, increasing AUC by an average of 5.1 percentage\npoints and increasing positive predictive value by 29.9 percent for the\nhighest-risk decile. These results highlight the value of integrating large\nlanguage models (LLMs) into clinical prediction tasks and underscore the\nbroader potential for using LLMs in any domain where unstructured text data\nremains an underutilized resource.\n", "date": "2025-04-14", "categories": []}
{"id": "2504.12417", "title": "Interpretable AI-driven Guidelines for Type 2 Diabetes Treatment from\n  Observational Data", "abstract": "  Objective: Create precise, structured, data-backed guidelines for type 2\ndiabetes treatment progression, suitable for clinical adoption.\n  Research Design and Methods: Our training cohort was composed of patient\n(with type 2 diabetes) visits from Boston Medical Center (BMC) from 1998 to\n2014. We divide visits into 4 groups based on the patient's treatment regimen\nbefore the visit, and further divide them into subgroups based on the\nrecommended treatment during the visit. Since each subgroup has observational\ndata, which has confounding bias (sicker patients are prescribed more\naggressive treatments), we used machine learning and optimization to remove\nsome datapoints so that the remaining data resembles a randomized trial. On\neach subgroup, we train AI-backed tree-based models to prescribe treatment\nchanges. Once we train these tree models, we manually combine the models for\nevery group to create an end-to-end prescription pipeline for all patients in\nthat group. In this process, we prioritize stepping up to a more aggressive\ntreatment before considering less aggressive options. We tested this pipeline\non unseen data from BMC, and an external dataset from Hartford healthcare (type\n2 diabetes patient visits from January 2020 to May 2024).\n  Results: The median HbA1c reduction achieved by our pipelines is 0.26% more\nthan what the doctors achieved on the unseen BMC patients. For the Hartford\ncohort, our pipelines were better by 0.13%.\n  Conclusions: This precise, interpretable, and efficient AI-backed approach to\ntreatment progression in type 2 diabetes is predicted to outperform the current\npractice and can be deployed to improve patient outcomes.\n", "date": "2025-04-16", "categories": []}
{"id": "2504.12494", "title": "Accelerating Clinical NLP at Scale with a Hybrid Framework with Reduced\n  GPU Demands: A Case Study in Dementia Identification", "abstract": "  Clinical natural language processing (NLP) is increasingly in demand in both\nclinical research and operational practice. However, most of the\nstate-of-the-art solutions are transformers-based and require high\ncomputational resources, limiting their accessibility. We propose a hybrid NLP\nframework that integrates rule-based filtering, a Support Vector Machine (SVM)\nclassifier, and a BERT-based model to improve efficiency while maintaining\naccuracy. We applied this framework in a dementia identification case study\ninvolving 4.9 million veterans with incident hypertension, analyzing 2.1\nbillion clinical notes. At the patient level, our method achieved a precision\nof 0.90, a recall of 0.84, and an F1-score of 0.87. Additionally, this NLP\napproach identified over three times as many dementia cases as structured data\nmethods. All processing was completed in approximately two weeks using a single\nmachine with dual A40 GPUs. This study demonstrates the feasibility of hybrid\nNLP solutions for large-scale clinical text analysis, making state-of-the-art\nmethods more accessible to healthcare organizations with limited computational\nresources.\n", "date": "2025-04-16", "categories": []}
{"id": "2311.05418", "title": "Generalization in medical AI: a perspective on developing scalable\n  models", "abstract": "  The scientific community is increasingly recognizing the importance of\ngeneralization in medical AI for translating research into practical clinical\napplications. A three-level scale is introduced to characterize\nout-of-distribution generalization performance of medical AI models. This scale\naddresses the diversity of real-world medical scenarios as well as whether\ntarget domain data and labels are available for model recalibration. It serves\nas a tool to help researchers characterize their development settings and\ndetermine the best approach to tackling the challenge of out-of-distribution\ngeneralization.\n", "date": "2023-11-09", "categories": []}
{"id": "2504.11792", "title": "Large Language Models for Drug Overdose Prediction from Longitudinal\n  Medical Records", "abstract": "  The ability to predict drug overdose risk from a patient's medical records is\ncrucial for timely intervention and prevention. Traditional machine learning\nmodels have shown promise in analyzing longitudinal medical records for this\ntask. However, recent advancements in large language models (LLMs) offer an\nopportunity to enhance prediction performance by leveraging their ability to\nprocess long textual data and their inherent prior knowledge across diverse\ntasks. In this study, we assess the effectiveness of Open AI's GPT-4o LLM in\npredicting drug overdose events using patients' longitudinal insurance claims\nrecords. We evaluate its performance in both fine-tuned and zero-shot settings,\ncomparing them to strong traditional machine learning methods as baselines. Our\nresults show that LLMs not only outperform traditional models in certain\nsettings but can also predict overdose risk in a zero-shot setting without\ntask-specific training. These findings highlight the potential of LLMs in\nclinical decision support, particularly for drug overdose risk prediction.\n", "date": "2025-04-16", "categories": []}
{"id": "2504.09421", "title": "ClinicalGPT-R1: Pushing reasoning capability of generalist disease\n  diagnosis with large language model", "abstract": "  Recent advances in reasoning with large language models (LLMs)has shown\nremarkable reasoning capabilities in domains such as mathematics and coding,\nyet their application to clinical diagnosis remains underexplored. Here, we\nintroduce ClinicalGPT-R1, a reasoning enhanced generalist large language model\nfor disease diagnosis. Trained on a dataset of 20,000 real-world clinical\nrecords, ClinicalGPT-R1 leverages diverse training strategies to enhance\ndiagnostic reasoning. To benchmark performance, we curated MedBench-Hard, a\nchallenging dataset spanning seven major medical specialties and representative\ndiseases. Experimental results demonstrate that ClinicalGPT-R1 outperforms\nGPT-4o in Chinese diagnostic tasks and achieves comparable performance to GPT-4\nin English settings. This comparative study effectively validates the superior\nperformance of ClinicalGPT-R1 in disease diagnosis tasks. Resources are\navailable at https://github.com/medfound/medfound.\n", "date": "2025-04-13", "categories": []}
{"id": "2409.00041", "title": "Needles in Needle Stacks: Meaningful Clinical Information Buried in\n  Noisy Waveform Data", "abstract": "  Central Venous Lines (C-Lines) and Arterial Lines (A-Lines) are routinely\nused in the Critical Care Unit (CCU) for blood sampling, medication\nadministration, and high-frequency blood pressure measurement. Judiciously\naccessing these lines is important, as over-utilization is associated with\nsignificant in-hospital morbidity and mortality. Documenting the frequency of\nline-access is an important step in reducing these adverse outcomes.\nUnfortunately, the current gold-standard for documentation is manual and\nsubject to error, omission, and bias. The high-frequency blood pressure\nwaveform data from sensors in these lines are often noisy and full of\nartifacts. Standard approaches in signal processing remove noise artifacts\nbefore meaningful analysis. However, from bedside observations, we\ncharacterized a distinct artifact that occurs during each instance of C-Line or\nA-Line use. These artifacts are buried amongst physiological waveform and\nextraneous noise. We focus on Machine Learning (ML) models that can detect\nthese artifacts from waveform data in real-time - finding needles in needle\nstacks, in order to automate the documentation of line-access. We built and\nevaluated ML classifiers running in real-time at a major children's hospital to\nachieve this goal. We demonstrate the utility of these tools for reducing\ndocumentation burden, increasing available information for bedside clinicians,\nand informing unit-level initiatives to improve patient safety.\n", "date": "2024-08-18", "categories": []}
{"id": "2504.08877", "title": "The SERENADE project: Sensor-Based Explainable Detection of Cognitive\n  Decline", "abstract": "  Mild Cognitive Impairment (MCI) affects 12-18% of individuals over 60. MCI\npatients exhibit cognitive dysfunctions without significant daily functional\nloss. While MCI may progress to dementia, predicting this transition remains a\nclinical challenge due to limited and unreliable indicators. Behavioral\nchanges, like in the execution of Activities of Daily Living (ADLs), can signal\nsuch progression. Sensorized smart homes and wearable devices offer an\ninnovative solution for continuous, non-intrusive monitoring ADLs for MCI\npatients. However, current machine learning models for detecting behavioral\nchanges lack transparency, hindering clinicians' trust. This paper introduces\nthe SERENADE project, a European Union-funded initiative that aims to detect\nand explain behavioral changes associated with cognitive decline using\nexplainable AI methods. SERENADE aims at collecting one year of data from 30\nMCI patients living alone, leveraging AI to support clinical decision-making\nand offering a new approach to early dementia detection.\n", "date": "2025-04-11", "categories": []}
{"id": "2504.09299", "title": "Beyond Glucose-Only Assessment: Advancing Nocturnal Hypoglycemia\n  Prediction in Children with Type 1 Diabetes", "abstract": "  The dead-in-bed syndrome describes the sudden and unexplained death of young\nindividuals with Type 1 Diabetes (T1D) without prior long-term complications.\nOne leading hypothesis attributes this phenomenon to nocturnal hypoglycemia\n(NH), a dangerous drop in blood glucose during sleep. This study aims to\nimprove NH prediction in children with T1D by leveraging physiological data and\nmachine learning (ML) techniques. We analyze an in-house dataset collected from\n16 children with T1D, integrating physiological metrics from wearable sensors.\nWe explore predictive performance through feature engineering, model selection,\narchitectures, and oversampling. To address data limitations, we apply transfer\nlearning from a publicly available adult dataset. Our results achieve an AUROC\nof 0.75 +- 0.21 on the in-house dataset, further improving to 0.78 +- 0.05 with\ntransfer learning. This research moves beyond glucose-only predictions by\nincorporating physiological parameters, showcasing the potential of ML to\nenhance NH detection and improve clinical decision-making for pediatric\ndiabetes management.\n", "date": "2025-04-12", "categories": []}
{"id": "2504.09302", "title": "Application of Contrastive Learning on ECG Data: Evaluating Performance\n  in Japanese and Classification with Around 100 Labels", "abstract": "  The electrocardiogram (ECG) is a fundamental tool in cardiovascular\ndiagnostics due to its powerful and non-invasive nature. One of the most\ncritical usages is to determine whether more detailed examinations are\nnecessary, with users ranging across various levels of expertise. Given this\ndiversity in expertise, it is essential to assist users to avoid critical\nerrors. Recent studies in machine learning have addressed this challenge by\nextracting valuable information from ECG data. Utilizing language models, these\nstudies have implemented multimodal models aimed at classifying ECGs according\nto labeled terms. However, the number of classes was reduced, and it remains\nuncertain whether the technique is effective for languages other than English.\nTo move towards practical application, we utilized ECG data from regular\npatients visiting hospitals in Japan, maintaining a large number of Japanese\nlabels obtained from actual ECG readings. Using a contrastive learning\nframework, we found that even with 98 labels for classification, our\nJapanese-based language model achieves accuracy comparable to previous\nresearch. This study extends the applicability of multimodal machine learning\nframeworks to broader clinical studies and non-English languages.\n", "date": "2025-04-12", "categories": []}
{"id": "2504.08150", "title": "Beyond Feature Importance: Feature Interactions in Predicting\n  Post-Stroke Rigidity with Graph Explainable AI", "abstract": "  This study addresses the challenge of predicting post-stroke rigidity by\nemphasizing feature interactions through graph-based explainable AI.\nPost-stroke rigidity, characterized by increased muscle tone and stiffness,\nsignificantly affects survivors' mobility and quality of life. Despite its\nprevalence, early prediction remains limited, delaying intervention. We analyze\n519K stroke hospitalization records from the Healthcare Cost and Utilization\nProject dataset, where 43% of patients exhibited rigidity. We compare\ntraditional approaches such as Logistic Regression, XGBoost, and Transformer\nwith graph-based models like Graphormer and Graph Attention Network. These\ngraph models inherently capture feature interactions and incorporate intrinsic\nor post-hoc explainability. Our results show that graph-based methods\noutperform others (AUROC 0.75), identifying key predictors such as NIH Stroke\nScale and APR-DRG mortality risk scores. They also uncover interactions missed\nby conventional models. This research provides a novel application of\ngraph-based XAI in stroke prognosis, with potential to guide early\nidentification and personalized rehabilitation strategies.\n", "date": "2025-04-10", "categories": []}
{"id": "2504.07278", "title": "A Multi-Phase Analysis of Blood Culture Stewardship: Machine Learning\n  Prediction, Expert Recommendation Assessment, and LLM Automation", "abstract": "  Blood cultures are often over ordered without clear justification, straining\nhealthcare resources and contributing to inappropriate antibiotic use pressures\nworsened by the global shortage. In study of 135483 emergency department (ED)\nblood culture orders, we developed machine learning (ML) models to predict the\nrisk of bacteremia using structured electronic health record (EHR) data and\nprovider notes via a large language model (LLM). The structured models AUC\nimproved from 0.76 to 0.79 with note embeddings and reached 0.81 with added\ndiagnosis codes. Compared to an expert recommendation framework applied by\nhuman reviewers and an LLM-based pipeline, our ML approach offered higher\nspecificity without compromising sensitivity. The recommendation framework\nachieved sensitivity 86%, specificity 57%, while the LLM maintained high\nsensitivity (96%) but over classified negatives, reducing specificity (16%).\nThese findings demonstrate that ML models integrating structured and\nunstructured data can outperform consensus recommendations, enhancing\ndiagnostic stewardship beyond existing standards of care.\n", "date": "2025-04-09", "categories": []}
{"id": "2504.07373", "title": "ChronoFormer: Time-Aware Transformer Architectures for Structured\n  Clinical Event Modeling", "abstract": "  The temporal complexity of electronic health record (EHR) data presents\nsignificant challenges for predicting clinical outcomes using machine learning.\nThis paper proposes ChronoFormer, an innovative transformer based architecture\nspecifically designed to encode and leverage temporal dependencies in\nlongitudinal patient data. ChronoFormer integrates temporal embeddings,\nhierarchical attention mechanisms, and domain specific masking techniques.\nExtensive experiments conducted on three benchmark tasks mortality prediction,\nreadmission prediction, and long term comorbidity onset demonstrate substantial\nimprovements over current state of the art methods. Furthermore, detailed\nanalyses of attention patterns underscore ChronoFormer's capability to capture\nclinically meaningful long range temporal relationships.\n", "date": "2025-04-10", "categories": []}
{"id": "2504.06306", "title": "Predicting Survivability of Cancer Patients with Metastatic Patterns\n  Using Explainable AI", "abstract": "  Cancer remains a leading global health challenge and a major cause of\nmortality. This study leverages machine learning (ML) to predict the\nsurvivability of cancer patients with metastatic patterns using the\ncomprehensive MSK-MET dataset, which includes genomic and clinical data from\n25,775 patients across 27 cancer types. We evaluated five ML models-XGBoost,\nNa\\\"ive Bayes, Decision Tree, Logistic Regression, and Random Fores using\nhyperparameter tuning and grid search. XGBoost emerged as the best performer\nwith an area under the curve (AUC) of 0.82. To enhance model interpretability,\nSHapley Additive exPlanations (SHAP) were applied, revealing key predictors\nsuch as metastatic site count, tumor mutation burden, fraction of genome\naltered, and organ-specific metastases. Further survival analysis using\nKaplan-Meier curves, Cox Proportional Hazards models, and XGBoost Survival\nAnalysis identified significant predictors of patient outcomes, offering\nactionable insights for clinicians. These findings could aid in personalized\nprognosis and treatment planning, ultimately improving patient care.\n", "date": "2025-04-07", "categories": []}
{"id": "2504.06979", "title": "Artificial Intelligence for Pediatric Height Prediction Using\n  Large-Scale Longitudinal Body Composition Data", "abstract": "  This study developed an accurate artificial intelligence model for predicting\nfuture height in children and adolescents using anthropometric and body\ncomposition data from the GP Cohort Study (588,546 measurements from 96,485\nchildren aged 7-18). The model incorporated anthropometric measures, body\ncomposition, standard deviation scores, and growth velocity parameters, with\nperformance evaluated using RMSE, MAE, and MAPE. Results showed high accuracy\nwith males achieving average RMSE, MAE, and MAPE of 2.51 cm, 1.74 cm, and\n1.14%, and females showing 2.28 cm, 1.68 cm, and 1.13%, respectively.\nExplainable AI approaches identified height SDS, height velocity, and soft lean\nmass velocity as crucial predictors. The model generated personalized growth\ncurves by estimating individual-specific height trajectories, offering a robust\ntool for clinical decision support, early identification of growth disorders,\nand optimization of growth outcomes.\n", "date": "2025-04-09", "categories": []}
{"id": "2504.00053", "title": "Integrating Large Language Models with Human Expertise for Disease\n  Detection in Electronic Health Records", "abstract": "  Objective: Electronic health records (EHR) are widely available to complement\nadministrative data-based disease surveillance and healthcare performance\nevaluation. Defining conditions from EHR is labour-intensive and requires\nextensive manual labelling of disease outcomes. This study developed an\nefficient strategy based on advanced large language models to identify multiple\nconditions from EHR clinical notes. Methods: We linked a cardiac registry\ncohort in 2015 with an EHR system in Alberta, Canada. We developed a pipeline\nthat leveraged a generative large language model (LLM) to analyze, understand,\nand interpret EHR notes by prompts based on specific diagnosis, treatment\nmanagement, and clinical guidelines. The pipeline was applied to detect acute\nmyocardial infarction (AMI), diabetes, and hypertension. The performance was\ncompared against clinician-validated diagnoses as the reference standard and\nwidely adopted International Classification of Diseases (ICD) codes-based\nmethods. Results: The study cohort accounted for 3,088 patients and 551,095\nclinical notes. The prevalence was 55.4%, 27.7%, 65.9% and for AMI, diabetes,\nand hypertension, respectively. The performance of the LLM-based pipeline for\ndetecting conditions varied: AMI had 88% sensitivity, 63% specificity, and 77%\npositive predictive value (PPV); diabetes had 91% sensitivity, 86% specificity,\nand 71% PPV; and hypertension had 94% sensitivity, 32% specificity, and 72%\nPPV. Compared with ICD codes, the LLM-based method demonstrated improved\nsensitivity and negative predictive value across all conditions. The monthly\npercentage trends from the detected cases by LLM and reference standard showed\nconsistent patterns.\n", "date": "2025-03-31", "categories": []}
{"id": "2504.05338", "title": "Improving Early Prediction of Type 2 Diabetes Mellitus with ECG-DiaNet:\n  A Multimodal Neural Network Leveraging Electrocardiogram and Clinical Risk\n  Factors", "abstract": "  Type 2 Diabetes Mellitus (T2DM) remains a global health challenge,\nunderscoring the need for early and accurate risk prediction. This study\npresents ECG-DiaNet, a multimodal deep learning model that integrates\nelectrocardiogram (ECG) features with clinical risk factors (CRFs) to enhance\nT2DM onset prediction. Using data from Qatar Biobank (QBB), we trained and\nvalidated models on a development cohort (n=2043) and evaluated performance on\na longitudinal test set (n=395) with five-year follow-up. ECG-DiaNet\noutperformed unimodal ECG-only and CRF-only models, achieving a higher AUROC\n(0.845 vs 0.8217) than the CRF-only model, with statistical significance\n(DeLong p<0.001). Reclassification metrics further confirmed improvements: Net\nReclassification Improvement (NRI=0.0153) and Integrated Discrimination\nImprovement (IDI=0.0482). Risk stratification into low-, medium-, and high-risk\ngroups showed ECG-DiaNet achieved superior positive predictive value (PPV) in\nhigh-risk individuals. The model's reliance on non-invasive and widely\navailable ECG signals supports its feasibility in clinical and community health\nsettings. By combining cardiac electrophysiology and systemic risk profiles,\nECG-DiaNet addresses the multifactorial nature of T2DM and supports precision\nprevention. These findings highlight the value of multimodal AI in advancing\nearly detection and prevention strategies for T2DM, particularly in\nunderrepresented Middle Eastern populations.\n", "date": "2025-04-05", "categories": []}
{"id": "2504.05691", "title": "StayLTC: A Cost-Effective Multimodal Framework for Hospital Length of\n  Stay Forecasting", "abstract": "  Accurate prediction of Length of Stay (LOS) in hospitals is crucial for\nimproving healthcare services, resource management, and cost efficiency. This\npaper presents StayLTC, a multimodal deep learning framework developed to\nforecast real-time hospital LOS using Liquid Time-Constant Networks (LTCs).\nLTCs, with their continuous-time recurrent dynamics, are evaluated against\ntraditional models using structured data from Electronic Health Records (EHRs)\nand clinical notes. Our evaluation, conducted on the MIMIC-III dataset,\ndemonstrated that LTCs significantly outperform most of the other time series\nmodels, offering enhanced accuracy, robustness, and efficiency in resource\nutilization. Additionally, LTCs demonstrate a comparable performance in LOS\nprediction compared to time series large language models, while requiring\nsignificantly less computational power and memory, underscoring their potential\nto advance Natural Language Processing (NLP) tasks in healthcare.\n", "date": "2025-04-08", "categories": []}
{"id": "2408.07673", "title": "Deep Learning: a Heuristic Three-stage Mechanism for Grid Searches to\n  Optimize the Future Risk Prediction of Breast Cancer Metastasis Using\n  EHR-based Clinical Data", "abstract": "  A grid search, at the cost of training and testing a large number of models,\nis an effective way to optimize the prediction performance of deep learning\nmodels. A challenging task concerning grid search is the time management.\nWithout a good time management scheme, a grid search can easily be set off as a\nmission that will not finish in our lifetime. In this study, we introduce a\nheuristic three-stage mechanism for managing the running time of low-budget\ngrid searches, and the sweet-spot grid search (SSGS) and randomized grid search\n(RGS) strategies for improving model prediction performance, in predicting the\n5-year, 10-year, and 15-year risk of breast cancer metastasis. We develop deep\nfeedforward neural network (DFNN) models and optimize them through grid\nsearches. We conduct eight cycles of grid searches by applying our three-stage\nmechanism and SSGS and RGS strategies. We conduct various SHAP analyses\nincluding unique ones that interpret the importance of the DFNN-model\nhyperparameters. Our results show that grid search can greatly improve model\nprediction. The grid searches we conducted improved the risk prediction of\n5-year, 10-year, and 15-year breast cancer metastasis by 18.6%, 16.3%, and\n17.3% respectively, over the average performance of all corresponding models we\ntrained using the RGS strategy. We not only demonstrate best model performance\nbut also characterize grid searches from various aspects such as their\ncapabilities of discovering decent models and the unit grid search time. The\nthree-stage mechanism worked effectively. It made our low-budget grid searches\nfeasible and manageable, and in the meantime helped improve model prediction\nperformance. Our SHAP analyses identified both clinical risk factors important\nfor the prediction of future risk of breast cancer metastasis, and DFNN-model\nhyperparameters important to the prediction of performance scores.\n", "date": "2024-08-14", "categories": []}
{"id": "2503.20508", "title": "Explainable ICD Coding via Entity Linking", "abstract": "  Clinical coding is a critical task in healthcare, although traditional\nmethods for automating clinical coding may not provide sufficient explicit\nevidence for coders in production environments. This evidence is crucial, as\nmedical coders have to make sure there exists at least one explicit passage in\nthe input health record that justifies the attribution of a code. We therefore\npropose to reframe the task as an entity linking problem, in which each\ndocument is annotated with its set of codes and respective textual evidence,\nenabling better human-machine collaboration. By leveraging parameter-efficient\nfine-tuning of Large Language Models (LLMs), together with constrained\ndecoding, we introduce three approaches to solve this problem that prove\neffective at disambiguating clinical mentions and that perform well in few-shot\nscenarios.\n", "date": "2025-03-26", "categories": []}
{"id": "2504.03703", "title": "Hierarchical Attention Network for Interpretable ECG-based Heart Disease\n  Classification", "abstract": "  Cardiovascular disease remains one of the leading causes of mortality\nworldwide, underscoring the need for accurate as well as interpretable\ndiagnostic machine learning tools. In this work, we investigate heart disease\nclassification using electrocardiogram (ECG) data from two widely-utilized\ndatasets: The MIT-BIH Arrhythmia and the PTB-XL datasets. We adapt a\nhierarchical attention network (HAN), originally developed for text\nclassification, into an ECG-based heart-disease classification task. Our\nadapted HAN incorporates two attention layers that focus on ECG data segments\nof varying sizes. We conduct a comparative analysis between our adapted HAN and\na more sophisticated state-of-the-art architecture, featuring a network with\nconvolution, attention, and transformer layers (CAT-Net). Our empirical\nevaluation encompasses multiple aspects including test accuracy (quantified by\n0-1 loss); model complexity (measured by the number of model parameters); and\ninterpretability (through attention map visualization). Our adapted HAN\ndemonstrates comparable test accuracy with significant reductions in model\ncomplexity and enhanced interpretability analysis: For the MIT-BIH dataset, our\nadapted HAN achieves 98.55\\% test accuracy compared to 99.14\\% for CAT-Net,\nwhile reducing the number of model parameters by a factor of 15.6. For the\nPTB-XL dataset, our adapted HAN achieves a 19.3-fold reduction in model\ncomplexity compared to CAT-Net, with only a 5\\% lower test accuracy. From an\ninterpretability perspective, the significantly simpler architecture and the\nhierarchical nature of our adapted HAN model facilitate a more straightforward\ninterpretability analysis based on visualizing attention weights. Building on\nthis advantage, we conduct an interpretability analysis of our HAN that\nhighlights the regions of the ECG signal most relevant to the model's\ndecisions.\n", "date": "2025-03-25", "categories": []}
{"id": "2504.04262", "title": "Improving Chronic Kidney Disease Detection Efficiency: Fine Tuned\n  CatBoost and Nature-Inspired Algorithms with Explainable AI", "abstract": "  Chronic Kidney Disease (CKD) is a major global health issue which is\naffecting million people around the world and with increasing rate of\nmortality. Mitigation of progression of CKD and better patient outcomes\nrequires early detection. Nevertheless, limitations lie in traditional\ndiagnostic methods, especially in resource constrained settings. This study\nproposes an advanced machine learning approach to enhance CKD detection by\nevaluating four models: Random Forest (RF), Multi-Layer Perceptron (MLP),\nLogistic Regression (LR), and a fine-tuned CatBoost algorithm. Specifically,\namong these, the fine-tuned CatBoost model demonstrated the best overall\nperformance having an accuracy of 98.75%, an AUC of 0.9993 and a Kappa score of\n97.35% of the studies. The proposed CatBoost model has used a nature inspired\nalgorithm such as Simulated Annealing to select the most important features,\nCuckoo Search to adjust outliers and grid search to fine tune its settings in\nsuch a way to achieve improved prediction accuracy. Features significance is\nexplained by SHAP-a well-known XAI technique-for gaining transparency in the\ndecision-making process of proposed model and bring up trust in diagnostic\nsystems. Using SHAP, the significant clinical features were identified as\nspecific gravity, serum creatinine, albumin, hemoglobin, and diabetes mellitus.\nThe potential of advanced machine learning techniques in CKD detection is shown\nin this research, particularly for low income and middle-income healthcare\nsettings where prompt and correct diagnoses are vital. This study seeks to\nprovide a highly accurate, interpretable, and efficient diagnostic tool to add\nto efforts for early intervention and improved healthcare outcomes for all CKD\npatients.\n", "date": "2025-04-05", "categories": []}
{"id": "2504.04891", "title": "Leveraging Large Language Models for Cost-Effective, Multilingual\n  Depression Detection and Severity Assessment", "abstract": "  Depression is a prevalent mental health disorder that is difficult to detect\nearly due to subjective symptom assessments. Recent advancements in large\nlanguage models have offered efficient and cost-effective approaches for this\nobjective. In this study, we evaluated the performance of four LLMs in\ndepression detection using clinical interview data. We selected the best\nperforming model and further tested it in the severity evaluation scenario and\nknowledge enhanced scenario. The robustness was evaluated in complex diagnostic\nscenarios using a dataset comprising 51074 statements from six different mental\ndisorders. We found that DeepSeek V3 is the most reliable and cost-effective\nmodel for depression detection, performing well in both zero-shot and few-shot\nscenarios, with zero-shot being the most efficient choice. The evaluation of\nseverity showed low agreement with the human evaluator, particularly for mild\ndepression. The model maintains stably high AUCs for detecting depression in\ncomplex diagnostic scenarios. These findings highlight DeepSeek V3s strong\npotential for text-based depression detection in real-world clinical\napplications. However, they also underscore the need for further refinement in\nseverity assessment and the mitigation of potential biases to enhance clinical\nreliability.\n", "date": "2025-04-07", "categories": []}
{"id": "2501.02014", "title": "Machine Learning-Based Differential Diagnosis of Parkinson's Disease\n  Using Kinematic Feature Extraction and Selection", "abstract": "  Parkinson's disease (PD), the second most common neurodegenerative disorder,\nis characterized by dopaminergic neuron loss and the accumulation of abnormal\nsynuclein. PD presents both motor and non-motor symptoms that progressively\nimpair daily functioning. The severity of these symptoms is typically assessed\nusing the MDS-UPDRS rating scale, which is subjective and dependent on the\nphysician's experience. Additionally, PD shares symptoms with other\nneurodegenerative diseases, such as progressive supranuclear palsy (PSP) and\nmultiple system atrophy (MSA), complicating accurate diagnosis. To address\nthese diagnostic challenges, we propose a machine learning-based system for\ndifferential diagnosis of PD, PSP, MSA, and healthy controls (HC). This system\nutilizes a kinematic feature-based hierarchical feature extraction and\nselection approach. Initially, 18 kinematic features are extracted, including\ntwo newly proposed features: Thumb-to-index vector velocity and acceleration,\nwhich provide insights into motor control patterns. In addition, 41 statistical\nfeatures were extracted here from each kinematic feature, including some new\napproaches such as Average Absolute Change, Rhythm, Amplitude, Frequency,\nStandard Deviation of Frequency, and Slope. Feature selection is performed\nusing One-way ANOVA to rank features, followed by Sequential Forward Floating\nSelection (SFFS) to identify the most relevant ones, aiming to reduce the\ncomputational complexity. The final feature set is used for classification,\nachieving a classification accuracy of 66.67% for each dataset and 88.89% for\neach patient, with particularly high performance for the MSA and HC groups\nusing the SVM algorithm. This system shows potential as a rapid and accurate\ndiagnostic tool in clinical practice, though further data collection and\nrefinement are needed to enhance its reliability.\n", "date": "2025-01-02", "categories": []}
{"id": "2504.03439", "title": "Early detection of diabetes through transfer learning-based eye (vision)\n  screening and improvement of machine learning model performance and advanced\n  parameter setting algorithms", "abstract": "  Diabetic Retinopathy (DR) is a serious and common complication of diabetes,\ncaused by prolonged high blood sugar levels that damage the small retinal blood\nvessels. If left untreated, DR can progress to retinal vein occlusion and\nstimulate abnormal blood vessel growth, significantly increasing the risk of\nblindness. Traditional diabetes diagnosis methods often utilize convolutional\nneural networks (CNNs) to extract visual features from retinal images, followed\nby classification algorithms such as decision trees and k-nearest neighbors\n(KNN) for disease detection. However, these approaches face several challenges,\nincluding low accuracy and sensitivity, lengthy machine learning (ML) model\ntraining due to high data complexity and volume, and the use of limited\ndatasets for testing and evaluation. This study investigates the application of\ntransfer learning (TL) to enhance ML model performance in DR detection. Key\nimprovements include dimensionality reduction, optimized learning rate\nadjustments, and advanced parameter tuning algorithms, aimed at increasing\nefficiency and diagnostic accuracy. The proposed model achieved an overall\naccuracy of 84% on the testing dataset, outperforming prior studies. The\nhighest class-specific accuracy reached 89%, with a maximum sensitivity of 97%\nand an F1-score of 92%, demonstrating strong performance in identifying DR\ncases. These findings suggest that TL-based DR screening is a promising\napproach for early diagnosis, enabling timely interventions to prevent vision\nloss and improve patient outcomes.\n", "date": "2025-04-04", "categories": []}
{"id": "2409.18257", "title": "Developing a Dual-Stage Vision Transformer Model for Lung Disease\n  Classification", "abstract": "  Lung diseases have become a prevalent problem throughout the United States,\naffecting over 34 million people. Accurate and timely diagnosis of the\ndifferent types of lung diseases is critical, and Artificial Intelligence (AI)\nmethods could speed up these processes. A dual-stage vision transformer is\nbuilt throughout this research by integrating a Vision Transformer (ViT) and a\nSwin Transformer to classify 14 different lung diseases from X-ray scans of\npatients with these diseases. The proposed model achieved an accuracy of 92.06%\non a label-level when making predictions on an unseen testing subset of the\ndataset after data preprocessing and training the neural network. The model\nshowed promise for accurately classifying lung diseases and diagnosing patients\nwho suffer from these harmful diseases.\n", "date": "2024-09-26", "categories": []}
{"id": "2402.09975", "title": "Current and future roles of artificial intelligence in retinopathy of\n  prematurity", "abstract": "  Retinopathy of prematurity (ROP) is a severe condition affecting premature\ninfants, leading to abnormal retinal blood vessel growth, retinal detachment,\nand potential blindness. While semi-automated systems have been used in the\npast to diagnose ROP-related plus disease by quantifying retinal vessel\nfeatures, traditional machine learning (ML) models face challenges like\naccuracy and overfitting. Recent advancements in deep learning (DL), especially\nconvolutional neural networks (CNNs), have significantly improved ROP detection\nand classification. The i-ROP deep learning (i-ROP-DL) system also shows\npromise in detecting plus disease, offering reliable ROP diagnosis potential.\nThis research comprehensively examines the contemporary progress and challenges\nassociated with using retinal imaging and artificial intelligence (AI) to\ndetect ROP, offering valuable insights that can guide further investigation in\nthis domain. Based on 89 original studies in this field (out of 1487 studies\nthat were comprehensively reviewed), we concluded that traditional methods for\nROP diagnosis suffer from subjectivity and manual analysis, leading to\ninconsistent clinical decisions. AI holds great promise for improving ROP\nmanagement. This review explores AI's potential in ROP detection,\nclassification, diagnosis, and prognosis.\n", "date": "2024-02-15", "categories": []}
{"id": "2402.17493", "title": "The Foundational Capabilities of Large Language Models in Predicting\n  Postoperative Risks Using Clinical Notes", "abstract": "  Clinical notes recorded during a patient's perioperative journey holds\nimmense informational value. Advances in large language models (LLMs) offer\nopportunities for bridging this gap. Using 84,875 pre-operative notes and its\nassociated surgical cases from 2018 to 2021, we examine the performance of LLMs\nin predicting six postoperative risks using various fine-tuning strategies.\nPretrained LLMs outperformed traditional word embeddings by an absolute AUROC\nof 38.3% and AUPRC of 33.2%. Self-supervised fine-tuning further improved\nperformance by 3.2% and 1.5%. Incorporating labels into training further\nincreased AUROC by 1.8% and AUPRC by 2%. The highest performance was achieved\nwith a unified foundation model, with improvements of 3.6% for AUROC and 2.6%\nfor AUPRC compared to self-supervision, highlighting the foundational\ncapabilities of LLMs in predicting postoperative risks, which could be\npotentially beneficial when deployed for perioperative care\n", "date": "2024-02-27", "categories": []}
{"id": "2504.00022", "title": "Autonomous AI for Multi-Pathology Detection in Chest X-Rays: A\n  Multi-Site Study in the Indian Healthcare System", "abstract": "  Study Design: The study outlines the development of an autonomous AI system\nfor chest X-ray (CXR) interpretation, trained on a vast dataset of over 5\nmillion X rays sourced from healthcare systems across India. This AI system\nintegrates advanced architectures including Vision Transformers, Faster R-CNN,\nand various U Net models (such as Attention U-Net, U-Net++, and Dense U-Net) to\nenable comprehensive classification, detection, and segmentation of 75 distinct\npathologies. To ensure robustness, the study design includes subgroup analyses\nacross age, gender, and equipment type, validating the model's adaptability and\nperformance across diverse patient demographics and imaging environments.\n  Performance: The AI system achieved up to 98% precision and over 95% recall\nfor multi pathology classification, with stable performance across demographic\nand equipment subgroups. For normal vs. abnormal classification, it reached\n99.8% precision, 99.6% recall, and 99.9% negative predictive value (NPV). It\nwas deployed in 17 major healthcare systems in India including diagnostic\ncenters, large hospitals, and government hospitals. Over the deployment period,\nthe system processed over 150,000 scans, averaging 2,000 chest X rays daily,\nresulting in reduced reporting times and improved diagnostic accuracy.\n  Conclusion: The high precision and recall validate the AI's capability as a\nreliable tool for autonomous normal abnormal classification, pathology\nlocalization, and segmentation. This scalable AI model addresses diagnostic\ngaps in underserved areas, optimizing radiology workflows and enhancing patient\ncare across diverse healthcare settings in India.\n", "date": "2025-03-28", "categories": []}
{"id": "2406.06723", "title": "Leveraging Large Language Models for Knowledge-free Weak Supervision in\n  Clinical Natural Language Processing", "abstract": "  The performance of deep learning-based natural language processing systems is\nbased on large amounts of labeled training data which, in the clinical domain,\nare not easily available or affordable. Weak supervision and in-context\nlearning offer partial solutions to this issue, particularly using large\nlanguage models (LLMs), but their performance still trails traditional\nsupervised methods with moderate amounts of gold-standard data. In particular,\ninferencing with LLMs is computationally heavy. We propose an approach\nleveraging fine-tuning LLMs and weak supervision with virtually no domain\nknowledge that still achieves consistently dominant performance. Using a\nprompt-based approach, the LLM is used to generate weakly-labeled data for\ntraining a downstream BERT model. The weakly supervised model is then further\nfine-tuned on small amounts of gold standard data. We evaluate this approach\nusing Llama2 on three different n2c2 datasets. With no more than 10 gold\nstandard notes, our final BERT models weakly supervised by fine-tuned\nLlama2-13B consistently outperformed out-of-the-box PubMedBERT by 4.7% to 47.9%\nin F1 scores. With only 50 gold standard notes, our models achieved close\nperformance to fully fine-tuned systems.\n", "date": "2024-06-10", "categories": []}
{"id": "2501.16373", "title": "Unveiling Discrete Clues: Superior Healthcare Predictions for Rare\n  Diseases", "abstract": "  Accurate healthcare prediction is essential for improving patient outcomes.\nExisting work primarily leverages advanced frameworks like attention or graph\nnetworks to capture the intricate collaborative (CO) signals in electronic\nhealth records. However, prediction for rare diseases remains challenging due\nto limited co-occurrence and inadequately tailored approaches. To address this\nissue, this paper proposes UDC, a novel method that unveils discrete clues to\nbridge consistent textual knowledge and CO signals within a unified semantic\nspace, thereby enriching the representation semantics of rare diseases.\nSpecifically, we focus on addressing two key sub-problems: (1) acquiring\ndistinguishable discrete encodings for precise disease representation and (2)\nachieving semantic alignment between textual knowledge and the CO signals at\nthe code level. For the first sub-problem, we refine the standard vector\nquantized process to include condition awareness. Additionally, we develop an\nadvanced contrastive approach in the decoding stage, leveraging synthetic and\nmixed-domain targets as hard negatives to enrich the perceptibility of the\nreconstructed representation for downstream tasks. For the second sub-problem,\nwe introduce a novel codebook update strategy using co-teacher distillation.\nThis approach facilitates bidirectional supervision between textual knowledge\nand CO signals, thereby aligning semantically equivalent information in a\nshared discrete latent space. Extensive experiments on three datasets\ndemonstrate our superiority.\n", "date": "2025-01-23", "categories": []}
{"id": "2504.00036", "title": "Improving Diseases Predictions Utilizing External Bio-Banks", "abstract": "  Machine learning has been successfully used in critical domains, such as\nmedicine. However, extracting meaningful insights from biomedical data is often\nconstrained by the lack of their available disease labels. In this research, we\ndemonstrate how machine learning can be leveraged to enhance explainability and\nuncover biologically meaningful associations, even when predictive improvements\nin disease modeling are limited. We train LightGBM models from scratch on our\ndataset (10K) to impute metabolomics features and apply them to the UK Biobank\n(UKBB) for downstream analysis. The imputed metabolomics features are then used\nin survival analysis to assess their impact on disease-related risk factors. As\na result, our approach successfully identified biologically relevant\nconnections that were not previously known to the predictive models.\nAdditionally, we applied a genome-wide association study (GWAS) on key\nmetabolomics features, revealing a link between vascular dementia and smoking.\nAlthough being a well-established epidemiological relationship, this link was\nnot embedded in the model's training data, which validated the method's ability\nto extract meaningful signals. Furthermore, by integrating survival models as\ninputs in the 10K data, we uncovered associations between metabolic substances\nand obesity, demonstrating the ability to infer disease risk for future\npatients without requiring direct outcome labels. These findings highlight the\npotential of leveraging external bio-banks to extract valuable biomedical\ninsights, even in data-limited scenarios. Our results demonstrate that machine\nlearning models trained on smaller datasets can still be used to uncover real\nbiological associations when carefully integrated with survival analysis and\ngenetic studies.\n", "date": "2025-03-30", "categories": []}
{"id": "2504.00232", "title": "Opportunistic Screening for Pancreatic Cancer using Computed Tomography\n  Imaging and Radiology Reports", "abstract": "  Pancreatic ductal adenocarcinoma (PDAC) is a highly aggressive cancer, with\nmost cases diagnosed at stage IV and a five-year overall survival rate below\n5%. Early detection and prognosis modeling are crucial for improving patient\noutcomes and guiding early intervention strategies. In this study, we developed\nand evaluated a deep learning fusion model that integrates radiology reports\nand CT imaging to predict PDAC risk. The model achieved a concordance index\n(C-index) of 0.6750 (95% CI: 0.6429, 0.7121) and 0.6435 (95% CI: 0.6055,\n0.6789) on the internal and external dataset, respectively, for 5-year survival\nrisk estimation. Kaplan-Meier analysis demonstrated significant separation\n(p<0.0001) between the low and high risk groups predicted by the fusion model.\nThese findings highlight the potential of deep learning-based survival models\nin leveraging clinical and imaging data for pancreatic cancer.\n", "date": "2025-03-31", "categories": []}
{"id": "2503.22706", "title": "Validating Emergency Department Admission Predictions Based on Local\n  Data Through MIMIC-IV", "abstract": "  The effective management of Emergency Department (ED) overcrowding is\nessential for improving patient outcomes and optimizing healthcare resource\nallocation. This study validates hospital admission prediction models initially\ndeveloped using a small local dataset from a Greek hospital by leveraging the\ncomprehensive MIMIC-IV dataset. After preprocessing the MIMIC-IV data, five\nalgorithms were evaluated: Linear Discriminant Analysis (LDA), K-Nearest\nNeighbors (KNN), Random Forest (RF), Recursive Partitioning and Regression\nTrees (RPART), and Support Vector Machines (SVM Radial). Among these, RF\ndemonstrated superior performance, achieving an Area Under the Receiver\nOperating Characteristic Curve (AUC-ROC) of 0.9999, sensitivity of 0.9997, and\nspecificity of 0.9999 when applied to the MIMIC-IV data. These findings\nhighlight the robustness of RF in handling complex datasets for admission\nprediction, establish MIMIC-IV as a valuable benchmark for validating models\nbased on smaller local datasets, and provide actionable insights for improving\nED management strategies.\n", "date": "2025-03-18", "categories": []}
{"id": "2503.22773", "title": "Congenital Heart Disease Classification Using Phonocardiograms: A\n  Scalable Screening Tool for Diverse Environments", "abstract": "  Congenital heart disease (CHD) is a critical condition that demands early\ndetection, particularly in infancy and childhood. This study presents a deep\nlearning model designed to detect CHD using phonocardiogram (PCG) signals, with\na focus on its application in global health. We evaluated our model on several\ndatasets, including the primary dataset from Bangladesh, achieving a high\naccuracy of 94.1%, sensitivity of 92.7%, specificity of 96.3%. The model also\ndemonstrated robust performance on the public PhysioNet Challenge 2022 and 2016\ndatasets, underscoring its generalizability to diverse populations and data\nsources. We assessed the performance of the algorithm for single and multiple\nauscultation sites on the chest, demonstrating that the model maintains over\n85% accuracy even when using a single location. Furthermore, our algorithm was\nable to achieve an accuracy of 80% on low-quality recordings, which\ncardiologists deemed non-diagnostic. This research suggests that an AI- driven\ndigital stethoscope could serve as a cost-effective screening tool for CHD in\nresource-limited settings, enhancing clinical decision support and ultimately\nimproving patient outcomes.\n", "date": "2025-03-28", "categories": []}
{"id": "2503.23042", "title": "MIL vs. Aggregation: Evaluating Patient-Level Survival Prediction\n  Strategies Using Graph-Based Learning", "abstract": "  Oncologists often rely on a multitude of data, including whole-slide images\n(WSIs), to guide therapeutic decisions, aiming for the best patient outcome.\nHowever, predicting the prognosis of cancer patients can be a challenging task\ndue to tumor heterogeneity and intra-patient variability, and the complexity of\nanalyzing WSIs. These images are extremely large, containing billions of\npixels, making direct processing computationally expensive and requiring\nspecialized methods to extract relevant information. Additionally, multiple\nWSIs from the same patient may capture different tumor regions, some being more\ninformative than others. This raises a fundamental question: Should we use all\nWSIs to characterize the patient, or should we identify the most representative\nslide for prognosis? Our work seeks to answer this question by performing a\ncomparison of various strategies for predicting survival at the WSI and patient\nlevel. The former treats each WSI as an independent sample, mimicking the\nstrategy adopted in other works, while the latter comprises methods to either\naggregate the predictions of the several WSIs or automatically identify the\nmost relevant slide using multiple-instance learning (MIL). Additionally, we\nevaluate different Graph Neural Networks architectures under these strategies.\nWe conduct our experiments using the MMIST-ccRCC dataset, which comprises\npatients with clear cell renal cell carcinoma (ccRCC). Our results show that\nMIL-based selection improves accuracy, suggesting that choosing the most\nrepresentative slide benefits survival prediction.\n", "date": "2025-03-29", "categories": []}
{"id": "2503.23050", "title": "Prediction of 30-day hospital readmission with clinical notes and EHR\n  information", "abstract": "  High hospital readmission rates are associated with significant costs and\nhealth risks for patients. Therefore, it is critical to develop predictive\nmodels that can support clinicians to determine whether or not a patient will\nreturn to the hospital in a relatively short period of time (e.g, 30-days).\nNowadays, it is possible to collect both structured (electronic health records\n- EHR) and unstructured information (clinical notes) about a patient hospital\nevent, all potentially containing relevant information for a predictive model.\nHowever, their integration is challenging. In this work we explore the\ncombination of clinical notes and EHRs to predict 30-day hospital readmissions.\nWe address the representation of the various types of information available in\nthe EHR data, as well as exploring LLMs to characterize the clinical notes. We\ncollect both information sources as the nodes of a graph neural network (GNN).\nOur model achieves an AUROC of 0.72 and a balanced accuracy of 66.7\\%,\nhighlighting the importance of combining the multimodal information.\n", "date": "2025-03-29", "categories": []}
{"id": "2503.23072", "title": "TRACE: Intra-visit Clinical Event Nowcasting via Effective Patient\n  Trajectory Encoding", "abstract": "  Electronic Health Records (EHR) have become a valuable resource for a wide\nrange of predictive tasks in healthcare. However, existing approaches have\nlargely focused on inter-visit event predictions, overlooking the importance of\nintra-visit nowcasting, which provides prompt clinical insights during an\nongoing patient visit. To address this gap, we introduce the task of laboratory\nmeasurement prediction within a hospital visit. We study the laboratory data\nthat, however, remained underexplored in previous work. We propose TRACE, a\nTransformer-based model designed for clinical event nowcasting by encoding\npatient trajectories. TRACE effectively handles long sequences and captures\ntemporal dependencies through a novel timestamp embedding that integrates decay\nproperties and periodic patterns of data. Additionally, we introduce a smoothed\nmask for denoising, improving the robustness of the model. Experiments on two\nlarge-scale electronic health record datasets demonstrate that the proposed\nmodel significantly outperforms previous methods, highlighting its potential\nfor improving patient care through more accurate laboratory measurement\nnowcasting. The code is available at https://github.com/Amehi/TRACE.\n", "date": "2025-03-29", "categories": []}
{"id": "2503.24006", "title": "Comparing representations of long clinical texts for the task of patient\n  note-identification", "abstract": "  In this paper, we address the challenge of patient-note identification, which\ninvolves accurately matching an anonymized clinical note to its corresponding\npatient, represented by a set of related notes. This task has broad\napplications, including duplicate records detection and patient similarity\nanalysis, which require robust patient-level representations. We explore\nvarious embedding methods, including Hierarchical Attention Networks (HAN),\nthree-level Hierarchical Transformer Networks (HTN), LongFormer, and advanced\nBERT-based models, focusing on their ability to process mediumto-long clinical\ntexts effectively. Additionally, we evaluate different pooling strategies\n(mean, max, and mean_max) for aggregating wordlevel embeddings into\npatient-level representations and we examine the impact of sliding windows on\nmodel performance. Our results indicate that BERT-based embeddings outperform\ntraditional and hierarchical models, particularly in processing lengthy\nclinical notes and capturing nuanced patient representations. Among the pooling\nstrategies, mean_max pooling consistently yields the best results, highlighting\nits ability to capture critical features from clinical notes. Furthermore, the\nreproduction of our results on both MIMIC dataset and Necker hospital data\nwarehouse illustrates the generalizability of these approaches to real-world\napplications, emphasizing the importance of both embedding methods and\naggregation strategies in optimizing patient-note identification and enhancing\npatient-level modeling.\n", "date": "2025-03-31", "categories": []}
{"id": "2503.24165", "title": "Predicting Targeted Therapy Resistance in Non-Small Cell Lung Cancer\n  Using Multimodal Machine Learning", "abstract": "  Lung cancer is the primary cause of cancer death globally, with non-small\ncell lung cancer (NSCLC) emerging as its most prevalent subtype. Among NSCLC\npatients, approximately 32.3% have mutations in the epidermal growth factor\nreceptor (EGFR) gene. Osimertinib, a third-generation EGFR-tyrosine kinase\ninhibitor (TKI), has demonstrated remarkable efficacy in the treatment of NSCLC\npatients with activating and T790M resistance EGFR mutations. Despite its\nestablished efficacy, drug resistance poses a significant challenge for\npatients to fully benefit from osimertinib. The absence of a standard tool to\naccurately predict TKI resistance, including that of osimertinib, remains a\ncritical obstacle. To bridge this gap, in this study, we developed an\ninterpretable multimodal machine learning model designed to predict patient\nresistance to osimertinib among late-stage NSCLC patients with activating EGFR\nmutations, achieving a c-index of 0.82 on a multi-institutional dataset. This\nmachine learning model harnesses readily available data routinely collected\nduring patient visits and medical assessments to facilitate precision lung\ncancer management and informed treatment decisions. By integrating various data\ntypes such as histology images, next generation sequencing (NGS) data,\ndemographics data, and clinical records, our multimodal model can generate\nwell-informed recommendations. Our experiment results also demonstrated the\nsuperior performance of the multimodal model over single modality models\n(c-index 0.82 compared with 0.75 and 0.77), thus underscoring the benefit of\ncombining multiple modalities in patient outcome prediction.\n", "date": "2025-03-31", "categories": []}
{"id": "2406.10119", "title": "A Progressive Risk Formulation for Enhanced Deep Learning based Total\n  Knee Replacement Prediction in Knee Osteoarthritis", "abstract": "  We developed deep learning models for predicting Total Knee Replacement (TKR)\nneed within various time horizons in knee osteoarthritis patients, with a novel\ncapability: the models can perform TKR prediction using a single scan, and\nfurthermore when a previous scan is available, they leverage a progressive risk\nformulation to improve their predictions. Unlike conventional approaches that\ntreat each scan of a patient independently, our method incorporates a\nconstraint based on disease's progressive nature, ensuring that predicted TKR\nrisk either increases or remains stable over time when multiple scans of a knee\nare available. This was achieved by enforcing a progressive risk formulation\nconstraint during training with patients who have more than one available scan\nin the studies. Knee radiographs and MRIs from the Osteoarthritis Initiative\n(OAI) and Multicenter Osteoarthritis Study (MOST) were used in this work and\ndeep learning models were trained to predict TKR within 1, 2, and 4-year time\nperiods. The proposed approach, utilizing a dual-model risk constraint\narchitecture, demonstrated superior performance compared to baseline -\nconventional models trained with standard binary cross entropy loss. It\nachieved an AUROC of 0.87 and AUPRC of 0.47 for 1-year TKR prediction on the\nOAI radiograph test set, considerably improving over the baseline AUROC of 0.79\nand AUPRC of 0.34. For the MOST radiograph test set, the proposed approach\nachieved an AUROC of 0.77 and AUPRC of 0.25 for 1-year predictions,\noutperforming the baseline AUROC of 0.71 and AUPRC of 0.19. Similar trends were\nobserved in the MRI testsets\n", "date": "2024-06-14", "categories": []}
{"id": "2503.21818", "title": "Deep Learning-Based Quantitative Assessment of Renal Chronicity Indices\n  in Lupus Nephritis", "abstract": "  Background: Renal chronicity indices (CI) have been identified as strong\npredictors of long-term outcomes in lupus nephritis (LN) patients. However,\nassessment by pathologists is hindered by challenges such as substantial time\nrequirements, high interobserver variation, and susceptibility to fatigue. This\nstudy aims to develop an effective deep learning (DL) pipeline that automates\nthe assessment of CI and provides valuable prognostic insights from a\ndisease-specific perspective. Methods: We curated a dataset comprising 282\nslides obtained from 141 patients across two independent cohorts with a\ncomplete 10-years follow-up. Our DL pipeline was developed on 60 slides (22,410\npatch images) from 30 patients in the training cohort and evaluated on both an\ninternal testing set (148 slides, 77,605 patch images) and an external testing\nset (74 slides, 27,522 patch images). Results: The study included two cohorts\nwith slight demographic differences, particularly in age and hemoglobin levels.\nThe DL pipeline showed high segmentation performance across tissue compartments\nand histopathologic lesions, outperforming state-of-the-art methods. The DL\npipeline also demonstrated a strong correlation with pathologists in assessing\nCI, significantly improving interobserver agreement. Additionally, the DL\npipeline enhanced prognostic accuracy, particularly in outcome prediction, when\ncombined with clinical parameters and pathologist-assessed CIs Conclusions: The\nDL pipeline demonstrated accuracy and efficiency in assessing CI in LN, showing\npromise in improving interobserver agreement among pathologists. It also\nexhibited significant value in prognostic analysis and enhancing outcome\nprediction in LN patients, offering a valuable tool for clinical\ndecision-making.\n", "date": "2025-03-26", "categories": []}
{"id": "2503.22069", "title": "Contrasting Low and High-Resolution Features for HER2 Scoring using Deep\n  Learning", "abstract": "  Breast cancer, the most common malignancy among women, requires precise\ndetection and classification for effective treatment. Immunohistochemistry\n(IHC) biomarkers like HER2, ER, and PR are critical for identifying breast\ncancer subtypes. However, traditional IHC classification relies on\npathologists' expertise, making it labor-intensive and subject to significant\ninter-observer variability. To address these challenges, this study introduces\nthe India Pathology Breast Cancer Dataset (IPD-Breast), comprising of 1,272 IHC\nslides (HER2, ER, and PR) aimed at automating receptor status classification.\nThe primary focus is on developing predictive models for HER2 3-way\nclassification (0, Low, High) to enhance prognosis. Evaluation of multiple deep\nlearning models revealed that an end-to-end ConvNeXt network utilizing\nlow-resolution IHC images achieved an AUC, F1, and accuracy of 91.79%, 83.52%,\nand 83.56%, respectively, for 3-way classification, outperforming patch-based\nmethods by over 5.35% in F1 score. This study highlights the potential of\nsimple yet effective deep learning techniques to significantly improve accuracy\nand reproducibility in breast cancer classification, supporting their\nintegration into clinical workflows for better patient outcomes.\n", "date": "2025-03-28", "categories": []}
{"id": "2503.22092", "title": "Leveraging LLMs for Predicting Unknown Diagnoses from Clinical Notes", "abstract": "  Electronic Health Records (EHRs) often lack explicit links between\nmedications and diagnoses, making clinical decision-making and research more\ndifficult. Even when links exist, diagnosis lists may be incomplete, especially\nduring early patient visits. Discharge summaries tend to provide more complete\ninformation, which can help infer accurate diagnoses, especially with the help\nof large language models (LLMs). This study investigates whether LLMs can\npredict implicitly mentioned diagnoses from clinical notes and link them to\ncorresponding medications. We address two research questions: (1) Does majority\nvoting across diverse LLM configurations outperform the best single\nconfiguration in diagnosis prediction? (2) How sensitive is majority voting\naccuracy to LLM hyperparameters such as temperature, top-p, and summary length?\nTo evaluate, we created a new dataset of 240 expert-annotated\nmedication-diagnosis pairs from 20 MIMIC-IV notes. Using GPT-3.5 Turbo, we ran\n18 prompting configurations across short and long summary lengths, generating\n8568 test cases. Results show that majority voting achieved 75 percent\naccuracy, outperforming the best single configuration at 66 percent. No single\nhyperparameter setting dominated, but combining deterministic, balanced, and\nexploratory strategies improved performance. Shorter summaries generally led to\nhigher accuracy.In conclusion, ensemble-style majority voting with diverse LLM\nconfigurations improves diagnosis prediction in EHRs and offers a promising\nmethod to link medications and diagnoses in clinical texts.\n", "date": "2025-03-28", "categories": []}
{"id": "2503.22176", "title": "A Multi-Site Study on AI-Driven Pathology Detection and Osteoarthritis\n  Grading from Knee X-Ray", "abstract": "  Introduction: Bone health disorders like osteoarthritis and osteoporosis pose\nmajor global health challenges, often leading to delayed diagnoses due to\nlimited diagnostic tools. This study presents an AI-powered system that\nanalyzes knee X-rays to detect key pathologies, including joint space\nnarrowing, sclerosis, osteophytes, tibial spikes, alignment issues, and soft\ntissue anomalies. It also grades osteoarthritis severity, enabling timely,\npersonalized treatment.\n  Study Design: The research used 1.3 million knee X-rays from a multi-site\nIndian clinical trial across government, private, and SME hospitals. The\ndataset ensured diversity in demographics, imaging equipment, and clinical\nsettings. Rigorous annotation and preprocessing yielded high-quality training\ndatasets for pathology-specific models like ResNet15 for joint space narrowing\nand DenseNet for osteoarthritis grading.\n  Performance: The AI system achieved strong diagnostic accuracy across diverse\nimaging environments. Pathology-specific models excelled in precision, recall,\nand NPV, validated using Mean Squared Error (MSE), Intersection over Union\n(IoU), and Dice coefficient. Subgroup analyses across age, gender, and\nmanufacturer variations confirmed generalizability for real-world applications.\n  Conclusion: This scalable, cost-effective solution for bone health\ndiagnostics demonstrated robust performance in a multi-site trial. It holds\npromise for widespread adoption, especially in resource-limited healthcare\nsettings, transforming bone health management and enabling proactive patient\ncare.\n", "date": "2025-03-28", "categories": []}
{"id": "2411.10684", "title": "HIST-AID: Leveraging Historical Patient Reports for Enhanced Multi-Modal\n  Automatic Diagnosis", "abstract": "  Chest X-ray imaging is a widely accessible and non-invasive diagnostic tool\nfor detecting thoracic abnormalities. While numerous AI models assist\nradiologists in interpreting these images, most overlook patients' historical\ndata. To bridge this gap, we introduce Temporal MIMIC dataset, which integrates\nfive years of patient history, including radiographic scans and reports from\nMIMIC-CXR and MIMIC-IV, encompassing 12,221 patients and thirteen pathologies.\nBuilding on this, we present HIST-AID, a framework that enhances automatic\ndiagnostic accuracy using historical reports. HIST-AID emulates the\nradiologist's comprehensive approach, leveraging historical data to improve\ndiagnostic accuracy. Our experiments demonstrate significant improvements, with\nAUROC increasing by 6.56% and AUPRC by 9.51% compared to models that rely\nsolely on radiographic scans. These gains were consistently observed across\ndiverse demographic groups, including variations in gender, age, and racial\ncategories. We show that while recent data boost performance, older data may\nreduce accuracy due to changes in patient conditions. Our work paves the\npotential of incorporating historical data for more reliable automatic\ndiagnosis, providing critical support for clinical decision-making.\n", "date": "2024-11-16", "categories": []}
{"id": "2503.21241", "title": "Feature-Enhanced Machine Learning for All-Cause Mortality Prediction in\n  Healthcare Data", "abstract": "  Accurate patient mortality prediction enables effective risk stratification,\nleading to personalized treatment plans and improved patient outcomes. However,\npredicting mortality in healthcare remains a significant challenge, with\nexisting studies often focusing on specific diseases or limited predictor sets.\nThis study evaluates machine learning models for all-cause in-hospital\nmortality prediction using the MIMIC-III database, employing a comprehensive\nfeature engineering approach. Guided by clinical expertise and literature, we\nextracted key features such as vital signs (e.g., heart rate, blood pressure),\nlaboratory results (e.g., creatinine, glucose), and demographic information.\nThe Random Forest model achieved the highest performance with an AUC of 0.94,\nsignificantly outperforming other machine learning and deep learning\napproaches. This demonstrates Random Forest's robustness in handling\nhigh-dimensional, noisy clinical data and its potential for developing\neffective clinical decision support tools. Our findings highlight the\nimportance of careful feature engineering for accurate mortality prediction. We\nconclude by discussing implications for clinical adoption and propose future\ndirections, including enhancing model robustness and tailoring prediction\nmodels for specific diseases.\n", "date": "2025-03-27", "categories": []}
{"id": "2503.20103", "title": "Bigger But Not Better: Small Neural Language Models Outperform Large\n  Language Models in Detection of Thought Disorder", "abstract": "  Disorganized thinking is a key diagnostic indicator of schizophrenia-spectrum\ndisorders. Recently, clinical estimates of the severity of disorganized\nthinking have been shown to correlate with measures of how difficult speech\ntranscripts would be for large language models (LLMs) to predict. However,\nLLMs' deployment challenges -- including privacy concerns, computational and\nfinancial costs, and lack of transparency of training data -- limit their\nclinical utility. We investigate whether smaller neural language models can\nserve as effective alternatives for detecting positive formal thought disorder,\nusing the same sliding window based perplexity measurements that proved\neffective with larger models. Surprisingly, our results show that smaller\nmodels are more sensitive to linguistic differences associated with formal\nthought disorder than their larger counterparts. Detection capability declines\nbeyond a certain model size and context length, challenging the common\nassumption of ``bigger is better'' for LLM-based applications. Our findings\ngeneralize across audio diaries and clinical interview speech samples from\nindividuals with psychotic symptoms, suggesting a promising direction for\ndeveloping efficient, cost-effective, and privacy-preserving screening tools\nthat can be deployed in both clinical and naturalistic settings.\n", "date": "2025-03-25", "categories": []}
{"id": "2503.20496", "title": "Enhancing Depression Detection via Question-wise Modality Fusion", "abstract": "  Depression is a highly prevalent and disabling condition that incurs\nsubstantial personal and societal costs. Current depression diagnosis involves\ndetermining the depression severity of a person through self-reported\nquestionnaires or interviews conducted by clinicians. This often leads to\ndelayed treatment and involves substantial human resources. Thus, several works\ntry to automate the process using multimodal data. However, they usually\noverlook the following: i) The variable contribution of each modality for each\nquestion in the questionnaire and ii) Using ordinal classification for the\ntask. This results in sub-optimal fusion and training methods. In this work, we\npropose a novel Question-wise Modality Fusion (QuestMF) framework trained with\na novel Imbalanced Ordinal Log-Loss (ImbOLL) function to tackle these issues.\nThe performance of our framework is comparable to the current state-of-the-art\nmodels on the E-DAIC dataset and enhances interpretability by predicting scores\nfor each question. This will help clinicians identify an individual's symptoms,\nallowing them to customise their interventions accordingly. We also make the\ncode for the QuestMF framework publicly available.\n", "date": "2025-03-26", "categories": []}
{"id": "2503.13281", "title": "LLM-Match: An Open-Sourced Patient Matching Model Based on Large\n  Language Models and Retrieval-Augmented Generation", "abstract": "  Patient matching is the process of linking patients to appropriate clinical\ntrials by accurately identifying and matching their medical records with trial\neligibility criteria. We propose LLM-Match, a novel framework for patient\nmatching leveraging fine-tuned open-source large language models. Our approach\nconsists of four key components. First, a retrieval-augmented generation (RAG)\nmodule extracts relevant patient context from a vast pool of electronic health\nrecords (EHRs). Second, a prompt generation module constructs input prompts by\nintegrating trial eligibility criteria (both inclusion and exclusion criteria),\npatient context, and system instructions. Third, a fine-tuning module with a\nclassification head optimizes the model parameters using structured prompts and\nground-truth labels. Fourth, an evaluation module assesses the fine-tuned\nmodel's performance on the testing datasets. We evaluated LLM-Match on four\nopen datasets - n2c2, SIGIR, TREC 2021, and TREC 2022 - using open-source\nmodels, comparing it against TrialGPT, Zero-Shot, and GPT-4-based closed\nmodels. LLM-Match outperformed all baselines.\n", "date": "2025-03-17", "categories": []}
{"id": "2503.18996", "title": "Enhanced prediction of spine surgery outcomes using advanced machine\n  learning techniques and oversampling methods", "abstract": "  The study proposes an advanced machine learning approach to predict spine\nsurgery outcomes by incorporating oversampling techniques and grid search\noptimization. A variety of models including GaussianNB, ComplementNB, KNN,\nDecision Tree, and optimized versions with RandomOverSampler and SMOTE were\ntested on a dataset of 244 patients, which included pre-surgical, psychometric,\nsocioeconomic, and analytical variables. The enhanced KNN models achieved up to\n76% accuracy and a 67% F1-score, while grid-search optimization further\nimproved performance. The findings underscore the potential of these advanced\ntechniques to aid healthcare professionals in decision-making, with future\nresearch needed to refine these models on larger and more diverse datasets.\n", "date": "2025-03-23", "categories": []}
{"id": "2503.19820", "title": "A Systematic Review of EEG-based Machine Intelligence Algorithms for\n  Depression Diagnosis, and Monitoring", "abstract": "  Depression disorder is a serious health condition that has affected the lives\nof millions of people around the world. Diagnosis of depression is a\nchallenging practice that relies heavily on subjective studies and, in most\ncases, suffers from late findings. Electroencephalography (EEG) biomarkers have\nbeen suggested and investigated in recent years as a potential transformative\nobjective practice. In this article, for the first time, a detailed systematic\nreview of EEG-based depression diagnosis approaches is conducted using advanced\nmachine learning techniques and statistical analyses. For this, 938 potentially\nrelevant articles (since 1985) were initially detected and filtered into 139\nrelevant articles based on the review scheme 'preferred reporting items for\nsystematic reviews and meta-analyses (PRISMA).' This article compares and\ndiscusses the selected articles and categorizes them according to the type of\nmachine learning techniques and statistical analyses. Algorithms, preprocessing\ntechniques, extracted features, and data acquisition systems are discussed and\nsummarized. This review paper explains the existing challenges of the current\nalgorithms and sheds light on the future direction of the field. This\nsystematic review outlines the issues and challenges in machine intelligence\nfor the diagnosis of EEG depression that can be addressed in future studies and\npossibly in future wearable technologies.\n", "date": "2025-03-25", "categories": []}
{"id": "2503.13999", "title": "BI-RADS prediction of mammographic masses using uncertainty information\n  extracted from a Bayesian Deep Learning model", "abstract": "  The BI_RADS score is a probabilistic reporting tool used by radiologists to\nexpress the level of uncertainty in predicting breast cancer based on some\nmorphological features in mammography images. There is a significant\nvariability in describing masses which sometimes leads to BI_RADS\nmisclassification. Using a BI_RADS prediction system is required to support the\nfinal radiologist decisions. In this study, the uncertainty information\nextracted by a Bayesian deep learning model is utilized to predict the BI_RADS\nscore. The investigation results based on the pathology information demonstrate\nthat the f1-scores of the predictions of the radiologist are 42.86%, 48.33% and\n48.28%, meanwhile, the f1-scores of the model performance are 73.33%, 59.60%\nand 59.26% in the BI_RADS 2, 3 and 5 dataset samples, respectively. Also, the\nmodel can distinguish malignant from benign samples in the BI_RADS 0 category\nof the used dataset with an accuracy of 75.86% and correctly identify all\nmalignant samples as BI_RADS 5. The Grad-CAM visualization shows the model pays\nattention to the morphological features of the lesions. Therefore, this study\nshows the uncertainty-aware Bayesian Deep Learning model can report his\nuncertainty about the malignancy of a lesion based on morphological features,\nlike a radiologist.\n", "date": "2025-03-18", "categories": []}
{"id": "2503.17664", "title": "CardioTabNet: A Novel Hybrid Transformer Model for Heart Disease\n  Prediction using Tabular Medical Data", "abstract": "  The early detection and prediction of cardiovascular diseases are crucial for\nreducing the severe morbidity and mortality associated with these conditions\nworldwide. A multi-headed self-attention mechanism, widely used in natural\nlanguage processing (NLP), is operated by Transformers to understand feature\ninteractions in feature spaces. However, the relationships between various\nfeatures within biological systems remain ambiguous in these spaces,\nhighlighting the necessity of early detection and prediction of cardiovascular\ndiseases to reduce the severe morbidity and mortality with these conditions\nworldwide. We handle this issue with CardioTabNet, which exploits the strength\nof tab transformer to extract feature space which carries strong understanding\nof clinical cardiovascular data and its feature ranking. As a result,\nperformance of downstream classical models significantly showed outstanding\nresult. Our study utilizes the open-source dataset for heart disease prediction\nwith 1190 instances and 11 features. In total, 11 features are divided into\nnumerical (age, resting blood pressure, cholesterol, maximum heart rate, old\npeak, weight, and fasting blood sugar) and categorical (resting ECG, exercise\nangina, and ST slope). Tab transformer was used to extract important features\nand ranked them using random forest (RF) feature ranking algorithm. Ten\nmachine-learning models were used to predict heart disease using selected\nfeatures. After extracting high-quality features, the top downstream model (a\nhyper-tuned ExtraTree classifier) achieved an average accuracy rate of 94.1%\nand an average Area Under Curve (AUC) of 95.0%. Furthermore, a nomogram\nanalysis was conducted to evaluate the model's effectiveness in cardiovascular\nrisk assessment. A benchmarking study was conducted using state-of-the-art\nmodels to evaluate our transformer-driven framework.\n", "date": "2025-03-22", "categories": []}
{"id": "2503.18141", "title": "AGIR: Assessing 3D Gait Impairment with Reasoning based on LLMs", "abstract": "  Assessing gait impairment plays an important role in early diagnosis, disease\nmonitoring, and treatment evaluation for neurodegenerative diseases. Despite\nits widespread use in clinical practice, it is limited by subjectivity and a\nlack of precision. While recent deep learning-based approaches have\nconsistently improved classification accuracies, they often lack\ninterpretability, hindering their utility in clinical decision-making. To\novercome these challenges, we introduce AGIR, a novel pipeline consisting of a\npre-trained VQ-VAE motion tokenizer and a subsequent Large Language Model (LLM)\nfine-tuned over pairs of motion tokens and Chain-of-Thought (CoT) reasonings.\nTo fine-tune an LLM for pathological gait analysis, we first introduce a\nmultimodal dataset by adding rationales dedicated to MDS-UPDRS gait score\nassessment to an existing PD gait dataset. We then introduce a two-stage\nsupervised fine-tuning (SFT) strategy to enhance the LLM's motion comprehension\nwith pathology-specific knowledge. This strategy includes: 1) a generative\nstage that aligns gait motions with analytic descriptions through bidirectional\nmotion-description generation, 2) a reasoning stage that integrates logical\nChain-of-Thought (CoT) reasoning for impairment assessment with UPDRS gait\nscore. Validation on an existing dataset and comparisons with state-of-the-art\nmethods confirm the robustness and accuracy of our pipeline, demonstrating its\nability to assign gait impairment scores from motion input with clinically\nmeaningful rationales.\n", "date": "2025-03-23", "categories": []}
{"id": "2503.16560", "title": "Early Prediction of Alzheimer's and Related Dementias: A Machine\n  Learning Approach Utilizing Social Determinants of Health Data", "abstract": "  Alzheimer's disease and related dementias (AD/ADRD) represent a growing\nhealthcare crisis affecting over 6 million Americans. While genetic factors\nplay a crucial role, emerging research reveals that social determinants of\nhealth (SDOH) significantly influence both the risk and progression of\ncognitive functioning, such as cognitive scores and cognitive decline. This\nreport examines how these social, environmental, and structural factors impact\ncognitive health trajectories, with a particular focus on Hispanic populations,\nwho face disproportionate risk for AD/ADRD. Using data from the Mexican Health\nand Aging Study (MHAS) and its cognitive assessment sub study (Mex-Cog), we\nemployed ensemble of regression trees models to predict 4-year and 9-year\ncognitive scores and cognitive decline based on SDOH. This approach identified\nkey predictive SDOH factors to inform potential multilevel interventions to\naddress cognitive health disparities in this population.\n", "date": "2025-03-20", "categories": []}
{"id": "2503.16577", "title": "Feature selection strategies for optimized heart disease diagnosis using\n  ML and DL models", "abstract": "  Heart disease remains one of the leading causes of morbidity and mortality\nworldwide, necessitating the development of effective diagnostic tools to\nenable early diagnosis and clinical decision-making. This study evaluates the\nimpact of feature selection techniques Mutual Information (MI), Analysis of\nVariance (ANOVA), and Chi-Square on the predictive performance of various\nmachine learning (ML) and deep learning (DL) models using a dataset of clinical\nindicators for heart disease. Eleven ML/DL models were assessed using metrics\nsuch as precision, recall, AUC score, F1-score, and accuracy. Results indicate\nthat MI outperformed other methods, particularly for advanced models like\nneural networks, achieving the highest accuracy of 82.3% and recall score of\n0.94. Logistic regression (accuracy 82.1%) and random forest (accuracy 80.99%)\nalso demonstrated improved performance with MI. Simpler models such as Naive\nBayes and decision trees achieved comparable results with ANOVA and Chi-Square,\nyielding accuracies of 76.45% and 75.99%, respectively, making them\ncomputationally efficient alternatives. Conversely, k Nearest Neighbors (KNN)\nand Support Vector Machines (SVM) exhibited lower performance, with accuracies\nranging between 51.52% and 54.43%, regardless of the feature selection method.\nThis study provides a comprehensive comparison of feature selection methods for\nheart disease prediction, demonstrating the critical role of feature selection\nin optimizing model performance. The results offer practical guidance for\nselecting appropriate feature selection techniques based on the chosen\nclassification algorithm, contributing to the development of more accurate and\nefficient diagnostic tools for enhanced clinical decision-making in cardiology.\n", "date": "2025-03-20", "categories": []}
{"id": "2503.16708", "title": "NeuroSep-CP-LCB: A Deep Learning-based Contextual Multi-armed Bandit\n  Algorithm with Uncertainty Quantification for Early Sepsis Prediction", "abstract": "  In critical care settings, timely and accurate predictions can significantly\nimpact patient outcomes, especially for conditions like sepsis, where early\nintervention is crucial. We aim to model patient-specific reward functions in a\ncontextual multi-armed bandit setting. The goal is to leverage patient-specific\nclinical features to optimize decision-making under uncertainty. This paper\nproposes NeuroSep-CP-LCB, a novel integration of neural networks with\ncontextual bandits and conformal prediction tailored for early sepsis\ndetection. Unlike the algorithm pool selection problem in the previous paper,\nwhere the primary focus was identifying the most suitable pre-trained model for\nprediction tasks, this work directly models the reward function using a neural\nnetwork, allowing for personalized and adaptive decision-making. Combining the\nrepresentational power of neural networks with the robustness of conformal\nprediction intervals, this framework explicitly accounts for uncertainty in\noffline data distributions and provides actionable confidence bounds on\npredictions.\n", "date": "2025-03-20", "categories": []}
{"id": "2503.17105", "title": "A Comparative Analysis of Image Descriptors for Histopathological\n  Classification of Gastric Cancer", "abstract": "  Gastric cancer ranks as the fifth most common and fourth most lethal cancer\nglobally, with a dismal 5-year survival rate of approximately 20%. Despite\nextensive research on its pathobiology, the prognostic predictability remains\ninadequate, compounded by pathologists' high workload and potential diagnostic\nerrors. Thus, automated, accurate histopathological diagnosis tools are\ncrucial. This study employs Machine Learning and Deep Learning techniques to\nclassify histopathological images into healthy and cancerous categories. Using\nhandcrafted and deep features with shallow learning classifiers on the\nGasHisSDB dataset, we offer a comparative analysis and insights into the most\nrobust and high-performing combinations of features and classifiers for\ndistinguishing between normal and abnormal histopathological images without\nfine-tuning strategies. With the RF classifier, our approach can reach F1 of\n93.4%, demonstrating its validity.\n", "date": "2025-03-21", "categories": []}
{"id": "2503.07667", "title": "CLIMB: Data Foundations for Large Scale Multimodal Clinical Foundation\n  Models", "abstract": "  Recent advances in clinical AI have enabled remarkable progress across many\nclinical domains. However, existing benchmarks and models are primarily limited\nto a small set of modalities and tasks, which hinders the development of\nlarge-scale multimodal methods that can make holistic assessments of patient\nhealth and well-being. To bridge this gap, we introduce Clinical Large-Scale\nIntegrative Multimodal Benchmark (CLIMB), a comprehensive clinical benchmark\nunifying diverse clinical data across imaging, language, temporal, and graph\nmodalities. CLIMB comprises 4.51 million patient samples totaling 19.01\nterabytes distributed across 2D imaging, 3D video, time series, graphs, and\nmultimodal data. Through extensive empirical evaluation, we demonstrate that\nmultitask pretraining significantly improves performance on understudied\ndomains, achieving up to 29% improvement in ultrasound and 23% in ECG analysis\nover single-task learning. Pretraining on CLIMB also effectively improves\nmodels' generalization capability to new tasks, and strong unimodal encoder\nperformance translates well to multimodal performance when paired with\ntask-appropriate fusion strategies. Our findings provide a foundation for new\narchitecture designs and pretraining strategies to advance clinical AI\nresearch. Code is released at https://github.com/DDVD233/climb.\n", "date": "2025-03-09", "categories": []}
{"id": "2503.16091", "title": "AIMI: Leveraging Future Knowledge and Personalization in Sparse Event\n  Forecasting for Treatment Adherence", "abstract": "  Adherence to prescribed treatments is crucial for individuals with chronic\nconditions to avoid costly or adverse health outcomes. For certain patient\ngroups, intensive lifestyle interventions are vital for enhancing medication\nadherence. Accurate forecasting of treatment adherence can open pathways to\ndeveloping an on-demand intervention tool, enabling timely and personalized\nsupport. With the increasing popularity of smartphones and wearables, it is now\neasier than ever to develop and deploy smart activity monitoring systems.\nHowever, effective forecasting systems for treatment adherence based on\nwearable sensors are still not widely available. We close this gap by proposing\nAdherence Forecasting and Intervention with Machine Intelligence (AIMI). AIMI\nis a knowledge-guided adherence forecasting system that leverages smartphone\nsensors and previous medication history to estimate the likelihood of\nforgetting to take a prescribed medication. A user study was conducted with 27\nparticipants who took daily medications to manage their cardiovascular\ndiseases. We designed and developed CNN and LSTM-based forecasting models with\nvarious combinations of input features and found that LSTM models can forecast\nmedication adherence with an accuracy of 0.932 and an F-1 score of 0.936.\nMoreover, through a series of ablation studies involving convolutional and\nrecurrent neural network architectures, we demonstrate that leveraging known\nknowledge about future and personalized training enhances the accuracy of\nmedication adherence forecasting. Code available:\nhttps://github.com/ab9mamun/AIMI.\n", "date": "2025-03-20", "categories": []}
{"id": "2503.16286", "title": "Explainable Graph-theoretical Machine Learning: with Application to\n  Alzheimer's Disease Prediction", "abstract": "  Alzheimer's disease (AD) affects 50 million people worldwide and is projected\nto overwhelm 152 million by 2050. AD is characterized by cognitive decline due\npartly to disruptions in metabolic brain connectivity. Thus, early and accurate\ndetection of metabolic brain network impairments is crucial for AD management.\nChief to identifying such impairments is FDG-PET data. Despite advancements,\nmost graph-based studies using FDG-PET data rely on group-level analysis or\nthresholding. Yet, group-level analysis can veil individual differences and\nthresholding may overlook weaker but biologically critical brain connections.\nAdditionally, machine learning-based AD prediction largely focuses on\nunivariate outcomes, such as disease status. Here, we introduce explainable\ngraph-theoretical machine learning (XGML), a framework employing kernel density\nestimation and dynamic time warping to construct individual metabolic brain\ngraphs that capture the distance between pair-wise brain regions and identify\nsubgraphs most predictive of multivariate AD-related outcomes. Using FDG-PET\ndata from the Alzheimer's Disease Neuroimaging Initiative, XGML builds\nmetabolic brain graphs and uncovers subgraphs predictive of eight AD-related\ncognitive scores in new subjects. XGML shows robust performance, particularly\nfor predicting scores measuring learning, memory, language, praxis, and\norientation, such as CDRSB ($r = 0.74$), ADAS11 ($r = 0.73$), and ADAS13 ($r =\n0.71$). Moreover, XGML unveils key edges jointly but differentially predictive\nof several AD-related outcomes; they may serve as potential network biomarkers\nfor assessing overall cognitive decline. Together, we show the promise of\ngraph-theoretical machine learning in biomarker discovery and disease\nprediction and its potential to improve our understanding of network neural\nmechanisms underlying AD.\n", "date": "2025-03-20", "categories": []}
{"id": "2412.16178", "title": "Context Clues: Evaluating Long Context Models for Clinical Prediction\n  Tasks on EHRs", "abstract": "  Foundation Models (FMs) trained on Electronic Health Records (EHRs) have\nachieved state-of-the-art results on numerous clinical prediction tasks.\nHowever, most existing EHR FMs have context windows of <1k tokens. This\nprevents them from modeling full patient EHRs which can exceed 10k's of events.\nRecent advancements in subquadratic long-context architectures (e.g., Mamba)\noffer a promising solution. However, their application to EHR data has not been\nwell-studied. We address this gap by presenting the first systematic evaluation\nof the effect of context length on modeling EHR data. We find that longer\ncontext models improve predictive performance -- our Mamba-based model\nsurpasses the prior state-of-the-art on 9/14 tasks on the EHRSHOT prediction\nbenchmark. For clinical applications, however, model performance alone is\ninsufficient -- robustness to the unique properties of EHR is crucial. Thus, we\nalso evaluate models across three previously underexplored properties of EHR\ndata: (1) the prevalence of \"copy-forwarded\" diagnoses which creates artificial\nrepetition of tokens within EHR sequences; (2) the irregular time intervals\nbetween EHR events which can lead to a wide range of timespans within a context\nwindow; and (3) the natural increase in disease complexity over time which\nmakes later tokens in the EHR harder to predict than earlier ones. Stratifying\nour EHRSHOT results, we find that higher levels of each property correlate\nnegatively with model performance, but that longer context models are more\nrobust to more extreme levels of these properties. Our work highlights the\npotential for using long-context architectures to model EHR data, and offers a\ncase study for identifying new challenges in modeling sequential data motivated\nby domains outside of natural language. We release our models and code at:\nhttps://github.com/som-shahlab/long_context_clues\n", "date": "2024-12-09", "categories": []}
{"id": "2503.09659", "title": "Edge AI for Real-time Fetal Assessment in Rural Guatemala", "abstract": "  Perinatal complications, defined as conditions that arise during pregnancy,\nchildbirth, and the immediate postpartum period, represent a significant burden\non maternal and neonatal health worldwide. Factors contributing to these\ndisparities include limited access to quality healthcare, socioeconomic\ninequalities, and variations in healthcare infrastructure. Addressing these\nissues is crucial for improving health outcomes for mothers and newborns,\nparticularly in underserved communities. To mitigate these challenges, we have\ndeveloped an AI-enabled smartphone application designed to provide decision\nsupport at the point-of-care. This tool aims to enhance health monitoring\nduring pregnancy by leveraging machine learning (ML) techniques. The intended\nuse of this application is to assist midwives during routine home visits by\noffering real-time analysis and providing feedback based on collected data. The\napplication integrates TensorFlow Lite (TFLite) and other Python-based\nalgorithms within a Kotlin framework to process data in real-time. It is\ndesigned for use in low-resource settings, where traditional healthcare\ninfrastructure may be lacking. The intended patient population includes\npregnant women and new mothers in underserved areas and the developed system\nwas piloted in rural Guatemala. This ML-based solution addresses the critical\nneed for accessible and quality perinatal care by empowering healthcare\nproviders with decision support tools to improve maternal and neonatal health\noutcomes.\n", "date": "2025-03-12", "categories": []}
{"id": "2503.14512", "title": "Machine learning algorithms to predict stroke in China based on causal\n  inference of time series analysis", "abstract": "  Participants: This study employed a combination of Vector Autoregression\n(VAR) model and Graph Neural Networks (GNN) to systematically construct dynamic\ncausal inference. Multiple classic classification algorithms were compared,\nincluding Random Forest, Logistic Regression, XGBoost, Support Vector Machine\n(SVM), K-Nearest Neighbor (KNN), Gradient Boosting, and Multi Layer Perceptron\n(MLP). The SMOTE algorithm was used to undersample a small number of samples\nand employed Stratified K-fold Cross Validation. Results: This study included a\ntotal of 11,789 participants, including 6,334 females (53.73%) and 5,455 males\n(46.27%), with an average age of 65 years. Introduction of dynamic causal\ninference features has significantly improved the performance of almost all\nmodels. The area under the ROC curve of each model ranged from 0.78 to 0.83,\nindicating significant difference (P < 0.01). Among all the models, the\nGradient Boosting model demonstrated the highest performance and stability.\nModel explanation and feature importance analysis generated model\ninterpretation that illustrated significant contributors associated with risks\nof stroke. Conclusions and Relevance: This study proposes a stroke risk\nprediction method that combines dynamic causal inference with machine learning\nmodels, significantly improving prediction accuracy and revealing key health\nfactors that affect stroke. The research results indicate that dynamic causal\ninference features have important value in predicting stroke risk, especially\nin capturing the impact of changes in health status over time on stroke risk.\nBy further optimizing the model and introducing more variables, this study\nprovides theoretical basis and practical guidance for future stroke prevention\nand intervention strategies.\n", "date": "2025-03-10", "categories": []}
{"id": "2503.14542", "title": "AI-Driven Rapid Identification of Bacterial and Fungal Pathogens in\n  Blood Smears of Septic Patients", "abstract": "  Sepsis is a life-threatening condition which requires rapid diagnosis and\ntreatment. Traditional microbiological methods are time-consuming and\nexpensive. In response to these challenges, deep learning algorithms were\ndeveloped to identify 14 bacteria species and 3 yeast-like fungi from\nmicroscopic images of Gram-stained smears of positive blood samples from sepsis\npatients.\n  A total of 16,637 Gram-stained microscopic images were used in the study. The\nanalysis used the Cellpose 3 model for segmentation and Attention-based Deep\nMultiple Instance Learning for classification. Our model achieved an accuracy\nof 77.15% for bacteria and 71.39% for fungi, with ROC AUC of 0.97 and 0.88,\nrespectively. The highest values, reaching up to 96.2%, were obtained for\nCutibacterium acnes, Enterococcus faecium, Stenotrophomonas maltophilia and\nNakaseomyces glabratus. Classification difficulties were observed in closely\nrelated species, such as Staphylococcus hominis and Staphylococcus\nhaemolyticus, due to morphological similarity, and within Candida albicans due\nto high morphotic diversity.\n  The study confirms the potential of our model for microbial classification,\nbut it also indicates the need for further optimisation and expansion of the\ntraining data set. In the future, this technology could support microbial\ndiagnosis, reducing diagnostic time and improving the effectiveness of sepsis\ntreatment due to its simplicity and accessibility. Part of the results\npresented in this publication was covered by a patent application at the\nEuropean Patent Office EP24461637.1 \"A computer implemented method for\nidentifying a microorganism in a blood and a data processing system therefor\".\n", "date": "2025-03-17", "categories": []}
{"id": "2503.14546", "title": "The Impact of Artificial Intelligence on Emergency Medicine: A Review of\n  Recent Advances", "abstract": "  Artificial Intelligence (AI) is revolutionizing emergency medicine by\nenhancing diagnostic processes and improving patient outcomes. This article\nprovides a review of the current applications of AI in emergency imaging\nstudies, focusing on the last five years of advancements. AI technologies,\nparticularly machine learning and deep learning, are pivotal in interpreting\ncomplex imaging data, offering rapid, accurate diagnoses and potentially\nsurpassing traditional diagnostic methods. Studies highlighted within the\narticle demonstrate AI's capabilities in accurately detecting conditions such\nas fractures, pneumothorax, and pulmonary diseases from various imaging\nmodalities including X-rays, CT scans, and MRIs. Furthermore, AI's ability to\npredict clinical outcomes like mechanical ventilation needs illustrates its\npotential in crisis resource optimization. Despite these advancements, the\nintegration of AI into clinical practice presents challenges such as data\nprivacy, algorithmic bias, and the need for extensive validation across diverse\nsettings. This review underscores the transformative potential of AI in\nemergency settings, advocating for a future where AI and clinical expertise\nsynergize to elevate patient care standards.\n", "date": "2025-03-17", "categories": []}
{"id": "2503.14562", "title": "Analysis of human visual field information using machine learning\n  methods and assessment of their accuracy", "abstract": "  Subject of research: is the study of methods for analyzing perimetric images\nfor the diagnosis and control of glaucoma diseases. Objects of research: is a\ndataset collected on the ophthalmological perimeter with the results of various\npatient pathologies, since the ophthalmological community is acutely aware of\nthe issue of disease control and import substitution. [5]. Purpose of research:\nis to consider various machine learning methods that can classify glaucoma.\nThis is possible thanks to the classifier built after labeling the dataset. It\nis able to determine from the image whether the visual fields depicted on it\nare the results of the impact of glaucoma on the eyes or other visual diseases.\nEarlier in the work [3], a dataset was described that was collected on the\nTomey perimeter. The average age of the examined patients ranged from 30 to 85\nyears. Methods of research: machine learning methods for classifying image\nresults (stochastic gradient descent, logistic regression, random forest, naive\nBayes). Main results of research: the result of the study is computer modeling\nthat can determine from the image whether the result is glaucoma or another\ndisease (binary classification).\n", "date": "2025-03-18", "categories": []}
{"id": "2503.14621", "title": "Reducing False Ventricular Tachycardia Alarms in ICU Settings: A Machine\n  Learning Approach", "abstract": "  False arrhythmia alarms in intensive care units (ICUs) are a significant\nchallenge, contributing to alarm fatigue and potentially compromising patient\nsafety. Ventricular tachycardia (VT) alarms are particularly difficult to\ndetect accurately due to their complex nature. This paper presents a machine\nlearning approach to reduce false VT alarms using the VTaC dataset, a benchmark\ndataset of annotated VT alarms from ICU monitors. We extract time-domain and\nfrequency-domain features from waveform data, preprocess the data, and train\ndeep learning models to classify true and false VT alarms. Our results\ndemonstrate high performance, with ROC-AUC scores exceeding 0.96 across various\ntraining configurations. This work highlights the potential of machine learning\nto improve the accuracy of VT alarm detection in clinical settings.\n", "date": "2025-03-18", "categories": []}
{"id": "2503.14663", "title": "Sepsyn-OLCP: An Online Learning-based Framework for Early Sepsis\n  Prediction with Uncertainty Quantification using Conformal Prediction", "abstract": "  Sepsis is a life-threatening syndrome with high morbidity and mortality in\nhospitals. Early prediction of sepsis plays a crucial role in facilitating\nearly interventions for septic patients. However, early sepsis prediction\nsystems with uncertainty quantification and adaptive learning are scarce. This\npaper proposes Sepsyn-OLCP, a novel online learning algorithm for early sepsis\nprediction by integrating conformal prediction for uncertainty quantification\nand Bayesian bandits for adaptive decision-making. By combining the robustness\nof Bayesian models with the statistical uncertainty guarantees of conformal\nprediction methodologies, this algorithm delivers accurate and trustworthy\npredictions, addressing the critical need for reliable and adaptive systems in\nhigh-stakes healthcare applications such as early sepsis prediction. We\nevaluate the performance of Sepsyn-OLCP in terms of regret in stochastic bandit\nsetting, the area under the receiver operating characteristic curve (AUROC),\nand F-measure. Our results show that Sepsyn-OLCP outperforms existing\nindividual models, increasing AUROC of a neural network from 0.64 to 0.73\nwithout retraining and high computational costs. And the model selection policy\nconverges to the optimal strategy in the long run. We propose a novel\nreinforcement learning-based framework integrated with conformal prediction\ntechniques to provide uncertainty quantification for early sepsis prediction.\nThe proposed methodology delivers accurate and trustworthy predictions,\naddressing a critical need in high-stakes healthcare applications like early\nsepsis prediction.\n", "date": "2025-03-18", "categories": []}
{"id": "2503.15374", "title": "Real-world validation of a multimodal LLM-powered pipeline for\n  High-Accuracy Clinical Trial Patient Matching leveraging EHR data", "abstract": "  Background: Patient recruitment in clinical trials is hindered by complex\neligibility criteria and labor-intensive chart reviews. Prior research using\ntext-only models have struggled to address this problem in a reliable and\nscalable way due to (1) limited reasoning capabilities, (2) information loss\nfrom converting visual records to text, and (3) lack of a generic EHR\nintegration to extract patient data.\n  Methods: We introduce a broadly applicable, integration-free, LLM-powered\npipeline that automates patient-trial matching using unprocessed documents\nextracted from EHRs. Our approach leverages (1) the new reasoning-LLM paradigm,\nenabling the assessment of even the most complex criteria, (2) visual\ncapabilities of latest LLMs to interpret medical records without lossy\nimage-to-text conversions, and (3) multimodal embeddings for efficient medical\nrecord search. The pipeline was validated on the n2c2 2018 cohort selection\ndataset (288 diabetic patients) and a real-world dataset composed of 485\npatients from 30 different sites matched against 36 diverse trials.\n  Results: On the n2c2 dataset, our method achieved a new state-of-the-art\ncriterion-level accuracy of 93\\%. In real-world trials, the pipeline yielded an\naccuracy of 87\\%, undermined by the difficulty to replicate human\ndecision-making when medical records lack sufficient information. Nevertheless,\nusers were able to review overall eligibility in under 9 minutes per patient on\naverage, representing an 80\\% improvement over traditional manual chart\nreviews.\n  Conclusion: This pipeline demonstrates robust performance in clinical trial\npatient matching without requiring custom integration with site systems or\ntrial-specific tailoring, thereby enabling scalable deployment across sites\nseeking to leverage AI for patient matching.\n", "date": "2025-03-19", "categories": []}
{"id": "2501.05661", "title": "TAMER: A Test-Time Adaptive MoE-Driven Framework for EHR Representation\n  Learning", "abstract": "  We propose TAMER, a Test-time Adaptive MoE-driven framework for Electronic\nHealth Record (EHR) Representation learning. TAMER introduces a framework where\na Mixture-of-Experts (MoE) architecture is co-designed with Test-Time\nAdaptation (TTA) to jointly mitigate the intertwined challenges of patient\nheterogeneity and distribution shifts in EHR modeling. The MoE focuses on\nlatent patient subgroups through domain-aware expert specialization, while TTA\nenables real-time adaptation to evolving health status distributions when new\npatient samples are introduced. Extensive experiments across four real-world\nEHR datasets demonstrate that TAMER consistently improves predictive\nperformance for both mortality and readmission risk tasks when combined with\ndiverse EHR modeling backbones. TAMER offers a promising approach for dynamic\nand personalized EHR-based predictions in practical clinical settings.\n", "date": "2025-01-10", "categories": []}
{"id": "2503.13475", "title": "Cross-Subject Depression Level Classification Using EEG Signals with a\n  Sample Confidence Method", "abstract": "  Electroencephalogram (EEG) is a non-invasive tool for real-time neural\nmonitoring,widely used in depression detection via deep learning. However,\nexisting models primarily focus on binary classification (depression/normal),\nlacking granularity for severity assessment. To address this, we proposed the\nDepL-GCN, i.e., Depression Level classification based on GCN model. This model\ntackles two key challenges: (1) subjectivity in depres-sion-level labeling due\nto patient self-report biases, and (2) class imbalance across severity\ncategories. Inspired by the model learning patterns, we introduced two novel\nmodules: the sample confidence module and the minority sample penalty module.\nThe former leverages the L2-norm of prediction errors to progressively filter\nEEG samples with weak label alignment during training, thereby reducing the\nimpact of subjectivity; the latter automatically upweights misclassified\nminority-class samples to address imbalance issues. After testing on two public\nEEG datasets, DepL-GCN achieved accuracies of 81.13% and 81.36% for multi-class\nseverity recognition, outperforming baseline models.Ablation studies confirmed\nboth modules' contributions. We further discussed the strengths and limitations\nof regression-based models for depression-level recognition.\n", "date": "2025-03-04", "categories": []}
{"id": "2503.14239", "title": "Predicting Cardiopulmonary Exercise Testing Outcomes in Congenital Heart\n  Disease Through Multi-modal Data Integration and Geometric Learning", "abstract": "  Cardiopulmonary exercise testing (CPET) provides a comprehensive assessment\nof functional capacity by measuring key physiological variables including\noxygen consumption ($VO_2$), carbon dioxide production ($VCO_2$), and pulmonary\nventilation ($VE$) during exercise. Previous research has established that\nparameters such as peak $VO_2$ and $VE/VCO_2$ ratio serve as robust predictors\nof mortality risk in chronic heart failure patients. In this study, we leverage\nCPET variables as surrogate mortality endpoints for patients with Congenital\nHeart Disease (CHD). To our knowledge, this represents the first successful\nimplementation of an advanced machine learning approach that predicts CPET\noutcomes by integrating electrocardiograms (ECGs) with information derived from\nclinical letters. Our methodology began with extracting unstructured patient\ninformation-including intervention history, diagnoses, and medication\nregimens-from clinical letters using natural language processing techniques,\norganizing this data into a structured database. We then digitized ECGs to\nobtain quantifiable waveforms and established comprehensive data linkages. The\ncore innovation of our approach lies in exploiting the Riemannian geometric\nproperties of covariance matrices derived from both 12-lead ECGs and clinical\ntext data to develop robust regression and classification models. Through\nextensive ablation studies, we demonstrated that the integration of ECG signals\nwith clinical documentation, enhanced by covariance augmentation techniques in\nRiemannian space, consistently produced superior predictive performance\ncompared to conventional approaches.\n", "date": "2025-03-18", "categories": []}
{"id": "2503.14442", "title": "Inducing Causal Structure for Interpretable Neural Networks Applied to\n  Glucose Prediction for T1DM Patients", "abstract": "  Causal abstraction techniques such as Interchange Intervention Training (IIT)\nhave been proposed to infuse neural network with expert knowledge encoded in\ncausal models, but their application to real-world problems remains limited.\nThis article explores the application of IIT in predicting blood glucose levels\nin Type 1 Diabetes Mellitus (T1DM) patients. The study utilizes an acyclic\nversion of the simglucose simulator approved by the FDA to train a Multi-Layer\nPerceptron (MLP) model, employing IIT to impose causal relationships. Results\nshow that the model trained with IIT effectively abstracted the causal\nstructure and outperformed the standard one in terms of predictive performance\nacross different prediction horizons (PHs) post-meal. Furthermore, the\nbreakdown of the counterfactual loss can be leveraged to explain which part of\nthe causal mechanism are more or less effectively captured by the model. These\npreliminary results suggest the potential of IIT in enhancing predictive models\nin healthcare by effectively complying with expert knowledge.\n", "date": "2025-03-18", "categories": []}
{"id": "2501.10240", "title": "Challenges and recommendations for Electronic Health Records data\n  extraction and preparation for dynamic prediction modelling in hospitalized\n  patients -- a practical guide", "abstract": "  Dynamic predictive modelling using electronic health record (EHR) data has\ngained significant attention in recent years. The reliability and\ntrustworthiness of such models depend heavily on the quality of the underlying\ndata, which is, in part, determined by the stages preceding the model\ndevelopment: data extraction from EHR systems and data preparation. In this\narticle, we identified over forty challenges encountered during these stages\nand provide actionable recommendations for addressing them. These challenges\nare organized into four categories: cohort definition, outcome definition,\nfeature engineering, and data cleaning. This comprehensive list serves as a\npractical guide for data extraction engineers and researchers, promoting best\npractices and improving the quality and real-world applicability of dynamic\nprediction models in clinical settings.\n", "date": "2025-01-17", "categories": []}
{"id": "2503.07650", "title": "Insights into Schizophrenia: Leveraging Machine Learning for Early\n  Identification via EEG, ERP, and Demographic Attributes", "abstract": "  The research presents a machine learning (ML) classifier designed to\ndifferentiate between schizophrenia patients and healthy controls by utilising\nfeatures extracted from electroencephalogram (EEG) data, specifically focusing\non event-related potentials (ERPs) and certain demographic variables. The\ndataset comprises data from 81 participants, encompassing 32 healthy controls\nand 49 schizophrenia patients, all sourced from an online dataset. After\npreprocessing the dataset, our ML model achieved an accuracy of 99.930%. This\nperformance outperforms earlier research, including those that used deep\nlearning methods. Additionally, an analysis was conducted to assess individual\nfeatures' contribution to improving classification accuracy. This involved\nsystematically excluding specific features from the original dataset one at a\ntime, and another technique involved an iterative process of removing features\nbased on their entropy scores incrementally. The impact of these removals on\nmodel performance was evaluated to identify the most informative features.\n", "date": "2025-03-06", "categories": []}
{"id": "2503.11973", "title": "Machine Learning-Based Model for Postoperative Stroke Prediction in\n  Coronary Artery Disease", "abstract": "  Coronary artery disease remains one of the leading causes of mortality\nglobally. Despite advances in revascularization treatments like PCI and CABG,\npostoperative stroke is inevitable. This study aims to develop and evaluate a\nsophisticated machine learning prediction model to assess postoperative stroke\nrisk in coronary revascularization patients.This research employed data from\nthe MIMIC-IV database, consisting of a cohort of 7023 individuals. Study data\nincluded clinical, laboratory, and comorbidity variables. To reduce\nmulticollinearity, variables with over 30% missing values and features with a\ncorrelation coefficient larger than 0.9 were deleted. The dataset has 70%\ntraining and 30% test. The Random Forest technique interpolated residual\ndataset missing values. Numerical values were normalized, whereas categorical\nvariables were one-hot encoded. LASSO regularization selected features, and\ngrid search found model hyperparameters. Finally, Logistic Regression, XGBoost,\nSVM, and CatBoost were employed for predictive modeling, and SHAP analysis\nassessed stroke risk for each variable. AUC of 0.855 (0.829-0.878) showed that\nSVM model outperformed logistic regression and CatBoost models in prior\nresearch. SHAP research showed that the Charlson Comorbidity Index (CCI),\ndiabetes, chronic kidney disease, and heart failure are significant prognostic\nfactors for postoperative stroke. This study shows that improved machine\nlearning reduces overfitting and improves model predictive accuracy. Models\nusing the CCI alone cannot predict postoperative stroke risk as accurately as\nthose using independent comorbidity variables. The suggested technique provides\na more thorough and individualized risk assessment by encompassing a wider\nrange of clinically relevant characteristics, making it a better reference for\npreoperative risk assessments and targeted intervention.\n", "date": "2025-03-15", "categories": []}
{"id": "2503.12317", "title": "A Transformer-based survival model for prediction of all-cause mortality\n  in heart failure patients: a multi-cohort study", "abstract": "  We developed and validated TRisk, a Transformer-based AI model predicting\n36-month mortality in heart failure patients by analysing temporal patient\njourneys from UK electronic health records (EHR). Our study included 403,534\nheart failure patients (ages 40-90) from 1,418 English general practices, with\n1,063 practices for model derivation and 355 for external validation. TRisk was\ncompared against the MAGGIC-EHR model across various patient subgroups. With\nmedian follow-up of 9 months, TRisk achieved a concordance index of 0.845 (95%\nconfidence interval: [0.841, 0.849]), significantly outperforming MAGGIC-EHR's\n0.728 (0.723, 0.733) for predicting 36-month all-cause mortality. TRisk showed\nmore consistent performance across sex, age, and baseline characteristics,\nsuggesting less bias. We successfully adapted TRisk to US hospital data through\ntransfer learning, achieving a C-index of 0.802 (0.789, 0.816) with 21,767\npatients. Explainability analyses revealed TRisk captured established risk\nfactors while identifying underappreciated predictors like cancers and hepatic\nfailure that were important across both cohorts. Notably, cancers maintained\nstrong prognostic value even a decade after diagnosis. TRisk demonstrated\nwell-calibrated mortality prediction across both healthcare systems. Our\nfindings highlight the value of tracking longitudinal health profiles and\nrevealed risk factors not included in previous expert-driven models.\n", "date": "2025-03-16", "categories": []}
{"id": "2503.13205", "title": "MAP: Evaluation and Multi-Agent Enhancement of Large Language Models for\n  Inpatient Pathways", "abstract": "  Inpatient pathways demand complex clinical decision-making based on\ncomprehensive patient information, posing critical challenges for clinicians.\nDespite advancements in large language models (LLMs) in medical applications,\nlimited research focused on artificial intelligence (AI) inpatient pathways\nsystems, due to the lack of large-scale inpatient datasets. Moreover, existing\nmedical benchmarks typically concentrated on medical question-answering and\nexaminations, ignoring the multifaceted nature of clinical decision-making in\ninpatient settings. To address these gaps, we first developed the Inpatient\nPathway Decision Support (IPDS) benchmark from the MIMIC-IV database,\nencompassing 51,274 cases across nine triage departments and 17 major disease\ncategories alongside 16 standardized treatment options. Then, we proposed the\nMulti-Agent Inpatient Pathways (MAP) framework to accomplish inpatient pathways\nwith three clinical agents, including a triage agent managing the patient\nadmission, a diagnosis agent serving as the primary decision maker at the\ndepartment, and a treatment agent providing treatment plans. Additionally, our\nMAP framework includes a chief agent overseeing the inpatient pathways to guide\nand promote these three clinician agents. Extensive experiments showed our MAP\nimproved the diagnosis accuracy by 25.10% compared to the state-of-the-art LLM\nHuatuoGPT2-13B. It is worth noting that our MAP demonstrated significant\nclinical compliance, outperforming three board-certified clinicians by 10%-12%,\nestablishing a foundation for inpatient pathways systems.\n", "date": "2025-03-17", "categories": []}
{"id": "2503.10741", "title": "Predicting Treatment Response in Body Dysmorphic Disorder with\n  Interpretable Machine Learning", "abstract": "  Body Dysmorphic Disorder (BDD) is a highly prevalent and frequently\nunderdiagnosed condition characterized by persistent, intrusive preoccupations\nwith perceived defects in physical appearance. In this extended analysis, we\nemploy multiple machine learning approaches to predict treatment outcomes --\nspecifically treatment response and remission -- with an emphasis on\ninterpretability to ensure clinical relevance and utility. Across the various\nmodels investigated, treatment credibility emerged as the most potent\npredictor, surpassing traditional markers such as baseline symptom severity or\ncomorbid conditions. Notably, while simpler models (e.g., logistic regression\nand support vector machines) achieved competitive predictive performance,\ndecision tree analyses provided unique insights by revealing clinically\ninterpretable threshold values in credibility scores. These thresholds can\nserve as practical guideposts for clinicians when tailoring interventions or\nallocating treatment resources. We further contextualize our findings within\nthe broader literature on BDD, addressing technology-based therapeutics,\ndigital interventions, and the psychosocial determinants of treatment\nengagement. An extensive array of references situates our results within\ncurrent research on BDD prevalence, suicidality risks, and digital innovation.\nOur work underscores the potential of integrating rigorous statistical\nmethodologies with transparent machine learning models. By systematically\nidentifying modifiable predictors -- such as treatment credibility -- we\npropose a pathway toward more targeted, personalized, and ultimately\nefficacious interventions for individuals with BDD.\n", "date": "2025-03-13", "categories": []}
{"id": "2503.10925", "title": "Predicting Clinical Outcomes with Waveform LSTMs", "abstract": "  Data mining and machine learning hold great potential to enable health\nsystems to systematically use data and analytics to identify inefficiencies and\nbest practices that improve care and reduce costs. Waveform data offers\nparticularly detailed information on how patient health evolves over time and\nhas the potential to significantly improve prediction accuracy on multiple\nbenchmarks, but has been widely under-utilized, largely because of the\nchallenges in working with these large and complex datasets. This study\nevaluates the potential of leveraging clinical waveform data to improve\nprediction accuracy on a single benchmark task: the risk of mortality in the\nintensive care unit. We identify significant potential from this data, beating\nthe existing baselines for both logistic regression and deep learning models.\n", "date": "2025-03-13", "categories": []}
{"id": "2503.11282", "title": "OPTIMUS: Predicting Multivariate Outcomes in Alzheimer's Disease Using\n  Multi-modal Data amidst Missing Values", "abstract": "  Alzheimer's disease, a neurodegenerative disorder, is associated with neural,\ngenetic, and proteomic factors while affecting multiple cognitive and\nbehavioral faculties. Traditional AD prediction largely focuses on univariate\ndisease outcomes, such as disease stages and severity. Multimodal data encode\nbroader disease information than a single modality and may, therefore, improve\ndisease prediction; but they often contain missing values. Recent \"deeper\"\nmachine learning approaches show promise in improving prediction accuracy, yet\nthe biological relevance of these models needs to be further charted.\nIntegrating missing data analysis, predictive modeling, multimodal data\nanalysis, and explainable AI, we propose OPTIMUS, a predictive, modular, and\nexplainable machine learning framework, to unveil the many-to-many predictive\npathways between multimodal input data and multivariate disease outcomes amidst\nmissing values. OPTIMUS first applies modality-specific imputation to uncover\ndata from each modality while optimizing overall prediction accuracy. It then\nmaps multimodal biomarkers to multivariate outcomes using machine-learning and\nextracts biomarkers respectively predictive of each outcome. Finally, OPTIMUS\nincorporates XAI to explain the identified multimodal biomarkers. Using data\nfrom 346 cognitively normal subjects, 608 persons with mild cognitive\nimpairment, and 251 AD patients, OPTIMUS identifies neural and transcriptomic\nsignatures that jointly but differentially predict multivariate outcomes\nrelated to executive function, language, memory, and visuospatial function. Our\nwork demonstrates the potential of building a predictive and biologically\nexplainable machine-learning framework to uncover multimodal biomarkers that\ncapture disease profiles across varying cognitive landscapes. The results\nimprove our understanding of the complex many-to-many pathways in AD.\n", "date": "2025-03-14", "categories": []}
{"id": "2503.11384", "title": "Optimizing Large Language Models for Detecting Symptoms of Comorbid\n  Depression or Anxiety in Chronic Diseases: Insights from Patient Messages", "abstract": "  Patients with diabetes are at increased risk of comorbid depression or\nanxiety, complicating their management. This study evaluated the performance of\nlarge language models (LLMs) in detecting these symptoms from secure patient\nmessages. We applied multiple approaches, including engineered prompts,\nsystemic persona, temperature adjustments, and zero-shot and few-shot learning,\nto identify the best-performing model and enhance performance. Three out of\nfive LLMs demonstrated excellent performance (over 90% of F-1 and accuracy),\nwith Llama 3.1 405B achieving 93% in both F-1 and accuracy using a zero-shot\napproach. While LLMs showed promise in binary classification and handling\ncomplex metrics like Patient Health Questionnaire-4, inconsistencies in\nchallenging cases warrant further real-life assessment. The findings highlight\nthe potential of LLMs to assist in timely screening and referrals, providing\nvaluable empirical knowledge for real-world triage systems that could improve\nmental health care for patients with chronic diseases.\n", "date": "2025-03-14", "categories": []}
{"id": "2503.06571", "title": "SHIP: A Shapelet-based Approach for Interpretable Patient-Ventilator\n  Asynchrony Detection", "abstract": "  Patient-ventilator asynchrony (PVA) is a common and critical issue during\nmechanical ventilation, affecting up to 85% of patients. PVA can result in\nclinical complications such as discomfort, sleep disruption, and potentially\nmore severe conditions like ventilator-induced lung injury and diaphragm\ndysfunction. Traditional PVA management, which relies on manual adjustments by\nhealthcare providers, is often inadequate due to delays and errors. While\nvarious computational methods, including rule-based, statistical, and deep\nlearning approaches, have been developed to detect PVA events, they face\nchallenges related to dataset imbalances and lack of interpretability. In this\nwork, we propose a shapelet-based approach SHIP for PVA detection, utilizing\nshapelets - discriminative subsequences in time-series data - to enhance\ndetection accuracy and interpretability. Our method addresses dataset\nimbalances through shapelet-based data augmentation and constructs a shapelet\npool to transform the dataset for more effective classification. The combined\nshapelet and statistical features are then used in a classifier to identify PVA\nevents. Experimental results on medical datasets show that SHIP significantly\nimproves PVA detection while providing interpretable insights into model\ndecisions.\n", "date": "2025-03-09", "categories": []}
{"id": "2503.09927", "title": "Developing and Evaluating an AI-Assisted Prediction Model for Unplanned\n  Intensive Care Admissions following Elective Neurosurgery using Natural\n  Language Processing within an Electronic Healthcare Record System", "abstract": "  Introduction: Timely care in a specialised neuro-intensive therapy unit (ITU)\nreduces mortality and hospital stays, with planned admissions being safer than\nunplanned ones. However, post-operative care decisions remain subjective. This\nstudy used artificial intelligence (AI), specifically natural language\nprocessing (NLP) to analyse electronic health records (EHRs) and predict ITU\nadmissions for elective surgery patients. Methods: This study analysed the EHRs\nof elective neurosurgery patients from University College London Hospital\n(UCLH) using NLP. Patients were categorised into planned high dependency unit\n(HDU) or ITU admission; unplanned HDU or ITU admission; or ward / overnight\nrecovery (ONR). The Medical Concept Annotation Tool (MedCAT) was used to\nidentify SNOMED-CT concepts within the clinical notes. We then explored the\nutility of these identified concepts for a range of AI algorithms trained to\npredict ITU admission. Results: The CogStack-MedCAT NLP model, initially\ntrained on hospital-wide EHRs, underwent two refinements: first with data from\npatients with Normal Pressure Hydrocephalus (NPH) and then with data from\nVestibular Schwannoma (VS) patients, achieving a concept detection F1-score of\n0.93. This refined model was then used to extract concepts from EHR notes of\n2,268 eligible neurosurgical patients. We integrated the extracted concepts\ninto AI models, including a decision tree model and a neural time-series model.\nUsing the simpler decision tree model, we achieved a recall of 0.87 (CI 0.82 -\n0.91) for ITU admissions, reducing the proportion of unplanned ITU cases missed\nby human experts from 36% to 4%. Conclusion: The NLP model, refined for\naccuracy, has proven its efficiency in extracting relevant concepts, providing\na reliable basis for predictive AI models to use in clinically valid\napplications.\n", "date": "2025-03-13", "categories": []}
{"id": "2503.10362", "title": "BioSerenity-E1: a self-supervised EEG model for medical applications", "abstract": "  Electroencephalography (EEG) serves as an essential diagnostic tool in\nneurology; however, its accurate manual interpretation is a time-intensive\nprocess that demands highly specialized expertise, which remains relatively\nscarce and not consistently accessible. To address these limitations, the\nimplementation of automated pre-screening and analysis systems for EEG data\nholds considerable promise. Advances in self-supervised learning made it\npossible to pre-train complex deep learning architectures on large volumes of\nunlabeled EEG data to learn generalizable representations, that can later be\nused to enhance performance on multiple tasks while needing less downstream\ndata. In the present paper, we introduce BioSerenity-E1, the first of a family\nof self-supervised foundation models for clinical EEG applications that\ncombines spectral tokenization with masked prediction to achieve\nstate-of-the-art performance across relevant diagnostic tasks. The two-phase\nself-supervised pretraining framework initially acquires compressed EEG\nrepresentations via a transformer-based VQ-VAE architecture designed to\nreconstruct log-multitaper spectral projections, then implements extensive (70%\nblock) masked token prediction to force the model to learn complex\nspatiotemporal dependencies in EEG signals. BioSerenity-E1 achieves strong\nperformance across three clinical tasks, either in line or above\nstate-of-the-art methods: seizure detection (AUROC = 0.926, Sensitivity =\n0.909), normal/abnormal classification (AUPRC = 0.970 on proprietary data;\n0.910 on TUH-Abnormal), and multiclass pathology differentiation on unbalanced\ndata (Weighted F1 = 0.730). The utility of BioSerenity-E1 is further confirmed\nin low-data regimes scenarios, showing clear improvements in AUPRC (from +2% to\n17%) when trained on less than 10% of the available data.\n", "date": "2025-03-13", "categories": []}
{"id": "2503.10382", "title": "Subgroup Performance Analysis in Hidden Stratifications", "abstract": "  Machine learning (ML) models may suffer from significant performance\ndisparities between patient groups. Identifying such disparities by monitoring\nperformance at a granular level is crucial for safely deploying ML to each\npatient. Traditional subgroup analysis based on metadata can expose performance\ndisparities only if the available metadata (e.g., patient sex) sufficiently\nreflects the main reasons for performance variability, which is not common.\nSubgroup discovery techniques that identify cohesive subgroups based on learned\nfeature representations appear as a potential solution: They could expose\nhidden stratifications and provide more granular subgroup performance reports.\nHowever, subgroup discovery is challenging to evaluate even as a standalone\ntask, as ground truth stratification labels do not exist in real data. Subgroup\ndiscovery has thus neither been applied nor evaluated for the application of\nsubgroup performance monitoring. Here, we apply subgroup discovery for\nperformance monitoring in chest x-ray and skin lesion classification. We\npropose novel evaluation strategies and show that a simplified subgroup\ndiscovery method without access to classification labels or metadata can expose\nlarger performance disparities than traditional metadata-based subgroup\nanalysis. We provide the first compelling evidence that subgroup discovery can\nserve as an important tool for comprehensive performance validation and\nmonitoring of trustworthy AI in medicine.\n", "date": "2025-03-13", "categories": []}
{"id": "2501.18415", "title": "Consensus statement on the credibility assessment of ML predictors", "abstract": "  The rapid integration of machine learning (ML) predictors into in silico\nmedicine has revolutionized the estimation of quantities of interest (QIs) that\nare otherwise challenging to measure directly. However, the credibility of\nthese predictors is critical, especially when they inform high-stakes\nhealthcare decisions. This position paper presents a consensus statement\ndeveloped by experts within the In Silico World Community of Practice. We\noutline twelve key statements forming the theoretical foundation for evaluating\nthe credibility of ML predictors, emphasizing the necessity of causal\nknowledge, rigorous error quantification, and robustness to biases. By\ncomparing ML predictors with biophysical models, we highlight unique challenges\nassociated with implicit causal knowledge and propose strategies to ensure\nreliability and applicability. Our recommendations aim to guide researchers,\ndevelopers, and regulators in the rigorous assessment and deployment of ML\npredictors in clinical and biomedical contexts.\n", "date": "2025-01-30", "categories": []}
{"id": "2503.09226", "title": "Towards Regulatory-Confirmed Adaptive Clinical Trials: Machine Learning\n  Opportunities and Solutions", "abstract": "  Randomized Controlled Trials (RCTs) are the gold standard for evaluating the\neffect of new medical treatments. Treatments must pass stringent regulatory\nconditions in order to be approved for widespread use, yet even after the\nregulatory barriers are crossed, real-world challenges might arise: Who should\nget the treatment? What is its true clinical utility? Are there discrepancies\nin the treatment effectiveness across diverse and under-served populations? We\nintroduce two new objectives for future clinical trials that integrate\nregulatory constraints and treatment policy value for both the entire\npopulation and under-served populations, thus answering some of the questions\nabove in advance. Designed to meet these objectives, we formulate Randomize\nFirst Augment Next (RFAN), a new framework for designing Phase III clinical\ntrials. Our framework consists of a standard randomized component followed by\nan adaptive one, jointly meant to efficiently and safely acquire and assign\npatients into treatment arms during the trial. Then, we propose strategies for\nimplementing RFAN based on causal, deep Bayesian active learning. Finally, we\nempirically evaluate the performance of our framework using synthetic and\nreal-world semi-synthetic datasets.\n", "date": "2025-03-12", "categories": []}
{"id": "2406.13750", "title": "Empowering Tuberculosis Screening with Explainable Self-Supervised Deep\n  Neural Networks", "abstract": "  Tuberculosis persists as a global health crisis, especially in\nresource-limited populations and remote regions, with more than 10 million\nindividuals newly infected annually. It stands as a stark symbol of inequity in\npublic health. Tuberculosis impacts roughly a quarter of the global populace,\nwith the majority of cases concentrated in eight countries, accounting for\ntwo-thirds of all tuberculosis infections. Although a severe ailment,\ntuberculosis is both curable and manageable. However, early detection and\nscreening of at-risk populations are imperative. Chest x-ray stands as the\npredominant imaging technique utilized in tuberculosis screening efforts.\nHowever, x-ray screening necessitates skilled radiologists, a resource often\nscarce, particularly in remote regions with limited resources. Consequently,\nthere is a pressing need for artificial intelligence (AI)-powered systems to\nsupport clinicians and healthcare providers in swift screening. However,\ntraining a reliable AI model necessitates large-scale high-quality data, which\ncan be difficult and costly to acquire. Inspired by these challenges, in this\nwork, we introduce an explainable self-supervised self-train learning network\ntailored for tuberculosis case screening. The network achieves an outstanding\noverall accuracy of 98.14% and demonstrates high recall and precision rates of\n95.72% and 99.44%, respectively, in identifying tuberculosis cases, effectively\ncapturing clinically significant features.\n", "date": "2024-06-19", "categories": []}
{"id": "2503.07883", "title": "Cross-platform Prediction of Depression Treatment Outcome Using Location\n  Sensory Data on Smartphones", "abstract": "  Currently, depression treatment relies on closely monitoring patients\nresponse to treatment and adjusting the treatment as needed. Using\nself-reported or physician-administrated questionnaires to monitor treatment\nresponse is, however, burdensome, costly and suffers from recall bias. In this\npaper, we explore using location sensory data collected passively on\nsmartphones to predict treatment outcome. To address heterogeneous data\ncollection on Android and iOS phones, the two predominant smartphone platforms,\nwe explore using domain adaptation techniques to map their data to a common\nfeature space, and then use the data jointly to train machine learning models.\nOur results show that this domain adaptation approach can lead to significantly\nbetter prediction than that with no domain adaptation. In addition, our results\nshow that using location features and baseline self-reported questionnaire\nscore can lead to F1 score up to 0.67, comparable to that obtained using\nperiodic self-reported questionnaires, indicating that using location data is a\npromising direction for predicting depression treatment outcome.\n", "date": "2025-03-10", "categories": []}
{"id": "2404.12827", "title": "An Evaluation Benchmark for Adverse Drug Event Prediction from Clinical\n  Trial Results", "abstract": "  Adverse drug events (ADEs) are a major safety issue in clinical trials. Thus,\npredicting ADEs is key to developing safer medications and enhancing patient\noutcomes. To support this effort, we introduce CT-ADE, a dataset for multilabel\nADE prediction in monopharmacy treatments. CT-ADE encompasses 2,497 drugs and\n168,984 drug-ADE pairs from clinical trial results, annotated using the MedDRA\nontology. Unlike existing resources, CT-ADE integrates treatment and target\npopulation data, enabling comparative analyses under varying conditions, such\nas dosage, administration route, and demographics. In addition, CT-ADE\nsystematically collects all ADEs in the study population, including positive\nand negative cases. To provide a baseline for ADE prediction performance using\nthe CT-ADE dataset, we conducted analyses using large language models (LLMs).\nThe best LLM achieved an F1-score of 56%, with models incorporating treatment\nand patient information outperforming by 21%-38% those relying solely on the\nchemical structure. These findings underscore the importance of contextual\ninformation in ADE prediction and establish CT-ADE as a robust resource for\nsafety risk assessment in pharmaceutical research and development.\n", "date": "2024-04-19", "categories": []}
{"id": "2503.05746", "title": "Unsupervised Clustering Approaches for Autism Screening: Achieving\n  95.31% Accuracy with a Gaussian Mixture Model", "abstract": "  Autism spectrum disorder (ASD) remains a challenging condition to diagnose\neffectively and promptly, despite global efforts in public health, clinical\nscreening, and scientific research. Traditional diagnostic methods, primarily\nreliant on supervised learning approaches, presuppose the availability of\nlabeled data, which can be both time-consuming and resource-intensive to\nobtain. Unsupervised learning, in contrast, offers a means of gaining insights\nfrom unlabeled datasets in a manner that can expedite or support the diagnostic\nprocess. This paper explores the use of four distinct unsupervised clustering\nalgorithms K-Means, Gaussian Mixture Model (GMM), Agglomerative Clustering, and\nDBSCAN to analyze a publicly available dataset of 704 adult individuals\nscreened for ASD. After extensive hyperparameter tuning via cross-validation,\nthe study documents how the Gaussian Mixture Model achieved the highest\nclustering-to-label accuracy (95.31%) when mapped to the original ASD/NO\nclassification (4). Other key performance metrics included the Adjusted Rand\nIndex (ARI) and silhouette scores, which further illustrated the internal\ncoherence of each cluster. The dataset underwent preprocessing procedures\nincluding data cleaning, label encoding of categorical features, and standard\nscaling, followed by a thorough cross-validation approach to assess and compare\nthe four clustering methods (5). These results highlight the significant\npotential of unsupervised methods in assisting ASD screening, especially in\ncontexts where labeled data may be sparse, uncertain, or prohibitively\nexpensive to obtain. With continued methodological refinements, unsupervised\napproaches hold promise for augmenting early detection initiatives and guiding\nresource allocation to individuals at high risk.\n", "date": "2025-02-20", "categories": []}
{"id": "2503.05809", "title": "Sample size determination for machine learning in medical research", "abstract": "  Machine learning (ML) methods are being increasingly used across various\ndomains of medicine research. However, despite advancements in the use of ML in\nmedicine, clear and definitive guidelines for determining sample sizes in\nmedical ML research are lacking. This article proposes a method for determining\nsample sizes for medical research utilizing ML methods, beginning with the\ndetermination of the testing set sample size, followed with the determination\nof the training set and total sample sizes.\n", "date": "2025-03-04", "categories": []}
{"id": "2503.06059", "title": "MANDARIN: Mixture-of-Experts Framework for Dynamic Delirium and Coma\n  Prediction in ICU Patients: Development and Validation of an Acute Brain\n  Dysfunction Prediction Model", "abstract": "  Acute brain dysfunction (ABD) is a common, severe ICU complication,\npresenting as delirium or coma and leading to prolonged stays, increased\nmortality, and cognitive decline. Traditional screening tools like the Glasgow\nComa Scale (GCS), Confusion Assessment Method (CAM), and Richmond\nAgitation-Sedation Scale (RASS) rely on intermittent assessments, causing\ndelays and inconsistencies. In this study, we propose MANDARIN\n(Mixture-of-Experts Framework for Dynamic Delirium and Coma Prediction in ICU\nPatients), a 1.5M-parameter mixture-of-experts neural network to predict ABD in\nreal-time among ICU patients. The model integrates temporal and static data\nfrom the ICU to predict the brain status in the next 12 to 72 hours, using a\nmulti-branch approach to account for current brain status. The MANDARIN model\nwas trained on data from 92,734 patients (132,997 ICU admissions) from 2\nhospitals between 2008-2019 and validated externally on data from 11,719\npatients (14,519 ICU admissions) from 15 hospitals and prospectively on data\nfrom 304 patients (503 ICU admissions) from one hospital in 2021-2024. Three\ndatasets were used: the University of Florida Health (UFH) dataset, the\nelectronic ICU Collaborative Research Database (eICU), and the Medical\nInformation Mart for Intensive Care (MIMIC)-IV dataset. MANDARIN significantly\noutperforms the baseline neurological assessment scores (GCS, CAM, and RASS)\nfor delirium prediction in both external (AUROC 75.5% CI: 74.2%-76.8% vs 68.3%\nCI: 66.9%-69.5%) and prospective (AUROC 82.0% CI: 74.8%-89.2% vs 72.7% CI:\n65.5%-81.0%) cohorts, as well as for coma prediction (external AUROC 87.3% CI:\n85.9%-89.0% vs 72.8% CI: 70.6%-74.9%, and prospective AUROC 93.4% CI:\n88.5%-97.9% vs 67.7% CI: 57.7%-76.8%) with a 12-hour lead time. This tool has\nthe potential to assist clinicians in decision-making by continuously\nmonitoring the brain status of patients in the ICU.\n", "date": "2025-03-08", "categories": []}
{"id": "2503.06797", "title": "Multimodal AI-driven Biomarker for Early Detection of Cancer Cachexia", "abstract": "  Cancer cachexia is a multifactorial syndrome characterized by progressive\nmuscle wasting, metabolic dysfunction, and systemic inflammation, leading to\nreduced quality of life and increased mortality. Despite extensive research, no\nsingle definitive biomarker exists, as cachexia-related indicators such as\nserum biomarkers, skeletal muscle measurements, and metabolic abnormalities\noften overlap with other conditions. Existing composite indices, including the\nCancer Cachexia Index (CXI), Modified CXI (mCXI), and Cachexia Score (CASCO),\nintegrate multiple biomarkers but lack standardized thresholds, limiting their\nclinical utility. This study proposes a multimodal AI-based biomarker for early\ncancer cachexia detection, leveraging open-source large language models (LLMs)\nand foundation models trained on medical data. The approach integrates\nheterogeneous patient data, including demographics, disease status, lab\nreports, radiological imaging (CT scans), and clinical notes, using a machine\nlearning framework that can handle missing data. Unlike previous AI-based\nmodels trained on curated datasets, this method utilizes routinely collected\nclinical data, enhancing real-world applicability. Additionally, the model\nincorporates confidence estimation, allowing the identification of cases\nrequiring expert review for precise clinical interpretation. Preliminary\nfindings demonstrate that integrating multiple data modalities improves\ncachexia prediction accuracy at the time of cancer diagnosis. The AI-based\nbiomarker dynamically adapts to patient-specific factors such as age, race,\nethnicity, weight, cancer type, and stage, avoiding the limitations of\nfixed-threshold biomarkers. This multimodal AI biomarker provides a scalable\nand clinically viable solution for early cancer cachexia detection,\nfacilitating personalized interventions and potentially improving treatment\noutcomes and patient survival.\n", "date": "2025-03-09", "categories": []}
{"id": "2503.07276", "title": "A Systematic Review of ECG Arrhythmia Classification: Adherence to\n  Standards, Fair Evaluation, and Embedded Feasibility", "abstract": "  The classification of electrocardiogram (ECG) signals is crucial for early\ndetection of arrhythmias and other cardiac conditions. However, despite\nadvances in machine learning, many studies fail to follow standardization\nprotocols, leading to inconsistencies in performance evaluation and real-world\napplicability. Additionally, hardware constraints essential for practical\ndeployment, such as in pacemakers, Holter monitors, and wearable ECG patches,\nare often overlooked. Since real-world impact depends on feasibility in\nresource-constrained devices, ensuring efficient deployment is critical for\ncontinuous monitoring. This review systematically analyzes ECG classification\nstudies published between 2017 and 2024, focusing on those adhering to the E3C\n(Embedded, Clinical, and Comparative Criteria), which include inter-patient\nparadigm implementation, compliance with Association for the Advancement of\nMedical Instrumentation (AAMI) recommendations, and model feasibility for\nembedded systems. While many studies report high accuracy, few properly\nconsider patient-independent partitioning and hardware limitations. We identify\nstate-of-the-art methods meeting E3C criteria and conduct a comparative\nanalysis of accuracy, inference time, energy consumption, and memory usage.\nFinally, we propose standardized reporting practices to ensure fair comparisons\nand practical applicability of ECG classification models. By addressing these\ngaps, this study aims to guide future research toward more robust and\nclinically viable ECG classification systems.\n", "date": "2025-03-10", "categories": []}
{"id": "2503.04802", "title": "The order in speech disorder: a scoping review of state of the art\n  machine learning methods for clinical speech classification", "abstract": "  Background:Speech patterns have emerged as potential diagnostic markers for\nconditions with varying etiologies. Machine learning (ML) presents an\nopportunity to harness these patterns for accurate disease diagnosis.\n  Objective: This review synthesized findings from studies exploring ML's\ncapability in leveraging speech for the diagnosis of neurological, laryngeal\nand mental disorders.\n  Methods: A systematic examination of 564 articles was conducted with 91\narticles included in the study, which encompassed a wide spectrum of\nconditions, ranging from voice pathologies to mental and neurological\ndisorders. Methods for speech classifications were assessed based on the\nrelevant studies and scored between 0-10 based on the reported diagnostic\naccuracy of their ML models.\n  Results: High diagnostic accuracies were consistently observed for laryngeal\ndisorders, dysarthria, and changes related to speech in Parkinsons disease.\nThese findings indicate the robust potential of speech as a diagnostic tool.\nDisorders like depression, schizophrenia, mild cognitive impairment and\nAlzheimers dementia also demonstrated high accuracies, albeit with some\nvariability across studies. Meanwhile, disorders like OCD and autism\nhighlighted the need for more extensive research to ascertain the relationship\nbetween speech patterns and the respective conditions.\n  Conclusion: ML models utilizing speech patterns demonstrate promising\npotential in diagnosing a range of mental, laryngeal, and neurological\ndisorders. However, the efficacy varies across conditions, and further research\nis needed. The integration of these models into clinical practice could\npotentially revolutionize the evaluation and diagnosis of a number of different\nmedical conditions.\n", "date": "2025-03-03", "categories": []}
{"id": "2503.04946", "title": "Federated Inverse Probability Treatment Weighting for Individual\n  Treatment Effect Estimation", "abstract": "  Individual treatment effect (ITE) estimation is to evaluate the causal\neffects of treatment strategies on some important outcomes, which is a crucial\nproblem in healthcare. Most existing ITE estimation methods are designed for\ncentralized settings. However, in real-world clinical scenarios, the raw data\nare usually not shareable among hospitals due to the potential privacy and\nsecurity risks, which makes the methods not applicable. In this work, we study\nthe ITE estimation task in a federated setting, which allows us to harness the\ndecentralized data from multiple hospitals. Due to the unavoidable confounding\nbias in the collected data, a model directly learned from it would be\ninaccurate. One well-known solution is Inverse Probability Treatment Weighting\n(IPTW), which uses the conditional probability of treatment given the\ncovariates to re-weight each training example. Applying IPTW in a federated\nsetting, however, is non-trivial. We found that even with a well-estimated\nconditional probability, the local model training step using each hospital's\ndata alone would still suffer from confounding bias. To address this, we\npropose FED-IPTW, a novel algorithm to extend IPTW into a federated setting\nthat enforces both global (over all the data) and local (within each hospital)\ndecorrelation between covariates and treatments. We validated our approach on\nthe task of comparing the treatment effects of mechanical ventilation on\nimproving survival probability for patients with breadth difficulties in the\nintensive care unit (ICU). We conducted experiments on both synthetic and\nreal-world eICU datasets and the results show that FED-IPTW outperform\nstate-of-the-art methods on all the metrics on factual prediction and ITE\nestimation tasks, paving the way for personalized treatment strategy design in\nmechanical ventilation usage.\n", "date": "2025-03-06", "categories": []}
{"id": "2503.05119", "title": "AI-driven Prediction of Insulin Resistance in Normal Populations:\n  Comparing Models and Criteria", "abstract": "  Insulin resistance (IR) is a key precursor to diabetes and a significant risk\nfactor for cardiovascular disease. Traditional IR assessment methods require\nmultiple blood tests. We developed a simple AI model using only fasting blood\nglucose to predict IR in non-diabetic populations. Data from the NHANES\n(1999-2020) and CHARLS (2015) studies were used for model training and\nvalidation. Input features included age, gender, height, weight, blood\npressure, waist circumference, and fasting blood glucose. The CatBoost\nalgorithm achieved AUC values of 0.8596 (HOMA-IR) and 0.7777 (TyG index) in\nNHANES, with an external AUC of 0.7442 for TyG. For METS-IR prediction, the\nmodel achieved AUC values of 0.9731 (internal) and 0.9591 (external), with RMSE\nvalues of 3.2643 (internal) and 3.057 (external). SHAP analysis highlighted\nwaist circumference as a key predictor of IR. This AI model offers a minimally\ninvasive and effective tool for IR prediction, supporting early diabetes and\ncardiovascular disease prevention.\n", "date": "2025-03-07", "categories": []}
{"id": "2503.04137", "title": "A Comparative Study of Diabetes Prediction Based on Lifestyle Factors\n  Using Machine Learning", "abstract": "  Diabetes is a prevalent chronic disease with significant health and economic\nburdens worldwide. Early prediction and diagnosis can aid in effective\nmanagement and prevention of complications. This study explores the use of\nmachine learning models to predict diabetes based on lifestyle factors using\ndata from the Behavioral Risk Factor Surveillance System (BRFSS) 2015 survey.\nThe dataset consists of 21 lifestyle and health-related features, capturing\naspects such as physical activity, diet, mental health, and socioeconomic\nstatus. Three classification models, Decision Tree, K-Nearest Neighbors (KNN),\nand Logistic Regression, are implemented and evaluated to determine their\npredictive performance. The models are trained and tested using a balanced\ndataset, and their performances are assessed based on accuracy, precision,\nrecall, and F1-score. The results indicate that the Decision Tree, KNN, and\nLogistic Regression achieve an accuracy of 0.74, 0.72, and 0.75, respectively,\nwith varying strengths in precision and recall. The findings highlight the\npotential of machine learning in diabetes prediction and suggest future\nimprovements through feature selection and ensemble learning techniques.\n", "date": "2025-03-06", "categories": []}
{"id": "2503.04176", "title": "TIMER: Temporal Instruction Modeling and Evaluation for Longitudinal\n  Clinical Records", "abstract": "  Large language models (LLMs) have emerged as promising tools for assisting in\nmedical tasks, yet processing Electronic Health Records (EHRs) presents unique\nchallenges due to their longitudinal nature. While LLMs' capabilities to\nperform medical tasks continue to improve, their ability to reason over\ntemporal dependencies across multiple patient visits and time frames remains\nunexplored. We introduce TIMER (Temporal Instruction Modeling and Evaluation\nfor Longitudinal Clinical Records), a framework that incorporate\ninstruction-response pairs grounding to different parts of a patient's record\nas a critical dimension in both instruction evaluation and tuning for\nlongitudinal clinical records. We develop TIMER-Bench, the first time-aware\nbenchmark that evaluates temporal reasoning capabilities over longitudinal\nEHRs, as well as TIMER-Instruct, an instruction-tuning methodology for LLMs to\nlearn reasoning over time. We demonstrate that models fine-tuned with\nTIMER-Instruct improve performance by 7.3% on human-generated benchmarks and\n9.2% on TIMER-Bench, indicating that temporal instruction-tuning improves model\nperformance for reasoning over EHR.\n", "date": "2025-03-06", "categories": []}
{"id": "2503.02906", "title": "Diagnosis of Patients with Viral, Bacterial, and Non-Pneumonia Based on\n  Chest X-Ray Images Using Convolutional Neural Networks", "abstract": "  According to the World Health Organization (WHO), pneumonia is a disease that\ncauses a significant number of deaths each year. In response to this issue, the\ndevelopment of a decision support system for the classification of patients\ninto those without pneumonia and those with viral or bacterial pneumonia is\nproposed. This is achieved by implementing transfer learning (TL) using\npre-trained convolutional neural network (CNN) models on chest x-ray (CXR)\nimages. The system is further enhanced by integrating Relief and Chi-square\nmethods as dimensionality reduction techniques, along with support vector\nmachines (SVM) for classification. The performance of a series of experiments\nwas evaluated to build a model capable of distinguishing between patients\nwithout pneumonia and those with viral or bacterial pneumonia. The obtained\nresults include an accuracy of 91.02%, precision of 97.73%, recall of 98.03%,\nand an F1 Score of 97.88% for discriminating between patients without pneumonia\nand those with pneumonia. In addition, accuracy of 93.66%, precision of 94.26%,\nrecall of 92.66%, and an F1 Score of 93.45% were achieved for discriminating\nbetween patients with viral pneumonia and those with bacterial pneumonia.\n", "date": "2025-03-03", "categories": []}
{"id": "2503.03489", "title": "Federated Learning for Predicting Mild Cognitive Impairment to Dementia\n  Conversion", "abstract": "  Dementia is a progressive condition that impairs an individual's cognitive\nhealth and daily functioning, with mild cognitive impairment (MCI) often\nserving as its precursor. The prediction of MCI to dementia conversion has been\nwell studied, but previous studies have almost always focused on traditional\nMachine Learning (ML) based methods that require sharing sensitive clinical\ninformation to train predictive models. This study proposes a privacy-enhancing\nsolution using Federated Learning (FL) to train predictive models for MCI to\ndementia conversion without sharing sensitive data, leveraging socio\ndemographic and cognitive measures. We simulated and compared two network\narchitectures, Peer to Peer (P2P) and client-server, to enable collaborative\nlearning. Our results demonstrated that FL had comparable predictive\nperformance to centralized ML, and each clinical site showed similar\nperformance without sharing local data. Moreover, the predictive performance of\nFL models was superior to site specific models trained without collaboration.\nThis work highlights that FL can eliminate the need for data sharing without\ncompromising model efficacy.\n", "date": "2025-03-05", "categories": []}
{"id": "2503.03724", "title": "Deep Causal Behavioral Policy Learning: Applications to Healthcare", "abstract": "  We present a deep learning-based approach to studying dynamic clinical\nbehavioral regimes in diverse non-randomized healthcare settings. Our proposed\nmethodology - deep causal behavioral policy learning (DC-BPL) - uses deep\nlearning algorithms to learn the distribution of high-dimensional clinical\naction paths, and identifies the causal link between these action paths and\npatient outcomes. Specifically, our approach: (1) identifies the causal effects\nof provider assignment on clinical outcomes; (2) learns the distribution of\nclinical actions a given provider would take given evolving patient\ninformation; (3) and combines these steps to identify the optimal provider for\na given patient type and emulate that provider's care decisions. Underlying\nthis strategy, we train a large clinical behavioral model (LCBM) on electronic\nhealth records data using a transformer architecture, and demonstrate its\nability to estimate clinical behavioral policies. We propose a novel\ninterpretation of a behavioral policy learned using the LCBM: that it is an\nefficient encoding of complex, often implicit, knowledge used to treat a\npatient. This allows us to learn a space of policies that are critical to a\nwide range of healthcare applications, in which the vast majority of clinical\nknowledge is acquired tacitly through years of practice and only a tiny\nfraction of information relevant to patient care is written down (e.g. in\ntextbooks, studies or standardized guidelines).\n", "date": "2025-03-05", "categories": []}
{"id": "2503.02124", "title": "A Hybrid CNN-Transformer Model for Heart Disease Prediction Using Life\n  History Data", "abstract": "  This study proposed a hybrid model of a convolutional neural network (CNN)\nand a Transformer to predict and diagnose heart disease. Based on CNN's\nstrength in detecting local features and the Transformer's high capacity in\nsensing global relations, the model is able to successfully detect risk factors\nof heart disease from high-dimensional life history data. Experimental results\nshow that the proposed model outperforms traditional benchmark models like\nsupport vector machine (SVM), convolutional neural network (CNN), and long\nshort-term memory network (LSTM) on several measures like accuracy, precision,\nand recall. This demonstrates its strong ability to deal with multi-dimensional\nand unstructured data. In order to verify the effectiveness of the model,\nexperiments removing certain parts were carried out, and the results of the\nexperiments showed that it is important to use both CNN and Transformer modules\nin enhancing the model. This paper also discusses the incorporation of\nadditional features and approaches in future studies to enhance the model's\nperformance and enable it to operate effectively in diverse conditions. This\nstudy presents novel insights and methods for predicting heart disease using\nmachine learning, with numerous potential applications especially in\npersonalized medicine and health management.\n", "date": "2025-03-03", "categories": []}
{"id": "2405.03239", "title": "Deep Learning for Detecting and Early Predicting Chronic Obstructive\n  Pulmonary Disease from Spirogram Time Series", "abstract": "  Chronic Obstructive Pulmonary Disease (COPD) is a chronic lung condition\ncharacterized by airflow obstruction. Current diagnostic methods primarily rely\non identifying prominent features in spirometry (Volume-Flow time series) to\ndetect COPD, but they are not adept at predicting future COPD risk based on\nsubtle data patterns. In this study, we introduce a novel deep learning-based\napproach, DeepSpiro, aimed at the early prediction of future COPD risk.\nDeepSpiro consists of four key components: SpiroSmoother for stabilizing the\nVolume-Flow curve, SpiroEncoder for capturing volume variability-pattern\nthrough key patches of varying lengths, SpiroExplainer for integrating\nheterogeneous data and explaining predictions through volume attention, and\nSpiroPredictor for predicting the disease risk of undiagnosed high-risk\npatients based on key patch concavity, with prediction horizons of 1, 2, 3, 4,\n5 years, or even longer. Evaluated on the UK Biobank dataset, DeepSpiro\nachieved an AUC of 0.8328 for COPD detection and demonstrated strong predictive\nperformance for future COPD risk (p-value < 0.001). In summary, DeepSpiro can\neffectively predicts the long-term progression of the COPD disease.\n", "date": "2024-05-06", "categories": []}
{"id": "2412.19495", "title": "Disparate Model Performance and Stability in Machine Learning Clinical\n  Support for Diabetes and Heart Diseases", "abstract": "  Machine Learning (ML) algorithms are vital for supporting clinical\ndecision-making in biomedical informatics. However, their predictive\nperformance can vary across demographic groups, often due to the\nunderrepresentation of historically marginalized populations in training\ndatasets. The investigation reveals widespread sex- and age-related inequities\nin chronic disease datasets and their derived ML models. Thus, a novel\nanalytical framework is introduced, combining systematic arbitrariness with\ntraditional metrics like accuracy and data complexity. The analysis of data\nfrom over 25,000 individuals with chronic diseases revealed mild sex-related\ndisparities, favoring predictive accuracy for males, and significant\nage-related differences, with better accuracy for younger patients. Notably,\nolder patients showed inconsistent predictive accuracy across seven datasets,\nlinked to higher data complexity and lower model performance. This highlights\nthat representativeness in training data alone does not guarantee equitable\noutcomes, and model arbitrariness must be addressed before deploying models in\nclinical settings.\n", "date": "2024-12-27", "categories": []}
{"id": "2502.08813", "title": "Measuring Anxiety Levels with Head Motion Patterns in Severe Depression\n  Population", "abstract": "  Depression and anxiety are prevalent mental health disorders that frequently\ncooccur, with anxiety significantly influencing both the manifestation and\ntreatment of depression. An accurate assessment of anxiety levels in\nindividuals with depression is crucial to develop effective and personalized\ntreatment plans. This study proposes a new noninvasive method for quantifying\nanxiety severity by analyzing head movements -- specifically speed,\nacceleration, and angular displacement -- during video-recorded interviews with\npatients suffering from severe depression. Using data from a new CALYPSO\nDepression Dataset, we extracted head motion characteristics and applied\nregression analysis to predict clinically evaluated anxiety levels. Our results\ndemonstrate a high level of precision, achieving a mean absolute error (MAE) of\n0.35 in predicting the severity of psychological anxiety based on head movement\npatterns. This indicates that our approach can enhance the understanding of\nanxiety's role in depression and assist psychiatrists in refining treatment\nstrategies for individuals.\n", "date": "2025-02-12", "categories": []}
{"id": "2502.21264", "title": "Foundation Models -- A Panacea for Artificial Intelligence in Pathology?", "abstract": "  The role of artificial intelligence (AI) in pathology has evolved from aiding\ndiagnostics to uncovering predictive morphological patterns in whole slide\nimages (WSIs). Recently, foundation models (FMs) leveraging self-supervised\npre-training have been widely advocated as a universal solution for diverse\ndownstream tasks. However, open questions remain about their clinical\napplicability and generalization advantages over end-to-end learning using\ntask-specific (TS) models. Here, we focused on AI with clinical-grade\nperformance for prostate cancer diagnosis and Gleason grading. We present the\nlargest validation of AI for this task, using over 100,000 core needle biopsies\nfrom 7,342 patients across 15 sites in 11 countries. We compared two FMs with a\nfully end-to-end TS model in a multiple instance learning framework. Our\nfindings challenge assumptions that FMs universally outperform TS models. While\nFMs demonstrated utility in data-scarce scenarios, their performance converged\nwith - and was in some cases surpassed by - TS models when sufficient labeled\ntraining data were available. Notably, extensive task-specific training\nmarkedly reduced clinically significant misgrading, misdiagnosis of challenging\nmorphologies, and variability across different WSI scanners. Additionally, FMs\nused up to 35 times more energy than the TS model, raising concerns about their\nsustainability. Our results underscore that while FMs offer clear advantages\nfor rapid prototyping and research, their role as a universal solution for\nclinically applicable medical AI remains uncertain. For high-stakes clinical\napplications, rigorous validation and consideration of task-specific training\nremain critically important. We advocate for integrating the strengths of FMs\nand end-to-end learning to achieve robust and resource-efficient AI pathology\nsolutions fit for clinical use.\n", "date": "2025-02-28", "categories": []}
{"id": "2503.00028", "title": "Genetics-Driven Personalized Disease Progression Model", "abstract": "  Modeling disease progression through multiple stages is critical for clinical\ndecision-making for chronic diseases, e.g., cancer, diabetes, chronic kidney\ndiseases, and so on. Existing approaches often model the disease progression as\na uniform trajectory pattern at the population level. However, chronic diseases\nare highly heterogeneous and often have multiple progression patterns depending\non a patient's individual genetics and environmental effects due to lifestyles.\nWe propose a personalized disease progression model to jointly learn the\nheterogeneous progression patterns and groups of genetic profiles. In\nparticular, an end-to-end pipeline is designed to simultaneously infer the\ncharacteristics of patients from genetic markers using a variational\nautoencoder and how it drives the disease progressions using an RNN-based\nstate-space model based on clinical observations. Our proposed model shows\nimprovement on real-world and synthetic clinical data.\n", "date": "2025-02-24", "categories": []}
{"id": "2503.00210", "title": "Foundation-Model-Boosted Multimodal Learning for fMRI-based Neuropathic\n  Pain Drug Response Prediction", "abstract": "  Neuropathic pain, affecting up to 10% of adults, remains difficult to treat\ndue to limited therapeutic efficacy and tolerability. Although resting-state\nfunctional MRI (rs-fMRI) is a promising non-invasive measurement of brain\nbiomarkers to predict drug response in therapeutic development, the complexity\nof fMRI demands machine learning models with substantial capacity. However,\nextreme data scarcity in neuropathic pain research limits the application of\nhigh-capacity models. To address the challenge of data scarcity, we propose\nFMM$_{TC}$, a Foundation-Model-boosted Multimodal learning framework for\nfMRI-based neuropathic pain drug response prediction, which leverages both\ninternal multimodal information in pain-specific data and external knowledge\nfrom large pain-agnostic data. Specifically, to maximize the value of limited\npain-specific data, FMM$_{TC}$ integrates complementary information from two\nrs-fMRI modalities: Time series and functional Connectivity. FMM$_{TC}$ is\nfurther boosted by an fMRI foundation model with its external knowledge from\nextensive pain-agnostic fMRI datasets enriching limited pain-specific\ninformation. Evaluations with an in-house dataset and a public dataset from\nOpenNeuro demonstrate FMM$_{TC}$'s superior representation ability,\ngeneralizability, and cross-dataset adaptability over existing unimodal fMRI\nmodels that only consider one of the rs-fMRI modalities. The ablation study\nvalidates the effectiveness of multimodal learning and foundation-model-powered\nexternal knowledge transfer in FMM$_{TC}$. An integrated gradient-based\ninterpretation study explains how FMM$_{TC}$'s cross-dataset dynamic behaviors\nenhance its adaptability. In conclusion, FMM$_{TC}$ boosts clinical trials in\nneuropathic pain therapeutic development by accurately predicting drug\nresponses to improve the participant stratification efficiency.\n", "date": "2025-02-28", "categories": []}
{"id": "2503.00586", "title": "Cross-Attention Fusion of MRI and Jacobian Maps for Alzheimer's Disease\n  Diagnosis", "abstract": "  Early diagnosis of Alzheimer's disease (AD) is critical for intervention\nbefore irreversible neurodegeneration occurs. Structural MRI (sMRI) is widely\nused for AD diagnosis, but conventional deep learning approaches primarily rely\non intensity-based features, which require large datasets to capture subtle\nstructural changes. Jacobian determinant maps (JSM) provide complementary\ninformation by encoding localized brain deformations, yet existing multimodal\nfusion strategies fail to fully integrate these features with sMRI. We propose\na cross-attention fusion framework to model the intrinsic relationship between\nsMRI intensity and JSM-derived deformations for AD classification. Using the\nAlzheimer's Disease Neuroimaging Initiative (ADNI) dataset, we compare\ncross-attention, pairwise self-attention, and bottleneck attention with four\npre-trained 3D image encoders. Cross-attention fusion achieves superior\nperformance, with mean ROC-AUC scores of 0.903 (+/-0.033) for AD vs.\ncognitively normal (CN) and 0.692 (+/-0.061) for mild cognitive impairment\n(MCI) vs. CN. Despite its strong performance, our model remains highly\nefficient, with only 1.56 million parameters--over 40 times fewer than\nResNet-34 (63M) and Swin UNETR (61.98M). These findings demonstrate the\npotential of cross-attention fusion for improving AD diagnosis while\nmaintaining computational efficiency.\n", "date": "2025-03-01", "categories": []}
{"id": "2502.16622", "title": "Diagnosing COVID-19 Severity from Chest X-Ray Images Using ViT and CNN\n  Architectures", "abstract": "  The COVID-19 pandemic strained healthcare resources and prompted discussion\nabout how machine learning can alleviate physician burdens and contribute to\ndiagnosis. Chest x-rays (CXRs) are used for diagnosis of COVID-19, but few\nstudies predict the severity of a patient's condition from CXRs. In this study,\nwe produce a large COVID severity dataset by merging three sources and\ninvestigate the efficacy of transfer learning using ImageNet- and\nCXR-pretrained models and vision transformers (ViTs) in both severity\nregression and classification tasks. A pretrained DenseNet161 model performed\nthe best on the three class severity prediction problem, reaching 80% accuracy\noverall and 77.3%, 83.9%, and 70% on mild, moderate and severe cases,\nrespectively. The ViT had the best regression results, with a mean absolute\nerror of 0.5676 compared to radiologist-predicted severity scores. The\nproject's source code is publicly available.\n", "date": "2025-02-23", "categories": []}
{"id": "2409.07347", "title": "The Role of Explainable AI in Revolutionizing Human Health Monitoring: A\n  Review", "abstract": "  The complex nature of disease mechanisms and the variability of patient\nsymptoms pose significant challenges in developing effective diagnostic tools.\nAlthough machine learning (ML) has made substantial advances in medical\ndiagnosis, the decision-making processes of these models often lack\ntransparency, potentially jeopardizing patient outcomes. This review aims to\nhighlight the role of Explainable AI (XAI) in addressing the interpretability\nissues of ML models in healthcare, with a focus on chronic conditions such as\nParkinson's, stroke, depression, cancer, heart disease, and Alzheimer's\ndisease. A comprehensive literature search was conducted across multiple\ndatabases to identify studies that applied XAI techniques in healthcare. The\nsearch focused on XAI algorithms used in diagnosing and monitoring chronic\ndiseases. The review identified the application of nine trending XAI\nalgorithms, each evaluated for their advantages and limitations in various\nhealthcare contexts. The findings underscore the importance of transparency in\nML models, which is crucial for improving trust and outcomes in clinical\npractice. While XAI provides significant potential to bridge the gap between\ncomplex ML models and clinical practice, challenges such as scalability,\nvalidation, and clinician acceptance remain. The review also highlights areas\nrequiring further research, particularly in integrating XAI into healthcare\nsystems. The study concludes that XAI methods offer a promising path forward\nfor enhancing human health monitoring and patient care, though significant\nchallenges must be addressed to fully realize their potential in clinical\nsettings.\n", "date": "2024-09-11", "categories": []}
{"id": "2502.19593", "title": "Improving Representation Learning of Complex Critical Care Data with\n  ICU-BERT", "abstract": "  The multivariate, asynchronous nature of real-world clinical data, such as\nthat generated in Intensive Care Units (ICUs), challenges traditional AI-based\ndecision-support systems. These often assume data regularity and feature\nindependence and frequently rely on limited data scopes and manual feature\nengineering. The potential of generative AI technologies has not yet been fully\nexploited to analyze clinical data. We introduce ICU-BERT, a transformer-based\nmodel pre-trained on the MIMIC-IV database using a multi-task scheme to learn\nrobust representations of complex ICU data with minimal preprocessing. ICU-BERT\nemploys a multi-token input strategy, incorporating dense embeddings from a\nbiomedical Large Language Model to learn a generalizable representation of\ncomplex and multivariate ICU data. With an initial evaluation of five tasks and\nfour additional ICU datasets, ICU-BERT results indicate that ICU-BERT either\ncompares to or surpasses current performance benchmarks by leveraging\nfine-tuning. By integrating structured and unstructured data, ICU-BERT advances\nthe use of foundational models in medical informatics, offering an adaptable\nsolution for clinical decision support across diverse applications.\n", "date": "2025-02-26", "categories": []}
{"id": "2406.00036", "title": "EMERGE: Enhancing Multimodal Electronic Health Records Predictive\n  Modeling with Retrieval-Augmented Generation", "abstract": "  The integration of multimodal Electronic Health Records (EHR) data has\nsignificantly advanced clinical predictive capabilities. Existing models, which\nutilize clinical notes and multivariate time-series EHR data, often fall short\nof incorporating the necessary medical context for accurate clinical tasks,\nwhile previous approaches with knowledge graphs (KGs) primarily focus on\nstructured knowledge extraction. In response, we propose EMERGE, a\nRetrieval-Augmented Generation (RAG) driven framework to enhance multimodal EHR\npredictive modeling. We extract entities from both time-series data and\nclinical notes by prompting Large Language Models (LLMs) and align them with\nprofessional PrimeKG, ensuring consistency. In addition to triplet\nrelationships, we incorporate entities' definitions and descriptions for richer\nsemantics. The extracted knowledge is then used to generate task-relevant\nsummaries of patients' health statuses. Finally, we fuse the summary with other\nmodalities using an adaptive multimodal fusion network with cross-attention.\nExtensive experiments on the MIMIC-III and MIMIC-IV datasets' in-hospital\nmortality and 30-day readmission tasks demonstrate the superior performance of\nthe EMERGE framework over baseline models. Comprehensive ablation studies and\nanalysis highlight the efficacy of each designed module and robustness to data\nsparsity. EMERGE contributes to refining the utilization of multimodal EHR data\nin healthcare, bridging the gap with nuanced medical contexts essential for\ninformed clinical predictions. We have publicly released the code at\nhttps://github.com/yhzhu99/EMERGE.\n", "date": "2024-05-27", "categories": []}
{"id": "2410.02551", "title": "ColaCare: Enhancing Electronic Health Record Modeling through Large\n  Language Model-Driven Multi-Agent Collaboration", "abstract": "  We introduce ColaCare, a framework that enhances Electronic Health Record\n(EHR) modeling through multi-agent collaboration driven by Large Language\nModels (LLMs). Our approach seamlessly integrates domain-specific expert models\nwith LLMs to bridge the gap between structured EHR data and text-based\nreasoning. Inspired by the Multidisciplinary Team (MDT) approach used in\nclinical settings, ColaCare employs two types of agents: DoctorAgents and a\nMetaAgent, which collaboratively analyze patient data. Expert models process\nand generate predictions from numerical EHR data, while LLM agents produce\nreasoning references and decision-making reports within the MDT-driven\ncollaborative consultation framework. The MetaAgent orchestrates the\ndiscussion, facilitating consultations and evidence-based debates among\nDoctorAgents, simulating diverse expertise in clinical decision-making. We\nadditionally incorporate the Merck Manual of Diagnosis and Therapy (MSD)\nmedical guideline within a retrieval-augmented generation (RAG) module for\nmedical evidence support, addressing the challenge of knowledge currency.\nExtensive experiments conducted on three EHR datasets demonstrate ColaCare's\nsuperior performance in clinical mortality outcome and readmission prediction\ntasks, underscoring its potential to revolutionize clinical decision support\nsystems and advance personalized precision medicine. All code, case studies and\na questionnaire are available at the project website:\nhttps://colacare.netlify.app.\n", "date": "2024-10-03", "categories": []}
{"id": "2502.18823", "title": "Evidence-Driven Marker Extraction for Social Media Suicide Risk\n  Detection", "abstract": "  Early detection of suicide risk from social media text is crucial for timely\nintervention. While Large Language Models (LLMs) offer promising capabilities\nin this domain, challenges remain in terms of interpretability and\ncomputational efficiency. This paper introduces Evidence-Driven LLM (ED-LLM), a\nnovel approach for clinical marker extraction and suicide risk classification.\nED-LLM employs a multi-task learning framework, jointly training a Mistral-7B\nbased model to identify clinical marker spans and classify suicide risk levels.\nThis evidence-driven strategy enhances interpretability by explicitly\nhighlighting textual evidence supporting risk assessments. Evaluated on the\nCLPsych datasets, ED-LLM demonstrates competitive performance in risk\nclassification and superior capability in clinical marker span identification\ncompared to baselines including fine-tuned LLMs, traditional machine learning,\nand prompt-based methods. The results highlight the effectiveness of multi-task\nlearning for interpretable and efficient LLM-based suicide risk assessment,\npaving the way for clinically relevant applications.\n", "date": "2025-02-26", "categories": []}
{"id": "2405.01617", "title": "An Explainable and Conformal AI Model to Detect Temporomandibular Joint\n  Involvement in Children Suffering from Juvenile Idiopathic Arthritis", "abstract": "  Juvenile idiopathic arthritis (JIA) is the most common rheumatic disease\nduring childhood and adolescence. The temporomandibular joints (TMJ) are among\nthe most frequently affected joints in patients with JIA, and mandibular growth\nis especially vulnerable to arthritic changes of the TMJ in children. A\nclinical examination is the most cost-effective method to diagnose TMJ\ninvolvement, but clinicians find it difficult to interpret and inaccurate when\nused only on clinical examinations. This study implemented an explainable\nartificial intelligence (AI) model that can help clinicians assess TMJ\ninvolvement. The classification model was trained using Random Forest on 6154\nclinical examinations of 1035 pediatric patients (67% female, 33% male) and\nevaluated on its ability to correctly classify TMJ involvement or not on a\nseparate test set. Most notably, the results show that the model can classify\npatients within two years of their first examination as having TMJ involvement\nwith a precision of 0.86 and a sensitivity of 0.7. The results show promise for\nan AI model in the assessment of TMJ involvement in children and as a decision\nsupport tool.\n", "date": "2024-05-02", "categories": []}
{"id": "2502.17463", "title": "SincPD: An Explainable Method based on Sinc Filters to Diagnose\n  Parkinson's Disease Severity by Gait Cycle Analysis", "abstract": "  In this paper, an explainable deep learning-based classifier based on\nadaptive sinc filters for Parkinson's Disease diagnosis (PD) along with\ndetermining its severity, based on analyzing the gait cycle (SincPD) is\npresented. Considering the effects of PD on the gait cycle of patients, the\nproposed method utilizes raw data in the form of vertical Ground Reaction Force\n(vGRF) measured by wearable sensors placed in soles of subjects' shoes. The\nproposed method consists of Sinc layers that model adaptive bandpass filters to\nextract important frequency-bands in gait cycle of patients along with healthy\nsubjects. Therefore, by considering these frequencies, the reasons behind the\nclassification a person as a patient or healthy can be explained. In this\nmethod, after applying some preprocessing processes, a large model equipped\nwith many filters is first trained. Next, to prune the extra units and reach a\nmore explainable and parsimonious structure, the extracted filters are clusters\nbased on their cut-off frequencies using a centroid-based clustering approach.\nAfterward, the medoids of the extracted clusters are considered as the final\nfilters. Therefore, only 15 bandpass filters for each sensor are derived to\nclassify patients and healthy subjects. Finally, the most effective filters\nalong with the sensors are determined by comparing the energy of each filter\nencountering patients and healthy subjects.\n", "date": "2025-02-10", "categories": []}
{"id": "2502.17476", "title": "Fusion of ECG Foundation Model Embeddings to Improve Early Detection of\n  Acute Coronary Syndromes", "abstract": "  Acute Coronary Syndrome (ACS) is a life-threatening cardiovascular condition\nwhere early and accurate diagnosis is critical for effective treatment and\nimproved patient outcomes. This study explores the use of ECG foundation\nmodels, specifically ST-MEM and ECG-FM, to enhance ACS risk assessment using\nprehospital ECG data collected in ambulances. Both models leverage\nself-supervised learning (SSL), with ST-MEM using a reconstruction-based\napproach and ECG-FM employing contrastive learning, capturing unique spatial\nand temporal ECG features. We evaluate the performance of these models\nindividually and through a fusion approach, where their embeddings are combined\nfor enhanced prediction. Results demonstrate that both foundation models\noutperform a baseline ResNet-50 model, with the fusion-based approach achieving\nthe highest performance (AUROC: 0.843 +/- 0.006, AUCPR: 0.674 +/- 0.012). These\nfindings highlight the potential of ECG foundation models for early ACS\ndetection and motivate further exploration of advanced fusion strategies to\nmaximize complementary feature utilization.\n", "date": "2025-02-17", "categories": []}
{"id": "2502.17484", "title": "Urinary Tract Infection Detection in Digital Remote Monitoring:\n  Strategies for Managing Participant-Specific Prediction Complexity", "abstract": "  Urinary tract infections (UTIs) are a significant health concern,\nparticularly for people living with dementia (PLWD), as they can lead to severe\ncomplications if not detected and treated early. This study builds on previous\nwork that utilised machine learning (ML) to detect UTIs in PLWD by analysing\nin-home activity and physiological data collected through low-cost, passive\nsensors. The current research focuses on improving the performance of previous\nmodels, particularly by refining the Multilayer Perceptron (MLP), to better\nhandle variations in home environments and improve sex fairness in predictions\nby making use of concepts from multitask learning. This study implemented three\nprimary model designs: feature clustering, loss-dependent clustering, and\nparticipant ID embedding which were compared against a baseline MLP model. The\nresults demonstrated that the loss-dependent MLP achieved the most significant\nimprovements, increasing validation precision from 48.92% to 72.60% and\nsensitivity from 27.44% to 70.52%, while also enhancing model fairness across\nsexes. These findings suggest that the refined models offer a more reliable and\nequitable approach to early UTI detection in PLWD, addressing\nparticipant-specific data variations and enabling clinicians to detect and\nscreen for UTI risks more effectively, thereby facilitating earlier and more\naccurate treatment decisions.\n", "date": "2025-02-18", "categories": []}
{"id": "2502.17887", "title": "Arrhythmia Classification from 12-Lead ECG Signals Using Convolutional\n  and Transformer-Based Deep Learning Models", "abstract": "  In Romania, cardiovascular problems are the leading cause of death,\naccounting for nearly one-third of annual fatalities. The severity of this\nsituation calls for innovative diagnosis method for cardiovascular diseases.\nThis article aims to explore efficient, light-weight and rapid methods for\narrhythmia diagnosis, in resource-constrained healthcare settings. Due to the\nlack of Romanian public medical data, we trained our systems using\ninternational public datasets, having in mind that the ECG signals are the same\nregardless the patients' nationality. Within this purpose, we combined multiple\ndatasets, usually used in the field of arrhythmias classification: PTB-XL\nelectrocardiography dataset , PTB Diagnostic ECG Database, China 12-Lead ECG\nChallenge Database, Georgia 12-Lead ECG Challenge Database, and St. Petersburg\nINCART 12-lead Arrhythmia Database. For the input data, we employed ECG signal\nprocessing methods, specifically a variant of the Pan-Tompkins algorithm,\nuseful in arrhythmia classification because it provides a robust and efficient\nmethod for detecting QRS complexes in ECG signals. Additionally, we used\nmachine learning techniques, widely used for the task of classification,\nincluding convolutional neural networks (1D CNNs, 2D CNNs, ResNet) and Vision\nTransformers (ViTs). The systems were evaluated in terms of accuracy and F1\nscore. We annalysed our dataset from two perspectives. First, we fed the\nsystems with the ECG signals and the GRU-based 1D CNN model achieved the\nhighest accuracy of 93.4% among all the tested architectures. Secondly, we\ntransformed ECG signals into images and the CNN2D model achieved an accuracy of\n92.16%.\n", "date": "2025-02-25", "categories": []}
{"id": "2502.18009", "title": "Patient Trajectory Prediction: Integrating Clinical Notes with\n  Transformers", "abstract": "  Predicting disease trajectories from electronic health records (EHRs) is a\ncomplex task due to major challenges such as data non-stationarity, high\ngranularity of medical codes, and integration of multimodal data. EHRs contain\nboth structured data, such as diagnostic codes, and unstructured data, such as\nclinical notes, which hold essential information often overlooked. Current\nmodels, primarily based on structured data, struggle to capture the complete\nmedical context of patients, resulting in a loss of valuable information. To\naddress this issue, we propose an approach that integrates unstructured\nclinical notes into transformer-based deep learning models for sequential\ndisease prediction. This integration enriches the representation of patients'\nmedical histories, thereby improving the accuracy of diagnosis predictions.\nExperiments on MIMIC-IV datasets demonstrate that the proposed approach\noutperforms traditional models relying solely on structured data.\n", "date": "2025-02-25", "categories": []}
{"id": "2502.18050", "title": "Uncertainty-aware abstention in medical diagnosis based on medical texts", "abstract": "  This study addresses the critical issue of reliability for AI-assisted\nmedical diagnosis. We focus on the selection prediction approach that allows\nthe diagnosis system to abstain from providing the decision if it is not\nconfident in the diagnosis. Such selective prediction (or abstention)\napproaches are usually based on the modeling predictive uncertainty of machine\nlearning models involved.\n  This study explores uncertainty quantification in machine learning models for\nmedical text analysis, addressing diverse tasks across multiple datasets. We\nfocus on binary mortality prediction from textual data in MIMIC-III,\nmulti-label medical code prediction using ICD-10 codes from MIMIC-IV, and\nmulti-class classification with a private outpatient visits dataset.\nAdditionally, we analyze mental health datasets targeting depression and\nanxiety detection, utilizing various text-based sources, such as essays, social\nmedia posts, and clinical descriptions.\n  In addition to comparing uncertainty methods, we introduce HUQ-2, a new\nstate-of-the-art method for enhancing reliability in selective prediction\ntasks. Our results provide a detailed comparison of uncertainty quantification\nmethods. They demonstrate the effectiveness of HUQ-2 in capturing and\nevaluating uncertainty, paving the way for more reliable and interpretable\napplications in medical text analysis.\n", "date": "2025-02-25", "categories": []}
{"id": "2502.15607", "title": "Benchmarking machine learning for bowel sound pattern classification\n  from tabular features to pretrained models", "abstract": "  The development of electronic stethoscopes and wearable recording sensors\nopened the door to the automated analysis of bowel sound (BS) signals. This\nenables a data-driven analysis of bowel sound patterns, their interrelations,\nand their correlation to different pathologies. This work leverages a BS\ndataset collected from 16 healthy subjects that was annotated according to four\nestablished BS patterns. This dataset is used to evaluate the performance of\nmachine learning models to detect and/or classify BS patterns. The selection of\nconsidered models covers models using tabular features, convolutional neural\nnetworks based on spectrograms and models pre-trained on large audio datasets.\nThe results highlight the clear superiority of pre-trained models, particularly\nin detecting classes with few samples, achieving an AUC of 0.89 in\ndistinguishing BS from non-BS using a HuBERT model and an AUC of 0.89 in\ndifferentiating bowel sound patterns using a Wav2Vec 2.0 model. These results\npave the way for an improved understanding of bowel sounds in general and\nfuture machine-learning-driven diagnostic applications for gastrointestinal\nexaminations\n", "date": "2025-02-21", "categories": []}
{"id": "2502.15762", "title": "SmartEdge: Smart Healthcare End-to-End Integrated Edge and Cloud\n  Computing System for Diabetes Prediction Enabled by Ensemble Machine Learning", "abstract": "  The Internet of Things (IoT) revolutionizes smart city domains such as\nhealthcare, transportation, industry, and education. The Internet of Medical\nThings (IoMT) is gaining prominence, particularly in smart hospitals and Remote\nPatient Monitoring (RPM). The vast volume of data generated by IoMT devices\nshould be analyzed in real-time for health surveillance, prognosis, and\nprediction of diseases. Current approaches relying on Cloud computing to\nprovide the necessary computing and storage capabilities do not scale for these\nlatency-sensitive applications. Edge computing emerges as a solution by\nbringing cloud services closer to IoMT devices. This paper introduces\nSmartEdge, an AI-powered smart healthcare end-to-end integrated edge and cloud\ncomputing system for diabetes prediction. This work addresses latency concerns\nand demonstrates the efficacy of edge resources in healthcare applications\nwithin an end-to-end system. The system leverages various risk factors for\ndiabetes prediction. We propose an Edge and Cloud-enabled framework to deploy\nthe proposed diabetes prediction models on various configurations using edge\nnodes and main cloud servers. Performance metrics are evaluated using, latency,\naccuracy, and response time. By using ensemble machine learning voting\nalgorithms we can improve the prediction accuracy by 5% versus a single model\nprediction.\n", "date": "2025-02-14", "categories": []}
{"id": "2502.15825", "title": "Utilizing AI and Machine Learning for Predictive Analysis of\n  Post-Treatment Cancer Recurrence", "abstract": "  In oncology, recurrence after treatment is one of the major challenges,\nrelated to patients' survival and quality of life. Conventionally, prediction\nof cancer relapse has always relied on clinical observation with statistical\nmodel support, which almost fails to explain the complex, multifactorial nature\nof tumor recurrence. This research explores how AI and ML models may increase\nthe accuracy and reliability of recurrence prediction in cancer. Therefore, AI\nand ML create new opportunities not only for personalized medicine but also for\nproactive management of patients through analyzing large volumes of data on\ngenetics, clinical manifestations, and treatment. The paper describes the\nvarious AI and ML techniques for pattern identification and outcome prediction\nin cancer patients using supervised and unsupervised learning. Clinical\nimplications provide an opportunity to review how early interventions could\nhappen and the design of treatment planning.\n", "date": "2025-02-20", "categories": []}
{"id": "2502.16255", "title": "rECGnition_v2.0: Self-Attentive Canonical Fusion of ECG and Patient Data\n  using deep learning for effective Cardiac Diagnostics", "abstract": "  The variability in ECG readings influenced by individual patient\ncharacteristics has posed a considerable challenge to adopting automated ECG\nanalysis in clinical settings. A novel feature fusion technique termed SACC\n(Self Attentive Canonical Correlation) was proposed to address this. This\ntechnique is combined with DPN (Dual Pathway Network) and depth-wise separable\nconvolution to create a robust, interpretable, and fast end-to-end arrhythmia\nclassification model named rECGnition_v2.0 (robust ECG abnormality detection).\nThis study uses MIT-BIH, INCARTDB and EDB dataset to evaluate the efficiency of\nrECGnition_v2.0 for various classes of arrhythmias. To investigate the\ninfluence of constituting model components, various ablation studies were\nperformed, i.e. simple concatenation, CCA and proposed SACC were compared,\nwhile the importance of global and local ECG features were tested using DPN\nrECGnition_v2.0 model and vice versa. It was also benchmarked with\nstate-of-the-art CNN models for overall accuracy vs model parameters, FLOPs,\nmemory requirements, and prediction time. Furthermore, the inner working of the\nmodel was interpreted by comparing the activation locations in ECG before and\nafter the SACC layer. rECGnition_v2.0 showed a remarkable accuracy of 98.07%\nand an F1-score of 98.05% for classifying ten distinct classes of arrhythmia\nwith just 82.7M FLOPs per sample, thereby going beyond the performance metrics\nof current state-of-the-art (SOTA) models by utilizing MIT-BIH Arrhythmia\ndataset. Similarly, on INCARTDB and EDB datasets, excellent F1-scores of 98.01%\nand 96.21% respectively was achieved for AAMI classification. The compact\narchitectural footprint of the rECGnition_v2.0, characterized by its lesser\ntrainable parameters and diminished computational demands, unfurled several\nadvantages including interpretability and scalability.\n", "date": "2025-02-22", "categories": []}
{"id": "2502.16477", "title": "Unmasking Societal Biases in Respiratory Support for ICU Patients\n  through Social Determinants of Health", "abstract": "  In critical care settings, where precise and timely interventions are crucial\nfor health outcomes, evaluating disparities in patient outcomes is essential.\nCurrent approaches often fail to fully capture the impact of respiratory\nsupport interventions on individuals affected by social determinants of health.\nWhile attributes such as gender, race, and age are commonly assessed and\nprovide valuable insights, they offer only a partial view of the complexities\nfaced by diverse populations. In this study, we focus on two clinically\nmotivated tasks: prolonged mechanical ventilation and successful weaning.\nAdditionally, we conduct fairness audits on the models' predictions across\ndemographic groups and social determinants of health to better understand\nhealth inequities in respiratory interventions within the intensive care unit.\nFurthermore, we release a temporal benchmark dataset, verified by clinical\nexperts, to facilitate benchmarking of clinical respiratory intervention tasks.\n", "date": "2025-02-23", "categories": []}
{"id": "2502.16834", "title": "A Novel Multi-Task Teacher-Student Architecture with Self-Supervised\n  Pretraining for 48-Hour Vasoactive-Inotropic Trend Analysis in Sepsis\n  Mortality Prediction", "abstract": "  Sepsis is a major cause of ICU mortality, where early recognition and\neffective interventions are essential for improving patient outcomes. However,\nthe vasoactive-inotropic score (VIS) varies dynamically with a patient's\nhemodynamic status, complicated by irregular medication patterns, missing data,\nand confounders, making sepsis prediction challenging. To address this, we\npropose a novel Teacher-Student multitask framework with self-supervised VIS\npretraining via a Masked Autoencoder (MAE). The teacher model performs\nmortality classification and severity-score regression, while the student\ndistills robust time-series representations, enhancing adaptation to\nheterogeneous VIS data. Compared to LSTM-based methods, our approach achieves\nan AUROC of 0.82 on MIMIC-IV 3.0 (9,476 patients), outperforming the baseline\n(0.74). SHAP analysis revealed that SOFA score (0.147) had the greatest impact\non ICU mortality, followed by LODS (0.033), single marital status (0.031), and\nMedicaid insurance (0.023), highlighting the role of sociodemographic factors.\nSAPSII (0.020) also contributed significantly. These findings suggest that both\nclinical and social factors should be considered in ICU decision-making. Our\nnovel multitask and distillation strategies enable earlier identification of\nhigh-risk patients, improving prediction accuracy and disease management,\noffering new tools for ICU decision support.\n", "date": "2025-02-24", "categories": []}
{"id": "2502.14227", "title": "SleepGMUformer: A gated multimodal temporal neural network for sleep\n  staging", "abstract": "  Sleep staging is a key method for assessing sleep quality and diagnosing\nsleep disorders. However, current deep learning methods face challenges: 1)\npostfusion techniques ignore the varying contributions of different modalities;\n2) unprocessed sleep data can interfere with frequency-domain information. To\ntackle these issues, this paper proposes a gated multimodal temporal neural\nnetwork for multidomain sleep data, including heart rate, motion, steps, EEG\n(Fpz-Cz, Pz-Oz), and EOG from WristHR-Motion-Sleep and SleepEDF-78. The model\nintegrates: 1) a pre-processing module for feature alignment, missing value\nhandling, and EEG de-trending; 2) a feature extraction module for complex sleep\nfeatures in the time dimension; and 3) a dynamic fusion module for real-time\nmodality weighting.Experiments show classification accuracies of 85.03% on\nSleepEDF-78 and 94.54% on WristHR-Motion-Sleep datasets. The model handles\nheterogeneous datasets and outperforms state-of-the-art models by 1.00%-4.00%.\n", "date": "2025-02-20", "categories": []}
{"id": "2502.14430", "title": "Cardiac Evidence Backtracking for Eating Behavior Monitoring using\n  Collocative Electrocardiogram Imagining", "abstract": "  Eating monitoring has remained an open challenge in medical research for\nyears due to the lack of non-invasive sensors for continuous monitoring and the\nreliable methods for automatic behavior detection. In this paper, we present a\npilot study using the wearable 24-hour ECG for sensing and tailoring the\nsophisticated deep learning for ad-hoc and interpretable detection. This is\naccomplished using a collocative learning framework in which 1) we construct\ncollocative tensors as pseudo-images from 1D ECG signals to improve the\nfeasibility of 2D image-based deep models; 2) we formulate the cardiac logic of\nanalyzing the ECG data in a comparative way as periodic attention regulators so\nas to guide the deep inference to collect evidence in a human comprehensible\nmanner; and 3) we improve the interpretability of the framework by enabling the\nbacktracking of evidence with a set of methods designed for Class Activation\nMapping (CAM) decoding and decision tree/forest generation. The effectiveness\nof the proposed framework has been validated on the largest ECG dataset of\neating behavior with superior performance over conventional models, and its\ncapacity of cardiac evidence mining has also been verified through the\nconsistency of the evidence it backtracked and that of the previous medical\nstudies.\n", "date": "2025-02-20", "categories": []}
{"id": "2406.16913", "title": "L-SFAN: Lightweight Spatially-focused Attention Network for Pain\n  Behavior Detection", "abstract": "  Chronic Low Back Pain (CLBP) afflicts millions globally, significantly\nimpacting individuals' well-being and imposing economic burdens on healthcare\nsystems. While artificial intelligence (AI) and deep learning offer promising\navenues for analyzing pain-related behaviors to improve rehabilitation\nstrategies, current models, including convolutional neural networks (CNNs),\nrecurrent neural networks, and graph-based neural networks, have limitations.\nThese approaches often focus singularly on the temporal dimension or require\ncomplex architectures to exploit spatial interrelationships within multivariate\ntime series data. To address these limitations, we introduce \\hbox{L-SFAN}, a\nlightweight CNN architecture incorporating 2D filters designed to meticulously\ncapture the spatial-temporal interplay of data from motion capture and surface\nelectromyography sensors. Our proposed model, enhanced with an oriented global\npooling layer and multi-head self-attention mechanism, prioritizes critical\nfeatures to better understand CLBP and achieves competitive classification\naccuracy. Experimental results on the EmoPain database demonstrate that our\napproach not only enhances performance metrics with significantly fewer\nparameters but also promotes model interpretability, offering valuable insights\nfor clinicians in managing CLBP. This advancement underscores the potential of\nAI in transforming healthcare practices for chronic conditions like CLBP,\nproviding a sophisticated framework for the nuanced analysis of complex\nbiomedical data.\n", "date": "2024-06-07", "categories": []}
{"id": "2404.15352", "title": "TransfoRhythm: A Transformer Architecture Conductive to Blood Pressure\n  Estimation via Solo PPG Signal Capturing", "abstract": "  Recent statistics indicate that approximately 1.3 billion individuals\nworldwide suffer from hypertension, a leading cause of premature death\nglobally. Blood Pressure (BP) serves as a critical health indicator for\naccurate and timely diagnosis and/or treatment of hypertension. Traditional BP\nmeasurement methods rely on cuff-based approaches, which lack real-time,\ncontinuous, and reliable BP estimates, crucial for the timely\ndiagnosis/treatment of hypertension. Driven by recent advancements in\nArtificial Intelligence (AI) and Deep Neural Networks (DNNs), there has been a\nsurge of interest in developing data-driven and cuff-less BP estimation\nsolutions. In this context, current literature predominantly focuses on\ncoupling Electrocardiography (ECG) and Photoplethysmography (PPG) sensors,\nthough this approach is constrained by reliance on multiple sensor types. An\nalternative, utilizing standalone PPG signals, presents challenges due to the\nabsence of auxiliary sensors (ECG), requiring the use of morphological features\nwhile addressing motion artifacts and high-frequency noise. To address these\nissues, the paper introduces the TransfoRhythm framework, a Transformer-based\nDNN architecture built upon the recently released physiological database,\nMIMIC-IV. Leveraging the Multi-Head Attention (MHA) mechanism, TransfoRhythm\nidentifies dependencies and similarities across data segments, forming a robust\nframework for cuff-less BP estimation solely using PPG signals. To our\nknowledge, this paper represents the first study to apply the MIMIC IV dataset\nfor cuff-less BP estimation. TransfoRhythm achieves highly accurate results\nwith a Root Mean Square Error (RMSE) of [2.21, 1.84] and a Mean Absolute Error\n(MAE) of [1.37, 1.06] for systolic and diastolic blood pressures, respectively.\n", "date": "2024-04-15", "categories": []}
{"id": "2410.03514", "title": "Stabilized Neural Prediction of Potential Outcomes in Continuous Time", "abstract": "  Patient trajectories from electronic health records are widely used to\nestimate conditional average potential outcomes (CAPOs) of treatments over\ntime, which then allows to personalize care. Yet, existing neural methods for\nthis purpose have a key limitation: while some adjust for time-varying\nconfounding, these methods assume that the time series are recorded in discrete\ntime. In other words, they are constrained to settings where measurements and\ntreatments are conducted at fixed time steps, even though this is unrealistic\nin medical practice. In this work, we aim to estimate CAPOs in continuous time.\nThe latter is of direct practical relevance because it allows for modeling\npatient trajectories where measurements and treatments take place at arbitrary,\nirregular timestamps. We thus propose a new method called stabilized continuous\ntime inverse propensity network (SCIP-Net). For this, we further derive\nstabilized inverse propensity weights for robust estimation of the CAPOs. To\nthe best of our knowledge, our SCIP-Net is the first neural method that\nperforms proper adjustments for time-varying confounding in continuous time.\n", "date": "2024-10-04", "categories": []}
{"id": "2502.12277", "title": "Healthcare cost prediction for heterogeneous patient profiles using deep\n  learning models with administrative claims data", "abstract": "  Problem: How can we design patient cost prediction models that effectively\naddress the challenges of heterogeneity in administrative claims (AC) data to\nensure accurate, fair, and generalizable predictions, especially for high-need\n(HN) patients with complex chronic conditions?\n  Relevance: Accurate and equitable patient cost predictions are vital for\ndeveloping health management policies and optimizing resource allocation, which\ncan lead to significant cost savings for healthcare payers, including\ngovernment agencies and private insurers. Addressing disparities in prediction\noutcomes for HN patients ensures better economic and clinical decision-making,\nbenefiting both patients and payers.\n  Methodology: This study is grounded in socio-technical considerations that\nemphasize the interplay between technical systems (e.g., deep learning models)\nand humanistic outcomes (e.g., fairness in healthcare decisions). It\nincorporates representation learning and entropy measurement to address\nheterogeneity and complexity in data and patient profiles, particularly for HN\npatients. We propose a channel-wise deep learning framework that mitigates data\nheterogeneity by segmenting AC data into separate channels based on types of\ncodes (e.g., diagnosis, procedures) and costs. This approach is paired with a\nflexible evaluation design that uses multi-channel entropy measurement to\nassess patient heterogeneity.\n  Results: The proposed channel-wise models reduce prediction errors by 23%\ncompared to single-channel models, leading to 16.4% and 19.3% reductions in\noverpayments and underpayments, respectively. Notably, the reduction in\nprediction bias is significantly higher for HN patients, demonstrating\neffectiveness in handling heterogeneity and complexity in data and patient\nprofiles. This demonstrates the potential for applying channel-wise modeling to\ndomains with similar heterogeneity challenges.\n", "date": "2025-02-17", "categories": []}
{"id": "2307.05333", "title": "Wearable-based Fair and Accurate Pain Assessment Using Multi-Attribute\n  Fairness Loss in Convolutional Neural Networks", "abstract": "  The integration of diverse health data, such as IoT (Internet of Things), EHR\n(Electronic Health Record), and clinical surveys, with scalable AI(Artificial\nIntelligence) has enabled the identification of physical, behavioral, and\npsycho-social indicators of pain. However, the adoption of AI in clinical pain\nevaluation is hindered by challenges like personalization and fairness. Many AI\nmodels, including machine and deep learning, exhibit biases, discriminating\nagainst specific groups based on gender or ethnicity, causing skepticism among\nmedical professionals about their reliability. This paper proposes a\nMulti-attribute Fairness Loss (MAFL) based Convolutional Neural Network (CNN)\nmodel designed to account for protected attributes in data, ensuring fair pain\nstatus predictions while minimizing disparities between privileged and\nunprivileged groups. We evaluate whether a balance between accuracy and\nfairness is achievable by comparing the proposed model with existing mitigation\nmethods. Our findings indicate that the model performs favorably against\nstate-of-the-art techniques. Using the NIH All-Of-US dataset, comprising data\nfrom 868 individuals over 1500 days, we demonstrate our model's effectiveness,\nachieving accuracy rates between 75% and 85%.\n", "date": "2023-07-03", "categories": []}
{"id": "2501.09628", "title": "Artificial Intelligence-Driven Clinical Decision Support Systems", "abstract": "  As artificial intelligence (AI) becomes increasingly embedded in healthcare\ndelivery, this chapter explores the critical aspects of developing reliable and\nethical Clinical Decision Support Systems (CDSS). Beginning with the\nfundamental transition from traditional statistical models to sophisticated\nmachine learning approaches, this work examines rigorous validation strategies\nand performance assessment methods, including the crucial role of model\ncalibration and decision curve analysis. The chapter emphasizes that creating\ntrustworthy AI systems in healthcare requires more than just technical\naccuracy; it demands careful consideration of fairness, explainability, and\nprivacy. The challenge of ensuring equitable healthcare delivery through AI is\nstressed, discussing methods to identify and mitigate bias in clinical\npredictive models. The chapter then delves into explainability as a cornerstone\nof human-centered CDSS. This focus reflects the understanding that healthcare\nprofessionals must not only trust AI recommendations but also comprehend their\nunderlying reasoning. The discussion advances in an analysis of privacy\nvulnerabilities in medical AI systems, from data leakage in deep learning\nmodels to sophisticated attacks against model explanations. The text explores\nprivacy-preservation strategies such as differential privacy and federated\nlearning, while acknowledging the inherent trade-offs between privacy\nprotection and model performance. This progression, from technical validation\nto ethical considerations, reflects the multifaceted challenges of developing\nAI systems that can be seamlessly and reliably integrated into daily clinical\npractice while maintaining the highest standards of patient care and data\nprotection.\n", "date": "2025-01-16", "categories": []}
{"id": "2502.10481", "title": "Chronic Diseases Prediction Using ML", "abstract": "  The recent increase in morbidity is primarily due to chronic diseases\nincluding Diabetes, Heart disease, Lung cancer, and brain tumours. The results\nfor patients can be improved, and the financial burden on the healthcare system\ncan be lessened, through the early detection and prevention of certain\ndisorders. In this study, we built a machine-learning model for predicting the\nexistence of numerous diseases utilising datasets from various sources,\nincluding Kaggle, Dataworld, and the UCI repository, that are relevant to each\nof the diseases we intended to predict.\n  Following the acquisition of the datasets, we used feature engineering to\nextract pertinent features from the information, after which the model was\ntrained on a training set and improved using a validation set. A test set was\nthen used to assess the correctness of the final model. We provide an\neasy-to-use interface where users may enter the parameters for the selected\nailment. Once the right model has been run, it will indicate whether the user\nhas a certain ailment and offer suggestions for how to treat or prevent it.\n", "date": "2025-02-13", "categories": []}
{"id": "2502.10562", "title": "Detecting and Monitoring Bias for Subgroups in Breast Cancer Detection\n  AI", "abstract": "  Automated mammography screening plays an important role in early breast\ncancer detection. However, current machine learning models, developed on some\ntraining datasets, may exhibit performance degradation and bias when deployed\nin real-world settings. In this paper, we analyze the performance of\nhigh-performing AI models on two mammography datasets-the Emory Breast Imaging\nDataset (EMBED) and the RSNA 2022 challenge dataset. Specifically, we evaluate\nhow these models perform across different subgroups, defined by six attributes,\nto detect potential biases using a range of classification metrics. Our\nanalysis identifies certain subgroups that demonstrate notable\nunderperformance, highlighting the need for ongoing monitoring of these\nsubgroups' performance. To address this, we adopt a monitoring method designed\nto detect performance drifts over time. Upon identifying a drift, this method\nissues an alert, which can enable timely interventions. This approach not only\nprovides a tool for tracking the performance but also helps ensure that AI\nmodels continue to perform effectively across diverse populations.\n", "date": "2025-02-14", "categories": []}
{"id": "2502.10703", "title": "Artificial intelligence-enabled detection and assessment of Parkinson's\n  disease using multimodal data: A survey", "abstract": "  The rapid emergence of highly adaptable and reusable artificial intelligence\n(AI) models is set to revolutionize the medical field, particularly in the\ndiagnosis and management of Parkinson's disease (PD). Currently, there are no\neffective biomarkers for diagnosing PD, assessing its severity, or tracking its\nprogression. Numerous AI algorithms are now being used for PD diagnosis and\ntreatment, capable of performing various classification tasks based on\nmultimodal and heterogeneous disease symptom data, such as gait, hand\nmovements, and speech patterns of PD patients. They provide expressive\nfeedback, including predicting the potential likelihood of PD, assessing the\nseverity of individual or multiple symptoms, aiding in early detection, and\nevaluating rehabilitation and treatment effectiveness, thereby demonstrating\nadvanced medical diagnostic capabilities. Therefore, this work provides a\nsurveyed compilation of recent works regarding PD detection and assessment\nthrough biometric symptom recognition with a focus on machine learning and deep\nlearning approaches, emphasizing their benefits, and exposing their weaknesses,\nand their impact in opening up newer research avenues. Additionally, it also\npresents categorized and characterized descriptions of the datasets,\napproaches, and architectures employed to tackle associated constraints.\nFurthermore, the paper explores the potential opportunities and challenges\npresented by data-driven AI technologies in the diagnosis of PD.\n", "date": "2025-02-15", "categories": []}
{"id": "2412.17803", "title": "Examining Imbalance Effects on Performance and Demographic Fairness of\n  Clinical Language Models", "abstract": "  Data imbalance is a fundamental challenge in applying language models to\nbiomedical applications, particularly in ICD code prediction tasks where label\nand demographic distributions are uneven. While state-of-the-art language\nmodels have been increasingly adopted in biomedical tasks, few studies have\nsystematically examined how data imbalance affects model performance and\nfairness across demographic groups. This study fills the gap by statistically\nprobing the relationship between data imbalance and model performance in ICD\ncode prediction. We analyze imbalances in a standard benchmark data across\ngender, age, ethnicity, and social determinants of health by state-of-the-art\nbiomedical language models. By deploying diverse performance metrics and\nstatistical analyses, we explore the influence of data imbalance on performance\nvariations and demographic fairness. Our study shows that data imbalance\nsignificantly impacts model performance and fairness, but feature similarity to\nthe majority class may be a more critical factor. We believe this study\nprovides valuable insights for developing more equitable and robust language\nmodels in healthcare applications.\n", "date": "2024-12-23", "categories": []}
{"id": "2502.04794", "title": "MedMimic: Physician-Inspired Multimodal Fusion for Early Diagnosis of\n  Fever of Unknown Origin", "abstract": "  Fever of unknown origin FUO remains a diagnostic challenge. MedMimic is\nintroduced as a multimodal framework inspired by real-world diagnostic\nprocesses. It uses pretrained models such as DINOv2, Vision Transformer, and\nResNet-18 to convert high-dimensional 18F-FDG PET/CT imaging into\nlow-dimensional, semantically meaningful features. A learnable\nself-attention-based fusion network then integrates these imaging features with\nclinical data for classification. Using 416 FUO patient cases from Sichuan\nUniversity West China Hospital from 2017 to 2023, the multimodal fusion\nclassification network MFCN achieved macro-AUROC scores ranging from 0.8654 to\n0.9291 across seven tasks, outperforming conventional machine learning and\nsingle-modality deep learning methods. Ablation studies and five-fold\ncross-validation further validated its effectiveness. By combining the\nstrengths of pretrained large models and deep learning, MedMimic offers a\npromising solution for disease classification.\n", "date": "2025-02-07", "categories": []}
{"id": "2502.09686", "title": "Leveraging Machine Learning and Deep Learning Techniques for Improved\n  Pathological Staging of Prostate Cancer", "abstract": "  Prostate cancer (Pca) continues to be a leading cause of cancer-related\nmortality in men, and the limitations in precision of traditional diagnostic\nmethods such as the Digital Rectal Exam (DRE), Prostate-Specific Antigen (PSA)\ntesting, and biopsies underscore the critical importance of accurate staging\ndetection in enhancing treatment outcomes and improving patient prognosis. This\nstudy leverages machine learning and deep learning approaches, along with\nfeature selection and extraction methods, to enhance PCa pathological staging\npredictions using RNA sequencing data from The Cancer Genome Atlas (TCGA). Gene\nexpression profiles from 486 tumors were analyzed using advanced algorithms,\nincluding Random Forest (RF), Logistic Regression (LR), Extreme Gradient\nBoosting (XGB), and Support Vector Machine (SVM). The performance of the study\nis measured with respect to the F1-score, as well as precision and recall, all\nof which are calculated as weighted averages. The results reveal that the\nhighest test F1-score, approximately 83%, was achieved by the Random Forest\nalgorithm, followed by Logistic Regression at 80%, while both Extreme Gradient\nBoosting (XGB) and Support Vector Machine (SVM) scored around 79%. Furthermore,\ndeep learning models with data augmentation achieved an accuracy of 71. 23%,\nwhile PCA-based dimensionality reduction reached an accuracy of 69.86%. This\nresearch highlights the potential of AI-driven approaches in clinical oncology,\npaving the way for more reliable diagnostic tools that can ultimately improve\npatient outcomes.\n", "date": "2025-02-13", "categories": []}
{"id": "2502.09715", "title": "Evaluating GPT's Capability in Identifying Stages of Cognitive\n  Impairment from Electronic Health Data", "abstract": "  Identifying cognitive impairment within electronic health records (EHRs) is\ncrucial not only for timely diagnoses but also for facilitating research.\nInformation about cognitive impairment often exists within unstructured\nclinician notes in EHRs, but manual chart reviews are both time-consuming and\nerror-prone. To address this issue, our study evaluates an automated approach\nusing zero-shot GPT-4o to determine stage of cognitive impairment in two\ndifferent tasks. First, we evaluated the ability of GPT-4o to determine the\nglobal Clinical Dementia Rating (CDR) on specialist notes from 769 patients who\nvisited the memory clinic at Massachusetts General Hospital (MGH), and achieved\na weighted kappa score of 0.83. Second, we assessed GPT-4o's ability to\ndifferentiate between normal cognition, mild cognitive impairment (MCI), and\ndementia on all notes in a 3-year window from 860 Medicare patients. GPT-4o\nattained a weighted kappa score of 0.91 in comparison to specialist chart\nreviews and 0.96 on cases that the clinical adjudicators rated with high\nconfidence. Our findings demonstrate GPT-4o's potential as a scalable chart\nreview tool for creating research datasets and assisting diagnosis in clinical\nsettings in the future.\n", "date": "2025-02-13", "categories": []}
{"id": "2502.09919", "title": "AttenGluco: Multimodal Transformer-Based Blood Glucose Forecasting on\n  AI-READI Dataset", "abstract": "  Diabetes is a chronic metabolic disorder characterized by persistently high\nblood glucose levels (BGLs), leading to severe complications such as\ncardiovascular disease, neuropathy, and retinopathy. Predicting BGLs enables\npatients to maintain glucose levels within a safe range and allows caregivers\nto take proactive measures through lifestyle modifications. Continuous Glucose\nMonitoring (CGM) systems provide real-time tracking, offering a valuable tool\nfor monitoring BGLs. However, accurately forecasting BGLs remains challenging\ndue to fluctuations due to physical activity, diet, and other factors. Recent\ndeep learning models show promise in improving BGL prediction. Nonetheless,\nforecasting BGLs accurately from multimodal, irregularly sampled data over long\nprediction horizons remains a challenging research problem. In this paper, we\npropose AttenGluco, a multimodal Transformer-based framework for long-term\nblood glucose prediction. AttenGluco employs cross-attention to effectively\nintegrate CGM and activity data, addressing challenges in fusing data with\ndifferent sampling rates. Moreover, it employs multi-scale attention to capture\nlong-term dependencies in temporal data, enhancing forecasting accuracy. To\nevaluate the performance of AttenGluco, we conduct forecasting experiments on\nthe recently released AIREADI dataset, analyzing its predictive accuracy across\ndifferent subject cohorts including healthy individuals, people with\nprediabetes, and those with type 2 diabetes. Furthermore, we investigate its\nperformance improvements and forgetting behavior as new cohorts are introduced.\nOur evaluations show that AttenGluco improves all error metrics, such as root\nmean square error (RMSE), mean absolute error (MAE), and correlation, compared\nto the multimodal LSTM model. AttenGluco outperforms this baseline model by\nabout 10% and 15% in terms of RMSE and MAE, respectively.\n", "date": "2025-02-14", "categories": []}
{"id": "2501.18071", "title": "Towards Transparent and Accurate Diabetes Prediction Using Machine\n  Learning and Explainable Artificial Intelligence", "abstract": "  Diabetes mellitus (DM) is a global health issue of significance that must be\ndiagnosed as early as possible and managed well. This study presents a\nframework for diabetes prediction using Machine Learning (ML) models,\ncomplemented with eXplainable Artificial Intelligence (XAI) tools, to\ninvestigate both the predictive accuracy and interpretability of the\npredictions from ML models. Data Preprocessing is based on the Synthetic\nMinority Oversampling Technique (SMOTE) and feature scaling used on the\nDiabetes Binary Health Indicators dataset to deal with class imbalance and\nvariability of clinical features. The ensemble model provided high accuracy,\nwith a test accuracy of 92.50% and an ROC-AUC of 0.975. BMI, Age, General\nHealth, Income, and Physical Activity were the most influential predictors\nobtained from the model explanations. The results of this study suggest that ML\ncombined with XAI is a promising means of developing accurate and\ncomputationally transparent tools for use in healthcare systems.\n", "date": "2025-01-30", "categories": []}
{"id": "2502.08612", "title": "Continuous Cardiac Arrest Prediction in ICU using PPG Foundation Model", "abstract": "  Non-invasive patient monitoring for tracking and predicting adverse acute\nhealth events is an emerging area of research. We pursue in-hospital cardiac\narrest (IHCA) prediction using only single-channel finger photoplethysmography\n(PPG) signals. Our proposed two-stage model Feature Extractor-Aggregator\nNetwork (FEAN) leverages powerful representations from pre-trained PPG\nfoundation models (PPG-GPT of size up to 1 Billion) stacked with sequential\nclassification models. We propose two FEAN variants (\"1H\", \"FH\") which use the\nlatest one-hour and (max) 24-hour history to make decisions respectively. Our\nstudy is the first to present IHCA prediction results in ICU patients using\nonly unimodal (continuous PPG signal) waveform deep representations. With our\nbest model, we obtain an average of 0.79 AUROC over 24~h prediction window\nbefore CA event onset with our model peaking performance at 0.82 one hour\nbefore CA. We also provide a comprehensive analysis of our model through\narchitectural tuning and PaCMAP visualization of patient health trajectory in\nlatent space.\n", "date": "2025-02-12", "categories": []}
{"id": "2310.07799", "title": "Domain-invariant Clinical Representation Learning by Bridging Data\n  Distribution Shift across EMR Datasets", "abstract": "  Emerging diseases present challenges in symptom recognition and timely\nclinical intervention due to limited available information. An effective\nprognostic model could assist physicians in making accurate diagnoses and\ndesigning personalized treatment plans to prevent adverse outcomes. However, in\nthe early stages of disease emergence, several factors hamper model\ndevelopment: limited data collection, insufficient clinical experience, and\nprivacy and ethical concerns restrict data availability and complicate accurate\nlabel assignment. Furthermore, Electronic Medical Record (EMR) data from\ndifferent diseases or sources often exhibit significant cross-dataset feature\nmisalignment, severely impacting the effectiveness of deep learning models. We\npresent a domain-invariant representation learning method that constructs a\ntransition model between source and target datasets. By constraining the\ndistribution shift of features generated across different domains, we capture\ndomain-invariant features specifically relevant to downstream tasks, developing\na unified domain-invariant encoder that achieves better feature representation\nacross various task domains. Experimental results across multiple target tasks\ndemonstrate that our proposed model surpasses competing baseline methods and\nachieves faster training convergence, particularly when working with limited\ndata. Extensive experiments validate our method's effectiveness in providing\nmore accurate predictions for emerging pandemics and other diseases. Code is\npublicly available at https://github.com/wang1yuhang/domain_invariant_network.\n", "date": "2023-10-11", "categories": []}
{"id": "2411.09591", "title": "Handling missing values in clinical machine learning: Insights from an\n  expert study", "abstract": "  Inherently interpretable machine learning (IML) models offer valuable support\nfor clinical decision-making but face challenges when features contain missing\nvalues. Traditional approaches, such as imputation or discarding incomplete\nrecords, are often impractical in scenarios where data is missing at test time.\nWe surveyed 55 clinicians from 29 French trauma centers, collecting 20 complete\nresponses to study their interaction with three IML models in a real-world\nclinical setting for predicting hemorrhagic shock with missing values. Our\nfindings reveal that while clinicians recognize the value of interpretability\nand are familiar with common IML approaches, traditional imputation techniques\noften conflict with their intuition. Instead of imputing unobserved values,\nthey rely on observed features combined with medical intuition and experience.\nAs a result, methods that natively handle missing values are preferred. These\nfindings underscore the need to integrate clinical reasoning into future IML\nmodels to enhance human-computer interaction.\n", "date": "2024-11-14", "categories": []}
{"id": "2502.06907", "title": "Can ChatGPT Diagnose Alzheimer's Disease?", "abstract": "  Can ChatGPT diagnose Alzheimer's Disease (AD)? AD is a devastating\nneurodegenerative condition that affects approximately 1 in 9 individuals aged\n65 and older, profoundly impairing memory and cognitive function. This paper\nutilises 9300 electronic health records (EHRs) with data from Magnetic\nResonance Imaging (MRI) and cognitive tests to address an intriguing question:\nAs a general-purpose task solver, can ChatGPT accurately detect AD using EHRs?\nWe present an in-depth evaluation of ChatGPT using a black-box approach with\nzero-shot and multi-shot methods. This study unlocks ChatGPT's capability to\nanalyse MRI and cognitive test results, as well as its potential as a\ndiagnostic tool for AD. By automating aspects of the diagnostic process, this\nresearch opens a transformative approach for the healthcare system,\nparticularly in addressing disparities in resource-limited regions where AD\nspecialists are scarce. Hence, it offers a foundation for a promising method\nfor early detection, supporting individuals with timely interventions, which is\nparamount for Quality of Life (QoL).\n", "date": "2025-02-10", "categories": []}
{"id": "2502.07025", "title": "Detecting Neurodegenerative Diseases using Frame-Level Handwriting\n  Embeddings", "abstract": "  In this study, we explored the use of spectrograms to represent handwriting\nsignals for assessing neurodegenerative diseases, including 42 healthy controls\n(CTL), 35 subjects with Parkinson's Disease (PD), 21 with Alzheimer's Disease\n(AD), and 15 with Parkinson's Disease Mimics (PDM). We applied CNN and\nCNN-BLSTM models for binary classification using both multi-channel fixed-size\nand frame-based spectrograms. Our results showed that handwriting tasks and\nspectrogram channel combinations significantly impacted classification\nperformance. The highest F1-score (89.8%) was achieved for AD vs. CTL, while PD\nvs. CTL reached 74.5%, and PD vs. PDM scored 77.97%. CNN consistently\noutperformed CNN-BLSTM. Different sliding window lengths were tested for\nconstructing frame-based spectrograms. A 1-second window worked best for AD,\nlonger windows improved PD classification, and window length had little effect\non PD vs. PDM.\n", "date": "2025-02-10", "categories": []}
{"id": "2403.17428", "title": "Aligning Large Language Models for Enhancing Psychiatric Interviews\n  Through Symptom Delineation and Summarization: Pilot Study", "abstract": "  Background: Advancements in large language models (LLMs) have opened new\npossibilities in psychiatric interviews, an underexplored area where LLMs could\nbe valuable. This study focuses on enhancing psychiatric interviews by\nanalyzing counseling data from North Korean defectors who have experienced\ntrauma and mental health issues.\n  Objective: The study investigates whether LLMs can (1) identify parts of\nconversations that suggest psychiatric symptoms and recognize those symptoms,\nand (2) summarize stressors and symptoms based on interview transcripts.\n  Methods: LLMs are tasked with (1) extracting stressors from transcripts, (2)\nidentifying symptoms and their corresponding sections, and (3) generating\ninterview summaries using the extracted data. The transcripts were labeled by\nmental health experts for training and evaluation.\n  Results: In the zero-shot inference setting using GPT-4 Turbo, 73 out of 102\nsegments demonstrated a recall mid-token distance d < 20 in identifying\nsymptom-related sections. For recognizing specific symptoms, fine-tuning\noutperformed zero-shot inference, achieving an accuracy, precision, recall, and\nF1-score of 0.82. For the generative summarization task, LLMs using symptom and\nstressor information scored highly on G-Eval metrics: coherence (4.66),\nconsistency (4.73), fluency (2.16), and relevance (4.67). Retrieval-augmented\ngeneration showed no notable performance improvement.\n  Conclusions: LLMs, with fine-tuning or appropriate prompting, demonstrated\nstrong accuracy (over 0.8) for symptom delineation and achieved high coherence\n(4.6+) in summarization. This study highlights their potential to assist mental\nhealth practitioners in analyzing psychiatric interviews.\n", "date": "2024-03-26", "categories": []}
{"id": "2407.21124", "title": "Zero Shot Health Trajectory Prediction Using Transformer", "abstract": "  Integrating modern machine learning and clinical decision-making has great\npromise for mitigating healthcare's increasing cost and complexity. We\nintroduce the Enhanced Transformer for Health Outcome Simulation (ETHOS), a\nnovel application of the transformer deep-learning architecture for analyzing\nhigh-dimensional, heterogeneous, and episodic health data. ETHOS is trained\nusing Patient Health Timelines (PHTs)-detailed, tokenized records of health\nevents-to predict future health trajectories, leveraging a zero-shot learning\napproach. ETHOS represents a significant advancement in foundation model\ndevelopment for healthcare analytics, eliminating the need for labeled data and\nmodel fine-tuning. Its ability to simulate various treatment pathways and\nconsider patient-specific factors positions ETHOS as a tool for care\noptimization and addressing biases in healthcare delivery. Future developments\nwill expand ETHOS' capabilities to incorporate a wider range of data types and\ndata sources. Our work demonstrates a pathway toward accelerated AI development\nand deployment in healthcare.\n", "date": "2024-07-30", "categories": []}
{"id": "2502.05815", "title": "Image-Based Alzheimer's Disease Detection Using Pretrained Convolutional\n  Neural Network Models", "abstract": "  Alzheimer's disease is an untreatable, progressive brain disorder that slowly\nrobs people of their memory, thinking abilities, and ultimately their capacity\nto complete even the most basic tasks. Among older adults, it is the most\nfrequent cause of dementia. Although there is presently no treatment for\nAlzheimer's disease, scientific trials are ongoing to discover drugs to combat\nthe condition. Treatments to slow the signs of dementia are also available.\nMany researchers throughout the world became interested in developing\ncomputer-aided diagnosis systems to aid in the early identification of this\ndeadly disease and assure an accurate diagnosis. In particular, image based\napproaches have been coupled with machine learning techniques to address the\nchallenges of Alzheimer's disease detection. This study proposes a computer\naided diagnosis system to detect Alzheimer's disease from biomarkers captured\nusing neuroimaging techniques. The proposed approach relies on deep learning\ntechniques to extract the relevant visual features from the image collection to\naccurately predict the Alzheimer's class value. In the experiments, standard\ndatasets and pre-trained deep learning models were investigated. Moreover,\nstandard performance measures were used to assess the models' performances. The\nobtained results proved that VGG16-based models outperform the state of the art\nperformance.\n", "date": "2025-02-09", "categories": []}
{"id": "2502.04423", "title": "Primary Care Diagnoses as a Reliable Predictor for Orthopedic Surgical\n  Interventions", "abstract": "  Referral workflow inefficiencies, including misaligned referrals and delays,\ncontribute to suboptimal patient outcomes and higher healthcare costs. In this\nstudy, we investigated the possibility of predicting procedural needs based on\nprimary care diagnostic entries, thereby improving referral accuracy,\nstreamlining workflows, and providing better care to patients. A de-identified\ndataset of 2,086 orthopedic referrals from the University of Texas Health at\nTyler was analyzed using machine learning models built on Base General\nEmbeddings (BGE) for semantic extraction. To ensure real-world applicability,\nnoise tolerance experiments were conducted, and oversampling techniques were\nemployed to mitigate class imbalance. The selected optimum and parsimonious\nembedding model demonstrated high predictive accuracy (ROC-AUC: 0.874, Matthews\nCorrelation Coefficient (MCC): 0.540), effectively distinguishing patients\nrequiring surgical intervention. Dimensionality reduction techniques confirmed\nthe model's ability to capture meaningful clinical relationships. A threshold\nsensitivity analysis identified an optimal decision threshold (0.30) to balance\nprecision and recall, maximizing referral efficiency. In the predictive\nmodeling analysis, the procedure rate increased from 11.27% to an optimal\n60.1%, representing a 433% improvement with significant implications for\noperational efficiency and healthcare revenue.\n  The results of our study demonstrate that referral optimization can enhance\nprimary and surgical care integration. Through this approach, precise and\ntimely predictions of procedural requirements can be made, thereby minimizing\ndelays, improving surgical planning, and reducing administrative burdens. In\naddition, the findings highlight the potential of clinical decision support as\na scalable solution for improving patient outcomes and the efficiency of the\nhealthcare system.\n", "date": "2025-02-06", "categories": []}
{"id": "2502.04515", "title": "MedGNN: Towards Multi-resolution Spatiotemporal Graph Learning for\n  Medical Time Series Classification", "abstract": "  Medical time series has been playing a vital role in real-world healthcare\nsystems as valuable information in monitoring health conditions of patients.\nAccurate classification for medical time series, e.g., Electrocardiography\n(ECG) signals, can help for early detection and diagnosis. Traditional methods\ntowards medical time series classification rely on handcrafted feature\nextraction and statistical methods; with the recent advancement of artificial\nintelligence, the machine learning and deep learning methods have become more\npopular. However, existing methods often fail to fully model the complex\nspatial dynamics under different scales, which ignore the dynamic\nmulti-resolution spatial and temporal joint inter-dependencies. Moreover, they\nare less likely to consider the special baseline wander problem as well as the\nmulti-view characteristics of medical time series, which largely hinders their\nprediction performance. To address these limitations, we propose a\nMulti-resolution Spatiotemporal Graph Learning framework, MedGNN, for medical\ntime series classification. Specifically, we first propose to construct\nmulti-resolution adaptive graph structures to learn dynamic multi-scale\nembeddings. Then, to address the baseline wander problem, we propose Difference\nAttention Networks to operate self-attention mechanisms on the finite\ndifference for temporal modeling. Moreover, to learn the multi-view\ncharacteristics, we utilize the Frequency Convolution Networks to capture\ncomplementary information of medical time series from the frequency domain. In\naddition, we introduce the Multi-resolution Graph Transformer architecture to\nmodel the dynamic dependencies and fuse the information from different\nresolutions. Finally, we have conducted extensive experiments on multiple\nmedical real-world datasets that demonstrate the superior performance of our\nmethod. Our Code is available.\n", "date": "2025-02-06", "categories": []}
{"id": "2405.13082", "title": "A Survey of Artificial Intelligence in Gait-Based Neurodegenerative\n  Disease Diagnosis", "abstract": "  Recent years have witnessed an increasing global population affected by\nneurodegenerative diseases (NDs), which traditionally require extensive\nhealthcare resources and human effort for medical diagnosis and monitoring. As\na crucial disease-related motor symptom, human gait can be exploited to\ncharacterize different NDs. The current advances in artificial intelligence\n(AI) models enable automatic gait analysis for NDs identification and\nclassification, opening a new avenue to facilitate faster and more\ncost-effective diagnosis of NDs. In this paper, we provide a comprehensive\nsurvey on recent progress of machine learning and deep learning based AI\ntechniques applied to diagnosis of five typical NDs through gait. We provide an\noverview of the process of AI-assisted NDs diagnosis, and present a systematic\ntaxonomy of existing gait data and AI models. Meanwhile, a novel quality\nevaluation criterion is proposed to quantitatively assess the quality of\nexisting studies. Through an extensive review and analysis of 169 studies, we\npresent recent technical advancements, discuss existing challenges, potential\nsolutions, and future directions in this field. Finally, we envision the\nprospective utilization of 3D skeleton data for human gait representation and\nthe development of more efficient AI models for NDs diagnosis.\n", "date": "2024-05-21", "categories": []}
{"id": "2502.03591", "title": "Clinically-Inspired Hierarchical Multi-Label Classification of Chest\n  X-rays with a Penalty-Based Loss Function", "abstract": "  In this work, we present a novel approach to multi-label chest X-ray (CXR)\nimage classification that enhances clinical interpretability while maintaining\na streamlined, single-model, single-run training pipeline. Leveraging the\nCheXpert dataset and VisualCheXbert-derived labels, we incorporate hierarchical\nlabel groupings to capture clinically meaningful relationships between\ndiagnoses. To achieve this, we designed a custom hierarchical binary\ncross-entropy (HBCE) loss function that enforces label dependencies using\neither fixed or data-driven penalty types. Our model achieved a mean area under\nthe receiver operating characteristic curve (AUROC) of 0.903 on the test set.\nAdditionally, we provide visual explanations and uncertainty estimations to\nfurther enhance model interpretability. All code, model configurations, and\nexperiment details are made available.\n", "date": "2025-02-05", "categories": []}
{"id": "2502.03943", "title": "Multimodal Data-Driven Classification of Mental Disorders: A\n  Comprehensive Approach to Diagnosing Depression, Anxiety, and Schizophrenia", "abstract": "  This study investigates the potential of multimodal data integration, which\ncombines electroencephalogram (EEG) data with sociodemographic characteristics\nlike age, sex, education, and intelligence quotient (IQ), to diagnose mental\ndiseases like schizophrenia, depression, and anxiety. Using Apache Spark and\nconvolutional neural networks (CNNs), a data-driven classification pipeline has\nbeen developed for big data environment to effectively analyze massive\ndatasets. In order to evaluate brain activity and connection patterns\nassociated with mental disorders, EEG parameters such as power spectral density\n(PSD) and coherence are examined. The importance of coherence features is\nhighlighted by comparative analysis, which shows significant improvement in\nclassification accuracy and robustness. This study emphasizes the significance\nof holistic approaches for efficient diagnostic tools by integrating a variety\nof data sources. The findings open the door for creative, data-driven\napproaches to treating psychiatric diseases by demonstrating the potential of\nutilizing big data, sophisticated deep learning methods, and multimodal\ndatasets to enhance the precision, usability, and comprehension of mental\nhealth diagnostics.\n", "date": "2025-02-06", "categories": []}
{"id": "2502.04288", "title": "Leveraging Geolocation in Clinical Records to Improve Alzheimer's\n  Disease Diagnosis Using DMV Framework", "abstract": "  Alzheimer's Disease (AD) early detection is critical for enabling timely\nintervention and improving patient outcomes. This paper presents a DMV\nframework using Llama3-70B and GPT-4o as embedding models to analyze clinical\nnotes and predict a continuous risk score associated with early AD onset.\nFraming the task as a regression problem, we model the relationship between\nlinguistic features in clinical notes (inputs) and a target variable (data\nvalue) that answers specific questions related to AD risk within certain topic\ncategories. By leveraging a multi-faceted feature set that includes geolocation\ndata, we capture additional environmental context potentially linked to AD. Our\nresults demonstrate that the integration of the geolocation information\nsignificantly decreases the error of predicting early AD risk scores over prior\nmodels by 28.57% (Llama3-70B) and 33.47% (GPT4-o). Our findings suggest that\nthis combined approach can enhance the predictive accuracy of AD risk\nassessment, supporting early diagnosis and intervention in clinical settings.\nAdditionally, the framework's ability to incorporate geolocation data provides\na more comprehensive risk assessment model that could help healthcare providers\nbetter understand and address environmental factors contributing to AD\ndevelopment.\n", "date": "2025-02-06", "categories": []}
{"id": "2407.08442", "title": "How Deep is your Guess? A Fresh Perspective on Deep Learning for Medical\n  Time-Series Imputation", "abstract": "  We present a comprehensive analysis of deep learning approaches for\nElectronic Health Record (EHR) time-series imputation, examining how\narchitectural and framework biases combine to influence model performance. Our\ninvestigation reveals varying capabilities of deep imputers in capturing\ncomplex spatiotemporal dependencies within EHRs, and that model effectiveness\ndepends on how its combined biases align with medical time-series\ncharacteristics. Our experimental evaluation challenges common assumptions\nabout model complexity, demonstrating that larger models do not necessarily\nimprove performance. Rather, carefully designed architectures can better\ncapture the complex patterns inherent in clinical data. The study highlights\nthe need for imputation approaches that prioritise clinically meaningful data\nreconstruction over statistical accuracy. Our experiments show imputation\nperformance variations of up to 20\\% based on preprocessing and implementation\nchoices, emphasising the need for standardised benchmarking methodologies.\nFinally, we identify critical gaps between current deep imputation methods and\nmedical requirements, highlighting the importance of integrating clinical\ninsights to achieve more reliable imputation approaches for healthcare\napplications.\n", "date": "2024-07-11", "categories": []}
{"id": "2409.09727", "title": "From Challenges and Pitfalls to Recommendations and Opportunities:\n  Implementing Federated Learning in Healthcare", "abstract": "  Federated learning holds great potential for enabling large-scale healthcare\nresearch and collaboration across multiple centres while ensuring data privacy\nand security are not compromised. Although numerous recent studies suggest or\nutilize federated learning based methods in healthcare, it remains unclear\nwhich ones have potential clinical utility. This review paper considers and\nanalyzes the most recent studies up to May 2024 that describe federated\nlearning based methods in healthcare. After a thorough review, we find that the\nvast majority are not appropriate for clinical use due to their methodological\nflaws and/or underlying biases which include but are not limited to privacy\nconcerns, generalization issues, and communication costs. As a result, the\neffectiveness of federated learning in healthcare is significantly compromised.\nTo overcome these challenges, we provide recommendations and promising\nopportunities that might be implemented to resolve these problems and improve\nthe quality of model development in federated learning with healthcare.\n", "date": "2024-09-15", "categories": []}
{"id": "2502.01789", "title": "An Agentic AI Workflow for Detecting Cognitive Concerns in Real-world\n  Data", "abstract": "  Early identification of cognitive concerns is critical but often hindered by\nsubtle symptom presentation. This study developed and validated a fully\nautomated, multi-agent AI workflow using LLaMA 3 8B to identify cognitive\nconcerns in 3,338 clinical notes from Mass General Brigham. The agentic\nworkflow, leveraging task-specific agents that dynamically collaborate to\nextract meaningful insights from clinical notes, was compared to an\nexpert-driven benchmark. Both workflows achieved high classification\nperformance, with F1-scores of 0.90 and 0.91, respectively. The agentic\nworkflow demonstrated improved specificity (1.00) and achieved prompt\nrefinement in fewer iterations. Although both workflows showed reduced\nperformance on validation data, the agentic workflow maintained perfect\nspecificity. These findings highlight the potential of fully automated\nmulti-agent AI workflows to achieve expert-level accuracy with greater\nefficiency, offering a scalable and cost-effective solution for detecting\ncognitive concerns in clinical settings.\n", "date": "2025-02-03", "categories": []}
{"id": "2502.02109", "title": "Causally-informed Deep Learning towards Explainable and Generalizable\n  Outcomes Prediction in Critical Care", "abstract": "  Recent advances in deep learning (DL) have prompted the development of\nhigh-performing early warning score (EWS) systems, predicting clinical\ndeteriorations such as acute kidney injury, acute myocardial infarction, or\ncirculatory failure. DL models have proven to be powerful tools for various\ntasks but come with the cost of lacking interpretability and limited\ngeneralizability, hindering their clinical applications. To develop a practical\nEWS system applicable to various outcomes, we propose causally-informed\nexplainable early prediction model, which leverages causal discovery to\nidentify the underlying causal relationships of prediction and thus owns two\nunique advantages: demonstrating the explicit interpretation of the prediction\nwhile exhibiting decent performance when applied to unfamiliar environments.\nBenefiting from these features, our approach achieves superior accuracy for 6\ndifferent critical deteriorations and achieves better generalizability across\ndifferent patient groups, compared to various baseline algorithms. Besides, we\nprovide explicit causal pathways to serve as references for assistant clinical\ndiagnosis and potential interventions. The proposed approach enhances the\npractical application of deep learning in various medical scenarios.\n", "date": "2025-02-04", "categories": []}
{"id": "2404.10483", "title": "From Uncertainty to Trust: Kernel Dropout for AI-Powered Medical\n  Predictions", "abstract": "  AI-driven medical predictions with trustworthy confidence are essential for\nensuring the responsible use of AI in healthcare applications. The growing\ncapabilities of AI raise questions about their trustworthiness in healthcare,\nparticularly due to opaque decision-making and limited data availability. This\npaper proposes a novel approach to address these challenges, introducing a\nBayesian Monte Carlo Dropout model with kernel modelling. Our model is designed\nto enhance reliability on small medical datasets, a crucial barrier to the\nwider adoption of AI in healthcare. This model leverages existing language\nmodels for improved effectiveness and seamlessly integrates with current\nworkflows. Extensive evaluations of public medical datasets showcase our\nmodel's superior performance across diverse tasks. We demonstrate significant\nimprovements in reliability, even with limited data, offering a promising step\ntowards building trust in AI-driven medical predictions and unlocking its\npotential to improve patient care.\n", "date": "2024-04-16", "categories": []}
{"id": "2406.05189", "title": "Analyzing the factors that are involved in length of inpatient stay at\n  the hospital for diabetes patients", "abstract": "  The paper investigates the escalating concerns surrounding the surge in\ndiabetes cases, exacerbated by the COVID-19 pandemic, and the subsequent strain\non medical resources. The research aims to construct a predictive model\nquantifying factors influencing inpatient hospital stay durations for diabetes\npatients, offering insights to hospital administrators for improved patient\nmanagement strategies. The literature review highlights the increasing\nprevalence of diabetes, emphasizing the need for continued attention and\nanalysis of urban-rural disparities in healthcare access. International studies\nunderscore the financial implications and healthcare burden associated with\ndiabetes-related hospitalizations and complications, emphasizing the\nsignificance of effective management strategies. The methodology involves a\nquantitative approach, utilizing a dataset comprising 10,000 observations of\ndiabetic inpatient encounters in U.S. hospitals from 1999 to 2008. Predictive\nmodeling techniques, particularly Generalized Linear Models (GLM), are employed\nto develop a model predicting hospital stay durations based on patient\ndemographics, admission types, medical history, and treatment regimen. The\nresults highlight the influence of age, medical history, and treatment regimen\non hospital stay durations for diabetes patients. Despite model limitations,\nsuch as heteroscedasticity and deviations from normality in residual analysis,\nthe findings offer valuable insights for hospital administrators in patient\nmanagement. The paper concludes with recommendations for future research to\naddress model limitations and explore the implications of predictive models on\nhealthcare management strategies, ensuring equitable patient care and resource\nallocation.\n", "date": "2024-06-07", "categories": []}
{"id": "2502.00065", "title": "Blood Glucose Level Prediction in Type 1 Diabetes Using Machine Learning", "abstract": "  Type 1 Diabetes is a chronic autoimmune condition in which the immune system\nattacks and destroys insulin-producing beta cells in the pancreas, resulting in\nlittle to no insulin production. Insulin helps glucose in your blood enter your\nmuscle, fat, and liver cells so they can use it for energy or store it for\nlater use. If insulin is insufficient, it causes sugar to build up in the blood\nand leads to serious health problems. People with Type 1 Diabetes need\nsynthetic insulin every day. In diabetes management, continuous glucose\nmonitoring is an important feature that provides near real-time blood glucose\ndata. It is useful in deciding the synthetic insulin dose. In this research\nwork, we used machine learning tools, deep neural networks, deep reinforcement\nlearning, and voting and stacking regressors to predict blood glucose levels at\n30-min time intervals using the latest DiaTrend dataset. Predicting blood\nglucose levels is useful in better diabetes management systems. The trained\nmodels were compared using several evaluation metrics. Our evaluation results\ndemonstrate the performance of various models across different glycemic\nconditions for blood glucose prediction. The source codes of this work can be\nfound in: https://github.com/soon-jynn-chu/t1d_bg_prediction\n", "date": "2025-01-30", "categories": []}
{"id": "2502.00079", "title": "Advanced Assessment of Stroke in Retinal Fundus Imaging with Deep\n  Multi-view Learning", "abstract": "  Stroke is globally a major cause of mortality and morbidity, and hence\naccurate and rapid diagnosis of stroke is valuable. Retinal fundus imaging\nreveals the known markers of elevated stroke risk in the eyes, which are\nretinal venular widening, arteriolar narrowing, and increased tortuosity. In\ncontrast to other imaging techniques used for stroke diagnosis, the acquisition\nof fundus images is easy, non-invasive, fast, and inexpensive. Therefore, in\nthis study, we propose a multi-view stroke network (MVS-Net) to detect stroke\nand transient ischemic attack (TIA) using retinal fundus images. Contrary to\nexisting studies, our study proposes for the first time a solution to\ndiscriminate stroke and TIA with deep multi-view learning by proposing an\nend-to-end deep network, consisting of multi-view inputs of fundus images\ncaptured from both right and left eyes. Accordingly, the proposed MVS-Net\ndefines representative features from fundus images of both eyes and determines\nthe relation within their macula-centered and optic nerve head-centered views.\nExperiments performed on a dataset collected from stroke and TIA patients, in\naddition to healthy controls, show that the proposed framework achieves an AUC\nscore of 0.84 for stroke and TIA detection.\n", "date": "2025-01-31", "categories": []}
{"id": "2502.00973", "title": "A Wearable Device Dataset for Mental Health Assessment Using Laser\n  Doppler Flowmetry and Fluorescence Spectroscopy Sensors", "abstract": "  In this study, we introduce a novel method to predict mental health by\nbuilding machine learning models for a non-invasive wearable device equipped\nwith Laser Doppler Flowmetry (LDF) and Fluorescence Spectroscopy (FS) sensors.\nBesides, we present the corresponding dataset to predict mental health, e.g.\ndepression, anxiety, and stress levels via the DAS-21 questionnaire. To our\nbest knowledge, this is the world's largest and the most generalized dataset\never collected for both LDF and FS studies. The device captures cutaneous blood\nmicrocirculation parameters, and wavelet analysis of the LDF signal extracts\nkey rhythmic oscillations. The dataset, collected from 132 volunteers aged\n18-94 from 19 countries, explores relationships between physiological features,\ndemographics, lifestyle habits, and health conditions. We employed a variety of\nmachine learning methods to classify stress detection, in which LightGBM is\nidentified as the most effective model for stress detection, achieving a ROC\nAUC of 0.7168 and a PR AUC of 0.8852. In addition, we also incorporated\nExplainable Artificial Intelligence (XAI) techniques into our analysis to\ninvestigate deeper insights into the model's predictions. Our results suggest\nthat females, younger individuals and those with a higher Body Mass Index (BMI)\nor heart rate have a greater likelihood of experiencing mental health\nconditions like stress and anxiety. All related code and data are published\nonline: https://github.com/leduckhai/Wearable_LDF-FS.\n", "date": "2025-02-03", "categories": []}
{"id": "2212.00554", "title": "Early prediction of the risk of ICU mortality with Deep Federated\n  Learning", "abstract": "  Intensive Care Units usually carry patients with a serious risk of mortality.\nRecent research has shown the ability of Machine Learning to indicate the\npatients' mortality risk and point physicians toward individuals with a\nheightened need for care. Nevertheless, healthcare data is often subject to\nprivacy regulations and can therefore not be easily shared in order to build\nCentralized Machine Learning models that use the combined data of multiple\nhospitals. Federated Learning is a Machine Learning framework designed for data\nprivacy that can be used to circumvent this problem. In this study, we evaluate\nthe ability of deep Federated Learning to predict the risk of Intensive Care\nUnit mortality at an early stage. We compare the predictive performance of\nFederated, Centralized, and Local Machine Learning in terms of AUPRC, F1-score,\nand AUROC. Our results show that Federated Learning performs equally well as\nthe centralized approach and is substantially better than the local approach,\nthus providing a viable solution for early Intensive Care Unit mortality\nprediction. In addition, we show that the prediction performance is higher when\nthe patient history window is closer to discharge or death. Finally, we show\nthat using the F1-score as an early stopping metric can stabilize and increase\nthe performance of our approach for the task at hand.\n", "date": "2022-12-01", "categories": []}
{"id": "2405.09566", "title": "Detection of Sleep Oxygen Desaturations from Electroencephalogram\n  Signals", "abstract": "  In this work, we leverage machine learning techniques to identify potential\nbiomarkers of oxygen desaturation during sleep exclusively from\nelectroencephalogram (EEG) signals in pediatric patients with sleep apnea.\nDevelopment of a machine learning technique which can successfully identify EEG\nsignals from patients with sleep apnea as well as identify latent EEG signals\nwhich come from subjects who experience oxygen desaturations but do not\nthemselves occur during oxygen desaturation events would provide a strong step\ntowards developing a brain-based biomarker for sleep apnea in order to aid with\neasier diagnosis of this disease. We leverage a large corpus of data, and show\nthat machine learning enables us to classify EEG signals as occurring during\noxygen desaturations or not occurring during oxygen desaturations with an\naverage 66.8% balanced accuracy. We furthermore investigate the ability of\nmachine learning models to identify subjects who experience oxygen\ndesaturations from EEG data that does not occur during oxygen desaturations. We\nconclude that there is a potential biomarker for oxygen desaturation in EEG\ndata.\n", "date": "2024-05-08", "categories": []}
{"id": "2501.18614", "title": "Review and Recommendations for using Artificial Intelligence in\n  Intracoronary Optical Coherence Tomography Analysis", "abstract": "  Artificial intelligence (AI) methodologies hold great promise for the rapid\nand accurate diagnosis of coronary artery disease (CAD) from intravascular\noptical coherent tomography (IVOCT) images. Numerous papers have been published\ndescribing AI-based models for different diagnostic tasks, yet it remains\nunclear which models have potential clinical utility and have been properly\nvalidated. This systematic review considered published literature between\nJanuary 2015 and February 2023 describing AI-based diagnosis of CAD using\nIVOCT. Our search identified 5,576 studies, with 513 included after initial\nscreening and 35 studies included in the final systematic review after quality\nscreening. Our findings indicate that most of the identified models are not\ncurrently suitable for clinical use, primarily due to methodological flaws and\nunderlying biases. To address these issues, we provide recommendations to\nimprove model quality and research practices to enhance the development of\nclinically useful AI products.\n", "date": "2025-01-24", "categories": []}
{"id": "2501.18893", "title": "A machine learning approach for Premature Coronary Artery Disease\n  Diagnosis according to Different Ethnicities in Iran", "abstract": "  Premature coronary artery disease (PCAD) refers to the early onset of the\ndisease, usually before the age of 55 for men and 65 for women. Coronary Artery\nDisease (CAD) develops when coronary arteries, the major blood vessels\nsupplying the heart with blood, oxygen, and nutrients, become clogged or\ndiseased. This is often due to many risk factors, including lifestyle and\ncardiometabolic ones, but few studies were done on ethnicity as one of these\nrisk factors, especially in PCAD. In this study, we tested the rank of\nethnicity among the major risk factors of PCAD, including age, gender, body\nmass index (BMI), visceral obesity presented as waist circumference (WC),\ndiabetes mellitus (DM), high blood pressure (HBP), high low-density lipoprotein\ncholesterol (LDL-C), and smoking in a large national sample of patients with\nPCAD from different ethnicities. All patients who met the age criteria\nunderwent coronary angiography to confirm CAD diagnosis. The weight of\nethnicity was compared to the other eight features using feature weighting\nalgorithms in PCAD diagnosis. In addition, we conducted an experiment where we\nran predictive models (classification algorithms) to predict PCAD. We compared\nthe performance of these models under two conditions: we trained the\nclassification algorithms, including or excluding ethnicity. This study\nanalyzed various factors to determine their predictive power influencing PCAD\nprediction. Among these factors, gender and age were the most significant\npredictors, with ethnicity being the third most important. The results also\nshowed that if ethnicity is used as one of the input risk factors for\nclassification algorithms, it can improve their efficiency. Our results show\nthat ethnicity ranks as an influential factor in predicting PCAD. Therefore, it\nneeds to be addressed in the PCAD diagnostic and preventive measures.\n", "date": "2025-01-31", "categories": []}
{"id": "2402.12394", "title": "Improving Model's Interpretability and Reliability using Biomarkers", "abstract": "  Accurate and interpretable diagnostic models are crucial in the\nsafety-critical field of medicine. We investigate the interpretability of our\nproposed biomarker-based lung ultrasound diagnostic pipeline to enhance\nclinicians' diagnostic capabilities. The objective of this study is to assess\nwhether explanations from a decision tree classifier, utilizing biomarkers, can\nimprove users' ability to identify inaccurate model predictions compared to\nconventional saliency maps. Our findings demonstrate that decision tree\nexplanations, based on clinically established biomarkers, can assist clinicians\nin detecting false positives, thus improving the reliability of diagnostic\nmodels in medicine.\n", "date": "2024-02-16", "categories": []}
{"id": "2407.19540", "title": "Overcoming Uncertain Incompleteness for Robust Multimodal Sequential\n  Diagnosis Prediction via Curriculum Data Erasing Guided Knowledge\n  Distillation", "abstract": "  In this paper, we present NECHO v2, a novel framework designed to enhance the\npredictive accuracy of multimodal sequential patient diagnoses under uncertain\nmissing visit sequences, a common challenge in real clinical settings. Firstly,\nwe modify NECHO, designed in a diagnosis code-centric fashion, to handle\nuncertain modality representation dominance under the imperfect data. Secondly,\nwe develop a systematic knowledge distillation by employing the modified NECHO\nas both teacher and student. It encompasses a modality-wise contrastive and\nhierarchical distillation, transformer representation random distillation,\nalong with other distillations to align representations between teacher and\nstudent tightly and effectively. We also propose curriculum learning guided\nrandom data erasing within sequences during both training and distillation of\nthe teacher to lightly simulate scenario with missing visit information,\nthereby fostering effective knowledge transfer. As a result, NECHO v2 verifies\nitself by showing robust superiority in multimodal sequential diagnosis\nprediction under both balanced and imbalanced incomplete settings on multimodal\nhealthcare data.\n", "date": "2024-07-28", "categories": []}
{"id": "2501.17871", "title": "On the challenges of detecting MCI using EEG in the wild", "abstract": "  Recent studies have shown promising results in the detection of Mild\nCognitive Impairment (MCI) using easily accessible Electroencephalogram (EEG)\ndata which would help administer early and effective treatment for dementia\npatients. However, the reliability and practicality of such systems remains\nunclear. In this work, we investigate the potential limitations and challenges\nin developing a robust MCI detection method using two contrasting datasets: 1)\nCAUEEG, collected and annotated by expert neurologists in controlled settings\nand 2) GENEEG, a new dataset collected and annotated in general practice\nclinics, a setting where routine MCI diagnoses are typically made. We find that\ntraining on small datasets, as is done by most previous works, tends to produce\nhigh variance models that make overconfident predictions, and are unreliable in\npractice. Additionally, distribution shifts between datasets make cross-domain\ngeneralization challenging. Finally, we show that MCI detection using EEG may\nsuffer from fundamental limitations because of the overlapping nature of\nfeature distributions with control groups. We call for more effort in\nhigh-quality data collection in actionable settings (like general practice\nclinics) to make progress towards this salient goal of non-invasive MCI\ndetection.\n", "date": "2025-01-15", "categories": []}
{"id": "2501.18161", "title": "Using Computer Vision for Skin Disease Diagnosis in Bangladesh Enhancing\n  Interpretability and Transparency in Deep Learning Models for Skin Cancer\n  Classification", "abstract": "  With over 2 million new cases identified annually, skin cancer is the most\nprevalent type of cancer globally and the second most common in Bangladesh,\nfollowing breast cancer. Early detection and treatment are crucial for\nenhancing patient outcomes; however, Bangladesh faces a shortage of\ndermatologists and qualified medical professionals capable of diagnosing and\ntreating skin cancer. As a result, many cases are diagnosed only at advanced\nstages. Research indicates that deep learning algorithms can effectively\nclassify skin cancer images. However, these models typically lack\ninterpretability, making it challenging to understand their decision-making\nprocesses. This lack of clarity poses barriers to utilizing deep learning in\nimproving skin cancer detection and treatment. In this article, we present a\nmethod aimed at enhancing the interpretability of deep learning models for skin\ncancer classification in Bangladesh. Our technique employs a combination of\nsaliency maps and attention maps to visualize critical features influencing the\nmodel's diagnoses.\n", "date": "2025-01-30", "categories": []}
{"id": "2501.18237", "title": "Arbitrary Data as Images: Fusion of Patient Data Across Modalities and\n  Irregular Intervals with Vision Transformers", "abstract": "  A patient undergoes multiple examinations in each hospital stay, where each\nprovides different facets of the health status. These assessments include\ntemporal data with varying sampling rates, discrete single-point measurements,\ntherapeutic interventions such as medication administration, and images. While\nphysicians are able to process and integrate diverse modalities intuitively,\nneural networks need specific modeling for each modality complicating the\ntraining procedure. We demonstrate that this complexity can be significantly\nreduced by visualizing all information as images along with unstructured text\nand subsequently training a conventional vision-text transformer. Our approach,\nVision Transformer for irregular sampled Multi-modal Measurements (ViTiMM), not\nonly simplifies data preprocessing and modeling but also outperforms current\nstate-of-the-art methods in predicting in-hospital mortality and phenotyping,\nas evaluated on 6,175 patients from the MIMIC-IV dataset. The modalities\ninclude patient's clinical measurements, medications, X-ray images, and\nelectrocardiography scans. We hope our work inspires advancements in\nmulti-modal medical AI by reducing the training complexity to (visual) prompt\nengineering, thus lowering entry barriers and enabling no-code solutions for\ntraining. The source code will be made publicly available.\n", "date": "2025-01-30", "categories": []}
{"id": "2501.18294", "title": "A Comprehensive Analysis on Machine Learning based Methods for Lung\n  Cancer Level Classification", "abstract": "  Lung cancer is a major issue in worldwide public health, requiring early\ndiagnosis using stable techniques. This work begins a thorough investigation of\nthe use of machine learning (ML) methods for precise classification of lung\ncancer stages. A cautious analysis is performed to overcome overfitting issues\nin model performance, taking into account minimum child weight and learning\nrate. A set of machine learning (ML) models including XGBoost (XGB), LGBM,\nAdaboost, Logistic Regression (LR), Decision Tree (DT), Random Forest (RF),\nCatBoost, and k-Nearest Neighbor (k-NN) are run methodically and contrasted.\nFurthermore, the correlation between features and targets is examined using the\ndeep neural network (DNN) model and thus their capability in detecting complex\npatternsis established. It is argued that several ML models can be capable of\nclassifying lung cancer stages with great accuracy. In spite of the complexity\nof DNN architectures, traditional ML models like XGBoost, LGBM, and Logistic\nRegression excel with superior performance. The models perform better than the\nothers in lung cancer prediction on the complete set of comparative metrics\nlike accuracy, precision, recall, and F-1 score\n", "date": "2025-01-30", "categories": []}
{"id": "2501.18535", "title": "A Hybrid Data-Driven Approach For Analyzing And Predicting Inpatient\n  Length Of Stay In Health Centre", "abstract": "  Patient length of stay (LoS) is a critical metric for evaluating the efficacy\nof hospital management. The primary objectives encompass to improve efficiency\nand reduce costs while enhancing patient outcomes and hospital capacity within\nthe patient journey. By seamlessly merging data-driven techniques with\nsimulation methodologies, the study proposes an all-encompassing framework for\nthe optimization of patient flow. Using a comprehensive dataset of 2.3 million\nde-identified patient records, we analyzed demographics, diagnoses, treatments,\nservices, costs, and charges with machine learning models (Decision Tree,\nLogistic Regression, Random Forest, Adaboost, LightGBM) and Python tools\n(Spark, AWS clusters, dimensionality reduction). Our model predicts patient\nlength of stay (LoS) upon admission using supervised learning algorithms. This\nhybrid approach enables the identification of key factors influencing LoS,\noffering a robust framework for hospitals to streamline patient flow and\nresource utilization. The research focuses on patient flow, corroborating the\nefficacy of the approach, illustrating decreased patient length of stay within\na real healthcare environment. The findings underscore the potential of hybrid\ndata-driven models in transforming hospital management practices. This\ninnovative methodology provides generally flexible decision-making, training,\nand patient flow enhancement; such a system could have huge implications for\nhealthcare administration and overall satisfaction with healthcare.\n", "date": "2025-01-30", "categories": []}
{"id": "2407.15719", "title": "GFE-Mamba: Mamba-based AD Multi-modal Progression Assessment via\n  Generative Feature Extraction from MCI", "abstract": "  Alzheimer's Disease (AD) is a progressive, irreversible neurodegenerative\ndisorder that often originates from Mild Cognitive Impairment (MCI). This\nprogression results in significant memory loss and severely affects patients'\nquality of life. Clinical trials have consistently shown that early and\ntargeted interventions for individuals with MCI may slow or even prevent the\nadvancement of AD. Research indicates that accurate medical classification\nrequires diverse multimodal data, including detailed assessment scales and\nneuroimaging techniques like Magnetic Resonance Imaging (MRI) and Positron\nEmission Tomography (PET). However, simultaneously collecting the\naforementioned three modalities for training presents substantial challenges.\nTo tackle these difficulties, we propose GFE-Mamba, a multimodal classifier\nfounded on Generative Feature Extractor. The intermediate features provided by\nthis Extractor can compensate for the shortcomings of PET and achieve profound\nmultimodal fusion in the classifier. The Mamba block, as the backbone of the\nclassifier, enables it to efficiently extract information from long-sequence\nscale information. Pixel-level Bi-cross Attention supplements pixel-level\ninformation from MRI and PET. We provide our rationale for developing this\ncross-temporal progression prediction dataset and the pre-trained Extractor\nweights. Our experimental findings reveal that the GFE-Mamba model effectively\npredicts the progression from MCI to AD and surpasses several leading methods\nin the field. Our source code is available at\nhttps://github.com/Tinysqua/GFE-Mamba.\n", "date": "2024-07-22", "categories": []}
{"id": "2501.17326", "title": "Memorize and Rank: Elevating Large Language Models for Clinical\n  Diagnosis Prediction", "abstract": "  Clinical diagnosis prediction models, when provided with a patient's medical\nhistory, aim to detect potential diseases early, facilitating timely\nintervention and improving prognostic outcomes. However, the inherent scarcity\nof patient data and large disease candidate space often pose challenges in\ndeveloping satisfactory models for this intricate task. The exploration of\nleveraging Large Language Models (LLMs) for encapsulating clinical decision\nprocesses has been limited. We introduce MERA, a clinical diagnosis prediction\nmodel that bridges pertaining natural language knowledge with medical practice.\nWe apply hierarchical contrastive learning on a disease candidate ranking list\nto alleviate the large decision space issue. With concept memorization through\nfine-tuning, we bridge the natural language clinical knowledge with medical\ncodes. Experimental results on MIMIC-III and IV datasets show that MERA\nachieves the state-of-the-art diagnosis prediction performance and dramatically\nelevates the diagnosis prediction capabilities of generative LMs.\n", "date": "2025-01-28", "categories": []}
{"id": "2501.17510", "title": "LLM Assistance for Pediatric Depression", "abstract": "  Traditional depression screening methods, such as the PHQ-9, are particularly\nchallenging for children in pediatric primary care due to practical\nlimitations. AI has the potential to help, but the scarcity of annotated\ndatasets in mental health, combined with the computational costs of training,\nhighlights the need for efficient, zero-shot approaches. In this work, we\ninvestigate the feasibility of state-of-the-art LLMs for depressive symptom\nextraction in pediatric settings (ages 6-24). This approach aims to complement\ntraditional screening and minimize diagnostic errors.\n  Our findings show that all LLMs are 60% more efficient than word match, with\nFlan leading in precision (average F1: 0.65, precision: 0.78), excelling in the\nextraction of more rare symptoms like \"sleep problems\" (F1: 0.92) and\n\"self-loathing\" (F1: 0.8). Phi strikes a balance between precision (0.44) and\nrecall (0.60), performing well in categories like \"Feeling depressed\" (0.69)\nand \"Weight change\" (0.78). Llama 3, with the highest recall (0.90),\novergeneralizes symptoms, making it less suitable for this type of analysis.\nChallenges include the complexity of clinical notes and overgeneralization from\nPHQ-9 scores. The main challenges faced by LLMs include navigating the complex\nstructure of clinical notes with content from different times in the patient\ntrajectory, as well as misinterpreting elevated PHQ-9 scores.\n  We finally demonstrate the utility of symptom annotations provided by Flan as\nfeatures in an ML algorithm, which differentiates depression cases from\ncontrols with high precision of 0.78, showing a major performance boost\ncompared to a baseline that does not use these features.\n", "date": "2025-01-29", "categories": []}
{"id": "2402.06512", "title": "Multimodal Clinical Trial Outcome Prediction with Large Language Models", "abstract": "  The clinical trial is a pivotal and costly process, often spanning multiple\nyears and requiring substantial financial resources. Therefore, the development\nof clinical trial outcome prediction models aims to exclude drugs likely to\nfail and holds the potential for significant cost savings. Recent data-driven\nattempts leverage deep learning methods to integrate multimodal data for\npredicting clinical trial outcomes. However, these approaches rely on manually\ndesigned modal-specific encoders, which limits both the extensibility to adapt\nnew modalities and the ability to discern similar information patterns across\ndifferent modalities. To address these issues, we propose a multimodal\nmixture-of-experts (LIFTED) approach for clinical trial outcome prediction.\nSpecifically, LIFTED unifies different modality data by transforming them into\nnatural language descriptions. Then, LIFTED constructs unified noise-resilient\nencoders to extract information from modal-specific language descriptions.\nSubsequently, a sparse Mixture-of-Experts framework is employed to further\nrefine the representations, enabling LIFTED to identify similar information\npatterns across different modalities and extract more consistent\nrepresentations from those patterns using the same expert model. Finally, a\nmixture-of-experts module is further employed to dynamically integrate\ndifferent modality representations for prediction, which gives LIFTED the\nability to automatically weigh different modalities and pay more attention to\ncritical information. The experiments demonstrate that LIFTED significantly\nenhances performance in predicting clinical trial outcomes across all three\nphases compared to the best baseline, showcasing the effectiveness of our\nproposed key components.\n", "date": "2024-02-09", "categories": []}
{"id": "2411.10486", "title": "Artificial Intelligence for Infectious Disease Prediction and\n  Prevention: A Comprehensive Review", "abstract": "  Artificial Intelligence (AI) and infectious diseases prediction have recently\nexperienced a common development and advancement. Machine learning (ML)\napparition, along with deep learning (DL) emergence, extended many approaches\nagainst diseases apparition and their spread. And despite their outstanding\nresults in predicting infectious diseases, conflicts appeared regarding the\ntypes of data used and how they can be studied, analyzed, and exploited using\nvarious emerging methods. This has led to some ongoing discussions in the\nfield. This research aims not only to provide an overview of what has been\naccomplished, but also to highlight the difficulties related to the types of\ndata used, and the learning methods applied for each research objective. It\ncategorizes these contributions into three areas: predictions using Public\nHealth Data to prevent the spread of a transmissible disease within a region;\npredictions using Patients' Medical Data to detect whether a person is infected\nby a transmissible disease; and predictions using both Public and patient\nmedical data to estimate the extent of disease spread in a population. The\npaper also critically assesses the potential of AI and outlines its limitations\nin infectious disease management.\n", "date": "2024-11-14", "categories": []}
{"id": "2501.16334", "title": "RNN-Based Models for Predicting Seizure Onset in Epileptic Patients", "abstract": "  Early management and better clinical outcomes for epileptic patients depend\non seizure prediction. The accuracy and false alarm rates of existing systems\nare often compromised by their dependence on static thresholds and basic\nElectroencephalogram (EEG) properties. A novel Recurrent Neural Network\n(RNN)-based method for seizure start prediction is proposed in the article to\novercome these limitations. As opposed to conventional techniques, the proposed\nsystem makes use of Long Short-Term Memory (LSTM) networks to extract temporal\ncorrelations from unprocessed EEG data. It enables the system to adapt\ndynamically to the unique EEG patterns of each patient, improving prediction\naccuracy. The methodology of the system comprises thorough data collecting,\npreprocessing, and LSTM-based feature extraction. Annotated EEG datasets are\nthen used for model training and validation. Results show a considerable\nreduction in false alarm rates (average of 6.8%) and an improvement in\nprediction accuracy (90.2% sensitivity, 88.9% specificity, and AUC-ROC of 93).\nAdditionally, computational efficiency is significantly higher than that of\nexisting systems (12 ms processing time, 45 MB memory consumption). About\nimproving seizure prediction reliability, these results demonstrate the\neffectiveness of the proposed RNN-based strategy, opening up possibilities for\nits practical application to improve epilepsy treatment.\n", "date": "2024-12-24", "categories": []}
{"id": "2501.16386", "title": "ILETIA: An AI-enhanced method for individualized trigger-oocyte pickup\n  interval estimation of progestin-primed ovarian stimulation protocol", "abstract": "  In vitro fertilization-embryo transfer (IVF-ET) stands as one of the most\nprevalent treatments for infertility. During an IVF-ET cycle, the time interval\nbetween trigger shot and oocyte pickup (OPU) is a pivotal period for follicular\nmaturation, which determines mature oocytes yields and impacts the success of\nsubsequent procedures. However, accurately predicting this interval is severely\nhindered by the variability of clinicians'experience that often leads to\nsuboptimal oocyte retrieval rate. To address this challenge, we propose ILETIA,\nthe first machine learning-based method that could predict the optimal\ntrigger-OPU interval for patients receiving progestin-primed ovarian\nstimulation (PPOS) protocol. Specifically, ILETIA leverages a Transformer to\nlearn representations from clinical tabular data, and then employs\ngradient-boosted trees for interval prediction. For model training and\nevaluating, we compiled a dataset PPOS-DS of nearly ten thousand patients\nreceiving PPOS protocol, the largest such dataset to our knowledge.\nExperimental results demonstrate that our method achieves strong performance\n(AUROC = 0.889), outperforming both clinicians and other widely used\ncomputational models. Moreover, ILETIA also supports premature ovulation risk\nprediction in a specific OPU time (AUROC = 0.838). Collectively, by enabling\nmore precise and individualized decisions, ILETIA has the potential to improve\nclinical outcomes and lay the foundation for future IVF-ET research.\n", "date": "2025-01-25", "categories": []}
{"id": "2405.10883", "title": "Application of Artificial Intelligence in Schizophrenia Rehabilitation\n  Management: A Systematic Scoping Review", "abstract": "  This systematic review assessed the current state and future prospects of\nartificial intelligence (AI) in schizophrenia rehabilitation management. We\nreviewed 61 studies on AI-related data types, feature engineering methods,\nalgorithmic models, and evaluation metrics published from 2012-2024. The review\ncategorizes AI applications into the following key application areas: symptom\nmonitoring, medication management, risk management, functional training, and\npsychosocial support. Findings indicate that supervised machine learning\ntechniques, particularly for symptom monitoring and relapse risk management,\nremain the predominant approaches, effectively leveraging structured data while\nincorporating interpretable algorithms. This study underscores the potential of\nAI in transforming long-term management strategies for schizophrenia, offering\nvaluable insights into improving the quality of life of patients. Future\nresearch should focus on expanding data sources through multimodal data\nintegration, exploring deep learning models, and integrating AI-driven\ninterventions into training tasks to fully capitalize on AI's potential in\nschizophrenia rehabilitation.\n", "date": "2024-05-17", "categories": []}
{"id": "2406.06728", "title": "AI-Driven Predictive Analytics Approach for Early Prognosis of Chronic\n  Kidney Disease Using Ensemble Learning and Explainable AI", "abstract": "  Chronic Kidney Disease (CKD) is one of the widespread Chronic diseases with\nno known ultimo cure and high morbidity. Research demonstrates that progressive\nChronic Kidney Disease (CKD) is a heterogeneous disorder that significantly\nimpacts kidney structure and functions, eventually leading to kidney failure.\nWith the progression of time, chronic kidney disease has moved from a\nlife-threatening disease affecting few people to a common disorder of varying\nseverity. The goal of this research is to visualize dominating features,\nfeature scores, and values exhibited for early prognosis and detection of CKD\nusing ensemble learning and explainable AI. For that, an AI-driven predictive\nanalytics approach is proposed to aid clinical practitioners in prescribing\nlifestyle modifications for individual patients to reduce the rate of\nprogression of this disease. Our dataset is collected on body vitals from\nindividuals with CKD and healthy subjects to develop our proposed AI-driven\nsolution accurately. In this regard, blood and urine test results are provided,\nand ensemble tree-based machine-learning models are applied to predict unseen\ncases of CKD. Our research findings are validated after lengthy consultations\nwith nephrologists. Our experiments and interpretation results are compared\nwith existing explainable AI applications in various healthcare domains,\nincluding CKD. The comparison shows that our developed AI models, particularly\nthe Random Forest model, have identified more features as significant\ncontributors than XgBoost. Interpretability (I), which measures the ratio of\nimportant to masked features, indicates that our XgBoost model achieved a\nhigher score, specifically a Fidelity of 98\\%, in this metric and naturally in\nthe FII index compared to competing models.\n", "date": "2024-06-10", "categories": []}
{"id": "2408.01614", "title": "Advancing Mental Health Pre-Screening: A New Custom GPT for\n  Psychological Distress Assessment", "abstract": "  This study introduces 'Psycho Analyst', a custom GPT model based on OpenAI's\nGPT-4, optimized for pre-screening mental health disorders. Enhanced with\nDSM-5, PHQ-8, detailed data descriptions, and extensive training data, the\nmodel adeptly decodes nuanced linguistic indicators of mental health disorders.\nIt utilizes a dual-task framework that includes binary classification and a\nthree-stage PHQ-8 score computation involving initial assessment, detailed\nbreakdown, and independent assessment, showcasing refined analytic\ncapabilities. Validation with the DAIC-WOZ dataset reveals F1 and Macro-F1\nscores of 0.929 and 0.949, respectively, along with the lowest MAE and RMSE of\n2.89 and 3.69 in PHQ-8 scoring. These results highlight the model's precision\nand transformative potential in enhancing public mental health support,\nimproving accessibility, cost-effectiveness, and serving as a second opinion\nfor professionals.\n", "date": "2024-08-03", "categories": []}
{"id": "2411.10814", "title": "DEAL: Decoupled Classifier with Adaptive Linear Modulation for Group\n  Robust Early Diagnosis of MCI to AD Conversion", "abstract": "  While deep learning-based Alzheimer's disease (AD) diagnosis has recently\nmade significant advancements, particularly in predicting the conversion of\nmild cognitive impairment (MCI) to AD based on MRI images, there remains a\ncritical gap in research regarding the group robustness of the diagnosis.\nAlthough numerous studies pointed out that deep learning-based classifiers may\nexhibit poor performance in certain groups by relying on unimportant\nattributes, this issue has been largely overlooked in the early diagnosis of\nMCI to AD conversion. In this paper, we present the first comprehensive\ninvestigation of the group robustness in the early diagnosis of MCI to AD\nconversion using MRI images, focusing on disparities in accuracy between\ngroups, specifically sMCI and pMCI individuals divided by age. Our experiments\nreveal that standard classifiers consistently underperform for certain groups\nacross different architectures, highlighting the need for more tailored\napproaches. To address this, we propose a novel method, dubbed DEAL (DEcoupled\nclassifier with Adaptive Linear modulation), comprising two key components: (1)\na linear modulation of features from the penultimate layer, incorporating\neasily obtainable age and cognitive indicative tabular features, and (2) a\ndecoupled classifier that provides more tailored decision boundaries for each\ngroup, further improving performance. Through extensive experiments and\nevaluations across different architectures, we demonstrate the efficacy of DEAL\nin improving the group robustness of the MCI to AD conversion prediction.\n", "date": "2024-11-16", "categories": []}
{"id": "2501.13587", "title": "Contrastive Representation Learning Helps Cross-institutional Knowledge\n  Transfer: A Study in Pediatric Ventilation Management", "abstract": "  Clinical machine learning deployment across institutions faces significant\nchallenges when patient populations and clinical practices differ\nsubstantially. We present a systematic framework for cross-institutional\nknowledge transfer in clinical time series, demonstrated through pediatric\nventilation management between a general pediatric intensive care unit (PICU)\nand a cardiac-focused unit. Using contrastive predictive coding (CPC) for\nrepresentation learning, we investigate how different data regimes and\nfine-tuning strategies affect knowledge transfer across institutional\nboundaries. Our results show that while direct model transfer performs poorly,\nCPC with appropriate fine-tuning enables effective knowledge sharing between\ninstitutions, with benefits particularly evident in limited data scenarios.\nAnalysis of transfer patterns reveals an important asymmetry: temporal\nprogression patterns transfer more readily than point-of-care decisions,\nsuggesting practical pathways for cross-institutional deployment. Through a\nsystematic evaluation of fine-tuning approaches and transfer patterns, our work\nprovides insights for developing more generalizable clinical decision support\nsystems while enabling smaller specialized units to leverage knowledge from\nlarger centers.\n", "date": "2025-01-23", "categories": []}
{"id": "2501.15079", "title": "Salvaging Forbidden Treasure in Medical Data: Utilizing Surrogate\n  Outcomes and Single Records for Rare Event Modeling", "abstract": "  The vast repositories of Electronic Health Records (EHR) and medical claims\nhold untapped potential for studying rare but critical events, such as suicide\nattempt. Conventional setups often model suicide attempt as a univariate\noutcome and also exclude any ``single-record'' patients with a single\ndocumented encounter due to a lack of historical information. However, patients\nwho were diagnosed with suicide attempts at the only encounter could, to some\nsurprise, represent a substantial proportion of all attempt cases in the data,\nas high as 70--80%. We innovate a hybrid and integrative learning framework to\nleverage concurrent outcomes as surrogates and harness the forbidden yet\nprecious information from single-record data. Our approach employs a supervised\nlearning component to learn the latent variables that connect primary (e.g.,\nsuicide) and surrogate outcomes (e.g., mental disorders) to historical\ninformation. It simultaneously employs an unsupervised learning component to\nutilize the single-record data, through the shared latent variables. As such,\nour approach offers a general strategy for information integration that is\ncrucial to modeling rare conditions and events. With hospital inpatient data\nfrom Connecticut, we demonstrate that single-record data and concurrent\ndiagnoses indeed carry valuable information, and utilizing them can\nsubstantially improve suicide risk modeling.\n", "date": "2025-01-25", "categories": []}
{"id": "2501.15881", "title": "Multivariate Feature Selection and Autoencoder Embeddings of Ovarian\n  Cancer Clinical and Genetic Data", "abstract": "  This study explores a data-driven approach to discovering novel clinical and\ngenetic markers in ovarian cancer (OC). Two main analyses were performed: (1) a\nnonlinear examination of an OC dataset using autoencoders, which compress data\ninto a 3-dimensional latent space to detect potential intrinsic separability\nbetween platinum-sensitive and platinum-resistant groups; and (2) an adaptation\nof the informative variable identifier (IVI) to determine which features\n(clinical or genetic) are most relevant to disease progression. In the\nautoencoder analysis, a clearer pattern emerged when using clinical features\nand the combination of clinical and genetic data, indicating that disease\nprogression groups can be distinguished more effectively after supervised fine\ntuning. For genetic data alone, this separability was less apparent but became\nmore pronounced with a supervised approach. Using the IVI-based feature\nselection, key clinical variables (such as type of surgery and neoadjuvant\nchemotherapy) and certain gene mutations showed strong relevance, along with\nlow-risk genetic factors. These findings highlight the strength of combining\nmachine learning tools (autoencoders) with feature selection methods (IVI) to\ngain insights into ovarian cancer progression. They also underscore the\npotential for identifying new biomarkers that integrate clinical and genomic\nindicators, ultimately contributing to improved patient stratification and\npersonalized treatment strategies.\n", "date": "2025-01-27", "categories": []}
{"id": "2501.15969", "title": "An Explainable Disease Surveillance System for Early Prediction of\n  Multiple Chronic Diseases", "abstract": "  This study addresses a critical gap in the healthcare system by developing a\nclinically meaningful, practical, and explainable disease surveillance system\nfor multiple chronic diseases, utilizing routine EHR data from multiple U.S.\npractices integrated with CureMD's EMR/EHR system. Unlike traditional\nsystems--using AI models that rely on features from patients' labs--our\napproach focuses on routinely available data, such as medical history, vitals,\ndiagnoses, and medications, to preemptively assess the risks of chronic\ndiseases in the next year. We trained three distinct models for each chronic\ndisease: prediction models that forecast the risk of a disease 3, 6, and 12\nmonths before a potential diagnosis. We developed Random Forest models, which\nwere internally validated using F1 scores and AUROC as performance metrics and\nfurther evaluated by a panel of expert physicians for clinical relevance based\non inferences grounded in medical knowledge. Additionally, we discuss our\nimplementation of integrating these models into a practical EMR system. Beyond\nusing Shapley attributes and surrogate models for explainability, we also\nintroduce a new rule-engineering framework to enhance the intrinsic\nexplainability of Random Forests.\n", "date": "2025-01-27", "categories": []}
{"id": "2501.15973", "title": "Integrating Probabilistic Trees and Causal Networks for Clinical and\n  Epidemiological Data", "abstract": "  Healthcare decision-making requires not only accurate predictions but also\ninsights into how factors influence patient outcomes. While traditional Machine\nLearning (ML) models excel at predicting outcomes, such as identifying high\nrisk patients, they are limited in addressing what-if questions about\ninterventions. This study introduces the Probabilistic Causal Fusion (PCF)\nframework, which integrates Causal Bayesian Networks (CBNs) and Probability\nTrees (PTrees) to extend beyond predictions. PCF leverages causal relationships\nfrom CBNs to structure PTrees, enabling both the quantification of factor\nimpacts and simulation of hypothetical interventions. PCF was validated on\nthree real-world healthcare datasets i.e. MIMIC-IV, Framingham Heart Study, and\nDiabetes, chosen for their clinically diverse variables. It demonstrated\npredictive performance comparable to traditional ML models while providing\nadditional causal reasoning capabilities. To enhance interpretability, PCF\nincorporates sensitivity analysis and SHapley Additive exPlanations (SHAP).\nSensitivity analysis quantifies the influence of causal parameters on outcomes\nsuch as Length of Stay (LOS), Coronary Heart Disease (CHD), and Diabetes, while\nSHAP highlights the importance of individual features in predictive modeling.\nBy combining causal reasoning with predictive modeling, PCF bridges the gap\nbetween clinical intuition and data-driven insights. Its ability to uncover\nrelationships between modifiable factors and simulate hypothetical scenarios\nprovides clinicians with a clearer understanding of causal pathways. This\napproach supports more informed, evidence-based decision-making, offering a\nrobust framework for addressing complex questions in diverse healthcare\nsettings.\n", "date": "2025-01-27", "categories": []}
{"id": "2501.16077", "title": "RelCAT: Advancing Extraction of Clinical Inter-Entity Relationships from\n  Unstructured Electronic Health Records", "abstract": "  This study introduces RelCAT (Relation Concept Annotation Toolkit), an\ninteractive tool, library, and workflow designed to classify relations between\nentities extracted from clinical narratives. Building upon the CogStack MedCAT\nframework, RelCAT addresses the challenge of capturing complete clinical\nrelations dispersed within text. The toolkit implements state-of-the-art\nmachine learning models such as BERT and Llama along with proven evaluation and\ntraining methods. We demonstrate a dataset annotation tool (built within\nMedCATTrainer), model training, and evaluate our methodology on both openly\navailable gold-standard and real-world UK National Health Service (NHS)\nhospital clinical datasets. We perform extensive experimentation and a\ncomparative analysis of the various publicly available models with varied\napproaches selected for model fine-tuning. Finally, we achieve macro F1-scores\nof 0.977 on the gold-standard n2c2, surpassing the previous state-of-the-art\nperformance, and achieve performance of >=0.93 F1 on our NHS gathered datasets.\n", "date": "2025-01-27", "categories": []}
{"id": "2304.09466", "title": "MAMAF-Net: Motion-Aware and Multi-Attention Fusion Network for Stroke\n  Diagnosis", "abstract": "  Stroke is a major cause of mortality and disability worldwide from which one\nin four people are in danger of incurring in their lifetime. The pre-hospital\nstroke assessment plays a vital role in identifying stroke patients accurately\nto accelerate further examination and treatment in hospitals. Accordingly, the\nNational Institutes of Health Stroke Scale (NIHSS), Cincinnati Pre-hospital\nStroke Scale (CPSS) and Face Arm Speed Time (F.A.S.T.) are globally known tests\nfor stroke assessment. However, the validity of these tests is skeptical in the\nabsence of neurologists and access to healthcare may be limited. Therefore, in\nthis study, we propose a motion-aware and multi-attention fusion network\n(MAMAF-Net) that can detect stroke from multimodal examination videos. Contrary\nto other studies on stroke detection from video analysis, our study for the\nfirst time proposes an end-to-end solution from multiple video recordings of\neach subject with a dataset encapsulating stroke, transient ischemic attack\n(TIA), and healthy controls. The proposed MAMAF-Net consists of motion-aware\nmodules to sense the mobility of patients, attention modules to fuse the\nmulti-input video data, and 3D convolutional layers to perform diagnosis from\nthe attention-based extracted features. Experimental results over the collected\nStroke-data dataset show that the proposed MAMAF-Net achieves a successful\ndetection of stroke with 93.62% sensitivity and 95.33% AUC score.\n", "date": "2023-04-19", "categories": []}
{"id": "2501.05826", "title": "AI-Driven Diabetic Retinopathy Screening: Multicentric Validation of\n  AIDRSS in India", "abstract": "  Purpose: Diabetic retinopathy (DR) is a major cause of vision loss,\nparticularly in India, where access to retina specialists is limited in rural\nareas. This study aims to evaluate the Artificial Intelligence-based Diabetic\nRetinopathy Screening System (AIDRSS) for DR detection and prevalence\nassessment, addressing the growing need for scalable, automated screening\nsolutions in resource-limited settings.\n  Approach: A multicentric, cross-sectional study was conducted in Kolkata,\nIndia, involving 5,029 participants and 10,058 macula-centric retinal fundus\nimages. The AIDRSS employed a deep learning algorithm with 50 million trainable\nparameters, integrated with Contrast Limited Adaptive Histogram Equalization\n(CLAHE) preprocessing for enhanced image quality. DR was graded using the\nInternational Clinical Diabetic Retinopathy (ICDR) Scale, categorizing disease\ninto five stages (DR0 to DR4). Statistical metrics including sensitivity,\nspecificity, and prevalence rates were evaluated against expert retina\nspecialist assessments.\n  Results: The prevalence of DR in the general population was 13.7%, rising to\n38.2% among individuals with elevated random blood glucose levels. The AIDRSS\nachieved an overall sensitivity of 92%, specificity of 88%, and 100%\nsensitivity for detecting referable DR (DR3 and DR4). These results demonstrate\nthe system's robust performance in accurately identifying and grading DR in a\ndiverse population.\n  Conclusions: AIDRSS provides a reliable, scalable solution for early DR\ndetection in resource-constrained environments. Its integration of advanced AI\ntechniques ensures high diagnostic accuracy, with potential to significantly\nreduce the burden of diabetes-related vision loss in underserved regions.\n", "date": "2025-01-10", "categories": []}
{"id": "2501.14152", "title": "Multimodal Prescriptive Deep Learning", "abstract": "  We introduce a multimodal deep learning framework, Prescriptive Neural\nNetworks (PNNs), that combines ideas from optimization and machine learning,\nand is, to the best of our knowledge, the first prescriptive method to handle\nmultimodal data. The PNN is a feedforward neural network trained on embeddings\nto output an outcome-optimizing prescription. In two real-world multimodal\ndatasets, we demonstrate that PNNs prescribe treatments that are able to\nsignificantly improve estimated outcomes in transcatheter aortic valve\nreplacement (TAVR) procedures by reducing estimated postoperative complication\nrates by 32% and in liver trauma injuries by reducing estimated mortality rates\nby over 40%. In four real-world, unimodal tabular datasets, we demonstrate that\nPNNs outperform or perform comparably to other well-known, state-of-the-art\nprescriptive models; importantly, on tabular datasets, we also recover\ninterpretability through knowledge distillation, fitting interpretable Optimal\nClassification Tree models onto the PNN prescriptions as classification\ntargets, which is critical for many real-world applications. Finally, we\ndemonstrate that our multimodal PNN models achieve stability across randomized\ndata splits comparable to other prescriptive methods and produce realistic\nprescriptions across the different datasets.\n", "date": "2025-01-24", "categories": []}
{"id": "2501.14279", "title": "Deep Learning-Powered Classification of Thoracic Diseases in Chest\n  X-Rays", "abstract": "  Chest X-rays play a pivotal role in diagnosing respiratory diseases such as\npneumonia, tuberculosis, and COVID-19, which are prevalent and present unique\ndiagnostic challenges due to overlapping visual features and variability in\nimage quality. Severe class imbalance and the complexity of medical images\nhinder automated analysis. This study leverages deep learning techniques,\nincluding transfer learning on pre-trained models (AlexNet, ResNet, and\nInceptionNet), to enhance disease detection and classification. By fine-tuning\nthese models and incorporating focal loss to address class imbalance,\nsignificant performance improvements were achieved. Grad-CAM visualizations\nfurther enhance model interpretability, providing insights into clinically\nrelevant regions influencing predictions. The InceptionV3 model, for instance,\nachieved a 28% improvement in AUC and a 15% increase in F1-Score. These\nfindings highlight the potential of deep learning to improve diagnostic\nworkflows and support clinical decision-making.\n", "date": "2025-01-24", "categories": []}
{"id": "2501.14379", "title": "ECTIL: Label-efficient Computational Tumour Infiltrating Lymphocyte\n  (TIL) assessment in breast cancer: Multicentre validation in 2,340 patients\n  with breast cancer", "abstract": "  The level of tumour-infiltrating lymphocytes (TILs) is a prognostic factor\nfor patients with (triple-negative) breast cancer (BC). Computational TIL\nassessment (CTA) has the potential to assist pathologists in this\nlabour-intensive task, but current CTA models rely heavily on many detailed\nannotations. We propose and validate a fundamentally simpler deep learning\nbased CTA that can be trained in only ten minutes on hundredfold fewer\npathologist annotations. We collected whole slide images (WSIs) with TILs\nscores and clinical data of 2,340 patients with BC from six cohorts including\nthree randomised clinical trials. Morphological features were extracted from\nwhole slide images (WSIs) using a pathology foundation model. Our\nlabel-efficient Computational stromal TIL assessment model (ECTIL) directly\nregresses the TILs score from these features. ECTIL trained on only a few\nhundred samples (ECTIL-TCGA) showed concordance with the pathologist over five\nheterogeneous external cohorts (r=0.54-0.74, AUROC=0.80-0.94). Training on all\nslides of five cohorts (ECTIL-combined) improved results on a held-out test set\n(r=0.69, AUROC=0.85). Multivariable Cox regression analyses indicated that\nevery 10% increase of ECTIL scores was associated with improved overall\nsurvival independent of clinicopathological variables (HR 0.86, p<0.01),\nsimilar to the pathologist score (HR 0.87, p<0.001). We demonstrate that ECTIL\nis highly concordant with an expert pathologist and obtains a similar hazard\nratio. ECTIL has a fundamentally simpler design than existing methods and can\nbe trained on orders of magnitude fewer annotations. Such a CTA may be used to\npre-screen patients for, e.g., immunotherapy clinical trial inclusion, or as a\ntool to assist clinicians in the diagnostic work-up of patients with BC. Our\nmodel is available under an open source licence\n(https://github.com/nki-ai/ectil).\n", "date": "2025-01-24", "categories": []}
{"id": "2501.13247", "title": "Multimodal AI on Wound Images and Clinical Notes for Home Patient\n  Referral", "abstract": "  Chronic wounds affect 8.5 million Americans, particularly the elderly and\npatients with diabetes. These wounds can take up to nine months to heal, making\nregular care essential to ensure healing and prevent severe outcomes like limb\namputations. Many patients receive care at home from visiting nurses with\nvarying levels of wound expertise, leading to inconsistent care. Problematic,\nnon-healing wounds should be referred to wound specialists, but referral\ndecisions in non-clinical settings are often erroneous, delayed, or\nunnecessary.\n  This paper introduces the Deep Multimodal Wound Assessment Tool (DM-WAT), a\nmachine learning framework designed to assist visiting nurses in deciding\nwhether to refer chronic wound patients. DM-WAT analyzes smartphone-captured\nwound images and clinical notes from Electronic Health Records (EHRs). It uses\nDeiT-Base-Distilled, a Vision Transformer (ViT), to extract visual features\nfrom images and DeBERTa-base to extract text features from clinical notes.\nDM-WAT combines visual and text features using an intermediate fusion approach.\nTo address challenges posed by a small and imbalanced dataset, it integrates\nimage and text augmentation with transfer learning to achieve high performance.\nIn evaluations, DM-WAT achieved 77% with std 3% accuracy and a 70% with std 2%\nF1 score, outperforming prior approaches. Score-CAM and Captum interpretation\nalgorithms provide insights into specific parts of image and text inputs that\ninfluence recommendations, enhancing interpretability and trust.\n", "date": "2025-01-22", "categories": []}
{"id": "2501.12927", "title": "Longitudinal Missing Data Imputation for Predicting Disability Stage of\n  Patients with Multiple Sclerosis", "abstract": "  Multiple Sclerosis (MS) is a chronic disease characterized by progressive or\nalternate impairment of neurological functions (motor, sensory, visual, and\ncognitive). Predicting disease progression with a probabilistic and\ntime-dependent approach might help in suggesting interventions that can delay\nthe progression of the disease. However, extracting informative knowledge from\nirregularly collected longitudinal data is difficult, and missing data pose\nsignificant challenges. MS progression is measured through the Expanded\nDisability Status Scale (EDSS), which quantifies and monitors disability in MS\nover time. EDSS assesses impairment in eight functional systems (FS).\nFrequently, only the EDSS score assigned by clinicians is reported, while FS\nsub-scores are missing. Imputing these scores might be useful, especially to\nstratify patients according to their phenotype assessed over the disease\nprogression. This study aimed at i) exploring different methodologies for\nimputing missing FS sub-scores, and ii) predicting the EDSS score using\ncomplete clinical data. Results show that Exponential Weighted Moving Average\nachieved the lowest error rate in the missing data imputation task;\nfurthermore, the combination of Classification and Regression Trees for the\nimputation and SVM for the prediction task obtained the best accuracy.\n", "date": "2025-01-22", "categories": []}
{"id": "2501.09506", "title": "Multimodal Marvels of Deep Learning in Medical Diagnosis: A\n  Comprehensive Review of COVID-19 Detection", "abstract": "  This study presents a comprehensive review of the potential of multimodal\ndeep learning (DL) in medical diagnosis, using COVID-19 as a case example.\nMotivated by the success of artificial intelligence applications during the\nCOVID-19 pandemic, this research aims to uncover the capabilities of DL in\ndisease screening, prediction, and classification, and to derive insights that\nenhance the resilience, sustainability, and inclusiveness of science,\ntechnology, and innovation systems. Adopting a systematic approach, we\ninvestigate the fundamental methodologies, data sources, preprocessing steps,\nand challenges encountered in various studies and implementations. We explore\nthe architecture of deep learning models, emphasising their data-specific\nstructures and underlying algorithms. Subsequently, we compare different deep\nlearning strategies utilised in COVID-19 analysis, evaluating them based on\nmethodology, data, performance, and prerequisites for future research. By\nexamining diverse data types and diagnostic modalities, this research\ncontributes to scientific understanding and knowledge of the multimodal\napplication of DL and its effectiveness in diagnosis. We have implemented and\nanalysed 11 deep learning models using COVID-19 image, text, and speech (ie,\ncough) data. Our analysis revealed that the MobileNet model achieved the\nhighest accuracy of 99.97% for COVID-19 image data and 93.73% for speech data\n(i.e., cough). However, the BiGRU model demonstrated superior performance in\nCOVID-19 text classification with an accuracy of 99.89%. The broader\nimplications of this research suggest potential benefits for other domains and\ndisciplines that could leverage deep learning techniques for image, text, and\nspeech analysis.\n", "date": "2025-01-16", "categories": []}
{"id": "2501.10980", "title": "An analysis of the combination of feature selection and machine learning\n  methods for an accurate and timely detection of lung cancer", "abstract": "  One of the deadliest cancers, lung cancer necessitates an early and precise\ndiagnosis. Because patients have a better chance of recovering, early\nidentification of lung cancer is crucial. This review looks at how to diagnose\nlung cancer using sophisticated machine learning techniques like Random Forest\n(RF) and Support Vector Machine (SVM). The Chi-squared test is one feature\nselection strategy that has been successfully applied to find related features\nand enhance model performance. The findings demonstrate that these techniques\ncan improve detection efficiency and accuracy while also assisting in runtime\nreduction. This study produces recommendations for further research as well as\nideas to enhance diagnostic techniques. In order to improve healthcare and\ncreate automated methods for detecting lung cancer, this research is a critical\nfirst step.\n", "date": "2025-01-19", "categories": []}
{"id": "2501.11276", "title": "ITCFN: Incomplete Triple-Modal Co-Attention Fusion Network for Mild\n  Cognitive Impairment Conversion Prediction", "abstract": "  Alzheimer's disease (AD) is a common neurodegenerative disease among the\nelderly. Early prediction and timely intervention of its prodromal stage, mild\ncognitive impairment (MCI), can decrease the risk of advancing to AD. Combining\ninformation from various modalities can significantly improve predictive\naccuracy. However, challenges such as missing data and heterogeneity across\nmodalities complicate multimodal learning methods as adding more modalities can\nworsen these issues. Current multimodal fusion techniques often fail to adapt\nto the complexity of medical data, hindering the ability to identify\nrelationships between modalities. To address these challenges, we propose an\ninnovative multimodal approach for predicting MCI conversion, focusing\nspecifically on the issues of missing positron emission tomography (PET) data\nand integrating diverse medical information. The proposed incomplete\ntriple-modal MCI conversion prediction network is tailored for this purpose.\nThrough the missing modal generation module, we synthesize the missing PET data\nfrom the magnetic resonance imaging and extract features using specifically\ndesigned encoders. We also develop a channel aggregation module and a\ntriple-modal co-attention fusion module to reduce feature redundancy and\nachieve effective multimodal data fusion. Furthermore, we design a loss\nfunction to handle missing modality issues and align cross-modal features.\nThese components collectively harness multimodal data to boost network\nperformance. Experimental results on the ADNI1 and ADNI2 datasets show that our\nmethod significantly surpasses existing unimodal and other multimodal models.\nOur code is available at https://github.com/justinhxy/ITFC.\n", "date": "2025-01-20", "categories": []}
{"id": "2501.11535", "title": "A baseline for machine-learning-based hepatocellular carcinoma diagnosis\n  using multi-modal clinical data", "abstract": "  The objective of this paper is to provide a baseline for performing\nmulti-modal data classification on a novel open multimodal dataset of\nhepatocellular carcinoma (HCC), which includes both image data\n(contrast-enhanced CT and MRI images) and tabular data (the clinical laboratory\ntest data as well as case report forms). TNM staging is the classification\ntask. Features from the vectorized preprocessed tabular data and radiomics\nfeatures from contrast-enhanced CT and MRI images are collected. Feature\nselection is performed based on mutual information. An XGBoost classifier\npredicts the TNM staging and it shows a prediction accuracy of $0.89 \\pm 0.05$\nand an AUC of $0.93 \\pm 0.03$. The classifier shows that this high level of\nprediction accuracy can only be obtained by combining image and clinical\nlaboratory data and therefore is a good example case where multi-model\nclassification is mandatory to achieve accurate results.\n", "date": "2025-01-20", "categories": []}
{"id": "2501.11720", "title": "Prediction of Lung Metastasis from Hepatocellular Carcinoma using the\n  SEER Database", "abstract": "  Hepatocellular carcinoma (HCC) is a leading cause of cancer-related\nmortality, with lung metastases being the most common site of distant spread\nand significantly worsening prognosis. Despite the growing availability of\nclinical and demographic data, predictive models for lung metastasis in HCC\nremain limited in scope and clinical applicability. In this study, we develop\nand validate an end-to-end machine learning pipeline using data from the\nSurveillance, Epidemiology, and End Results (SEER) database. We evaluated three\nmachine learning models (Random Forest, XGBoost, and Logistic Regression)\nalongside a multilayer perceptron (MLP) neural network. Our models achieved\nhigh AUROC values and recall, with the Random Forest and MLP models\ndemonstrating the best overall performance (AUROC = 0.82). However, the low\nprecision across models highlights the challenges of accurately predicting\npositive cases. To address these limitations, we developed a custom loss\nfunction incorporating recall optimization, enabling the MLP model to achieve\nthe highest sensitivity. An ensemble approach further improved overall recall\nby leveraging the strengths of individual models. Feature importance analysis\nrevealed key predictors such as surgery status, tumor staging, and follow up\nduration, emphasizing the relevance of clinical interventions and disease\nprogression in metastasis prediction. While this study demonstrates the\npotential of machine learning for identifying high-risk patients, limitations\ninclude reliance on imbalanced datasets, incomplete feature annotations, and\nthe low precision of predictions. Future work should leverage the expanding\nSEER dataset, improve data imputation techniques, and explore advanced\npre-trained models to enhance predictive accuracy and clinical utility.\n", "date": "2025-01-20", "categories": []}
{"id": "2501.09999", "title": "Deep Learning for Early Alzheimer Disease Detection with MRI Scans", "abstract": "  Alzheimer's Disease is a neurodegenerative condition characterized by\ndementia and impairment in neurological function. The study primarily focuses\non the individuals above age 40, affecting their memory, behavior, and\ncognitive processes of the brain. Alzheimer's disease requires diagnosis by a\ndetailed assessment of MRI scans and neuropsychological tests of the patients.\nThis project compares existing deep learning models in the pursuit of enhancing\nthe accuracy and efficiency of AD diagnosis, specifically focusing on the\nConvolutional Neural Network, Bayesian Convolutional Neural Network, and the\nU-net model with the Open Access Series of Imaging Studies brain MRI dataset.\nBesides, to ensure robustness and reliability in the model evaluations, we\naddress the challenge of imbalance in data. We then perform rigorous evaluation\nto determine strengths and weaknesses for each model by considering\nsensitivity, specificity, and computational efficiency. This comparative\nanalysis would shed light on the future role of AI in revolutionizing AD\ndiagnostics but also paved ways for future innovation in medical imaging and\nthe management of neurodegenerative diseases.\n", "date": "2025-01-17", "categories": []}
{"id": "2303.13203", "title": "Confidence-Driven Deep Learning Framework for Early Detection of Knee\n  Osteoarthritis", "abstract": "  Knee Osteoarthritis (KOA) is a prevalent musculoskeletal disorder that\nseverely impacts mobility and quality of life, particularly among older adults.\nIts diagnosis often relies on subjective assessments using the\nKellgren-Lawrence (KL) grading system, leading to variability in clinical\nevaluations. To address these challenges, we propose a confidence-driven deep\nlearning framework for early KOA detection, focusing on distinguishing KL-0 and\nKL-2 stages. The Siamese-based framework integrates a novel multi-level feature\nextraction architecture with a hybrid loss strategy. Specifically, multi-level\nGlobal Average Pooling (GAP) layers are employed to extract features from\nvarying network depths, ensuring comprehensive feature representation, while\nthe hybrid loss strategy partitions training samples into high-, medium-, and\nlow-confidence subsets. Tailored loss functions are applied to improve model\nrobustness and effectively handle uncertainty in annotations. Experimental\nresults on the Osteoarthritis Initiative (OAI) dataset demonstrate that the\nproposed framework achieves competitive accuracy, sensitivity, and specificity,\ncomparable to those of expert radiologists. Cohen's kappa values (k > 0.85))\nconfirm substantial agreement, while McNemar's test (p > 0.05) indicates no\nstatistically significant differences between the model and radiologists.\nAdditionally, Confidence distribution analysis reveals that the model emulates\nradiologists' decision-making patterns. These findings highlight the potential\nof the proposed approach to serve as an auxiliary diagnostic tool, enhancing\nearly KOA detection and reducing clinical workload.\n", "date": "2023-03-23", "categories": []}
{"id": "2501.09480", "title": "Utilizing AI Language Models to Identify Prognostic Factors for Coronary\n  Artery Disease: A Study in Mashhad Residents", "abstract": "  Abstract: Background: Understanding cardiovascular artery disease risk\nfactors, the leading global cause of mortality, is crucial for influencing its\netiology, prevalence, and treatment. This study aims to evaluate prognostic\nmarkers for coronary artery disease in Mashhad using Naive Bayes, REP Tree,\nJ48, CART, and CHAID algorithms. Methods:\n  Using data from the 2009 MASHAD STUDY, prognostic factors for coronary artery\ndisease were determined with Naive Bayes, REP Tree, J48, CART, CHAID, and\nRandom Forest algorithms using R 3.5.3 and WEKA 3.9.4. Model efficiency was\ncompared by sensitivity, specificity, and accuracy. Cases were patients with\ncoronary artery disease; each had three controls (totally 940). Results:\nPrognostic factors for coronary artery disease in Mashhad residents varied by\nalgorithm. CHAID identified age, myocardial infarction history, and\nhypertension. CART included depression score and physical activity. REP added\neducation level and anxiety score. NB included diabetes and family history. J48\nhighlighted father's heart disease and weight loss. CHAID had the highest\naccuracy (0.80).\n  Conclusion:\n  Key prognostic factors for coronary artery disease in CART and CHAID models\ninclude age, myocardial infarction history, hypertension, depression score,\nphysical activity, and BMI. NB, REP Tree, and J48 identified numerous factors.\nCHAID had the highest accuracy, sensitivity, and specificity. CART offers\nsimpler interpretation, aiding physician and paramedic model selection based on\nspecific. Keywords: RF, Na\\\"ive Bayes, REP, J48 algorithms, Coronary Artery\nDisease (CAD).\n", "date": "2025-01-16", "categories": []}
{"id": "2311.06394", "title": "A design of Convolutional Neural Network model for the Diagnosis of the\n  COVID-19", "abstract": "  With the spread of COVID-19 around the globe over the past year, the usage of\nartificial intelligence (AI) algorithms and image processing methods to analyze\nthe X-ray images of patients' chest with COVID-19 has become essential. The\nCOVID-19 virus recognition in the lung area of a patient is one of the basic\nand essential needs of clicical centers and hospitals. Most research in this\nfield has been devoted to papers on the basis of deep learning methods\nutilizing CNNs (Convolutional Neural Network), which mainly deal with the\nscreening of sick and healthy people.In this study, a new structure of a\n19-layer CNN has been recommended for accurately recognition of the COVID-19\nfrom the X-ray pictures of chest. The offered CNN is developed to serve as a\nprecise diagnosis system for a three class (viral pneumonia, Normal, COVID) and\na four classclassification (Lung opacity, Normal, COVID-19, and pneumonia). A\ncomparison is conducted among the outcomes of the offered procedure and some\npopular pretrained networks, including Inception, Alexnet, ResNet50,\nSqueezenet, and VGG19 and based on Specificity, Accuracy, Precision,\nSensitivity, Confusion Matrix, and F1-score. The experimental results of the\noffered CNN method specify its dominance over the existing published\nprocedures. This method can be a useful tool for clinicians in deciding\nproperly about COVID-19.\n", "date": "2023-11-10", "categories": []}
{"id": "2501.08851", "title": "Digital Phenotyping for Adolescent Mental Health: A Feasibility Study\n  Employing Machine Learning to Predict Mental Health Risk From Active and\n  Passive Smartphone Data", "abstract": "  Background: Adolescents are particularly vulnerable to mental disorders, with\nover 75% of cases manifesting before the age of 25. Research indicates that\nonly 18 to 34% of young people experiencing high levels of depression or\nanxiety symptoms seek support. Digital tools leveraging smartphones offer\nscalable and early intervention opportunities. Objective: Using a novel machine\nlearning framework, this study evaluated the feasibility of integrating active\nand passive smartphone data to predict mental disorders in non-clinical\nadolescents. Specifically, we investigated the utility of the Mindcraft app in\npredicting risks for internalising and externalising disorders, eating\ndisorders, insomnia and suicidal ideation. Methods: Participants (N=103; mean\nage 16.1 years) were recruited from three London schools. Participants\ncompleted the Strengths and Difficulties Questionnaire, the Eating Disorders-15\nQuestionnaire, Sleep Condition Indicator Questionnaire and indicated the\npresence/absence of suicidal ideation. They used the Mindcraft app for 14 days,\ncontributing active data via self-reports and passive data from smartphone\nsensors. A contrastive pretraining phase was applied to enhance user-specific\nfeature stability, followed by supervised fine-tuning. The model evaluation\nemployed leave-one-subject-out cross-validation using balanced accuracy as the\nprimary metric. Results: The integration of active and passive data achieved\nsuperior performance compared to individual data sources, with mean balanced\naccuracies of 0.71 for SDQ-High risk, 0.67 for insomnia, 0.77 for suicidal\nideation and 0.70 for eating disorders. The contrastive learning framework\nstabilised daily behavioural representations, enhancing predictive robustness.\nThis study demonstrates the potential of integrating active and passive\nsmartphone data with advanced machine-learning techniques for predicting mental\nhealth risks.\n", "date": "2025-01-15", "categories": []}
{"id": "2501.08241", "title": "A Feature-Level Ensemble Model for COVID-19 Identification in CXR Images\n  using Choquet Integral and Differential Evolution Optimization", "abstract": "  The COVID-19 pandemic has profoundly impacted billions globally. It\nchallenges public health and healthcare systems due to its rapid spread and\nsevere respiratory effects. An effective strategy to mitigate the COVID-19\npandemic involves integrating testing to identify infected individuals. While\nRT-PCR is considered the gold standard for diagnosing COVID-19, it has some\nlimitations such as the risk of false negatives. To address this problem, this\npaper introduces a novel Deep Learning Diagnosis System that integrates\npre-trained Deep Convolutional Neural Networks (DCNNs) within an ensemble\nlearning framework to achieve precise identification of COVID-19 cases from\nChest X-ray (CXR) images. We combine feature vectors from the final hidden\nlayers of pre-trained DCNNs using the Choquet integral to capture interactions\nbetween different DCNNs that a linear approach cannot. We employed\nSugeno-$\\lambda$ measure theory to derive fuzzy measures for subsets of\nnetworks to enable aggregation. We utilized Differential Evolution to estimate\nfuzzy densities. We developed a TensorFlow-based layer for Choquet operation to\nfacilitate efficient aggregation, due to the intricacies involved in\naggregating feature vectors. Experimental results on the COVIDx dataset show\nthat our ensemble model achieved 98\\% accuracy in three-class classification\nand 99.50\\% in binary classification, outperforming its components-DenseNet-201\n(97\\% for three-class, 98.75\\% for binary), Inception-v3 (96.25\\% for\nthree-class, 98.50\\% for binary), and Xception (94.50\\% for three-class, 98\\%\nfor binary)-and surpassing many previous methods.\n", "date": "2025-01-14", "categories": []}
{"id": "2410.05450", "title": "AI-Driven Early Mental Health Screening: Analyzing Selfies of Pregnant\n  Women", "abstract": "  Major Depressive Disorder and anxiety disorders affect millions globally,\ncontributing significantly to the burden of mental health issues. Early\nscreening is crucial for effective intervention, as timely identification of\nmental health issues can significantly improve treatment outcomes. Artificial\nintelligence (AI) can be valuable for improving the screening of mental\ndisorders, enabling early intervention and better treatment outcomes. AI-driven\nscreening can leverage the analysis of multiple data sources, including facial\nfeatures in digital images. However, existing methods often rely on controlled\nenvironments or specialized equipment, limiting their broad applicability. This\nstudy explores the potential of AI models for ubiquitous depression-anxiety\nscreening given face-centric selfies. The investigation focuses on high-risk\npregnant patients, a population that is particularly vulnerable to mental\nhealth issues. To cope with limited training data resulting from our clinical\nsetup, pre-trained models were utilized in two different approaches:\nfine-tuning convolutional neural networks (CNNs) originally designed for facial\nexpression recognition and employing vision-language models (VLMs) for\nzero-shot analysis of facial expressions. Experimental results indicate that\nthe proposed VLM-based method significantly outperforms CNNs, achieving an\naccuracy of 77.6%. Although there is significant room for improvement, the\nresults suggest that VLMs can be a promising approach for mental health\nscreening.\n", "date": "2024-10-07", "categories": []}
{"id": "2410.10701", "title": "Early Diagnosis of Acute Lymphoblastic Leukemia Using YOLOv8 and YOLOv11\n  Deep Learning Models", "abstract": "  Leukemia, a severe form of blood cancer, claims thousands of lives each year.\nThis study focuses on the detection of Acute Lymphoblastic Leukemia (ALL) using\nadvanced image processing and deep learning techniques. By leveraging recent\nadvancements in artificial intelligence, the research evaluates the reliability\nof these methods in practical, real-world scenarios. Specifically, it examines\nthe performance of state-of-the-art YOLO models, including YOLOv8 and YOLOv11,\nto distinguish between malignant and benign white blood cells and accurately\nidentify different stages of ALL, including early stages. Moreover, the models\ndemonstrate the ability to detect hematogones, which are frequently\nmisclassified as ALL. With accuracy rates reaching 98.8%, this study highlights\nthe potential of these algorithms to provide robust and precise leukemia\ndetection across diverse datasets and conditions.\n", "date": "2024-10-14", "categories": []}
{"id": "2501.06432", "title": "Deep Learning on Hester Davis Scores for Inpatient Fall Prediction", "abstract": "  Fall risk prediction among hospitalized patients is a critical aspect of\npatient safety in clinical settings, and accurate models can help prevent\nadverse events. The Hester Davis Score (HDS) is commonly used to assess fall\nrisk, with current clinical practice relying on a threshold-based approach. In\nthis method, a patient is classified as high-risk when their HDS exceeds a\npredefined threshold. However, this approach may fail to capture dynamic\npatterns in fall risk over time. In this study, we model the threshold-based\napproach and propose two machine learning approaches for enhanced fall\nprediction: One-step ahead fall prediction and sequence-to-point fall\nprediction. The one-step ahead model uses the HDS at the current timestamp to\npredict the risk at the next timestamp, while the sequence-to-point model\nleverages all preceding HDS values to predict fall risk using deep learning. We\ncompare these approaches to assess their accuracy in fall risk prediction,\ndemonstrating that deep learning can outperform the traditional threshold-based\nmethod by capturing temporal patterns and improving prediction reliability.\nThese findings highlight the potential for data-driven approaches to enhance\npatient safety through more reliable fall prevention strategies.\n", "date": "2025-01-11", "categories": []}
{"id": "2501.06823", "title": "MEXA-CTP: Mode Experts Cross-Attention for Clinical Trial Outcome\n  Prediction", "abstract": "  Clinical trials are the gold standard for assessing the effectiveness and\nsafety of drugs for treating diseases. Given the vast design space of drug\nmolecules, elevated financial cost, and multi-year timeline of these trials,\nresearch on clinical trial outcome prediction has gained immense traction.\nAccurate predictions must leverage data of diverse modes such as drug\nmolecules, target diseases, and eligibility criteria to infer successes and\nfailures. Previous Deep Learning approaches for this task, such as HINT, often\nrequire wet lab data from synthesized molecules and/or rely on prior knowledge\nto encode interactions as part of the model architecture. To address these\nlimitations, we propose a light-weight attention-based model, MEXA-CTP, to\nintegrate readily-available multi-modal data and generate effective\nrepresentations via specialized modules dubbed \"mode experts\", while avoiding\nhuman biases in model design. We optimize MEXA-CTP with the Cauchy loss to\ncapture relevant interactions across modes. Our experiments on the Trial\nOutcome Prediction (TOP) benchmark demonstrate that MEXA-CTP improves upon\nexisting approaches by, respectively, up to 11.3% in F1 score, 12.2% in PR-AUC,\nand 2.5% in ROC-AUC, compared to HINT. Ablation studies are provided to\nquantify the effectiveness of each component in our proposed method.\n", "date": "2025-01-12", "categories": []}
{"id": "2501.06859", "title": "A Comprehensive Evaluation of Large Language Models on Mental Illnesses\n  in Arabic Context", "abstract": "  Mental health disorders pose a growing public health concern in the Arab\nworld, emphasizing the need for accessible diagnostic and intervention tools.\nLarge language models (LLMs) offer a promising approach, but their application\nin Arabic contexts faces challenges including limited labeled datasets,\nlinguistic complexity, and translation biases. This study comprehensively\nevaluates 8 LLMs, including general multi-lingual models, as well as bi-lingual\nones, on diverse mental health datasets (such as AraDepSu, Dreaddit, MedMCQA),\ninvestigating the impact of prompt design, language configuration (native\nArabic vs. translated English, and vice versa), and few-shot prompting on\ndiagnostic performance. We find that prompt engineering significantly\ninfluences LLM scores mainly due to reduced instruction following, with our\nstructured prompt outperforming a less structured variant on multi-class\ndatasets, with an average difference of 14.5\\%. While language influence on\nperformance was modest, model selection proved crucial: Phi-3.5 MoE excelled in\nbalanced accuracy, particularly for binary classification, while Mistral NeMo\nshowed superior performance in mean absolute error for severity prediction\ntasks. Few-shot prompting consistently improved performance, with particularly\nsubstantial gains observed for GPT-4o Mini on multi-class classification,\nboosting accuracy by an average factor of 1.58. These findings underscore the\nimportance of prompt optimization, multilingual analysis, and few-shot learning\nfor developing culturally sensitive and effective LLM-based mental health tools\nfor Arabic-speaking populations.\n", "date": "2025-01-12", "categories": []}
{"id": "2501.07206", "title": "A data-driven approach to discover and quantify systemic lupus\n  erythematosus etiological heterogeneity from electronic health records", "abstract": "  Systemic lupus erythematosus (SLE) is a complex heterogeneous disease with\nmany manifestational facets. We propose a data-driven approach to discover\nprobabilistic independent sources from multimodal imperfect EHR data. These\nsources represent exogenous variables in the data generation process causal\ngraph that estimate latent root causes of the presence of SLE in the health\nrecord. We objectively evaluated the sources against the original variables\nfrom which they were discovered by training supervised models to discriminate\nSLE from negative health records using a reduced set of labelled instances. We\nfound 19 predictive sources with high clinical validity and whose EHR\nsignatures define independent factors of SLE heterogeneity. Using the sources\nas input patient data representation enables models to provide with rich\nexplanations that better capture the clinical reasons why a particular record\nis (not) an SLE case. Providers may be willing to trade patient-level\ninterpretability for discrimination especially in challenging cases.\n", "date": "2025-01-13", "categories": []}
{"id": "2501.00190", "title": "SepsisCalc: Integrating Clinical Calculators into Early Sepsis\n  Prediction via Dynamic Temporal Graph Construction", "abstract": "  Sepsis is an organ dysfunction caused by a deregulated immune response to an\ninfection. Early sepsis prediction and identification allow for timely\nintervention, leading to improved clinical outcomes. Clinical calculators\n(e.g., the six-organ dysfunction assessment of SOFA) play a vital role in\nsepsis identification within clinicians' workflow, providing evidence-based\nrisk assessments essential for sepsis diagnosis. However, artificial\nintelligence (AI) sepsis prediction models typically generate a single sepsis\nrisk score without incorporating clinical calculators for assessing organ\ndysfunctions, making the models less convincing and transparent to clinicians.\nTo bridge the gap, we propose to mimic clinicians' workflow with a novel\nframework SepsisCalc to integrate clinical calculators into the predictive\nmodel, yielding a clinically transparent and precise model for utilization in\nclinical settings. Practically, clinical calculators usually combine\ninformation from multiple component variables in Electronic Health Records\n(EHR), and might not be applicable when the variables are (partially) missing.\nWe mitigate this issue by representing EHRs as temporal graphs and integrating\na learning module to dynamically add the accurately estimated calculator to the\ngraphs. Experimental results on real-world datasets show that the proposed\nmodel outperforms state-of-the-art methods on sepsis prediction tasks.\nMoreover, we developed a system to identify organ dysfunctions and potential\nsepsis risks, providing a human-AI interaction tool for deployment, which can\nhelp clinicians understand the prediction outputs and prepare timely\ninterventions for the corresponding dysfunctions, paving the way for actionable\nclinical decision-making support for early intervention.\n", "date": "2024-12-31", "categories": []}
{"id": "2409.10677", "title": "Mitigating Sex Bias in Audio Data-driven COPD and COVID-19 Breathing\n  Pattern Detection Models", "abstract": "  In the healthcare industry, researchers have been developing machine learning\nmodels to automate diagnosing patients with respiratory illnesses based on\ntheir breathing patterns. However, these models do not consider the demographic\nbiases, particularly sex bias, that often occur when models are trained with a\nskewed patient dataset. Hence, it is essential in such an important industry to\nreduce this bias so that models can make fair diagnoses. In this work, we\nexamine the bias in models used to detect breathing patterns of two major\nrespiratory diseases, i.e., chronic obstructive pulmonary disease (COPD) and\nCOVID-19. Using decision tree models trained with audio recordings of breathing\npatterns obtained from two open-source datasets consisting of 29 COPD and 680\nCOVID-19-positive patients, we analyze the effect of sex bias on the models.\nWith a threshold optimizer and two constraints (demographic parity and\nequalized odds) to mitigate the bias, we witness 81.43% (demographic parity\ndifference) and 71.81% (equalized odds difference) improvements. These findings\nare statistically significant.\n", "date": "2024-09-16", "categories": []}
{"id": "2501.04061", "title": "Causal Machine Learning Methods for Estimating Personalised Treatment\n  Effects -- Insights on validity from two large trials", "abstract": "  Causal machine learning (ML) methods hold great promise for advancing\nprecision medicine by estimating personalized treatment effects. However, their\nreliability remains largely unvalidated in empirical settings. In this study,\nwe assessed the internal and external validity of 17 mainstream causal\nheterogeneity ML methods -- including metalearners, tree-based methods, and\ndeep learning methods -- using data from two large randomized controlled\ntrials: the International Stroke Trial (N=19,435) and the Chinese Acute Stroke\nTrial (N=21,106). Our findings reveal that none of the ML methods reliably\nvalidated their performance, neither internal nor external, showing significant\ndiscrepancies between training and test data on the proposed evaluation\nmetrics. The individualized treatment effects estimated from training data\nfailed to generalize to the test data, even in the absence of distribution\nshifts. These results raise concerns about the current applicability of causal\nML models in precision medicine, and highlight the need for more robust\nvalidation techniques to ensure generalizability.\n", "date": "2025-01-07", "categories": []}
{"id": "2311.13015", "title": "Fast and Interpretable Mortality Risk Scores for Critical Care Patients", "abstract": "  Prediction of mortality in intensive care unit (ICU) patients typically\nrelies on black box models (that are unacceptable for use in hospitals) or\nhand-tuned interpretable models (that might lead to the loss in performance).\nWe aim to bridge the gap between these two categories by building on modern\ninterpretable ML techniques to design interpretable mortality risk scores that\nare as accurate as black boxes. We developed a new algorithm, GroupFasterRisk,\nwhich has several important benefits: it uses both hard and soft direct\nsparsity regularization, it incorporates group sparsity to allow more cohesive\nmodels, it allows for monotonicity constraint to include domain knowledge, and\nit produces many equally-good models, which allows domain experts to choose\namong them. For evaluation, we leveraged the largest existing public ICU\nmonitoring datasets (MIMIC III and eICU). Models produced by GroupFasterRisk\noutperformed OASIS and SAPS II scores and performed similarly to APACHE IV/IVa\nwhile using at most a third of the parameters. For patients with\nsepsis/septicemia, acute myocardial infarction, heart failure, and acute kidney\nfailure, GroupFasterRisk models outperformed OASIS and SOFA. Finally, different\nmortality prediction ML approaches performed better based on variables selected\nby GroupFasterRisk as compared to OASIS variables. GroupFasterRisk's models\nperformed better than risk scores currently used in hospitals, and on par with\nblack box ML models, while being orders of magnitude sparser. Because\nGroupFasterRisk produces a variety of risk scores, it allows design flexibility\n- the key enabler of practical model creation. GroupFasterRisk is a fast,\naccessible, and flexible procedure that allows learning a diverse set of sparse\nrisk scores for mortality prediction.\n", "date": "2023-11-21", "categories": []}
{"id": "2501.04073", "title": "Deep Learning for Ophthalmology: The State-of-the-Art and Future Trends", "abstract": "  The emergence of artificial intelligence (AI), particularly deep learning\n(DL), has marked a new era in the realm of ophthalmology, offering\ntransformative potential for the diagnosis and treatment of posterior segment\neye diseases. This review explores the cutting-edge applications of DL across a\nrange of ocular conditions, including diabetic retinopathy, glaucoma,\nage-related macular degeneration, and retinal vessel segmentation. We provide a\ncomprehensive overview of foundational ML techniques and advanced DL\narchitectures, such as CNNs, attention mechanisms, and transformer-based\nmodels, highlighting the evolving role of AI in enhancing diagnostic accuracy,\noptimizing treatment strategies, and improving overall patient care.\nAdditionally, we present key challenges in integrating AI solutions into\nclinical practice, including ensuring data diversity, improving algorithm\ntransparency, and effectively leveraging multimodal data. This review\nemphasizes AI's potential to improve disease diagnosis and enhance patient care\nwhile stressing the importance of collaborative efforts to overcome these\nbarriers and fully harness AI's impact in advancing eye care.\n", "date": "2025-01-07", "categories": []}
{"id": "2501.04441", "title": "Motif Discovery Framework for Psychiatric EEG Data Classification", "abstract": "  In current medical practice, patients undergoing depression treatment must\nwait four to six weeks before a clinician can assess medication response due to\nthe delayed noticeable effects of antidepressants. Identification of a\ntreatment response at any earlier stage is of great importance, since it can\nreduce the emotional and economic burden connected with the treatment. We\napproach the prediction of a patient response to a treatment as a\nclassification problem, by utilizing the dynamic properties of EEG recordings\non the 7th day of the treatment. We present a novel framework that applies\nmotif discovery to extract meaningful features from EEG data distinguishing\nbetween depression treatment responders and non-responders. We applied our\nframework also to classification tasks in other psychiatric EEG datasets,\nnamely to patients with symptoms of schizophrenia, pediatric patients with\nintractable seizures, and Alzheimer disease and dementia. We achieved high\nclassification precision in all data sets. The results demonstrate that the\ndynamic properties of the EEGs may support clinicians in decision making both\nin diagnosis and in the prediction depression treatment response as early as on\nthe 7th day of the treatment. To our best knowledge, our work is the first one\nusing motifs in the depression diagnostics in general.\n", "date": "2025-01-08", "categories": []}
{"id": "2501.04547", "title": "Medical artificial intelligence toolbox (MAIT): an explainable machine\n  learning framework for binary classification, survival modelling, and\n  regression analyses", "abstract": "  While machine learning offers diverse techniques suitable for exploring\nvarious medical research questions, a cohesive synergistic framework can\nfacilitate the integration and understanding of new approaches within unified\nmodel development and interpretation. We therefore introduce the Medical\nArtificial Intelligence Toolbox (MAIT), an explainable, open-source Python\npipeline for developing and evaluating binary classification, regression, and\nsurvival models on tabular datasets. MAIT addresses key challenges (e.g., high\ndimensionality, class imbalance, mixed variable types, and missingness) while\npromoting transparency in reporting (TRIPOD+AI compliant). Offering automated\nconfigurations for beginners and customizable source code for experts, MAIT\nstreamlines two primary use cases: Discovery (feature importance via unified\nscoring, e.g., SHapley Additive exPlanations - SHAP) and Prediction (model\ndevelopment and deployment with optimized solutions). Moreover, MAIT proposes\nnew techniques including fine-tuning of probability threshold in binary\nclassification, translation of cumulative hazard curves to binary\nclassification, enhanced visualizations for model interpretation for mixed data\ntypes, and handling censoring through semi-supervised learning, to adapt to a\nwide set of data constraints and study designs. We provide detailed tutorials\non GitHub, using four open-access data sets, to demonstrate how MAIT can be\nused to improve implementation and interpretation of ML models in medical\nresearch.\n", "date": "2025-01-08", "categories": []}
{"id": "2403.18873", "title": "Predicting risk of cardiovascular disease using retinal OCT imaging", "abstract": "  Cardiovascular diseases (CVD) are the leading cause of death globally.\nNon-invasive, cost-effective imaging techniques play a crucial role in early\ndetection and prevention of CVD. Optical coherence tomography (OCT) has gained\nrecognition as a potential tool for early CVD risk prediction, though its use\nremains underexplored. In this study, we investigated the potential of OCT as\nan additional imaging technique to predict future CVD events. We analysed\nretinal OCT data from the UK Biobank. The dataset included 612 patients who\nsuffered a myocardial infarction (MI) or stroke within five years of imaging\nand 2,234 controls without CVD (total: 2,846 participants). A self-supervised\ndeep learning approach based on Variational Autoencoders (VAE) was used to\nextract low-dimensional latent representations from high-dimensional 3D OCT\nimages, capturing distinct features of retinal layers. These latent features,\nalong with clinical data, were used to train a Random Forest (RF) classifier to\ndifferentiate between patients at risk of future CVD events (MI or stroke) and\nhealthy controls. Our model achieved an AUC of 0.75, sensitivity of 0.70,\nspecificity of 0.70, and accuracy of 0.70, outperforming the QRISK3 score (the\nthird version of the QRISK cardiovascular disease risk prediction algorithm;\nAUC = 0.60, sensitivity = 0.60, specificity = 0.55, accuracy = 0.55). The\nchoroidal layer in OCT images was identified as a key predictor of future CVD\nevents, revealed through a novel model explainability approach. This study\ndemonstrates that retinal OCT imaging is a cost-effective, non-invasive\nalternative for predicting CVD risk, offering potential for widespread\napplication in optometry practices and hospitals.\n", "date": "2024-03-26", "categories": []}
{"id": "2408.11876", "title": "From Glucose Patterns to Health Outcomes: A Generalizable Foundation\n  Model for Continuous Glucose Monitor Data Analysis", "abstract": "  Recent advances in SSL enabled novel medical AI models, known as foundation\nmodels, offer great potential for better characterizing health from diverse\nbiomedical data. CGM provides rich, temporal data on glycemic patterns, but its\nfull potential for predicting broader health outcomes remains underutilized.\nHere, we present GluFormer, a generative foundation model for CGM data that\nlearns nuanced glycemic patterns and translates them into predictive\nrepresentations of metabolic health. Trained on over 10 million CGM\nmeasurements from 10,812 adults, primarily without diabetes, GluFormer uses\nautoregressive token prediction to capture longitudinal glucose dynamics. We\nshow that GluFormer generalizes to 19 external cohorts (n=6,044) spanning\ndifferent ethnicities and ages, 5 countries, 8 CGM devices, and diverse\npathophysiological states. GluFormers representations exceed the performance of\ncurrent CGM metrics, such as the Glucose Management Indicator (GMI), for\nforecasting clinical measures. In a longitudinal study of 580 adults with CGM\ndata and 12-year follow-up, GluFormer identifies individuals at elevated risk\nof developing diabetes more effectively than blood HbA1C%, capturing 66% of all\nnew-onset diabetes diagnoses in the top quartile versus 7% in the bottom\nquartile. Similarly, 69% of cardiovascular-death events occurred in the top\nquartile with none in the bottom quartile, demonstrating powerful risk\nstratification beyond traditional glycemic metrics. We also show that CGM\nrepresentations from pre-intervention periods in Randomized Clinical Trials\noutperform other methods in predicting primary and secondary outcomes. When\nintegrating dietary data into GluFormer, we show that the multi-modal version\nof the model can accurately generate CGM data based on dietary intake data,\nsimulate outcomes of dietary interventions, and predict individual responses to\nspecific foods.\n", "date": "2024-08-20", "categories": []}
{"id": "2412.02801", "title": "Optimization of Transformer heart disease prediction model based on\n  particle swarm optimization algorithm", "abstract": "  Aiming at the latest particle swarm optimization algorithm, this paper\nproposes an improved Transformer model to improve the accuracy of heart disease\nprediction and provide a new algorithm idea. We first use three mainstream\nmachine learning classification algorithms - decision tree, random forest and\nXGBoost, and then output the confusion matrix of these three models. The\nresults showed that the random forest model had the best performance in\npredicting the classification of heart disease, with an accuracy of 92.2%.\nThen, we apply the Transformer model based on particle swarm optimization (PSO)\nalgorithm to the same dataset for classification experiment. The results show\nthat the classification accuracy of the model is as high as 96.5%, 4.3\npercentage points higher than that of random forest, which verifies the\neffectiveness of PSO in optimizing Transformer model. From the above research,\nwe can see that particle swarm optimization significantly improves Transformer\nperformance in heart disease prediction. Improving the ability to predict heart\ndisease is a global priority with benefits for all humankind. Accurate\nprediction can enhance public health, optimize medical resources, and reduce\nhealthcare costs, leading to healthier populations and more productive\nsocieties worldwide. This advancement paves the way for more efficient health\nmanagement and supports the foundation of a healthier, more resilient global\ncommunity.\n", "date": "2024-12-03", "categories": []}
{"id": "2408.07084", "title": "Dynamic Hypergraph-Enhanced Prediction of Sequential Medical Visits", "abstract": "  This study introduces a pioneering Dynamic Hypergraph Networks (DHCE) model\ndesigned to predict future medical diagnoses from electronic health records\nwith enhanced accuracy. The DHCE model innovates by identifying and\ndifferentiating acute and chronic diseases within a patient's visit history,\nconstructing dynamic hypergraphs that capture the complex, high-order\ninteractions between diseases. It surpasses traditional recurrent neural\nnetworks and graph neural networks by effectively integrating clinical event\ndata, reflected through medical language model-assisted encoding, into a robust\npatient representation. Through extensive experiments on two benchmark\ndatasets, MIMIC-III and MIMIC-IV, the DHCE model exhibits superior performance,\nsignificantly outpacing established baseline models in the precision of\nsequential diagnosis prediction.\n", "date": "2024-08-08", "categories": []}
{"id": "2410.19646", "title": "Deep learning-based identification of patients at increased risk of\n  cancer using routine laboratory markers", "abstract": "  Early screening for cancer has proven to improve the survival rate and spare\npatients from intensive and costly treatments due to late diagnosis. Cancer\nscreening in the healthy population involves an initial risk stratification\nstep to determine the screening method and frequency, primarily to optimize\nresource allocation by targeting screening towards individuals who draw most\nbenefit. For most screening programs, age and clinical risk factors such as\nfamily history are part of the initial risk stratification algorithm. In this\npaper, we focus on developing a blood marker-based risk stratification\napproach, which could be used to identify patients with elevated cancer risk to\nbe encouraged for taking a diagnostic test or participate in a screening\nprogram. We demonstrate that the combination of simple, widely available blood\ntests, such as complete blood count and complete metabolic panel, could\npotentially be used to identify patients at risk for colorectal, liver, and\nlung cancers with areas under the ROC curve of 0.76, 0.85, 0.78, respectively.\nFurthermore, we hypothesize that such an approach could not only be used as\npre-screening risk assessment for individuals but also as population health\nmanagement tool, for example to better interrogate the cancer risk in certain\nsub-populations.\n", "date": "2024-10-25", "categories": []}
{"id": "2501.01984", "title": "Leveraging AI for Automatic Classification of PCOS Using Ultrasound\n  Imaging", "abstract": "  The AUTO-PCOS Classification Challenge seeks to advance the diagnostic\ncapabilities of artificial intelligence (AI) in identifying Polycystic Ovary\nSyndrome (PCOS) through automated classification of healthy and unhealthy\nultrasound frames. This report outlines our methodology for building a robust\nAI pipeline utilizing transfer learning with the InceptionV3 architecture to\nachieve high accuracy in binary classification. Preprocessing steps ensured the\ndataset was optimized for training, validation, and testing, while\ninterpretability methods like LIME and saliency maps provided valuable insights\ninto the model's decision-making. Our approach achieved an accuracy of 90.52%,\nwith precision, recall, and F1-score metrics exceeding 90% on validation data,\ndemonstrating its efficacy. The project underscores the transformative\npotential of AI in healthcare, particularly in addressing diagnostic challenges\nlike PCOS. Key findings, challenges, and recommendations for future\nenhancements are discussed, highlighting the pathway for creating reliable,\ninterpretable, and scalable AI-driven medical diagnostic tools.\n", "date": "2024-12-30", "categories": []}
{"id": "2501.02025", "title": "RealDiffFusionNet: Neural Controlled Differential Equation Informed\n  Multi-Head Attention Fusion Networks for Disease Progression Modeling Using\n  Real-World Data", "abstract": "  This paper presents a novel deep learning-based approach named\nRealDiffFusionNet incorporating Neural Controlled Differential Equations\n(Neural CDE) - time series models that are robust in handling irregularly\nsampled data - and multi-head attention to align relevant multimodal context\n(image data, time invariant data, etc.) at each time point. Long short-term\nmemory (LSTM) models were also used as a baseline. Two different datasets were\nused: a data from the Open-Source Imaging Consortium (OSIC) containing\nstructured time series data of demographics and lung function with a baseline\nCT scan of the lungs and the second from the Alzheimer's Disease Neuroimaging\nInitiative (ADNI) containing a series of MRI scans along with demographics,\nphysical examinations, and cognitive assessment data. An ablation study was\nperformed to understand the role of CDEs, multimodal data, attention fusion,\nand interpolation strategies on model performance. When the baseline models\nwere evaluated, the use of multimodal data resulted in an improvement in Neural\nCDE performance, with a lower test RMSE. Additionally, the performance of\nmultimodal Neural CDE was also superior to multimodal LSTM. In the\nattention-based architectures, fusion through concatenation and rectilinear\ninterpolation were found to improve model performance. The performance of the\nproposed RealDiffFusionNet was found to be superior (0.2570) to all models. For\nthe ADNI dataset, between the Neural-CDE and LSTM models trained only on the\nstructured data, the test RMSE were comparable (0.471 for LSTM vs. 0.4581\nNeural-CDE). Furthermore, the addition of image features from patients' MRI\nseries resulted in an improvement in performance, with a lower test RMSE\n(0.4372 with multimodal vs 0.4581 with structured data). RealDiffFusionNet has\nshown promise in utilizing CDEs and multimodal data to accurately predict\ndisease progression.\n", "date": "2025-01-02", "categories": []}
{"id": "2501.02044", "title": "Advancing Pancreatic Cancer Prediction with a Next Visit Token\n  Prediction Head on top of Med-BERT", "abstract": "  Background: Recently, numerous foundation models pretrained on extensive data\nhave demonstrated efficacy in disease prediction using Electronic Health\nRecords (EHRs). However, there remains some unanswered questions on how to best\nutilize such models especially with very small fine-tuning cohorts. Methods: We\nutilized Med-BERT, an EHR-specific foundation model, and reformulated the\ndisease binary prediction task into a token prediction task and a next visit\nmask token prediction task to align with Med-BERT's pretraining task format in\norder to improve the accuracy of pancreatic cancer (PaCa) prediction in both\nfew-shot and fully supervised settings. Results: The reformulation of the task\ninto a token prediction task, referred to as Med-BERT-Sum, demonstrates\nslightly superior performance in both few-shot scenarios and larger data\nsamples. Furthermore, reformulating the prediction task as a Next Visit Mask\nToken Prediction task (Med-BERT-Mask) significantly outperforms the\nconventional Binary Classification (BC) prediction task (Med-BERT-BC) by 3% to\n7% in few-shot scenarios with data sizes ranging from 10 to 500 samples. These\nfindings highlight that aligning the downstream task with Med-BERT's\npretraining objectives substantially enhances the model's predictive\ncapabilities, thereby improving its effectiveness in predicting both rare and\ncommon diseases. Conclusion: Reformatting disease prediction tasks to align\nwith the pretraining of foundation models enhances prediction accuracy, leading\nto earlier detection and timely intervention. This approach improves treatment\neffectiveness, survival rates, and overall patient outcomes for PaCa and\npotentially other cancers.\n", "date": "2025-01-03", "categories": []}
{"id": "2412.19967", "title": "MobileNetV2: A lightweight classification model for home-based sleep\n  apnea screening", "abstract": "  This study proposes a novel lightweight neural network model leveraging\nfeatures extracted from electrocardiogram (ECG) and respiratory signals for\nearly OSA screening. ECG signals are used to generate feature spectrograms to\npredict sleep stages, while respiratory signals are employed to detect\nsleep-related breathing abnormalities. By integrating these predictions, the\nmethod calculates the apnea-hypopnea index (AHI) with enhanced accuracy,\nfacilitating precise OSA diagnosis.\n  The method was validated on three publicly available sleep apnea databases:\nthe Apnea-ECG database, the UCDDB dataset, and the MIT-BIH Polysomnographic\ndatabase. Results showed an overall OSA detection accuracy of 0.978,\nhighlighting the model's robustness. Respiratory event classification achieved\nan accuracy of 0.969 and an area under the receiver operating characteristic\ncurve (ROC-AUC) of 0.98. For sleep stage classification, in UCDDB dataset, the\nROC-AUC exceeded 0.85 across all stages, with recall for Sleep reaching 0.906\nand specificity for REM and Wake states at 0.956 and 0.937, respectively.\n  This study underscores the potential of integrating lightweight neural\nnetworks with multi-signal analysis for accurate, portable, and cost-effective\nOSA screening, paving the way for broader adoption in home-based and wearable\nhealth monitoring systems.\n", "date": "2024-12-28", "categories": []}
{"id": "2501.01462", "title": "Pan-infection Foundation Framework Enables Multiple Pathogen Prediction", "abstract": "  Host-response-based diagnostics can improve the accuracy of diagnosing\nbacterial and viral infections, thereby reducing inappropriate antibiotic\nprescriptions. However, the existing cohorts with limited sample size and\ncoarse infections types are unable to support the exploration of an accurate\nand generalizable diagnostic model. Here, we curate the largest infection\nhost-response transcriptome data, including 11,247 samples across 89 blood\ntranscriptome datasets from 13 countries and 21 platforms. We build a\ndiagnostic model for pathogen prediction starting from a pan-infection model as\nfoundation (AUC = 0.97) based on the pan-infection dataset. Then, we utilize\nknowledge distillation to efficiently transfer the insights from this \"teacher\"\nmodel to four lightweight pathogen \"student\" models, i.e., staphylococcal\ninfection (AUC = 0.99), streptococcal infection (AUC = 0.94), HIV infection\n(AUC = 0.93), and RSV infection (AUC = 0.94), as well as a sepsis \"student\"\nmodel (AUC = 0.99). The proposed knowledge distillation framework not only\nfacilitates the diagnosis of pathogens using pan-infection data, but also\nenables an across-disease study from pan-infection to sepsis. Moreover, the\nframework enables high-degree lightweight design of diagnostic models, which is\nexpected to be adaptively deployed in clinical settings.\n", "date": "2024-12-31", "categories": []}
{"id": "2311.01571", "title": "On Preserving the Knowledge of Long Clinical Texts", "abstract": "  Clinical texts, such as admission notes, discharge summaries, and progress\nnotes, contain rich and valuable information that can be used for clinical\ndecision making. However, a severe bottleneck in using transformer encoders for\nprocessing clinical texts comes from the input length limit of these models:\ntransformer-based encoders use fixed-length inputs. Therefore, these models\ndiscard part of the inputs while processing medical text. There is a risk of\nlosing vital knowledge from clinical text if only part of it is processed. This\npaper proposes a novel method to preserve the knowledge of long clinical texts\nin the models using aggregated ensembles of transformer encoders. Previous\nstudies used either ensemble or aggregation, but we studied the effects of\nfusing these methods. We trained several pre-trained BERT-like transformer\nencoders on two clinical outcome tasks: mortality prediction and length of stay\nprediction. Our method achieved better results than all baseline models for\nprediction tasks on long clinical notes. We conducted extensive experiments on\nthe MIMIC-III clinical database's admission notes by combining multiple\nunstructured and high-dimensional datasets, demonstrating our method's\neffectiveness and superiority over existing approaches. This study shows that\nfusing ensemble and aggregation improves the model performance for clinical\nprediction tasks, particularly the mortality and the length of hospital stay.\n", "date": "2023-11-02", "categories": []}
{"id": "2404.11760", "title": "Predictive Model Development to Identify Failed Healing in Patients\n  after Non-Union Fracture Surgery", "abstract": "  Bone non-union is among the most severe complications associated with trauma\nsurgery, occurring in 10-30% of cases after long bone fractures. Treating\nnon-unions requires a high level of surgical expertise and often involves\nmultiple revision surgeries, sometimes even leading to amputation. Thus, more\naccurate prognosis is crucial for patient well-being. Recent advances in\nmachine learning (ML) hold promise for developing models to predict non-union\nhealing, even when working with smaller datasets, a commonly encountered\nchallenge in clinical domains. To demonstrate the effectiveness of ML in\nidentifying candidates at risk of failed non-union healing, we applied three ML\nmodels (logistic regression, support vector machine, and XGBoost) to the\nclinical dataset TRUFFLE, which includes 797 patients with long bone non-union.\nThe models provided prediction results with 70% sensitivity, and the\nspecificities of 66% (XGBoost), 49% (support vector machine), and 43% (logistic\nregression). These findings offer valuable clinical insights because they\nenable early identification of patients at risk of failed non-union healing\nafter the initial surgical revision treatment protocol.\n", "date": "2024-04-17", "categories": []}
{"id": "2408.01612", "title": "Data-Driven Machine Learning Approaches for Predicting In-Hospital\n  Sepsis Mortality", "abstract": "  Sepsis is a severe condition responsible for many deaths in the United States\nand worldwide, making accurate prediction of outcomes crucial for timely and\neffective treatment. Previous studies employing machine learning faced\nlimitations in feature selection and model interpretability, reducing their\nclinical applicability. This research aimed to develop an interpretable and\naccurate machine learning model to predict in-hospital sepsis mortality,\naddressing these gaps. Using ICU patient records from the MIMIC-III database,\nwe extracted relevant data through a combination of literature review, clinical\ninput refinement, and Random Forest-based feature selection, identifying the\ntop 35 features. Data preprocessing included cleaning, imputation,\nstandardization, and applying the Synthetic Minority Over-sampling Technique\n(SMOTE) to address class imbalance, resulting in a dataset of 4,683 patients\nwith 17,429 admissions. Five models-Random Forest, Gradient Boosting, Logistic\nRegression, Support Vector Machine, and K-Nearest Neighbor-were developed and\nevaluated. The Random Forest model demonstrated the best performance, achieving\nan accuracy of 0.90, AUROC of 0.97, precision of 0.93, recall of 0.91, and\nF1-score of 0.92. These findings underscore the potential of data-driven\nmachine learning approaches to improve critical care, offering clinicians a\npowerful tool for predicting in-hospital sepsis mortality and enhancing patient\noutcomes.\n", "date": "2024-08-03", "categories": []}
{"id": "2501.00048", "title": "Stroke Prediction using Clinical and Social Features in Machine Learning", "abstract": "  Every year in the United States, 800,000 individuals suffer a stroke - one\nperson every 40 seconds, with a death occurring every four minutes. While\nindividual factors vary, certain predictors are more prevalent in determining\nstroke risk. As strokes are the second leading cause of death and disability\nworldwide, predicting stroke likelihood based on lifestyle factors is crucial.\nShowing individuals their stroke risk could motivate lifestyle changes, and\nmachine learning offers solutions to this prediction challenge. Neural networks\nexcel at predicting outcomes based on training features like lifestyle factors,\nhowever, they're not the only option. Logistic regression models can also\neffectively compute the likelihood of binary outcomes based on independent\nvariables, making them well-suited for stroke prediction. This analysis will\ncompare both neural networks (dense and convolutional) and logistic regression\nmodels for stroke prediction, examining their pros, cons, and differences to\ndevelop the most effective predictor that minimizes false negatives.\n", "date": "2024-12-27", "categories": []}
{"id": "2501.00199", "title": "GPT-4 on Clinic Depression Assessment: An LLM-Based Pilot Study", "abstract": "  Depression has impacted millions of people worldwide and has become one of\nthe most prevalent mental disorders. Early mental disorder detection can lead\nto cost savings for public health agencies and avoid the onset of other major\ncomorbidities. Additionally, the shortage of specialized personnel is a\ncritical issue because clinical depression diagnosis is highly dependent on\nexpert professionals and is time consuming.\n  In this study, we explore the use of GPT-4 for clinical depression assessment\nbased on transcript analysis. We examine the model's ability to classify\npatient interviews into binary categories: depressed and not depressed. A\ncomparative analysis is conducted considering prompt complexity (e.g., using\nboth simple and complex prompts) as well as varied temperature settings to\nassess the impact of prompt complexity and randomness on the model's\nperformance.\n  Results indicate that GPT-4 exhibits considerable variability in accuracy and\nF1-Score across configurations, with optimal performance observed at lower\ntemperature values (0.0-0.2) for complex prompts. However, beyond a certain\nthreshold (temperature >= 0.3), the relationship between randomness and\nperformance becomes unpredictable, diminishing the gains from prompt\ncomplexity.\n  These findings suggest that, while GPT-4 shows promise for clinical\nassessment, the configuration of the prompts and model parameters requires\ncareful calibration to ensure consistent results. This preliminary study\ncontributes to understanding the dynamics between prompt engineering and large\nlanguage models, offering insights for future development of AI-powered tools\nin clinical settings.\n", "date": "2024-12-31", "categories": []}
{"id": "2501.00464", "title": "Addressing Challenges in Data Quality and Model Generalization for\n  Malaria Detection", "abstract": "  Malaria remains a significant global health burden, particularly in\nresource-limited regions where timely and accurate diagnosis is critical to\neffective treatment and control. Deep Learning (DL) has emerged as a\ntransformative tool for automating malaria detection and it offers high\naccuracy and scalability. However, the effectiveness of these models is\nconstrained by challenges in data quality and model generalization including\nimbalanced datasets, limited diversity and annotation variability. These issues\nreduce diagnostic reliability and hinder real-world applicability.\n  This article provides a comprehensive analysis of these challenges and their\nimplications for malaria detection performance. Key findings highlight the\nimpact of data imbalances which can lead to a 20\\% drop in F1-score and\nregional biases which significantly hinder model generalization. Proposed\nsolutions, such as GAN-based augmentation, improved accuracy by 15-20\\% by\ngenerating synthetic data to balance classes and enhance dataset diversity.\nDomain adaptation techniques, including transfer learning, further improved\ncross-domain robustness by up to 25\\% in sensitivity.\n  Additionally, the development of diverse global datasets and collaborative\ndata-sharing frameworks is emphasized as a cornerstone for equitable and\nreliable malaria diagnostics. The role of explainable AI techniques in\nimproving clinical adoption and trustworthiness is also underscored. By\naddressing these challenges, this work advances the field of AI-driven malaria\ndetection and provides actionable insights for researchers and practitioners.\nThe proposed solutions aim to support the development of accessible and\naccurate diagnostic tools, particularly for resource-constrained populations.\n", "date": "2024-12-31", "categories": []}
{"id": "2501.01183", "title": "Machine Learning-Based Prediction of ICU Readmissions in Intracerebral\n  Hemorrhage Patients: Insights from the MIMIC Databases", "abstract": "  Intracerebral hemorrhage (ICH) is a life-risking condition characterized by\nbleeding within the brain parenchyma. ICU readmission in ICH patients is a\ncritical outcome, reflecting both clinical severity and resource utilization.\nAccurate prediction of ICU readmission risk is crucial for guiding clinical\ndecision-making and optimizing healthcare resources. This study utilized the\nMedical Information Mart for Intensive Care (MIMIC-III and MIMIC-IV) databases,\nwhich contain comprehensive clinical and demographic data on ICU patients.\nPatients with ICH were identified from both databases. Various clinical,\nlaboratory, and demographic features were extracted for analysis based on both\noverview literature and experts' opinions. Preprocessing methods like imputing\nand sampling were applied to improve the performance of our models. Machine\nlearning techniques, such as Artificial Neural Network (ANN), XGBoost, and\nRandom Forest, were employed to develop predictive models for ICU readmission\nrisk. Model performance was evaluated using metrics such as AUROC, accuracy,\nsensitivity, and specificity. The developed models demonstrated robust\npredictive accuracy for ICU readmission in ICH patients, with key predictors\nincluding demographic information, clinical parameters, and laboratory\nmeasurements. Our study provides a predictive framework for ICU readmission\nrisk in ICH patients, which can aid in clinical decision-making and improve\nresource allocation in intensive care settings.\n", "date": "2025-01-02", "categories": []}
{"id": "2501.01305", "title": "Large Language Models for Mental Health Diagnostic Assessments:\n  Exploring The Potential of Large Language Models for Assisting with Mental\n  Health Diagnostic Assessments -- The Depression and Anxiety Case", "abstract": "  Large language models (LLMs) are increasingly attracting the attention of\nhealthcare professionals for their potential to assist in diagnostic\nassessments, which could alleviate the strain on the healthcare system caused\nby a high patient load and a shortage of providers. For LLMs to be effective in\nsupporting diagnostic assessments, it is essential that they closely replicate\nthe standard diagnostic procedures used by clinicians. In this paper, we\nspecifically examine the diagnostic assessment processes described in the\nPatient Health Questionnaire-9 (PHQ-9) for major depressive disorder (MDD) and\nthe Generalized Anxiety Disorder-7 (GAD-7) questionnaire for generalized\nanxiety disorder (GAD). We investigate various prompting and fine-tuning\ntechniques to guide both proprietary and open-source LLMs in adhering to these\nprocesses, and we evaluate the agreement between LLM-generated diagnostic\noutcomes and expert-validated ground truth. For fine-tuning, we utilize the\nMentalllama and Llama models, while for prompting, we experiment with\nproprietary models like GPT-3.5 and GPT-4o, as well as open-source models such\nas llama-3.1-8b and mixtral-8x7b.\n", "date": "2025-01-02", "categories": []}
{"id": "2310.12595", "title": "Bayesian Meta-Learning for Improving Generalizability of Health\n  Prediction Models With Similar Causal Mechanisms", "abstract": "  Machine learning strategies like multi-task learning, meta-learning, and\ntransfer learning enable efficient adaptation of machine learning models to\nspecific applications in healthcare, such as prediction of various diseases, by\nleveraging generalizable knowledge across large datasets and multiple domains.\nIn particular, Bayesian meta-learning methods pool data across related\nprediction tasks to learn prior distributions for model parameters, which are\nthen used to derive models for specific tasks. However, inter- and intra-task\nvariability due to disease heterogeneity and other patient-level differences\npose challenges of negative transfer during shared learning and poor\ngeneralizability to new patients. We introduce a novel Bayesian meta-learning\napproach that aims to address this in two key settings: (1) predictions for new\npatients (same population as the training set) and (2) adapting to new patient\npopulations. Our main contribution is in modeling similarity between causal\nmechanisms of the tasks, for (1) mitigating negative transfer during training\nand (2) fine-tuning that pools information from tasks that are expected to aid\ngeneralizability. We propose an algorithm for implementing this approach for\nBayesian deep learning, and apply it to a case study for stroke prediction\ntasks using electronic health record data. Experiments for the UK Biobank\ndataset as the training population demonstrated significant generalizability\nimprovements compared to standard meta-learning, non-causal task similarity\nmeasures, and local baselines (separate models for each task). This was\nassessed for a variety of tasks that considered both new patients from the\ntraining population (UK Biobank) and a new population (FinnGen).\n", "date": "2023-10-19", "categories": []}
{"id": "2407.14876", "title": "Preictal Period Optimization for Deep Learning-Based Epileptic Seizure\n  Prediction", "abstract": "  Accurate prediction of epileptic seizures could prove critical for improving\npatient safety and quality of life in drug-resistant epilepsy. Although deep\nlearning-based approaches have shown promising seizure prediction performance\nusing scalp electroencephalogram (EEG) signals, substantial limitations still\nimpede their clinical adoption. Furthermore, identifying the optimal preictal\nperiod (OPP) for labeling EEG segments remains a challenge. Here, we not only\ndevelop a competitive deep learning model for seizure prediction but, more\nimportantly, leverage it to demonstrate a methodology to comprehensively\nevaluate the predictive performance in the seizure prediction task. For this,\nwe introduce a CNN-Transformer deep learning model to detect preictal\nspatiotemporal dynamics, alongside a novel Continuous Input-Output Performance\nRatio (CIOPR) metric to determine the OPP. We trained and evaluated our model\non 19 pediatric patients of the open-access CHB-MIT dataset in a\nsubject-specific manner. Using the OPP of each patient, preictal and interictal\nsegments were correctly identified with an average sensitivity of 99.31%,\nspecificity of 95.34%, AUC of 99.35%, and F1- score of 97.46%, while prediction\ntime averaged 76.8 minutes before onset. Notably, our novel CIOPR metric\nallowed outlining the impact of different preictal period definitions on\nprediction time, accuracy, output stability, and transition time between\ninterictal and preictal states in a comprehensive and quantitative way and\nhighlighted the importance of considering both inter- and intra-patient\nvariability in seizure prediction.\n", "date": "2024-07-20", "categories": []}
{"id": "2412.01950", "title": "A Novel Generative Multi-Task Representation Learning Approach for\n  Predicting Postoperative Complications in Cardiac Surgery Patients", "abstract": "  Early detection of surgical complications allows for timely therapy and\nproactive risk mitigation. Machine learning (ML) can be leveraged to identify\nand predict patient risks for postoperative complications. We developed and\nvalidated the effectiveness of predicting postoperative complications using a\nnovel surgical Variational Autoencoder (surgVAE) that uncovers intrinsic\npatterns via cross-task and cross-cohort presentation learning. This\nretrospective cohort study used data from the electronic health records of\nadult surgical patients over four years (2018 - 2021). Six key postoperative\ncomplications for cardiac surgery were assessed: acute kidney injury, atrial\nfibrillation, cardiac arrest, deep vein thrombosis or pulmonary embolism, blood\ntransfusion, and other intraoperative cardiac events. We compared prediction\nperformances of surgVAE against widely-used ML models and advanced\nrepresentation learning and generative models under 5-fold cross-validation.\n89,246 surgeries (49% male, median (IQR) age: 57 (45-69)) were included, with\n6,502 in the targeted cardiac surgery cohort (61% male, median (IQR) age: 60\n(53-70)). surgVAE demonstrated superior performance over existing ML solutions\nacross all postoperative complications of cardiac surgery patients, achieving\nmacro-averaged AUPRC of 0.409 and macro-averaged AUROC of 0.831, which were\n3.4% and 3.7% higher, respectively, than the best alternative method (by AUPRC\nscores). Model interpretation using Integrated Gradients highlighted key risk\nfactors based on preoperative variable importance. surgVAE showed excellent\ndiscriminatory performance for predicting postoperative complications and\naddressing the challenges of data complexity, small cohort sizes, and\nlow-frequency positive events. surgVAE enables data-driven predictions of\npatient risks and prognosis while enhancing the interpretability of patient\nrisk profiles.\n", "date": "2024-12-02", "categories": []}
{"id": "2412.19833", "title": "Multi-atlas Ensemble Graph Neural Network Model For Major Depressive\n  Disorder Detection Using Functional MRI Data", "abstract": "  Major depressive disorder (MDD) is one of the most common mental disorders,\nwith significant impacts on many daily activities and quality of life. It\nstands as one of the most common mental disorders globally and ranks as the\nsecond leading cause of disability. The current diagnostic approach for MDD\nprimarily relies on clinical observations and patient-reported symptoms,\noverlooking the diverse underlying causes and pathophysiological factors\ncontributing to depression. Therefore, scientific researchers and clinicians\nmust gain a deeper understanding of the pathophysiological mechanisms involved\nin MDD. There is growing evidence in neuroscience that depression is a brain\nnetwork disorder, and the use of neuroimaging, such as magnetic resonance\nimaging (MRI), plays a significant role in identifying and treating MDD.\nRest-state functional MRI (rs-fMRI) is among the most popular neuroimaging\ntechniques used to study MDD. Deep learning techniques have been widely applied\nto neuroimaging data to help with early mental health disorder detection.\nRecent years have seen a rise in interest in graph neural networks (GNNs),\nwhich are deep neural architectures specifically designed to handle\ngraph-structured data like rs-fMRI. This research aimed to develop an\nensemble-based GNN model capable of detecting discriminative features from\nrs-fMRI images for the purpose of diagnosing MDD. Specifically, we constructed\nan ensemble model by combining features from multiple brain region segmentation\natlases to capture brain complexity and detect distinct features more\naccurately than single atlas-based models. Further, the effectiveness of our\nmodel is demonstrated by assessing its performance on a large multi-site MDD\ndataset. The best performing model among all folds achieved an accuracy of\n75.80%, a sensitivity of 88.89%, a specificity of 61.84%, a precision of\n71.29%, and an F1-score of 79.12%.\n", "date": "2024-12-21", "categories": []}
{"id": "2412.20741", "title": "Depression and Anxiety Prediction Using Deep Language Models and\n  Transfer Learning", "abstract": "  Digital screening and monitoring applications can aid providers in the\nmanagement of behavioral health conditions. We explore deep language models for\ndetecting depression, anxiety, and their co-occurrence from conversational\nspeech collected during 16k user interactions with an application. Labels come\nfrom PHQ-8 and GAD-7 results also collected by the application. We find that\nresults for binary classification range from 0.86 to 0.79 AUC, depending on\ncondition and co-occurrence. Best performance is achieved when a user has\neither both or neither condition, and we show that this result is not\nattributable to data skew. Finally, we find evidence suggesting that underlying\nword sequence cues may be more salient for depression than for anxiety.\n", "date": "2024-12-30", "categories": []}
{"id": "2412.20744", "title": "Advancing Parkinson's Disease Progression Prediction: Comparing Long\n  Short-Term Memory Networks and Kolmogorov-Arnold Networks", "abstract": "  Parkinson's Disease (PD) is a degenerative neurological disorder that impairs\nmotor and non-motor functions, significantly reducing quality of life and\nincreasing mortality risk. Early and accurate detection of PD progression is\nvital for effective management and improved patient outcomes. Current\ndiagnostic methods, however, are often costly, time-consuming, and require\nspecialized equipment and expertise. This work proposes an innovative approach\nto predicting PD progression using regression methods, Long Short-Term Memory\n(LSTM) networks, and Kolmogorov Arnold Networks (KAN). KAN, utilizing\nspline-parametrized univariate functions, allows for dynamic learning of\nactivation patterns, unlike traditional linear models.\n  The Movement Disorder Society-Sponsored Revision of the Unified Parkinson's\nDisease Rating Scale (MDS-UPDRS) is a comprehensive tool for evaluating PD\nsymptoms and is commonly used to measure disease progression. Additionally,\nprotein or peptide abnormalities are linked to PD onset and progression.\nIdentifying these associations can aid in predicting disease progression and\nunderstanding molecular changes.\n  Comparing multiple models, including LSTM and KAN, this study aims to\nidentify the method that delivers the highest metrics. The analysis reveals\nthat KAN, with its dynamic learning capabilities, outperforms other approaches\nin predicting PD progression. This research highlights the potential of AI and\nmachine learning in healthcare, paving the way for advanced computational\nmodels to enhance clinical predictions and improve patient care and treatment\nstrategies in PD management.\n", "date": "2024-12-30", "categories": []}
{"id": "2412.21156", "title": "Unified dimensionality reduction techniques in chronic liver disease\n  detection", "abstract": "  Globally, chronic liver disease continues to be a major health concern that\nrequires precise predictive models for prompt detection and treatment. Using\nthe Indian Liver Patient Dataset (ILPD) from the University of California at\nIrvine's UCI Machine Learning Repository, a number of machine learning\nalgorithms are investigated in this study. The main focus of our research is\nthis dataset, which includes the medical records of 583 patients, 416 of whom\nhave been diagnosed with liver disease and 167 of whom have not. There are\nseveral aspects to this work, including feature extraction and dimensionality\nreduction methods like Linear Discriminant Analysis (LDA), Factor Analysis\n(FA), t-distributed Stochastic Neighbour Embedding (t-SNE), and Uniform\nManifold Approximation and Projection (UMAP). The purpose of the study is to\ninvestigate how well these approaches work for converting high-dimensional\ndatasets and improving prediction accuracy. To assess the prediction ability of\nthe improved models, a number of classification methods were used, such as\nMulti-layer Perceptron, Random Forest, K-nearest neighbours, and Logistic\nRegression. Remarkably, the improved models performed admirably, with Random\nForest having the highest accuracy of 98.31\\% in 10-fold cross-validation and\n95.79\\% in train-test split evaluation. Findings offer important new\nperspectives on the choice and use of customized feature extraction and\ndimensionality reduction methods, which improve predictive models for patients\nwith chronic liver disease.\n", "date": "2024-12-30", "categories": []}
{"id": "2412.19696", "title": "An Integrated Optimization and Deep Learning Pipeline for Predicting\n  Live Birth Success in IVF Using Feature Optimization and Transformer-Based\n  Models", "abstract": "  In vitro fertilization (IVF) is a widely utilized assisted reproductive\ntechnology, yet predicting its success remains challenging due to the\nmultifaceted interplay of clinical, demographic, and procedural factors. This\nstudy develops a robust artificial intelligence (AI) pipeline aimed at\npredicting live birth outcomes in IVF treatments. The pipeline uses anonymized\ndata from 2010 to 2018, obtained from the Human Fertilization and Embryology\nAuthority (HFEA). We evaluated the prediction performance of live birth success\nas a binary outcome (success/failure) by integrating different feature\nselection methods, such as principal component analysis (PCA) and particle\nswarm optimization (PSO), with different traditional machine learning-based\nclassifiers including random forest (RF) and decision tree, as well as deep\nlearning-based classifiers including custom transformer-based model and a tab\ntransformer model with an attention mechanism. Our research demonstrated that\nthe best performance was achieved by combining PSO for feature selection with\nthe TabTransformer-based deep learning model, yielding an accuracy of 99.50%\nand an AUC of 99.96%, highlighting its significant performance to predict live\nbirths. This study establishes a highly accurate AI pipeline for predicting\nlive birth outcomes in IVF, demonstrating its potential to enhance personalized\nfertility treatments.\n", "date": "2024-12-27", "categories": []}
{"id": "2412.18248", "title": "Detection and Forecasting of Parkinson Disease Progression from Speech\n  Signal Features Using MultiLayer Perceptron and LSTM", "abstract": "  Accurate diagnosis of Parkinson disease, especially in its early stages, can\nbe a challenging task. The application of machine learning techniques helps\nimprove the diagnostic accuracy of Parkinson disease detection but only few\nstudies have presented work towards the prediction of disease progression. In\nthis research work, Long Short Term Memory LSTM was trained using the\ndiagnostic features on Parkinson patients speech signals, to predict the\ndisease progression while a Multilayer Perceptron MLP was trained on the same\ndiagnostic features to detect the disease. Diagnostic features selected using\ntwo well-known feature selection methods named Relief-F and Sequential Forward\nSelection and applied on LSTM and MLP have shown to accurately predict the\ndisease progression as stage 2 and 3 and its existence respectively.\n", "date": "2024-12-24", "categories": []}
{"id": "2412.16768", "title": "A Comparative Study on Machine Learning Models to Classify Diseases\n  Based on Patient Behaviour and Habits", "abstract": "  In recent years, ML algorithms have been shown to be useful for predicting\ndiseases based on health data and posed a potential application area for these\nalgorithms such as modeling of diseases. The majority of these applications\nemploy supervised rather than unsupervised ML algorithms. In addition, each\nyear, the amount of data in medical science grows rapidly. Moreover, these data\ninclude clinical and Patient-Related Factors (PRF), such as height, weight,\nage, other physical characteristics, blood sugar, lipids, insulin, etc., all of\nwhich will change continually over time. Analysis of historical data can help\nidentify disease risk factors and their interactions, which is useful for\ndisease diagnosis and prediction. This wealth of valuable information in these\ndata will help doctors diagnose accurately and people can become more aware of\nthe risk factors and key indicators to act proactively. The purpose of this\nstudy is to use six supervised ML approaches to fill this gap by conducting a\ncomprehensive experiment to investigate the correlation between PRF and\nDiabetes, Stroke, Heart Disease (HD), and Kidney Disease (KD). Moreover, it\nwill investigate the link between Diabetes, Stroke, and KD and PRF with HD.\nFurther, the research aims to compare and evaluate various ML algorithms for\nclassifying diseases based on the PRF. Additionally, it aims to compare and\nevaluate ML algorithms for classifying HD based on PRF as well as Diabetes,\nStroke, Asthma, Skin Cancer, and KD as attributes. Lastly, HD predictions will\nbe provided through a Web-based application on the most accurate classifier,\nwhich allows the users to input their values and predict the output.\n", "date": "2024-12-21", "categories": []}
{"id": "2412.17527", "title": "Enhancing Cancer Diagnosis with Explainable & Trustworthy Deep Learning\n  Models", "abstract": "  This research presents an innovative approach to cancer diagnosis and\nprediction using explainable Artificial Intelligence (XAI) and deep learning\ntechniques. With cancer causing nearly 10 million deaths globally in 2020,\nearly and accurate diagnosis is crucial. Traditional methods often face\nchallenges in cost, accuracy, and efficiency. Our study develops an AI model\nthat provides precise outcomes and clear insights into its decision-making\nprocess, addressing the \"black box\" problem of deep learning models. By\nemploying XAI techniques, we enhance interpretability and transparency,\nbuilding trust among healthcare professionals and patients. Our approach\nleverages neural networks to analyse extensive datasets, identifying patterns\nfor cancer detection. This model has the potential to revolutionise diagnosis\nby improving accuracy, accessibility, and clarity in medical decision-making,\npossibly leading to earlier detection and more personalised treatment\nstrategies. Furthermore, it could democratise access to high-quality\ndiagnostics, particularly in resource-limited settings, contributing to global\nhealth equity. The model's applications extend beyond cancer diagnosis,\npotentially transforming various aspects of medical decision-making and saving\nmillions of lives worldwide.\n", "date": "2024-12-23", "categories": []}
{"id": "2403.10586", "title": "Reviewing AI's Role in Non-Muscle-Invasive Bladder Cancer Recurrence\n  Prediction", "abstract": "  Notorious for its 70-80% recurrence rate, Non-muscle-invasive Bladder Cancer\n(NMIBC) imposes a significant human burden and is one of the costliest cancers\nto manage. Current tools for predicting NMIBC recurrence rely on scoring\nsystems that often overestimate risk and have poor accuracy. This is where\nMachine learning (ML)-based techniques have emerged as a promising approach for\npredicting NMIBC recurrence by leveraging molecular and clinical data. This\ncomprehensive review paper critically analyses ML-based frameworks for\npredicting NMIBC recurrence, focusing on their statistical robustness and\nalgorithmic efficacy. We meticulously examine the strengths and weaknesses of\neach study, by focusing on various prediction tasks, data modalities, and ML\nmodels, highlighting their remarkable performance alongside inherent\nlimitations. A diverse array of ML algorithms that leverage multimodal data\nspanning radiomics, clinical, histopathological, and genomic data, exhibit\nsignificant promise in accurately predicting NMIBC recurrence. However, the\npath to widespread adoption faces challenges concerning the generalisability\nand interpretability of models, emphasising the need for collaborative efforts,\nrobust datasets, and the incorporation of cost-effectiveness. Our detailed\ncategorisation and in-depth analysis illuminate the nuances, complexities, and\ncontexts that influence real-world advancement and adoption of these AI-based\ntechniques. This rigorous analysis equips researchers with a deeper\nunderstanding of the intricacies of the ML algorithms employed. Researchers can\nuse these insights to refine approaches, address limitations, and boost\ngeneralisability of their ML models, ultimately leading to reduced healthcare\ncosts and improved patient outcomes.\n", "date": "2024-03-15", "categories": []}
{"id": "2412.10388", "title": "AI-assisted summary of suicide risk Formulation", "abstract": "  Background: Formulation, associated with suicide risk assessment, is an\nindividualised process that seeks to understand the idiosyncratic nature and\ndevelopment of an individual's problems. Auditing clinical documentation on an\nelectronic health record (EHR) is challenging as it requires resource-intensive\nmanual efforts to identify keywords in relevant sections of specific forms.\nFurthermore, clinicians and healthcare professionals often do not use keywords;\ntheir clinical language can vary greatly and may contain various jargon and\nacronyms. Also, the relevant information may be recorded elsewhere. This study\ndescribes how we developed advanced Natural Language Processing (NLP)\nalgorithms, a branch of Artificial Intelligence (AI), to analyse EHR data\nautomatically. Method: Advanced Optical Character Recognition techniques were\nused to process unstructured data sets, such as portable document format (pdf)\nfiles. Free text data was cleaned and pre-processed using Normalisation of Free\nText techniques. We developed algorithms and tools to unify the free text.\nFinally, the formulation was checked for the presence of each concept based on\nsimilarity using NLP-powered semantic matching techniques. Results: We\nextracted information indicative of formulation and assessed it to cover the\nrelevant concepts. This was achieved using a Weighted Score to obtain a\nConfidence Level. Conclusion: The rigour to which formulation is completed is\ncrucial to effectively using EHRs, ensuring correct and timely identification,\nengagement and interventions that may potentially avoid many suicide attempts\nand suicides.\n", "date": "2024-11-29", "categories": []}
{"id": "2412.16134", "title": "EF-Net: A Deep Learning Approach Combining Word Embeddings and Feature\n  Fusion for Patient Disposition Analysis", "abstract": "  One of the most urgent problems is the overcrowding in emergency departments\n(EDs), caused by an aging population and rising healthcare costs. Patient\ndispositions have become more complex as a result of the strain on hospital\ninfrastructure and the scarcity of medical resources. Individuals with more\ndangerous health issues should be prioritized in the emergency room. Thus, our\nresearch aims to develop a prediction model for patient disposition using\nEF-Net. This model will incorporate categorical features into the neural\nnetwork layer and add numerical features with the embedded categorical\nfeatures. We combine the EF-Net and XGBoost models to attain higher accuracy in\nour results. The result is generated using the soft voting technique. In\nEF-Net, we attained an accuracy of 95.33%, whereas in the Ensemble Model, we\nachieved an accuracy of 96%. The experiment's analysis shows that EF-Net\nsurpasses existing works in accuracy, AUROC, and F1-Score on the MIMIC-IV-ED\ndataset, demonstrating its potential as a scalable solution for patient\ndisposition assessment. Our code is available at\nhttps://github.com/nafisa67/thesis\n", "date": "2024-12-20", "categories": []}
{"id": "2402.02258", "title": "XTSFormer: Cross-Temporal-Scale Transformer for Irregular-Time Event\n  Prediction in Clinical Applications", "abstract": "  Adverse clinical events related to unsafe care are among the top ten causes\nof death in the U.S. Accurate modeling and prediction of clinical events from\nelectronic health records (EHRs) play a crucial role in patient safety\nenhancement. An example is modeling de facto care pathways that characterize\ncommon step-by-step plans for treatment or care. However, clinical event data\npose several unique challenges, including the irregularity of time intervals\nbetween consecutive events, the existence of cycles, periodicity, multi-scale\nevent interactions, and the high computational costs associated with long event\nsequences. Existing neural temporal point processes (TPPs) methods do not\neffectively capture the multi-scale nature of event interactions, which is\ncommon in many real-world clinical applications. To address these issues, we\npropose the cross-temporal-scale transformer (XTSFormer), specifically designed\nfor irregularly timed event data. Our model consists of two vital components: a\nnovel Feature-based Cycle-aware Time Positional Encoding (FCPE) that adeptly\ncaptures the cyclical nature of time, and a hierarchical multi-scale temporal\nattention mechanism, where different temporal scales are determined by a\nbottom-up clustering approach. Extensive experiments on several real-world EHR\ndatasets show that our XTSFormer outperforms multiple baseline methods. The\ncode is available at https://github.com/spatialdatasciencegroup/XTSFormer.\n", "date": "2024-02-03", "categories": []}
{"id": "2412.14769", "title": "PsyDraw: A Multi-Agent Multimodal System for Mental Health Screening in\n  Left-Behind Children", "abstract": "  Left-behind children (LBCs), numbering over 66 million in China, face severe\nmental health challenges due to parental migration for work. Early screening\nand identification of at-risk LBCs is crucial, yet challenging due to the\nsevere shortage of mental health professionals, especially in rural areas.\nWhile the House-Tree-Person (HTP) test shows higher child participation rates,\nits requirement for expert interpretation limits its application in\nresource-scarce regions. To address this challenge, we propose PsyDraw, a\nmulti-agent system based on Multimodal Large Language Models that assists\nmental health professionals in analyzing HTP drawings. The system employs\nspecialized agents for feature extraction and psychological interpretation,\noperating in two stages: comprehensive feature analysis and professional report\ngeneration. Evaluation of HTP drawings from 290 primary school students reveals\nthat 71.03% of the analyzes achieved High Consistency with professional\nevaluations, 26.21% Moderate Consistency and only 2.41% Low Consistency. The\nsystem identified 31.03% of cases requiring professional attention,\ndemonstrating its effectiveness as a preliminary screening tool. Currently\ndeployed in pilot schools, \\method shows promise in supporting mental health\nprofessionals, particularly in resource-limited areas, while maintaining high\nprofessional standards in psychological assessment.\n", "date": "2024-12-19", "categories": []}
{"id": "2412.13856", "title": "A Systematic Analysis of Input Modalities for Fracture Classification of\n  the Paediatric Wrist", "abstract": "  Fractures, particularly in the distal forearm, are among the most common\ninjuries in children and adolescents, with approximately 800 000 cases treated\nannually in Germany. The AO/OTA system provides a structured fracture type\nclassification, which serves as the foundation for treatment decisions.\nAlthough accurately classifying fractures can be challenging, current deep\nlearning models have demonstrated performance comparable to that of experienced\nradiologists. While most existing approaches rely solely on radiographs, the\npotential impact of incorporating other additional modalities, such as\nautomatic bone segmentation, fracture location, and radiology reports, remains\nunderexplored. In this work, we systematically analyse the contribution of\nthese three additional information types, finding that combining them with\nradiographs increases the AUROC from 91.71 to 93.25. Our code is available on\nGitHub.\n", "date": "2024-12-18", "categories": []}
{"id": "2407.00120", "title": "Automated Web-Based Malaria Detection System with Machine Learning and\n  Deep Learning Techniques", "abstract": "  Malaria parasites pose a significant global health burden, causing widespread\nsuffering and mortality. Detecting malaria infection accurately is crucial for\neffective treatment and control. However, existing automated detection\ntechniques have shown limitations in terms of accuracy and generalizability.\nMany studies have focused on specific features without exploring more\ncomprehensive approaches. In our case, we formulate a deep learning technique\nfor malaria-infected cell classification using traditional CNNs and transfer\nlearning models notably VGG19, InceptionV3, and Xception. The models were\ntrained using NIH datasets and tested using different performance metrics such\nas accuracy, precision, recall, and F1-score. The test results showed that deep\nCNNs achieved the highest accuracy -- 97%, followed by Xception with an\naccuracy of 95%. A machine learning model SVM achieved an accuracy of 83%,\nwhile an Inception-V3 achieved an accuracy of 94%. Furthermore, the system can\nbe accessed through a web interface, where users can upload blood smear images\nfor malaria detection.\n", "date": "2024-06-27", "categories": []}
{"id": "2412.12118", "title": "High-Throughput Detection of Risk Factors to Sudden Cardiac Arrest in\n  Youth Athletes: A Smartwatch-Based Screening Platform", "abstract": "  Sudden Cardiac Arrest (SCA) is the leading cause of death among athletes of\nall age levels worldwide. Current prescreening methods for cardiac risk factors\nare largely ineffective, and implementing the International Olympic Committee\nrecommendation for 12-lead ECG screening remains prohibitively expensive. To\naddress these challenges, a preliminary comprehensive screening system (CSS)\nwas developed to efficiently and economically screen large populations for risk\nfactors to SCA. A protocol was established to measure a 4-lead ECG using an\nApple Watch. Additionally, two key advances were introduced and validated: 1) A\ndecomposition regression model to upscale 4-lead data to 12 leads, reducing ECG\ncost and usage complexity. 2) A deep learning model, the Transformer\nAuto-Encoder System (TAES), was designed to extract spatial and temporal\nfeatures from the data for beat-based classification. TAES demonstrated an\naverage sensitivity of 95.3% and specificity of 99.1% respectively in the\ntesting dataset, outperforming human physicians in the same dataset (Se: 94%,\nSp: 93%). Human subject trials (n = 30) validated the smartwatch protocol, with\nBland-Altman analysis showing no statistical difference between the smartwatch\nvs. ECG protocol. Further validation of the complete CSS on a 20-subject cohort\n(10 affected, 10 controls) did not result in any misidentifications. This paper\npresents a mass screening system with the potential to achieve superior\naccuracy in high-throughput cardiac pre-participation evaluation compared to\nthe clinical gold standard.\n", "date": "2024-12-02", "categories": []}
{"id": "2412.12597", "title": "Distribution-Free Uncertainty Quantification in Mechanical Ventilation\n  Treatment: A Conformal Deep Q-Learning Framework", "abstract": "  Mechanical Ventilation (MV) is a critical life-support intervention in\nintensive care units (ICUs). However, optimal ventilator settings are\nchallenging to determine because of the complexity of balancing\npatient-specific physiological needs with the risks of adverse outcomes that\nimpact morbidity, mortality, and healthcare costs. This study introduces\nConformalDQN, a novel distribution-free conformal deep Q-learning approach for\noptimizing mechanical ventilation in intensive care units. By integrating\nconformal prediction with deep reinforcement learning, our method provides\nreliable uncertainty quantification, addressing the challenges of Q-value\noverestimation and out-of-distribution actions in offline settings. We trained\nand evaluated our model using ICU patient records from the MIMIC-IV database.\nConformalDQN extends the Double DQN architecture with a conformal predictor and\nemploys a composite loss function that balances Q-learning with well-calibrated\nprobability estimation. This enables uncertainty-aware action selection,\nallowing the model to avoid potentially harmful actions in unfamiliar states\nand handle distribution shifts by being more conservative in\nout-of-distribution scenarios. Evaluation against baseline models, including\nphysician policies, policy constraint methods, and behavior cloning,\ndemonstrates that ConformalDQN consistently makes recommendations within\nclinically safe and relevant ranges, outperforming other methods by increasing\nthe 90-day survival rate. Notably, our approach provides an interpretable\nmeasure of confidence in its decisions, which is crucial for clinical adoption\nand potential human-in-the-loop implementations.\n", "date": "2024-12-17", "categories": []}
{"id": "2412.12629", "title": "a2z-1 for Multi-Disease Detection in Abdomen-Pelvis CT: External\n  Validation and Performance Analysis Across 21 Conditions", "abstract": "  We present a comprehensive evaluation of a2z-1, an artificial intelligence\n(AI) model designed to analyze abdomen-pelvis CT scans for 21 time-sensitive\nand actionable findings. Our study focuses on rigorous assessment of the\nmodel's performance and generalizability. Large-scale retrospective analysis\ndemonstrates an average AUC of 0.931 across 21 conditions. External validation\nacross two distinct health systems confirms consistent performance (AUC 0.923),\nestablishing generalizability to different evaluation scenarios, with notable\nperformance in critical findings such as small bowel obstruction (AUC 0.958)\nand acute pancreatitis (AUC 0.961). Subgroup analysis shows consistent accuracy\nacross patient sex, age groups, and varied imaging protocols, including\ndifferent slice thicknesses and contrast administration types. Comparison of\nhigh-confidence model outputs to radiologist reports reveals instances where\na2z-1 identified overlooked findings, suggesting potential for quality\nassurance applications.\n", "date": "2024-12-17", "categories": []}
{"id": "2412.12909", "title": "PT: A Plain Transformer is Good Hospital Readmission Predictor", "abstract": "  Hospital readmission prediction is critical for clinical decision support,\naiming to identify patients at risk of returning within 30 days post-discharge.\nHigh readmission rates often indicate inadequate treatment or post-discharge\ncare, making effective prediction models essential for optimizing resources and\nimproving patient outcomes. We propose PT, a Transformer-based model that\nintegrates Electronic Health Records (EHR), medical images, and clinical notes\nto predict 30-day all-cause hospital readmissions. PT extracts features from\nraw data and uses specialized Transformer blocks tailored to the data's\ncomplexity. Enhanced with Random Forest for EHR feature selection and test-time\nensemble techniques, PT achieves superior accuracy, scalability, and\nrobustness. It performs well even when temporal information is missing. Our\nmain contributions are: (1)Simplicity: A powerful and efficient baseline model\noutperforming existing ones in prediction accuracy; (2)Scalability: Flexible\nhandling of various features from different modalities, achieving high\nperformance with just clinical notes or EHR data; (3)Robustness: Strong\npredictive performance even with missing or unclear temporal data.\n", "date": "2024-12-17", "categories": []}
{"id": "2406.13181", "title": "The Impact of Auxiliary Patient Data on Automated Chest X-Ray Report\n  Generation and How to Incorporate It", "abstract": "  This study investigates the integration of diverse patient data sources into\nmultimodal language models for automated chest X-ray (CXR) report generation.\nTraditionally, CXR report generation relies solely on CXR images and limited\nradiology data, overlooking valuable information from patient health records,\nparticularly from emergency departments. Utilising the MIMIC-CXR and\nMIMIC-IV-ED datasets, we incorporate detailed patient information such as vital\nsigns, medicines, and clinical history to enhance diagnostic accuracy. We\nintroduce a novel approach to transform these heterogeneous data sources into\nembeddings that prompt a multimodal language model; this significantly enhances\nthe diagnostic accuracy of generated radiology reports. Our comprehensive\nevaluation demonstrates the benefits of using a broader set of patient data,\nunderscoring the potential for enhanced diagnostic capabilities and better\npatient outcomes through the integration of multimodal data in CXR report\ngeneration.\n", "date": "2024-06-19", "categories": []}
{"id": "2412.10567", "title": "Cardiovascular Disease Detection By Leveraging Semi-Supervised Learning", "abstract": "  Cardiovascular disease (CVD) persists as a primary cause of death on a global\nscale, which requires more effective and timely detection methods. Traditional\nsupervised learning approaches for CVD detection rely heavily on large-labeled\ndatasets, which are often difficult to obtain. This paper employs\nsemi-supervised learning models to boost efficiency and accuracy of CVD\ndetection when there are few labeled samples. By leveraging both labeled and\nvast amounts of unlabeled data, our approach demonstrates improvements in\nprediction performance, while reducing the dependency on labeled data.\nExperimental results in a publicly available dataset show that semi-supervised\nmodels outperform traditional supervised learning techniques, providing an\nintriguing approach for the initial identification of cardiovascular disease\nwithin clinical environments.\n", "date": "2024-12-13", "categories": []}
{"id": "2412.10848", "title": "Large Language Models for Medical Forecasting -- Foresight 2", "abstract": "  Foresight 2 (FS2) is a large language model fine-tuned on hospital data for\nmodelling patient timelines (GitHub 'removed for anon'). It can understand\npatients' clinical notes and predict SNOMED codes for a wide range of\nbiomedical use cases, including diagnosis suggestions, risk forecasting, and\nprocedure and medication recommendations. FS2 is trained on the free text\nportion of the MIMIC-III dataset, firstly through extracting biomedical\nconcepts and then creating contextualised patient timelines, upon which the\nmodel is then fine-tuned. The results show significant improvement over the\nprevious state-of-the-art for the next new biomedical concept prediction (P/R -\n0.73/0.66 vs 0.52/0.32) and a similar improvement specifically for the next new\ndisorder prediction (P/R - 0.69/0.62 vs 0.46/0.25). Finally, on the task of\nrisk forecast, we compare our model to GPT-4-turbo (and a range of open-source\nbiomedical LLMs) and show that FS2 performs significantly better on such tasks\n(P@5 - 0.90 vs 0.65). This highlights the need to incorporate hospital data\ninto LLMs and shows that small models outperform much larger ones when\nfine-tuned on high-quality, specialised data.\n", "date": "2024-12-14", "categories": []}
{"id": "2412.10919", "title": "Predicting Survival of Hemodialysis Patients using Federated Learning", "abstract": "  Hemodialysis patients who are on donor lists for kidney transplant may get\nmisidentified, delaying their wait time. Thus, predicting their survival time\nis crucial for optimizing waiting lists and personalizing treatment plans.\nPredicting survival times for patients often requires large quantities of high\nquality but sensitive data. This data is siloed and since individual datasets\nare smaller and less diverse, locally trained survival models do not perform as\nwell as centralized ones. Hence, we propose the use of Federated Learning in\nthe context of predicting survival for hemodialysis patients. Federated\nLearning or FL can have comparatively better performances than local models\nwhile not sharing data between centers. However, despite the increased use of\nsuch technologies, the application of FL in survival and even more, dialysis\npatients remains sparse. This paper studies the performance of FL for data of\nhemodialysis patients from NephroPlus, the largest private network of dialysis\ncenters in India.\n", "date": "2024-12-14", "categories": []}
{"id": "2412.11839", "title": "Evaluating the Efficacy of Vectocardiographic and ECG Parameters for\n  Efficient Tertiary Cardiology Care Allocation Using Decision Tree Analysis", "abstract": "  Use real word data to evaluate the performance of the electrocardiographic\nmarkers of GEH as features in a machine learning model with Standard ECG\nfeatures and Risk Factors in Predicting Outcome of patients in a population\nreferred to a tertiary cardiology hospital.\n  Patients forwarded to specific evaluation in a cardiology specialized\nhospital performed an ECG and a risk factor anamnesis. A series of follow up\nattendances occurred in periods of 6 months, 12 months and 15 months to check\nfor cardiovascular related events (mortality or new nonfatal cardiovascular\nevents (Stroke, MI, PCI, CS), as identified during 1-year phone follow-ups.\n  The first attendance ECG was measured by a specialist and processed in order\nto obtain the global electric heterogeneity (GEH) using the Kors Matriz. The\nECG measurements, GEH parameters and risk factors were combined for training\nmultiple instances of XGBoost decision trees models. Each instance were\noptmized for the AUCPR and the instance with higher AUC is chosen as\nrepresentative to the model. The importance of each parameter for the winner\ntree model was compared to better understand the improvement from using GEH\nparameters.\n  The GEH parameters turned out to have statistical significance for this\npopulation specially the QRST angle and the SVG. The combined model with the\ntree parameters class had the best performance. The findings suggest that using\nVCG features can facilitate more accurate identification of patients who\nrequire tertiary care, thereby optimizing resource allocation and improving\npatient outcomes. Moreover, the decision tree model's transparency and ability\nto pinpoint critical features make it a valuable tool for clinical\ndecision-making and align well with existing clinical practices.\n", "date": "2024-12-16", "categories": []}
{"id": "2401.04402", "title": "IGNITE: Individualized GeNeration of Imputations in Time-series\n  Electronic health records", "abstract": "  Electronic Health Records present a valuable modality for driving\npersonalized medicine, where treatment is tailored to fit individual-level\ndifferences. For this purpose, many data-driven machine learning and\nstatistical models rely on the wealth of longitudinal EHRs to study patients'\nphysiological and treatment effects. However, longitudinal EHRs tend to be\nsparse and highly missing, where missingness could also be informative and\nreflect the underlying patient's health status. Therefore, the success of\ndata-driven models for personalized medicine highly depends on how the EHR data\nis represented from physiological data, treatments, and the missing values in\nthe data. To this end, we propose a novel deep-learning model that learns the\nunderlying patient dynamics over time across multivariate data to generate\npersonalized realistic values conditioning on an individual's demographic\ncharacteristics and treatments. Our proposed model, IGNITE (Individualized\nGeNeration of Imputations in Time-series Electronic health records), utilises a\nconditional dual-variational autoencoder augmented with dual-stage attention to\ngenerate missing values for an individual. In IGNITE, we further propose a\nnovel individualized missingness mask (IMM), which helps our model generate\nvalues based on the individual's observed data and missingness patterns. We\nfurther extend the use of IGNITE from imputing missingness to a personalized\ndata synthesizer, where it generates missing EHRs that were never observed\nprior or even generates new patients for various applications. We validate our\nmodel on three large publicly available datasets and show that IGNITE\noutperforms state-of-the-art approaches in missing data reconstruction and task\nprediction.\n", "date": "2024-01-09", "categories": []}
{"id": "2411.07853", "title": "Evidential time-to-event prediction with calibrated uncertainty\n  quantification", "abstract": "  Time-to-event analysis provides insights into clinical prognosis and\ntreatment recommendations. However, this task is more challenging than standard\nregression problems due to the presence of censored observations. Additionally,\nthe lack of confidence assessment, model robustness, and prediction calibration\nraises concerns about the reliability of predictions. To address these\nchallenges, we propose an evidential regression model specifically designed for\ntime-to-event prediction. The proposed model quantifies both epistemic and\naleatory uncertainties using Gaussian Random Fuzzy Numbers and belief\nfunctions, providing clinicians with uncertainty-aware survival time\npredictions. The model is trained by minimizing a generalized negative\nlog-likelihood function accounting for data censoring. Experimental evaluations\nusing simulated datasets with different data distributions and censoring\nconditions, as well as real-world datasets across diverse clinical\napplications, demonstrate that our model delivers both accurate and reliable\nperformance, outperforming state-of-the-art methods. These results highlight\nthe potential of our approach for enhancing clinical decision-making in\nsurvival analysis.\n", "date": "2024-11-12", "categories": []}
{"id": "2412.10288", "title": "Performance evaluation of predictive AI models to support medical\n  decisions: Overview and guidance", "abstract": "  A myriad of measures to illustrate performance of predictive artificial\nintelligence (AI) models have been proposed in the literature. Selecting\nappropriate performance measures is essential for predictive AI models that are\ndeveloped to be used in medical practice, because poorly performing models may\nharm patients and lead to increased costs. We aim to assess the merits of\nclassic and contemporary performance measures when validating predictive AI\nmodels for use in medical practice. We focus on models with a binary outcome.\nWe discuss 32 performance measures covering five performance domains\n(discrimination, calibration, overall, classification, and clinical utility)\nalong with accompanying graphical assessments. The first four domains cover\nstatistical performance, the fifth domain covers decision-analytic performance.\nWe explain why two key characteristics are important when selecting which\nperformance measures to assess: (1) whether the measure's expected value is\noptimized when it is calculated using the correct probabilities (i.e., a\n\"proper\" measure), and (2) whether they reflect either purely statistical\nperformance or decision-analytic performance by properly considering\nmisclassification costs. Seventeen measures exhibit both characteristics,\nfourteen measures exhibited one characteristic, and one measure possessed\nneither characteristic (the F1 measure). All classification measures (such as\nclassification accuracy and F1) are improper for clinically relevant decision\nthresholds other than 0.5 or the prevalence. We recommend the following\nmeasures and plots as essential to report: AUROC, calibration plot, a clinical\nutility measure such as net benefit with decision curve analysis, and a plot\nwith probability distributions per outcome category.\n", "date": "2024-12-13", "categories": []}
{"id": "2306.15194", "title": "Modified Feature Selection for Improved Classification of Resting-State\n  Raw EEG Signals in Chronic Knee Pain", "abstract": "  \\textit{Objective:} Diagnosing pain in research and clinical practices still\nrelies on self-report. This study aims to develop an automatic approach that\nworks on resting-state raw EEG data for chronic knee pain prediction.\n\\textit{Method:} A new feature selection algorithm called ``modified Sequential\nFloating Forward Selection'' (mSFFS) is proposed. The improved feature\nselection scheme can better avoid local minima and explore alternative search\nroutes. \\textit{Results:} The feature selection obtained by mSFFS displays\nbetter class separability as indicated by the Bhattacharyya distance measures\nand better visualization results. It also outperforms selections generated by\nother benchmark methods, boosting the test accuracy to 97.5\\%.\n\\textit{Conclusion:} The improved feature selection searches out a compact,\neffective subset of connectivity features that produces competitive performance\non chronic knee pain prediction. \\textit{Significance:} We have shown that an\nautomatic approach can be employed to find a compact connectivity feature set\nthat effectively predicts chronic knee pain from EEG. It may shed light on the\nresearch of chronic pains and lead to future clinical solutions for diagnosis\nand treatment.\n", "date": "2023-06-27", "categories": []}
{"id": "2412.08984", "title": "Predicting Emergency Department Visits for Patients with Type II\n  Diabetes", "abstract": "  Over 30 million Americans are affected by Type II diabetes (T2D), a treatable\ncondition with significant health risks. This study aims to develop and\nvalidate predictive models using machine learning (ML) techniques to estimate\nemergency department (ED) visits among patients with T2D. Data for these\npatients was obtained from the HealthShare Exchange (HSX), focusing on\ndemographic details, diagnoses, and vital signs. Our sample contained 34,151\npatients diagnosed with T2D which resulted in 703,065 visits overall between\n2017 and 2021. A workflow integrated EMR data with SDoH for ML predictions. A\ntotal of 87 out of 2,555 features were selected for model construction. Various\nmachine learning algorithms, including CatBoost, Ensemble Learning, K-nearest\nNeighbors (KNN), Support Vector Classification (SVC), Random Forest, and\nExtreme Gradient Boosting (XGBoost), were employed with tenfold\ncross-validation to predict whether a patient is at risk of an ED visit. The\nROC curves for Random Forest, XGBoost, Ensemble Learning, CatBoost, KNN, and\nSVC, were 0.82, 0.82, 0.82, 0.81, 0.72, 0.68, respectively. Ensemble Learning\nand Random Forest models demonstrated superior predictive performance in terms\nof discrimination, calibration, and clinical applicability. These models are\nreliable tools for predicting risk of ED visits among patients with T2D. They\ncan estimate future ED demand and assist clinicians in identifying critical\nfactors associated with ED utilization, enabling early interventions to reduce\nsuch visits. The top five important features were age, the difference between\nvisitation gaps, visitation gaps, R10 or abdominal and pelvic pain, and the\nIndex of Concentration at the Extremes (ICE) for income.\n", "date": "2024-12-12", "categories": []}
{"id": "2412.09472", "title": "A Novel Ensemble-Based Deep Learning Model with Explainable AI for\n  Accurate Kidney Disease Diagnosis", "abstract": "  Chronic Kidney Disease (CKD) represents a significant global health\nchallenge, characterized by the progressive decline in renal function, leading\nto the accumulation of waste products and disruptions in fluid balance within\nthe body. Given its pervasive impact on public health, there is a pressing need\nfor effective diagnostic tools to enable timely intervention. Our study delves\ninto the application of cutting-edge transfer learning models for the early\ndetection of CKD. Leveraging a comprehensive and publicly available dataset, we\nmeticulously evaluate the performance of several state-of-the-art models,\nincluding EfficientNetV2, InceptionNetV2, MobileNetV2, and the Vision\nTransformer (ViT) technique. Remarkably, our analysis demonstrates superior\naccuracy rates, surpassing the 90% threshold with MobileNetV2 and achieving\n91.5% accuracy with ViT. Moreover, to enhance predictive capabilities further,\nwe integrate these individual methodologies through ensemble modeling,\nresulting in our ensemble model exhibiting a remarkable 96% accuracy in the\nearly detection of CKD. This significant advancement holds immense promise for\nimproving clinical outcomes and underscores the critical role of machine\nlearning in addressing complex medical challenges.\n", "date": "2024-12-12", "categories": []}
{"id": "2412.07806", "title": "Diagnosis and Severity Assessment of Ulcerative Colitis using Self\n  Supervised Learning", "abstract": "  Ulcerative Colitis (UC) is an incurable inflammatory bowel disease that leads\nto ulcers along the large intestine and rectum. The increase in the prevalence\nof UC coupled with gastrointestinal physician shortages stresses the healthcare\nsystem and limits the care UC patients receive. A colonoscopy is performed to\ndiagnose UC and assess its severity based on the Mayo Endoscopic Score (MES).\nThe MES ranges between zero and three, wherein zero indicates no inflammation\nand three indicates that the inflammation is markedly high. Artificial\nIntelligence (AI)-based neural network models, such as convolutional neural\nnetworks (CNNs) are capable of analyzing colonoscopies to diagnose and\ndetermine the severity of UC by modeling colonoscopy analysis as a multi-class\nclassification problem. Prior research for AI-based UC diagnosis relies on\nsupervised learning approaches that require large annotated datasets to train\nthe CNNs. However, creating such datasets necessitates that domain experts\ninvest a significant amount of time, rendering the process expensive and\nchallenging. To address the challenge, this research employs self-supervised\nlearning (SSL) frameworks that can efficiently train on unannotated datasets to\nanalyze colonoscopies and, aid in diagnosing UC and its severity. A comparative\nanalysis with supervised learning models shows that SSL frameworks, such as\nSwAV and SparK outperform supervised learning models on the LIMUC dataset, the\nlargest publicly available annotated dataset of colonoscopy images for UC.\n", "date": "2024-12-09", "categories": []}
{"id": "2303.17573", "title": "Using AI to Measure Parkinson's Disease Severity at Home", "abstract": "  We present an artificial intelligence system to remotely assess the motor\nperformance of individuals with Parkinson's disease (PD). Participants\nperformed a motor task (i.e., tapping fingers) in front of a webcam, and data\nfrom 250 global participants were rated by three expert neurologists following\nthe Movement Disorder Society Unified Parkinson's Disease Rating Scale\n(MDS-UPDRS). The neurologists' ratings were highly reliable, with an\nintra-class correlation coefficient (ICC) of 0.88. We developed computer\nalgorithms to obtain objective measurements that align with the MDS-UPDRS\nguideline and are strongly correlated with the neurologists' ratings. Our\nmachine learning model trained on these measures outperformed an MDS-UPDRS\ncertified rater, with a mean absolute error (MAE) of 0.59 compared to the\nrater's MAE of 0.79. However, the model performed slightly worse than the\nexpert neurologists (0.53 MAE). The methodology can be replicated for similar\nmotor tasks, providing the possibility of evaluating individuals with PD and\nother movement disorders remotely, objectively, and in areas with limited\naccess to neurological care.\n", "date": "2023-03-30", "categories": []}
{"id": "2412.07050", "title": "Advancing clinical trial outcomes using deep learning and predictive\n  modelling: bridging precision medicine and patient-centered care", "abstract": "  The integration of artificial intelligence [AI] into clinical trials has\nrevolutionized the process of drug development and personalized medicine. Among\nthese advancements, deep learning and predictive modelling have emerged as\ntransformative tools for optimizing clinical trial design, patient recruitment,\nand real-time monitoring. This study explores the application of deep learning\ntechniques, such as convolutional neural networks [CNNs] and transformerbased\nmodels, to stratify patients, forecast adverse events, and personalize\ntreatment plans. Furthermore, predictive modelling approaches, including\nsurvival analysis and time-series forecasting, are employed to predict trial\noutcomes, enhancing efficiency and reducing trial failure rates. To address\nchallenges in analysing unstructured clinical data, such as patient notes and\ntrial protocols, natural language processing [NLP] techniques are utilized for\nextracting actionable insights. A custom dataset comprising structured patient\ndemographics, genomic data, and unstructured text is curated for training and\nvalidating these models. Key metrics, including precision, recall, and F1\nscores, are used to evaluate model performance, while trade-offs between\naccuracy and computational efficiency are examined to identify the optimal\nmodel for clinical deployment. This research underscores the potential of\nAI-driven methods to streamline clinical trial workflows, improve\npatient-centric outcomes, and reduce costs associated with trial\ninefficiencies. The findings provide a robust framework for integrating\npredictive analytics into precision medicine, paving the way for more adaptive\nand efficient clinical trials. By bridging the gap between technological\ninnovation and real-world applications, this study contributes to advancing the\nrole of AI in healthcare, particularly in fostering personalized care and\nimproving overall trial success rates.\n", "date": "2024-12-09", "categories": []}
{"id": "2412.07136", "title": "A multimodal ensemble approach for clear cell renal cell carcinoma\n  treatment outcome prediction", "abstract": "  Purpose: A reliable cancer prognosis model for clear cell renal cell\ncarcinoma (ccRCC) can enhance personalized treatment. We developed a\nmulti-modal ensemble model (MMEM) that integrates pretreatment clinical data,\nmulti-omics data, and histopathology whole slide image (WSI) data to predict\noverall survival (OS) and disease-free survival (DFS) for ccRCC patients.\nMethods: We analyzed 226 patients from The Cancer Genome Atlas Kidney Renal\nClear Cell Carcinoma (TCGA-KIRC) dataset, which includes OS, DFS follow-up\ndata, and five data modalities: clinical data, WSIs, and three multi-omics\ndatasets (mRNA, miRNA, and DNA methylation). Separate survival models were\nbuilt for OS and DFS. Cox-proportional hazards (CPH) model with forward feature\nselection is used for clinical and multi-omics data. Features from WSIs were\nextracted using ResNet and three general-purpose foundation models. A deep\nlearning-based CPH model predicted survival using encoded WSI features. Risk\nscores from all models were combined based on training performance. Results:\nPerformance was assessed using concordance index (C-index) and AUROC. The\nclinical feature-based CPH model received the highest weight for both OS and\nDFS tasks. Among WSI-based models, the general-purpose foundation model (UNI)\nachieved the best performance. The final MMEM model surpassed single-modality\nmodels, achieving C-indices of 0.820 (OS) and 0.833 (DFS), and AUROC values of\n0.831 (3-year patient death) and 0.862 (cancer recurrence). Using predicted\nrisk medians to stratify high- and low-risk groups, log-rank tests showed\nimproved performance in both OS and DFS compared to single-modality models.\nConclusion: MMEM is the first multi-modal model for ccRCC patients, integrating\nfive data modalities. It outperformed single-modality models in prognostic\nability and has the potential to assist in ccRCC patient management if\nindependently validated.\n", "date": "2024-12-10", "categories": []}
{"id": "2412.07743", "title": "Zero-Shot ATC Coding with Large Language Models for Clinical Assessments", "abstract": "  Manual assignment of Anatomical Therapeutic Chemical (ATC) codes to\nprescription records is a significant bottleneck in healthcare research and\noperations at Ontario Health and InterRAI Canada, requiring extensive expert\ntime and effort. To automate this process while maintaining data privacy, we\ndevelop a practical approach using locally deployable large language models\n(LLMs). Inspired by recent advances in automatic International Classification\nof Diseases (ICD) coding, our method frames ATC coding as a hierarchical\ninformation extraction task, guiding LLMs through the ATC ontology level by\nlevel. We evaluate our approach using GPT-4o as an accuracy ceiling and focus\ndevelopment on open-source Llama models suitable for privacy-sensitive\ndeployment. Testing across Health Canada drug product data, the RABBITS\nbenchmark, and real clinical notes from Ontario Health, our method achieves 78%\nexact match accuracy with GPT-4o and 60% with Llama 3.1 70B. We investigate\nknowledge grounding through drug definitions, finding modest improvements in\naccuracy. Further, we show that fine-tuned Llama 3.1 8B matches zero-shot Llama\n3.1 70B accuracy, suggesting that effective ATC coding is feasible with smaller\nmodels. Our results demonstrate the feasibility of automatic ATC coding in\nprivacy-sensitive healthcare environments, providing a foundation for future\ndeployments.\n", "date": "2024-12-10", "categories": []}
{"id": "2407.04753", "title": "Continuous Sleep Depth Index Annotation with Deep Learning Yields Novel\n  Digital Biomarkers for Sleep Health", "abstract": "  Traditional sleep staging categorizes sleep and wakefulness into five\ncoarse-grained classes, overlooking subtle variations within each stage. It\nprovides limited information about the duration of arousal and may hinder\nresearch on sleep fragmentation and relevant sleep disorders. To address this\nissue, we propose a deep learning method for automatic and scalable annotation\nof continuous sleep depth index (SDI) using existing discrete sleep staging\nlabels. Our approach was validated using polysomnography from over 10,000\nrecordings across four large-scale cohorts. The results showcased a strong\ncorrelation between the decrease in sleep depth index and the increase in\nduration of arousal. Specific case studies indicated that the sleep depth index\ncaptured more nuanced sleep structures than conventional sleep staging.\nGaussian mixture models based on the digital biomarkers extracted from the\nsleep depth index identified two subtypes of sleep, where participants in the\ndisturbed sleep group had a higher prevalence of sleep apnea, insomnia, poor\nsubjective sleep quality, hypertension, and cardiovascular disease. The\ndisturbed subtype was associated with a 42% (hazard ratio 1.42, 95% CI\n1.24-1.62) increased risk of mortality and a 29% (hazard ratio 1.29, 95% CI\n1.00-1.67) increased risk of fatal cardiovascular disease. Our study\nunderscores the utility of the proposed method for continuous sleep depth\nannotation, which could reveal more detailed information about the sleep\nstructure and yield novel digital biomarkers for routine clinical use in sleep\nmedicine.\n", "date": "2024-07-05", "categories": []}
{"id": "2412.05536", "title": "Comprehensive Evaluation of Multimodal AI Models in Medical Imaging\n  Diagnosis: From Data Augmentation to Preference-Based Comparison", "abstract": "  This study introduces an evaluation framework for multimodal models in\nmedical imaging diagnostics. We developed a pipeline incorporating data\npreprocessing, model inference, and preference-based evaluation, expanding an\ninitial set of 500 clinical cases to 3,000 through controlled augmentation. Our\nmethod combined medical images with clinical observations to generate\nassessments, using Claude 3.5 Sonnet for independent evaluation against\nphysician-authored diagnoses. The results indicated varying performance across\nmodels, with Llama 3.2-90B outperforming human diagnoses in 85.27% of cases. In\ncontrast, specialized vision models like BLIP2 and Llava showed preferences in\n41.36% and 46.77% of cases, respectively. This framework highlights the\npotential of large multimodal models to outperform human diagnostics in certain\ntasks.\n", "date": "2024-12-07", "categories": []}
{"id": "2412.05813", "title": "Risk factor identification and classification of malnutrition among\n  under-five children in Bangladesh: Machine learning and statistical approach", "abstract": "  This study aims to understand the factors that resulted in under-five\nchildren's malnutrition from the Multiple Indicator Cluster (MICS-2019)\nnationwide surveys and classify different malnutrition stages based on the four\nwell-established machine learning algorithms, namely - Decision Tree (DT),\nRandom Forest (RF), Support Vector Machine (SVM), and Multi-layer Perceptron\n(MLP) neural network. Accuracy, precision, recall, and F1 scores are obtained\nto evaluate the performance of each model. The statistical Pearson correlation\ncoefficient analysis is also done to understand the significant factors related\nto a child's malnutrition. The eligible data sample for analysis was 21,858\namong 24,686 samples from the dataset. Satisfactory and insightful results were\nobtained in each case and, the RF and MLP performed extraordinarily well. For\nRF, the accuracy was 98.55%, average precision 98.3%, recall value 95.68%, and\nF1 score 97.13%. For MLP, the accuracy was 98.69%, average precision 97.62%,\nrecall 90.96%, and F1 score of 97.39%. From the Pearson co-efficient, all\nnegative correlation results are enlisted, and the most significant impacts are\nfound for the WAZ2 (Weight for age Z score WHO) (-0.828\"), WHZ2 (Weight for\nheight Z score WHO) (-0.706\"), ZBMI (BMI Z score WHO) (-0.656\"), BD3 (whether\nchild is still being breastfed) (-0.59\"), HAZ2 (Height for age Z score WHO)\n(-0.452\"), CA1 (whether child had diarrhea in last 2 weeks) (-0.34\"), Windex5\n(Wealth index quantile) (-0.161\"), melevel (Mother's education) (-0.132\"), and\nCA14/CA16/CA17 (whether child had illness with fever, cough, and breathing)\n(-0.04) in successive order.\n", "date": "2024-12-08", "categories": []}
{"id": "2412.06709", "title": "Parkinson's Disease Diagnosis Through Deep Learning: A Novel LSTM-Based\n  Approach for Freezing of Gait Detection", "abstract": "  Deep learning holds tremendous potential in healthcare for uncovering hidden\npatterns within extensive clinical datasets, aiding in the diagnosis of various\ndiseases. Parkinson's disease (PD) is a neurodegenerative condition\ncharacterized by the deterioration of brain function. In the initial stages of\nPD, automatic diagnosis poses a challenge due to the similarity in behavior\nbetween individuals with PD and those who are healthy. Our objective is to\npropose an effective model that can aid in the early detection of Parkinson's\ndisease. We employed the VGRF gait signal dataset sourced from Physionet for\ndistinguishing between healthy individuals and those diagnosed with Parkinson's\ndisease. This paper introduces a novel deep learning architecture based on the\nLSTM network for automatically detecting freezing of gait episodes in\nParkinson's disease patients. In contrast to conventional machine learning\nalgorithms, this method eliminates manual feature engineering and proficiently\ncaptures prolonged temporal dependencies in gait patterns, thereby improving\nthe diagnosis of Parkinson's disease. The LSTM network resolves the issue of\nvanishing gradients by employing memory blocks in place of self-connected\nhidden units, allowing for optimal information assimilation. To prevent\noverfitting, dropout and L2 regularization techniques have been employed.\nAdditionally, the stochastic gradient-based optimizer Adam is used for the\noptimization process. The results indicate that our proposed approach surpasses\ncurrent state-of-the-art models in FOG episode detection, achieving an accuracy\nof 97.71%, sensitivity of 99%, precision of 98%, and specificity of 96%. This\ndemonstrates its potential as a superior classification method for Parkinson's\ndisease detection.\n", "date": "2024-12-09", "categories": []}
{"id": "2301.10829", "title": "TranSOP: Transformer-based Multimodal Classification for Stroke\n  Treatment Outcome Prediction", "abstract": "  Acute ischaemic stroke, caused by an interruption in blood flow to brain\ntissue, is a leading cause of disability and mortality worldwide. The selection\nof patients for the most optimal ischaemic stroke treatment is a crucial step\nfor a successful outcome, as the effect of treatment highly depends on the time\nto treatment. We propose a transformer-based multimodal network (TranSOP) for a\nclassification approach that employs clinical metadata and imaging information,\nacquired on hospital admission, to predict the functional outcome of stroke\ntreatment based on the modified Rankin Scale (mRS). This includes a fusion\nmodule to efficiently combine 3D non-contrast computed tomography (NCCT)\nfeatures and clinical information. In comparative experiments using unimodal\nand multimodal data on the MRCLEAN dataset, we achieve a state-of-the-art AUC\nscore of 0.85.\n", "date": "2023-01-25", "categories": []}
{"id": "2409.15586", "title": "TFT-multi: simultaneous forecasting of vital sign trajectories in the\n  ICU", "abstract": "  Trajectory forecasting in healthcare data has been an important area of\nresearch in precision care and clinical integration for computational methods.\nIn recent years, generative AI models have demonstrated promising results in\ncapturing short and long range dependencies in time series data. While these\nmodels have also been applied in healthcare, most of them only predict one\nvalue at a time, which is unrealistic in a clinical setting where multiple\nmeasures are taken at once. In this work, we extend the framework temporal\nfusion transformer (TFT), a multi-horizon time series prediction tool, and\npropose TFT-multi, an end-to-end framework that can predict multiple vital\ntrajectories simultaneously. We apply TFT-multi to forecast 5 vital signs\nrecorded in the intensive care unit: blood pressure, pulse, SpO2, temperature\nand respiratory rate. We hypothesize that by jointly predicting these measures,\nwhich are often correlated with one another, we can make more accurate\npredictions, especially in variables with large missingness. We validate our\nmodel on the public MIMIC dataset and an independent institutional dataset, and\ndemonstrate that this approach outperforms state-of-the-art univariate\nprediction tools including the original TFT and Prophet, as well as vector\nregression modeling for multivariate prediction. Furthermore, we perform a\nstudy case analysis by applying our pipeline to forecast blood pressure changes\nin response to actual and hypothetical pressor administration.\n", "date": "2024-09-23", "categories": []}
{"id": "2412.04749", "title": "Machine learning algorithms to predict the risk of rupture of\n  intracranial aneurysms: a systematic review", "abstract": "  Purpose: Subarachnoid haemorrhage is a potentially fatal consequence of\nintracranial aneurysm rupture, however, it is difficult to predict if aneurysms\nwill rupture. Prophylactic treatment of an intracranial aneurysm also involves\nrisk, hence identifying rupture-prone aneurysms is of substantial clinical\nimportance. This systematic review aims to evaluate the performance of machine\nlearning algorithms for predicting intracranial aneurysm rupture risk.\n  Methods: MEDLINE, Embase, Cochrane Library and Web of Science were searched\nuntil December 2023. Studies incorporating any machine learning algorithm to\npredict the risk of rupture of an intracranial aneurysm were included. Risk of\nbias was assessed using the Prediction Model Risk of Bias Assessment Tool\n(PROBAST). PROSPERO registration: CRD42023452509. Results: Out of 10,307\nrecords screened, 20 studies met the eligibility criteria for this review\nincorporating a total of 20,286 aneurysm cases. The machine learning models\ngave a 0.66-0.90 range for performance accuracy. The models were compared to\ncurrent clinical standards in six studies and gave mixed results. Most studies\nposed high or unclear risks of bias and concerns for applicability, limiting\nthe inferences that can be drawn from them. There was insufficient homogenous\ndata for a meta-analysis.\n  Conclusions: Machine learning can be applied to predict the risk of rupture\nfor intracranial aneurysms. However, the evidence does not comprehensively\ndemonstrate superiority to existing practice, limiting its role as a clinical\nadjunct. Further prospective multicentre studies of recent machine learning\ntools are needed to prove clinical validation before they are implemented in\nthe clinic.\n", "date": "2024-12-06", "categories": []}
{"id": "2412.04792", "title": "Multi-class heart disease Detection, Classification, and Prediction\n  using Machine Learning Models", "abstract": "  Heart disease is a leading cause of premature death worldwide, particularly\namong middle-aged and older adults, with men experiencing a higher prevalence.\nAccording to the World Health Organization (WHO), non-communicable diseases,\nincluding heart disease, account for 25\\% (17.9 million) of global deaths, with\nover 43,204 annual fatalities in Bangladesh. However, the development of heart\ndisease detection (HDD) systems tailored to the Bangladeshi population remains\nunderexplored due to the lack of benchmark datasets and reliance on manual or\nlimited-data approaches. This study addresses these challenges by introducing\nnew, ethically sourced HDD dataset, BIG-Dataset and CD dataset which\nincorporates comprehensive data on symptoms, examination techniques, and risk\nfactors. Using advanced machine learning techniques, including Logistic\nRegression and Random Forest, we achieved a remarkable testing accuracy of up\nto 96.6\\% with Random Forest. The proposed AI-driven system integrates these\nmodels and datasets to provide real-time, accurate diagnostics and personalized\nhealthcare recommendations. By leveraging structured datasets and\nstate-of-the-art machine learning algorithms, this research offers an\ninnovative solution for scalable and effective heart disease detection, with\nthe potential to reduce mortality rates and improve clinical outcomes.\n", "date": "2024-12-06", "categories": []}
{"id": "2412.04950", "title": "Bed-Attached Vibration Sensor System: A Machine Learning Approach for\n  Fall Detection in Nursing Homes", "abstract": "  The increasing shortage of nursing staff and the acute risk of falls in\nnursing homes pose significant challenges for the healthcare system. This study\npresents the development of an automated fall detection system integrated into\ncare beds, aimed at enhancing patient safety without compromising privacy\nthrough wearables or video monitoring. Mechanical vibrations transmitted\nthrough the bed frame are processed using a short-time Fourier transform,\nenabling robust classification of distinct human fall patterns with a\nconvolutional neural network. Challenges pertaining to the quantity and\ndiversity of the data are addressed, proposing the generation of additional\ndata with a specific emphasis on enhancing variation. While the model shows\npromising results in distinguishing fall events from noise using lab data,\nfurther testing in real-world environments is recommended for validation and\nimprovement. Despite limited available data, the proposed system shows the\npotential for an accurate and rapid response to falls, mitigating health\nimplications, and addressing the needs of an aging population. This case study\nwas performed as part of the ZIM Project. Further research on sensors enhanced\nby artificial intelligence will be continued in the ShapeFuture Project.\n", "date": "2024-12-06", "categories": []}
{"id": "2312.11502", "title": "Labrador: Exploring the Limits of Masked Language Modeling for\n  Laboratory Data", "abstract": "  In this work we introduce Labrador, a pre-trained Transformer model for\nlaboratory data. Labrador and BERT were pre-trained on a corpus of 100 million\nlab test results from electronic health records (EHRs) and evaluated on various\ndownstream outcome prediction tasks. Both models demonstrate mastery of the\npre-training task but neither consistently outperform XGBoost on downstream\nsupervised tasks. Our ablation studies reveal that transfer learning shows\nlimited effectiveness for BERT and achieves marginal success with Labrador. We\nexplore the reasons for the failure of transfer learning and suggest that the\ndata generating process underlying each patient cannot be characterized\nsufficiently using labs alone, among other factors. We encourage future work to\nfocus on joint modeling of multiple EHR data categories and to include\ntree-based baselines in their evaluations.\n", "date": "2023-12-09", "categories": []}
{"id": "2412.03593", "title": "CovidLLM: A Robust Large Language Model with Missing Value Adaptation\n  and Multi-Objective Learning Strategy for Predicting Disease Severity and\n  Clinical Outcomes in COVID-19 Patients", "abstract": "  Coronavirus Disease 2019 (COVID-19), which emerged in 2019, has caused\nmillions of deaths worldwide. Although effective vaccines have been developed\nto mitigate severe symptoms, certain populations, particularly the elderly and\nthose with comorbidities, remain at high risk for severe outcomes and increased\nmortality. Consequently, early identification of the severity and clinical\noutcomes of the disease in these patients is vital to prevent adverse\nprognoses. Although traditional machine learning and deep learning models have\nbeen widely employed in this area, the potential of large language models\n(LLMs) remains largely unexplored. Our research focuses primarily on\nconstructing specialized prompts and adopting multi-objective learning\nstrategies. We started by selecting serological indicators that significantly\ncorrelate with clinical outcomes and disease severity to serve as input data\nfor the model. Blood test samples often contain numerous missing values, and\ntraditional models generally rely on imputation to handle these gaps in the\ndata. In contrast, LLMs offer the advantage of robust semantic understanding.\nBy setting prompts, we can explicitly inform the model when a feature's value\nis missing, without the need for imputation. For the multi-objective learning\nstrategy, the model is designed to first predict disease severity and then\npredict clinical outcomes. Given that LLMs utilize both the input text and the\ngenerated tokens as input for generating the next token, the predicted severity\nis used as a basis for generating the clinical outcome. During the fine-tuning\nof the LLM, the two objectives influence and improve each other. Our\nexperiments were implemented based on the ChatGLM model. The results\ndemonstrate the effectiveness of LLMs in this task, suggesting promising\npotential for further development.\n", "date": "2024-11-28", "categories": []}
{"id": "2412.03737", "title": "Utilizing Machine Learning Models to Predict Acute Kidney Injury in\n  Septic Patients from MIMIC-III Database", "abstract": "  Sepsis is a severe condition that causes the body to respond incorrectly to\nan infection. This reaction can subsequently cause organ failure, a major one\nbeing acute kidney injury (AKI). For septic patients, approximately 50% develop\nAKI, with a mortality rate above 40%. Creating models that can accurately\npredict AKI based on specific qualities of septic patients is crucial for early\ndetection and intervention. Using medical data from septic patients during\nintensive care unit (ICU) admission from the Medical Information Mart for\nIntensive Care 3 (MIMIC-III) database, we extracted 3301 patients with sepsis,\nwith 73% of patients developing AKI. The data was randomly divided into a\ntraining set (n = 1980, 40%), a test set (n = 661, 10%), and a validation set\n(n = 660, 50%). The proposed model was logistic regression, and it was compared\nagainst five baseline models: XGBoost, K Nearest Neighbors (KNN), Support\nVector Machines (SVM), Random Forest (RF), and LightGBM. Area Under the Curve\n(AUC), Accuracy, F1-Score, and Recall were calculated for each model. After\nanalysis, we were able to select 23 features to include in our model, the top\nfeatures being urine output, maximum bilirubin, minimum bilirubin, weight,\nmaximum blood urea nitrogen, and minimum estimated glomerular filtration rate.\nThe logistic regression model performed the best, achieving an AUC score of\n0.887 (95% CI: [0.861-0.915]), an accuracy of 0.817, an F1 score of 0.866, a\nrecall score of 0.827, and a Brier score of 0.13. Compared to the best existing\nliterature in this field, our model achieved an 8.57% improvement in AUC while\nusing 13 fewer variables, showcasing its effectiveness in determining AKI in\nseptic patients. While the features selected for predicting AKI in septic\npatients are similar to previous literature, the top features that influenced\nour model's performance differ.\n", "date": "2024-12-04", "categories": []}
{"id": "2412.03961", "title": "Electronic Health Records-Based Data-Driven Diabetes Knowledge Unveiling\n  and Risk Prognosis", "abstract": "  In the healthcare sector, the application of deep learning technologies has\nrevolutionized data analysis and disease forecasting. This is particularly\nevident in the field of diabetes, where the deep analysis of Electronic Health\nRecords (EHR) has unlocked new opportunities for early detection and effective\nintervention strategies. Our research presents an innovative model that\nsynergizes the capabilities of Bidirectional Long Short-Term Memory\nNetworks-Conditional Random Field (BiLSTM-CRF) with a fusion of XGBoost and\nLogistic Regression. This model is designed to enhance the accuracy of diabetes\nrisk prediction by conducting an in-depth analysis of electronic medical\nrecords data. The first phase of our approach involves employing BiLSTM-CRF to\ndelve into the temporal characteristics and latent patterns present in EHR\ndata. This method effectively uncovers the progression trends of diabetes,\nwhich are often hidden in the complex data structures of medical records. The\nsecond phase leverages the combined strength of XGBoost and Logistic Regression\nto classify these extracted features and evaluate associated risks. This dual\napproach facilitates a more nuanced and precise prediction of diabetes,\noutperforming traditional models, particularly in handling multifaceted and\nnonlinear medical datasets. Our research demonstrates a notable advancement in\ndiabetes prediction over traditional methods, showcasing the effectiveness of\nour combined BiLSTM-CRF, XGBoost, and Logistic Regression model. This study\nhighlights the value of data-driven strategies in clinical decision-making,\nequipping healthcare professionals with precise tools for early detection and\nintervention. By enabling personalized treatment and timely care, our approach\nsignifies progress in incorporating advanced analytics in healthcare,\npotentially improving outcomes for diabetes and other chronic conditions.\n", "date": "2024-12-05", "categories": []}
{"id": "2412.04254", "title": "CLINICSUM: Utilizing Language Models for Generating Clinical Summaries\n  from Patient-Doctor Conversations", "abstract": "  This paper presents ClinicSum, a novel framework designed to automatically\ngenerate clinical summaries from patient-doctor conversations. It utilizes a\ntwo-module architecture: a retrieval-based filtering module that extracts\nSubjective, Objective, Assessment, and Plan (SOAP) information from\nconversation transcripts, and an inference module powered by fine-tuned\nPre-trained Language Models (PLMs), which leverage the extracted SOAP data to\ngenerate abstracted clinical summaries. To fine-tune the PLM, we created a\ntraining dataset of consisting 1,473 conversations-summaries pair by\nconsolidating two publicly available datasets, FigShare and MTS-Dialog, with\nground truth summaries validated by Subject Matter Experts (SMEs). ClinicSum's\neffectiveness is evaluated through both automatic metrics (e.g., ROUGE,\nBERTScore) and expert human assessments. Results show that ClinicSum\noutperforms state-of-the-art PLMs, demonstrating superior precision, recall,\nand F-1 scores in automatic evaluations and receiving high preference from SMEs\nin human assessment, making it a robust solution for automated clinical\nsummarization.\n", "date": "2024-12-05", "categories": []}
{"id": "2408.12838", "title": "Exploring Machine Learning Models for Lung Cancer Level Classification:\n  A comparative ML Approach", "abstract": "  This paper explores machine learning (ML) models for classifying lung cancer\nlevels to improve diagnostic accuracy and prognosis. Through parameter tuning\nand rigorous evaluation, we assess various ML algorithms. Techniques like\nminimum child weight and learning rate monitoring were used to reduce\noverfitting and optimize performance. Our findings highlight the robust\nperformance of Deep Neural Network (DNN) models across all phases. Ensemble\nmethods, including voting and bagging, also showed promise in enhancing\npredictive accuracy and robustness. However, Support Vector Machine (SVM)\nmodels with the Sigmoid kernel faced challenges, indicating a need for further\nrefinement. Overall, our study provides insights into ML-based lung cancer\nclassification, emphasizing the importance of parameter tuning to optimize\nmodel performance and improve diagnostic accuracy in oncological care.\n", "date": "2024-08-23", "categories": []}
{"id": "2408.12841", "title": "COVID-19 Probability Prediction Using Machine Learning: An Infectious\n  Approach", "abstract": "  The ongoing COVID-19 pandemic continues to pose significant challenges to\nglobal public health, despite the widespread availability of vaccines. Early\ndetection of the disease remains paramount in curbing its transmission and\nmitigating its impact on public health systems. In response, this study delves\ninto the application of advanced machine learning (ML) techniques for\npredicting COVID-19 infection probability. We conducted a rigorous\ninvestigation into the efficacy of various ML models, including XGBoost, LGBM,\nAdaBoost, Logistic Regression, Decision Tree, RandomForest, CatBoost, KNN, and\nDeep Neural Networks (DNN). Leveraging a dataset comprising 4000 samples, with\n3200 allocated for training and 800 for testing, our experiment offers\ncomprehensive insights into the performance of these models in COVID-19\nprediction. Our findings reveal that Deep Neural Networks (DNN) emerge as the\ntop-performing model, exhibiting superior accuracy and recall metrics. With an\nimpressive accuracy rate of 89%, DNN demonstrates remarkable potential in early\nCOVID-19 detection. This underscores the efficacy of deep learning approaches\nin leveraging complex data patterns to identify COVID-19 infections accurately.\nThis study underscores the critical role of machine learning, particularly deep\nlearning methodologies, in augmenting early detection efforts amidst the\nongoing pandemic. The success of DNN in accurately predicting COVID-19\ninfection probability highlights the importance of continued research and\ndevelopment in leveraging advanced technologies to combat infectious diseases.\n", "date": "2024-08-23", "categories": []}
{"id": "2412.01933", "title": "Recurrent Neural Network on PICTURE Model", "abstract": "  Intensive Care Units (ICUs) provide critical care and life support for most\nseverely ill and injured patients in the hospital. With the need for ICUs\ngrowing rapidly and unprecedentedly, especially during COVID-19, accurately\nidentifying the most critical patients helps hospitals to allocate resources\nmore efficiently and save more lives. The Predicting Intensive Care Transfers\nand Other Unforeseen Events (PICTURE) model predicts patient deterioration by\nseparating those at high risk for imminent intensive care unit transfer,\nrespiratory failure, or death from those at lower risk. This study aims to\nimplement a deep learning model to benchmark the performance from the XGBoost\nmodel, an existing model which has competitive results on prediction.\n", "date": "2024-12-02", "categories": []}
{"id": "2412.02062", "title": "Construction and optimization of health behavior prediction model for\n  the elderly in smart elderly care", "abstract": "  With the intensification of global aging, health management of the elderly\nhas become a focus of social attention. This study designs and implements a\nsmart elderly care service model to address issues such as data diversity,\nhealth status complexity, long-term dependence and data loss, sudden changes in\nbehavior, and data privacy in the prediction of health behaviors of the\nelderly. The model achieves accurate prediction and dynamic management of\nhealth behaviors of the elderly through modules such as multimodal data fusion,\ndata loss processing, nonlinear prediction, emergency detection, and privacy\nprotection. In the experimental design, based on multi-source data sets and\nmarket research results, the model demonstrates excellent performance in health\nbehavior prediction, emergency detection, and personalized services. The\nexperimental results show that the model can effectively improve the accuracy\nand robustness of health behavior prediction and meet the actual application\nneeds in the field of smart elderly care. In the future, with the integration\nof more data and further optimization of technology, the model will provide\nmore powerful technical support for smart elderly care services.\n", "date": "2024-12-03", "categories": []}
{"id": "2412.02173", "title": "Keeping Experts in the Loop: Expert-Guided Optimization for Clinical\n  Data Classification using Large Language Models", "abstract": "  Since the emergence of Large Language Models (LLMs), the challenge of\neffectively leveraging their potential in healthcare has taken center stage. A\ncritical barrier to using LLMs for extracting insights from unstructured\nclinical notes lies in the prompt engineering process. Despite its pivotal role\nin determining task performance, a clear framework for prompt optimization\nremains absent. Current methods to address this gap take either a manual prompt\nrefinement approach, where domain experts collaborate with prompt engineers to\ncreate an optimal prompt, which is time-intensive and difficult to scale, or\nthrough employing automatic prompt optimizing approaches, where the value of\nthe input of domain experts is not fully realized. To address this, we propose\nStructEase, a novel framework that bridges the gap between automation and the\ninput of human expertise in prompt engineering. A core innovation of the\nframework is SamplEase, an iterative sampling algorithm that identifies\nhigh-value cases where expert feedback drives significant performance\nimprovements. This approach minimizes expert intervention, to effectively\nenhance classification outcomes. This targeted approach reduces labeling\nredundancy, mitigates human error, and enhances classification outcomes. We\nevaluated the performance of StructEase using a dataset of de-identified\nclinical narratives from the US National Electronic Injury Surveillance System\n(NEISS), demonstrating significant gains in classification performance compared\nto current methods. Our findings underscore the value of expert integration in\nLLM workflows, achieving notable improvements in F1 score while maintaining\nminimal expert effort. By combining transparency, flexibility, and scalability,\nStructEase sets the foundation for a framework to integrate expert input into\nLLM workflows in healthcare and beyond.\n", "date": "2024-12-03", "categories": []}
{"id": "2412.02189", "title": "Comparative Performance of Machine Learning Algorithms for Early Genetic\n  Disorder and Subclass Classification", "abstract": "  A great deal of effort has been devoted to discovering a particular genetic\ndisorder, but its classification across a broad spectrum of disorder classes\nand types remains elusive. Early diagnosis of genetic disorders enables timely\ninterventions and improves outcomes. This study implements machine learning\nmodels using basic clinical indicators measurable at birth or infancy to enable\ndiagnosis in preliminary life stages. Supervised learning algorithms were\nimplemented on a dataset of 22083 instances with 42 features like family\nhistory, newborn metrics, and basic lab tests. Extensive hyperparameter tuning,\nfeature engineering, and selection were undertaken. Two multi-class classifiers\nwere developed: one for predicting disorder classes (mitochondrial,\nmultifactorial, and single-gene) and one for subtypes (9 disorders).\nPerformance was evaluated using accuracy, precision, recall, and the F1-score.\nThe CatBoost classifier achieved the highest accuracy of 77% for predicting\ngenetic disorder classes. For subtypes, SVM attained a maximum accuracy of 80%.\nThe study demonstrates the feasibility of using basic clinical data in machine\nlearning models for early categorization and diagnosis across various genetic\ndisorders. Applying ML with basic clinical indicators can enable timely\ninterventions once validated on larger datasets. It is necessary to conduct\nfurther studies to improve model performance on this dataset.\n", "date": "2024-12-03", "categories": []}
{"id": "2412.02265", "title": "Diabetic Retinopathy Classification from Retinal Images using Machine\n  Learning Approaches", "abstract": "  Diabetic Retinopathy is one of the most familiar diseases and is a diabetes\ncomplication that affects eyes. Initially, diabetic retinopathy may cause no\nsymptoms or only mild vision problems. Eventually, it can cause blindness. So\nearly detection of symptoms could help to avoid blindness. In this paper, we\npresent some experiments on some features of diabetic retinopathy, like\nproperties of exudates, properties of blood vessels and properties of\nmicroaneurysm. Using the features, we can classify healthy, mild\nnon-proliferative, moderate non-proliferative, severe non-proliferative and\nproliferative stages of DR. Support Vector Machine, Random Forest and Naive\nBayes classifiers are used to classify the stages. Finally, Random Forest is\nfound to be the best for higher accuracy, sensitivity and specificity of 76.5%,\n77.2% and 93.3% respectively.\n", "date": "2024-12-03", "categories": []}
{"id": "2412.02621", "title": "Medical Multimodal Foundation Models in Clinical Diagnosis and\n  Treatment: Applications, Challenges, and Future Directions", "abstract": "  Recent advancements in deep learning have significantly revolutionized the\nfield of clinical diagnosis and treatment, offering novel approaches to improve\ndiagnostic precision and treatment efficacy across diverse clinical domains,\nthus driving the pursuit of precision medicine. The growing availability of\nmulti-organ and multimodal datasets has accelerated the development of\nlarge-scale Medical Multimodal Foundation Models (MMFMs). These models, known\nfor their strong generalization capabilities and rich representational power,\nare increasingly being adapted to address a wide range of clinical tasks, from\nearly diagnosis to personalized treatment strategies. This review offers a\ncomprehensive analysis of recent developments in MMFMs, focusing on three key\naspects: datasets, model architectures, and clinical applications. We also\nexplore the challenges and opportunities in optimizing multimodal\nrepresentations and discuss how these advancements are shaping the future of\nhealthcare by enabling improved patient outcomes and more efficient clinical\nworkflows.\n", "date": "2024-12-03", "categories": []}
{"id": "2401.03482", "title": "Uncertainty Quantification on Clinical Trial Outcome Prediction", "abstract": "  The importance of uncertainty quantification is increasingly recognized in\nthe diverse field of machine learning. Accurately assessing model prediction\nuncertainty can help provide deeper understanding and confidence for\nresearchers and practitioners. This is especially critical in medical diagnosis\nand drug discovery areas, where reliable predictions directly impact research\nquality and patient health.\n  In this paper, we proposed incorporating uncertainty quantification into\nclinical trial outcome predictions. Our main goal is to enhance the model's\nability to discern nuanced differences, thereby significantly improving its\noverall performance.\n  We have adopted a selective classification approach to fulfill our objective,\nintegrating it seamlessly with the Hierarchical Interaction Network (HINT),\nwhich is at the forefront of clinical trial prediction modeling. Selective\nclassification, encompassing a spectrum of methods for uncertainty\nquantification, empowers the model to withhold decision-making in the face of\nsamples marked by ambiguity or low confidence, thereby amplifying the accuracy\nof predictions for the instances it chooses to classify. A series of\ncomprehensive experiments demonstrate that incorporating selective\nclassification into clinical trial predictions markedly enhances the model's\nperformance, as evidenced by significant upticks in pivotal metrics such as\nPR-AUC, F1, ROC-AUC, and overall accuracy.\n  Specifically, the proposed method achieved 32.37\\%, 21.43\\%, and 13.27\\%\nrelative improvement on PR-AUC over the base model (HINT) in phase I, II, and\nIII trial outcome prediction, respectively. When predicting phase III, our\nmethod reaches 0.9022 PR-AUC scores.\n  These findings illustrate the robustness and prospective utility of this\nstrategy within the area of clinical trial predictions, potentially setting a\nnew benchmark in the field.\n", "date": "2024-01-07", "categories": []}
{"id": "2407.06125", "title": "Depression Detection and Analysis using Large Language Models on Textual\n  and Audio-Visual Modalities", "abstract": "  Depression has proven to be a significant public health issue, profoundly\naffecting the psychological well-being of individuals. If it remains\nundiagnosed, depression can lead to severe health issues, which can manifest\nphysically and even lead to suicide. Generally, Diagnosing depression or any\nother mental disorder involves conducting semi-structured interviews alongside\nsupplementary questionnaires, including variants of the Patient Health\nQuestionnaire (PHQ) by Clinicians and mental health professionals. This\napproach places significant reliance on the experience and judgment of trained\nphysicians, making the diagnosis susceptible to personal biases. Given that the\nunderlying mechanisms causing depression are still being actively researched,\nphysicians often face challenges in diagnosing and treating the condition,\nparticularly in its early stages of clinical presentation. Recently,\nsignificant strides have been made in Artificial neural computing to solve\nproblems involving text, image, and speech in various domains. Our analysis has\naimed to leverage these state-of-the-art (SOTA) models in our experiments to\nachieve optimal outcomes leveraging multiple modalities. The experiments were\nperformed on the Extended Distress Analysis Interview Corpus Wizard of Oz\ndataset (E-DAIC) corpus presented in the Audio/Visual Emotion Challenge (AVEC)\n2019 Challenge. The proposed solutions demonstrate better results achieved by\nProprietary and Open-source Large Language Models (LLMs), which achieved a Root\nMean Square Error (RMSE) score of 3.98 on Textual Modality, beating the AVEC\n2019 challenge baseline results and current SOTA regression analysis\narchitectures. Additionally, the proposed solution achieved an accuracy of\n71.43% in the classification task. The paper also includes a novel audio-visual\nmulti-modal network that predicts PHQ-8 scores with an RMSE of 6.51.\n", "date": "2024-07-08", "categories": []}
{"id": "2412.00105", "title": "Predicting Extubation Failure in Intensive Care: The Development of a\n  Novel, End-to-End Actionable and Interpretable Prediction System", "abstract": "  Predicting extubation failure in intensive care is challenging due to complex\ndata and the severe consequences of inaccurate predictions. Machine learning\nshows promise in improving clinical decision-making but often fails to account\nfor temporal patient trajectories and model interpretability, highlighting the\nneed for innovative solutions.\n  This study aimed to develop an actionable, interpretable prediction system\nfor extubation failure using temporal modelling approaches such as Long\nShort-Term Memory (LSTM) and Temporal Convolutional Networks (TCN). A\nretrospective cohort study of 4,701 mechanically ventilated patients from the\nMIMIC-IV database was conducted. Data from the 6 hours before extubation,\nincluding static and dynamic features, were processed through novel techniques\naddressing data inconsistency and synthetic data challenges. Feature selection\nwas guided by clinical relevance and literature benchmarks.\n  Iterative experimentation involved training LSTM, TCN, and LightGBM models.\nInitial results showed a strong bias toward predicting extubation success,\ndespite advanced hyperparameter tuning and static data inclusion. Data was\nstratified by sampling frequency to reduce synthetic data impacts, leading to a\nfused decision system with improved performance. However, all architectures\nyielded modest predictive power (AUC-ROC ~0.6; F1 <0.5) with no clear advantage\nin incorporating static data or additional features. Ablation analysis\nindicated minimal impact of individual features on model performance.\n  This thesis highlights the challenges of synthetic data in extubation failure\nprediction and introduces strategies to mitigate bias, including\nclinician-informed preprocessing and novel feature subsetting. While\nperformance was limited, the study provides a foundation for future work,\nemphasising the need for reliable, interpretable models to optimise ICU\noutcomes.\n", "date": "2024-11-27", "categories": []}
{"id": "2412.00606", "title": "Fairness at Every Intersection: Uncovering and Mitigating Intersectional\n  Biases in Multimodal Clinical Predictions", "abstract": "  Biases in automated clinical decision-making using Electronic Healthcare\nRecords (EHR) impose significant disparities in patient care and treatment\noutcomes. Conventional approaches have primarily focused on bias mitigation\nstrategies stemming from single attributes, overlooking intersectional\nsubgroups -- groups formed across various demographic intersections (such as\nrace, gender, ethnicity, etc.). Rendering single-attribute mitigation\nstrategies to intersectional subgroups becomes statistically irrelevant due to\nthe varying distribution and bias patterns across these subgroups. The\nmultimodal nature of EHR -- data from various sources such as combinations of\ntext, time series, tabular, events, and images -- adds another layer of\ncomplexity as the influence on minority groups may fluctuate across modalities.\nIn this paper, we take the initial steps to uncover potential intersectional\nbiases in predictions by sourcing extensive multimodal datasets, MIMIC-Eye1 and\nMIMIC-IV ED, and propose mitigation at the intersectional subgroup level. We\nperform and benchmark downstream tasks and bias evaluation on the datasets by\nlearning a unified text representation from multimodal sources, harnessing the\nenormous capabilities of the pre-trained clinical Language Models (LM),\nMedBERT, Clinical BERT, and Clinical BioBERT. Our findings indicate that the\nproposed sub-group-specific bias mitigation is robust across different\ndatasets, subgroups, and embeddings, demonstrating effectiveness in addressing\nintersectional biases in multimodal settings.\n", "date": "2024-11-30", "categories": []}
{"id": "2412.01167", "title": "HumekaFL: Automated Detection of Neonatal Asphyxia Using Federated\n  Learning", "abstract": "  Birth Apshyxia (BA) is a severe condition characterized by insufficient\nsupply of oxygen to a newborn during the delivery. BA is one of the primary\ncauses of neonatal death in the world. Although there has been a decline in\nneonatal deaths over the past two decades, the developing world, particularly\nsub-Saharan Africa, continues to experience the highest under-five (<5)\nmortality rates. While evidence-based methods are commonly used to detect BA in\nAfrican healthcare settings, they can be subject to physician errors or delays\nin diagnosis, preventing timely interventions. Centralized Machine Learning\n(ML) methods demonstrated good performance in early detection of BA but require\nsensitive health data to leave their premises before training, which does not\nguarantee privacy and security. Healthcare institutions are therefore reluctant\nto adopt such solutions in Africa. To address this challenge, we suggest a\nfederated learning (FL)-based software architecture, a distributed learning\nmethod that prioritizes privacy and security by design. We have developed a\nuser-friendly and cost-effective mobile application embedding the FL pipeline\nfor early detection of BA. Our Federated SVM model outperformed centralized SVM\npipelines and Neural Networks (NN)-based methods in the existing literature\n", "date": "2024-12-02", "categories": []}
{"id": "2412.01331", "title": "Exploring Long-Term Prediction of Type 2 Diabetes Microvascular\n  Complications", "abstract": "  Electronic healthcare records (EHR) contain a huge wealth of data that can\nsupport the prediction of clinical outcomes. EHR data is often stored and\nanalysed using clinical codes (ICD10, SNOMED), however these can differ across\nregistries and healthcare providers. Integrating data across systems involves\nmapping between different clinical ontologies requiring domain expertise, and\nat times resulting in data loss. To overcome this, code-agnostic models have\nbeen proposed. We assess the effectiveness of a code-agnostic representation\napproach on the task of long-term microvascular complication prediction for\nindividuals living with Type 2 Diabetes. Our method encodes individual EHRs as\ntext using fine-tuned, pretrained clinical language models. Leveraging\nlarge-scale EHR data from the UK, we employ a multi-label approach to\nsimultaneously predict the risk of microvascular complications across 1-, 5-,\nand 10-year windows. We demonstrate that a code-agnostic approach outperforms a\ncode-based model and illustrate that performance is better with longer\nprediction windows but is biased to the first occurring complication. Overall,\nwe highlight that context length is vitally important for model performance.\nThis study highlights the possibility of including data from across different\nclinical ontologies and is a starting point for generalisable clinical models.\n", "date": "2024-12-02", "categories": []}
{"id": "2402.17621", "title": "Supervised machine learning for microbiomics: bridging the gap between\n  current and best practices", "abstract": "  Machine learning (ML) is poised to drive innovations in clinical\nmicrobiomics, such as in disease diagnostics and prognostics. However, the\nsuccessful implementation of ML in these domains necessitates the development\nof reproducible, interpretable models that meet the rigorous performance\nstandards set by regulatory agencies. This study aims to identify key areas in\nneed of improvement in current ML practices within microbiomics, with a focus\non bridging the gap between existing methodologies and the requirements for\nclinical application. To do so, we analyze 100 peer-reviewed articles from\n2021-2022. Within this corpus, datasets have a median size of 161.5 samples,\nwith over one-third containing fewer than 100 samples, signaling a high\npotential for overfitting. Limited demographic data further raises concerns\nabout generalizability and fairness, with 24% of studies omitting participants'\ncountry of residence, and attributes like race/ethnicity, education, and income\nrarely reported (11%, 2%, and 0%, respectively). Methodological issues are also\ncommon; for instance, for 86% of studies we could not confidently rule out test\nset omission and data leakage, suggesting a strong potential for inflated\nperformance estimates across the literature. Reproducibility is also a concern,\nwith 78% of studies abstaining from sharing their ML code publicly. Based on\nthis analysis, we provide guidance to avoid common pitfalls that can hinder\nmodel performance, generalizability, and trustworthiness. An interactive\ntutorial on applying ML to microbiomics data accompanies the discussion, to\nhelp establish and reinforce best practices within the community.\n", "date": "2024-02-27", "categories": []}
{"id": "2405.01799", "title": "Exploiting ChatGPT for Diagnosing Autism-Associated Language Disorders\n  and Identifying Distinct Features", "abstract": "  Diagnosing language disorders associated with autism is a complex challenge,\noften hampered by the subjective nature and variability of traditional\nassessment methods. Traditional diagnostic methods not only require intensive\nhuman effort but also often result in delayed interventions due to their lack\nof speed and precision. In this study, we explored the application of ChatGPT,\na large language model, to overcome these obstacles by enhancing sensitivity\nand profiling linguistic features for autism diagnosis. This research utilizes\nChatGPT natural language processing capabilities to simplify and improve the\ndiagnostic process, focusing on identifying autism related language patterns.\nSpecifically, we compared ChatGPT performance with that of conventional\nsupervised learning models, including BERT, a model acclaimed for its\neffectiveness in various natural language processing tasks. We showed that\nChatGPT substantially outperformed these models, achieving over 10% improvement\nin both sensitivity and positive predictive value, in a zero shot learning\nconfiguration. The findings underscore the model potential as a diagnostic\ntool, combining accuracy and applicability. We identified ten key features of\nautism associated language disorders across scenarios. Features such as\necholalia, pronoun reversal, and atypical language usage play a critical role\nin diagnosing ASD and informing tailored treatment plans. Together, our\nfindings advocate for adopting sophisticated AI tools like ChatGPT in clinical\nsettings to assess and diagnose developmental disorders. Our approach promises\nenhanced diagnostic precision and supports personalized medicine, potentially\ntransforming the evaluation landscape for autism and similar neurological\nconditions.\n", "date": "2024-05-03", "categories": []}
{"id": "2407.21323", "title": "STANet: A Novel Spatio-Temporal Aggregation Network for Depression\n  Classification with Small and Unbalanced FMRI Data", "abstract": "  Accurate diagnosis of depression is crucial for timely implementation of\noptimal treatments, preventing complications and reducing the risk of suicide.\nTraditional methods rely on self-report questionnaires and clinical assessment,\nlacking objective biomarkers. Combining fMRI with artificial intelligence can\nenhance depression diagnosis by integrating neuroimaging indicators. However,\nthe specificity of fMRI acquisition for depression often results in unbalanced\nand small datasets, challenging the sensitivity and accuracy of classification\nmodels. In this study, we propose the Spatio-Temporal Aggregation Network\n(STANet) for diagnosing depression by integrating CNN and RNN to capture both\ntemporal and spatial features of brain activity. STANet comprises the following\nsteps:(1) Aggregate spatio-temporal information via ICA. (2) Utilize\nmulti-scale deep convolution to capture detailed features. (3) Balance data\nusing the SMOTE to generate new samples for minority classes. (4) Employ the\nAFGRU classifier, which combines Fourier transformation with GRU, to capture\nlong-term dependencies, with an adaptive weight assignment mechanism to enhance\nmodel generalization. The experimental results demonstrate that STANet achieves\nsuperior depression diagnostic performance with 82.38% accuracy and a 90.72%\nAUC. The STFA module enhances classification by capturing deeper features at\nmultiple scales. The AFGRU classifier, with adaptive weights and stacked GRU,\nattains higher accuracy and AUC. SMOTE outperforms other oversampling methods.\nAdditionally, spatio-temporal aggregated features achieve better performance\ncompared to using only temporal or spatial features. STANet outperforms\ntraditional or deep learning classifiers, and functional connectivity-based\nclassifiers, as demonstrated by ten-fold cross-validation.\n", "date": "2024-07-31", "categories": []}
{"id": "2411.18640", "title": "A quantum inspired predictor of Parkinsons disease built on a diverse,\n  multimodal dataset", "abstract": "  Parkinsons disease, the fastest growing neurodegenerative disorder globally,\nhas seen a 50 percent increase in cases within just two years. As speech,\nmemory, and motor symptoms worsen over time, early diagnosis is crucial for\npreserving patients quality of life. While machine-learning-based detection has\nshown promise, relying on a single feature for classification can be\nerror-prone due to the variability of symptoms between patients. To address\nthis limitation we utilized the mPower database, which includes 150,000 samples\nacross four key biomarkers: voice, gait, tapping, and demographic data. From\nthese measurements, we extracted 64 features and trained a baseline Random\nForest model to select the features above the 80th percentile. For\nclassification, we designed a simulatable quantum support vector machine (qSVM)\nthat detects high-dimensional patterns, leveraging recent advancements in\nquantum machine learning. With a novel, simulatable architecture that can be\nrun on standard hardware rather than resource-intensive quantum computers, our\nmodel achieves an accuracy of 90 percent and an AUC of 0.98, surpassing\nbenchmark models. By utilizing an innovative classification framework built on\na diverse set of features, our model offers a pathway for accessible global\nParkinsons screening.\n", "date": "2024-11-25", "categories": []}
{"id": "2411.18759", "title": "Classification of Deceased Patients from Non-Deceased Patients using\n  Random Forest and Support Vector Machine Classifiers", "abstract": "  Analyzing large datasets and summarizing it into useful information is the\nheart of the data mining process. In healthcare, information can be converted\ninto knowledge about patient historical patterns and possible future trends.\nDuring the COVID-19 pandemic, data mining COVID-19 patient information poses an\nopportunity to discover patterns that may signal that the patient is at high\nrisk for death. COVID-19 patients die from sepsis, a complex disease process\ninvolving multiple organ systems. We extracted the variables physicians are\nmost concerned about regarding viral septic infections. With the aim of\ndistinguishing COVID-19 patients who survive their hospital stay and those\nCOVID-19 who do not, the authors of this study utilize the Support Vector\nMachine (SVM) and the Random Forest (RF) classification techniques to classify\npatients according to their demographics, laboratory test results, and\npreexisting health conditions. After conducting a 10-fold validation procedure,\nwe assessed the performance of the classification through a Receiver Operating\nCharacteristic (ROC) curve, and a Confusion Matrix was used to determine the\naccuracy of the classifiers. We also performed a cluster analysis on the binary\nfactors, such as if the patient had a preexisting condition and if sepsis was\nidentified, and the numeric values from patient demographics and laboratory\ntest results as predictors.\n", "date": "2024-11-27", "categories": []}
{"id": "2411.18940", "title": "Rephrasing Electronic Health Records for Pretraining Clinical Language\n  Models", "abstract": "  Clinical language models are important for many applications in healthcare,\nbut their development depends on access to extensive clinical text for\npretraining. However, obtaining clinical notes from electronic health records\n(EHRs) at scale is challenging due to patient privacy concerns. In this study,\nwe rephrase existing clinical notes using LLMs to generate synthetic\npretraining corpora, drawing inspiration from previous work on rephrasing web\ndata. We examine four popular small-sized LLMs (<10B) to create synthetic\nclinical text to pretrain both decoder-based and encoder-based language models.\nThe method yields better results in language modeling and downstream tasks than\nprevious synthesis approaches without referencing real clinical text. We find\nthat augmenting original clinical notes with synthetic corpora from different\nLLMs improves performances even at a small token budget, showing the potential\nof this method to support pretraining at the institutional level or be scaled\nto synthesize large-scale clinical corpora.\n", "date": "2024-11-28", "categories": []}
{"id": "2404.14815", "title": "Time-aware Heterogeneous Graph Transformer with Adaptive Attention\n  Merging for Health Event Prediction", "abstract": "  The widespread application of Electronic Health Records (EHR) data in the\nmedical field has led to early successes in disease risk prediction using deep\nlearning methods. These methods typically require extensive data for training\ndue to their large parameter sets. However, existing works do not exploit the\nfull potential of EHR data. A significant challenge arises from the infrequent\noccurrence of many medical codes within EHR data, limiting their clinical\napplicability. Current research often lacks in critical areas: 1) incorporating\ndisease domain knowledge; 2) heterogeneously learning disease representations\nwith rich meanings; 3) capturing the temporal dynamics of disease progression.\nTo overcome these limitations, we introduce a novel heterogeneous graph\nlearning model designed to assimilate disease domain knowledge and elucidate\nthe intricate relationships between drugs and diseases. This model innovatively\nincorporates temporal data into visit-level embeddings and leverages a\ntime-aware transformer alongside an adaptive attention mechanism to produce\npatient representations. When evaluated on two healthcare datasets, our\napproach demonstrated notable enhancements in both prediction accuracy and\ninterpretability over existing methodologies, signifying a substantial\nadvancement towards personalized and proactive healthcare management.\n", "date": "2024-04-23", "categories": []}
{"id": "2411.17709", "title": "Quantity versus Diversity: Influence of Data on Detecting EEG Pathology\n  with Advanced ML Models", "abstract": "  This study investigates the impact of quantity and diversity of data on the\nperformance of various machine-learning models for detecting general EEG\npathology. We utilized an EEG dataset of 2,993 recordings from Temple\nUniversity Hospital and a dataset of 55,787 recordings from Elmiko Biosignals\nsp. z o.o. The latter contains data from 39 hospitals and a diverse patient set\nwith varied conditions. Thus, we introduce the Elmiko dataset - the largest\npublicly available EEG corpus. Our findings show that small and consistent\ndatasets enable a wide range of models to achieve high accuracy; however,\nvariations in pathological conditions, recording protocols, and labeling\nstandards lead to significant performance degradation. Nonetheless, increasing\nthe number of available recordings improves predictive accuracy and may even\ncompensate for data diversity, particularly in neural networks based on\nattention mechanism or transformer architecture. A meta-model that combined\nthese networks with a gradient-boosting approach using handcrafted features\ndemonstrated superior performance across varied datasets.\n", "date": "2024-11-13", "categories": []}
{"id": "2411.17717", "title": "Comprehensive Methodology for Sample Augmentation in EEG Biomarker\n  Studies for Alzheimers Risk Classification", "abstract": "  Background: Dementia, marked by cognitive decline, is a global health\nchallenge. Alzheimer's disease (AD), the leading type, accounts for ~70% of\ncases. Electroencephalography (EEG) measures show promise in identifying AD\nrisk, but obtaining large samples for reliable comparisons is challenging.\nObjective: This study integrates signal processing, harmonization, and\nstatistical techniques to enhance sample size and improve AD risk\nclassification reliability. Methods: We used advanced EEG preprocessing,\nfeature extraction, harmonization, and propensity score matching (PSM) to\nbalance healthy non-carriers (HC) and asymptomatic E280A mutation carriers\n(ACr). Data from four databases were harmonized to adjust site effects while\npreserving covariates like age and sex. PSM ratios (2:1, 5:1, 10:1) were\napplied to assess sample size impact on model performance. The final dataset\nunderwent machine learning analysis with decision trees and cross-validation\nfor robust results. Results: Balancing sample sizes via PSM significantly\nimproved classification accuracy, ranging from 0.92 to 0.96 across ratios. This\napproach enabled precise risk identification even with limited samples.\nConclusion: Integrating data processing, harmonization, and balancing\ntechniques improves AD risk classification accuracy, offering potential for\nother neurodegenerative diseases.\n", "date": "2024-11-20", "categories": []}
{"id": "2411.15128", "title": "Health AI Developer Foundations", "abstract": "  Robust medical Machine Learning (ML) models have the potential to\nrevolutionize healthcare by accelerating clinical research, improving workflows\nand outcomes, and producing novel insights or capabilities. Developing such ML\nmodels from scratch is cost prohibitive and requires substantial compute, data,\nand time (e.g., expert labeling). To address these challenges, we introduce\nHealth AI Developer Foundations (HAI-DEF), a suite of pre-trained,\ndomain-specific foundation models, tools, and recipes to accelerate building ML\nfor health applications. The models cover various modalities and domains,\nincluding radiology (X-rays and computed tomography), histopathology,\ndermatological imaging, and audio. These models provide domain specific\nembeddings that facilitate AI development with less labeled data, shorter\ntraining times, and reduced computational costs compared to traditional\napproaches. In addition, we utilize a common interface and style across these\nmodels, and prioritize usability to enable developers to integrate HAI-DEF\nefficiently. We present model evaluations across various tasks and conclude\nwith a discussion of their application and evaluation, covering the importance\nof ensuring efficacy, fairness, and equity. Finally, while HAI-DEF and\nspecifically the foundation models lower the barrier to entry for ML in\nhealthcare, we emphasize the importance of validation with problem- and\npopulation-specific data for each desired usage setting. This technical report\nwill be updated over time as more modalities and features are added.\n", "date": "2024-11-22", "categories": []}
{"id": "2411.16818", "title": "Enhancing In-Hospital Mortality Prediction Using Multi-Representational\n  Learning with LLM-Generated Expert Summaries", "abstract": "  In-hospital mortality (IHM) prediction for ICU patients is critical for\ntimely interventions and efficient resource allocation. While structured\nphysiological data provides quantitative insights, clinical notes offer\nunstructured, context-rich narratives. This study integrates these modalities\nwith Large Language Model (LLM)-generated expert summaries to improve IHM\nprediction accuracy. Using the MIMIC-III database, we analyzed time-series\nphysiological data and clinical notes from the first 48 hours of ICU admission.\nClinical notes were concatenated chronologically for each patient and\ntransformed into expert summaries using Med42-v2 70B. A multi-representational\nlearning framework was developed to integrate these data sources, leveraging\nLLMs to enhance textual data while mitigating direct reliance on LLM\npredictions, which can introduce challenges in uncertainty quantification and\ninterpretability. The proposed model achieved an AUPRC of 0.6156 (+36.41%) and\nan AUROC of 0.8955 (+7.64%) compared to a time-series-only baseline. Expert\nsummaries outperformed clinical notes or time-series data alone, demonstrating\nthe value of LLM-generated knowledge. Performance gains were consistent across\ndemographic groups, with notable improvements in underrepresented populations,\nunderscoring the framework's equitable application potential. By integrating\nLLM-generated summaries with structured and unstructured data, the framework\ncaptures complementary patient information, significantly improving predictive\nperformance. This approach showcases the potential of LLMs to augment critical\ncare prediction models, emphasizing the need for domain-specific validation and\nadvanced integration strategies for broader clinical adoption.\n", "date": "2024-11-25", "categories": []}
{"id": "2410.03736", "title": "CliMB: An AI-enabled Partner for Clinical Predictive Modeling", "abstract": "  Despite its significant promise and continuous technical advances, real-world\napplications of artificial intelligence (AI) remain limited. We attribute this\nto the \"domain expert-AI-conundrum\": while domain experts, such as clinician\nscientists, should be able to build predictive models such as risk scores, they\nface substantial barriers in accessing state-of-the-art (SOTA) tools. While\nautomated machine learning (AutoML) has been proposed as a partner in clinical\npredictive modeling, many additional requirements need to be fulfilled to make\nmachine learning accessible for clinician scientists.\n  To address this gap, we introduce CliMB, a no-code AI-enabled partner\ndesigned to empower clinician scientists to create predictive models using\nnatural language. CliMB guides clinician scientists through the entire medical\ndata science pipeline, thus empowering them to create predictive models from\nreal-world data in just one conversation. CliMB also creates structured reports\nand interpretable visuals. In evaluations involving clinician scientists and\nsystematic comparisons against a baseline GPT-4, CliMB consistently\ndemonstrated superior performance in key areas such as planning, error\nprevention, code execution, and model performance. Moreover, in blinded\nassessments involving 45 clinicians from diverse specialties and career stages,\nmore than 80% preferred CliMB over GPT-4. Overall, by providing a no-code\ninterface with clear guidance and access to SOTA methods in the fields of\ndata-centric AI, AutoML, and interpretable ML, CliMB empowers clinician\nscientists to build robust predictive models.\n  The proof-of-concept version of CliMB is available as open-source software on\nGitHub: https://github.com/vanderschaarlab/climb.\n", "date": "2024-09-30", "categories": []}
{"id": "2411.11487", "title": "Look a Group at Once: Multi-Slide Modeling for Survival Prediction", "abstract": "  Survival prediction is a critical task in pathology. In clinical practice,\npathologists often examine multiple cases, leveraging a broader spectrum of\ncancer phenotypes to enhance pathological assessment. Despite significant\nadvancements in deep learning, current solutions typically model each slide as\na sample, struggling to effectively capture comparable and slide-agnostic\npathological features. In this paper, we introduce GroupMIL, a novel framework\ninspired by the clinical practice of collective analysis, which models multiple\nslides as a single sample and organizes groups of patches and slides\nsequentially to capture cross-slide prognostic features. We also present\nGPAMamba, a model designed to facilitate intra- and inter-slide feature\ninteractions, effectively capturing local micro-environmental characteristics\nwithin slide-level graphs while uncovering essential prognostic patterns across\nan extended patch sequence within the group framework. Furthermore, we develop\na dual-head predictor that delivers comprehensive survival risk and probability\nassessments for each patient. Extensive empirical evaluations demonstrate that\nour model significantly outperforms state-of-the-art approaches across five\ndatasets from The Cancer Genome Atlas.\n", "date": "2024-11-18", "categories": []}
{"id": "2411.16346", "title": "Towards Foundation Models for Critical Care Time Series", "abstract": "  Notable progress has been made in generalist medical large language models\nacross various healthcare areas. However, large-scale modeling of in-hospital\ntime series data - such as vital signs, lab results, and treatments in critical\ncare - remains underexplored. Existing datasets are relatively small, but\ncombining them can enhance patient diversity and improve model robustness. To\neffectively utilize these combined datasets for large-scale modeling, it is\nessential to address the distribution shifts caused by varying treatment\npolicies, necessitating the harmonization of treatment variables across the\ndifferent datasets. This work aims to establish a foundation for training\nlarge-scale multi-variate time series models on critical care data and to\nprovide a benchmark for machine learning models in transfer learning across\nhospitals to study and address distribution shift challenges. We introduce a\nharmonized dataset for sequence modeling and transfer learning research,\nrepresenting the first large-scale collection to include core treatment\nvariables. Future plans involve expanding this dataset to support further\nadvancements in transfer learning and the development of scalable,\ngeneralizable models for critical healthcare applications.\n", "date": "2024-11-25", "categories": []}
{"id": "2411.14471", "title": "Leveraging Gene Expression Data and Explainable Machine Learning for\n  Enhanced Early Detection of Type 2 Diabetes", "abstract": "  Diabetes, particularly Type 2 diabetes (T2D), poses a substantial global\nhealth burden, compounded by its associated complications such as\ncardiovascular diseases, kidney failure, and vision impairment. Early detection\nof T2D is critical for improving healthcare outcomes and optimizing resource\nallocation. In this study, we address the gap in early T2D detection by\nleveraging machine learning (ML) techniques on gene expression data obtained\nfrom T2D patients. Our primary objective was to enhance the accuracy of early\nT2D detection through advanced ML methodologies and increase the model's\ntrustworthiness using the explainable artificial intelligence (XAI) technique.\nAnalyzing the biological mechanisms underlying T2D through gene expression\ndatasets represents a novel research frontier, relatively less explored in\nprevious studies. While numerous investigations have focused on utilizing\nclinical and demographic data for T2D prediction, the integration of molecular\ninsights from gene expression datasets offers a unique and promising avenue for\nunderstanding the pathophysiology of the disease. By employing six ML\nclassifiers on data sourced from NCBI's Gene Expression Omnibus (GEO), we\nobserved promising performance across all models. Notably, the XGBoost\nclassifier exhibited the highest accuracy, achieving 97%. Our study addresses a\nnotable gap in early T2D detection methodologies, emphasizing the importance of\nleveraging gene expression data and advanced ML techniques.\n", "date": "2024-11-18", "categories": []}
{"id": "2411.14750", "title": "Ordinal Multiple-instance Learning for Ulcerative Colitis Severity\n  Estimation with Selective Aggregated Transformer", "abstract": "  Patient-level diagnosis of severity in ulcerative colitis (UC) is common in\nreal clinical settings, where the most severe score in a patient is recorded.\nHowever, previous UC classification methods (i.e., image-level estimation)\nmainly assumed the input was a single image. Thus, these methods can not\nutilize severity labels recorded in real clinical settings. In this paper, we\npropose a patient-level severity estimation method by a transformer with\nselective aggregator tokens, where a severity label is estimated from multiple\nimages taken from a patient, similar to a clinical setting. Our method can\neffectively aggregate features of severe parts from a set of images captured in\neach patient, and it facilitates improving the discriminative ability between\nadjacent severity classes. Experiments demonstrate the effectiveness of the\nproposed method on two datasets compared with the state-of-the-art MIL methods.\nMoreover, we evaluated our method in real clinical settings and confirmed that\nour method outperformed the previous image-level methods. The code is publicly\navailable at\nhttps://github.com/Shiku-Kaito/Ordinal-Multiple-instance-Learning-for-Ulcerative-Colitis-Severity-Estimation.\n", "date": "2024-11-22", "categories": []}
{"id": "2411.13855", "title": "A Multimodal Approach to The Detection and Classification of Skin\n  Diseases", "abstract": "  According to PBS, nearly one-third of Americans lack access to primary care\nservices, and another forty percent delay going to avoid medical costs. As a\nresult, many diseases are left undiagnosed and untreated, even if the disease\nshows many physical symptoms on the skin. With the rise of AI, self-diagnosis\nand improved disease recognition have become more promising than ever; in spite\nof that, existing methods suffer from a lack of large-scale patient databases\nand outdated methods of study, resulting in studies being limited to only a few\ndiseases or modalities. This study incorporates readily available and easily\naccessible patient information via image and text for skin disease\nclassification on a new dataset of 26 skin disease types that includes both\nskin disease images (37K) and associated patient narratives. Using this\ndataset, baselines for various image models were established that outperform\nexisting methods. Initially, the Resnet-50 model was only able to achieve an\naccuracy of 70% but, after various optimization techniques, the accuracy was\nimproved to 80%. In addition, this study proposes a novel fine-tuning strategy\nfor sequence classification Large Language Models (LLMs), Chain of Options,\nwhich breaks down a complex reasoning task into intermediate steps at training\ntime instead of inference. With Chain of Options and preliminary disease\nrecommendations from the image model, this method achieves state of the art\naccuracy 91% in diagnosing patient skin disease given just an image of the\nafflicted area as well as a patient description of the symptoms (such as\nitchiness or dizziness). Through this research, an earlier diagnosis of skin\ndiseases can occur, and clinicians can work with deep learning models to give a\nmore accurate diagnosis, improving quality of life and saving lives.\n", "date": "2024-11-21", "categories": []}
{"id": "2411.07372", "title": "Identifying Differential Patient Care Through Inverse Intent Inference", "abstract": "  Sepsis is a life-threatening condition defined by end-organ dysfunction due\nto a dysregulated host response to infection. Although the Surviving Sepsis\nCampaign has launched and has been releasing sepsis treatment guidelines to\nunify and normalize the care for sepsis patients, it has been reported in\nnumerous studies that disparities in care exist across the trajectory of\npatient stay in the emergency department and intensive care unit. Here, we\napply a number of reinforcement learning techniques including behavioral\ncloning, imitation learning, and inverse reinforcement learning, to learn the\noptimal policy in the management of septic patient subgroups using expert\ndemonstrations. Then we estimate the counterfactual optimal policies by\napplying the model to another subset of unseen medical populations and identify\nthe difference in cure by comparing it to the real policy. Our data comes from\nthe sepsis cohort of MIMIC-IV and the clinical data warehouses of the Mass\nGeneral Brigham healthcare system. The ultimate objective of this work is to\nuse the optimal learned policy function to estimate the counterfactual\ntreatment policy and identify deviations across sub-populations of interest. We\nhope this approach would help us identify any disparities in care and also\nchanges in cure in response to the publication of national sepsis treatment\nguidelines.\n", "date": "2024-11-11", "categories": []}
{"id": "2411.11863", "title": "Longitudinal Wrist PPG Analysis for Reliable Hypertension Risk Screening\n  Using Deep Learning", "abstract": "  Hypertension is a leading risk factor for cardiovascular diseases.\nTraditional blood pressure monitoring methods are cumbersome and inadequate for\ncontinuous tracking, prompting the development of PPG-based cuffless blood\npressure monitoring wearables. This study leverages deep learning models,\nincluding ResNet and Transformer, to analyze wrist PPG data collected with a\nsmartwatch for efficient hypertension risk screening, eliminating the need for\nhandcrafted PPG features. Using the Home Blood Pressure Monitoring (HBPM)\nlongitudinal dataset of 448 subjects and five-fold cross-validation, our model\nwas trained on over 68k spot-check instances from 358 subjects and tested on\nreal-world continuous recordings of 90 subjects. The compact ResNet model with\n0.124M parameters performed significantly better than traditional machine\nlearning methods, demonstrating its effectiveness in distinguishing between\nhealthy and abnormal cases in real-world scenarios.\n", "date": "2024-11-02", "categories": []}
{"id": "2411.11915", "title": "Phenome-wide causal proteomics enhance systemic lupus erythematosus\n  flare prediction: A study in Asian populations", "abstract": "  Objective: Systemic lupus erythematosus (SLE) is a complex autoimmune disease\ncharacterized by unpredictable flares. This study aimed to develop a novel\nproteomics-based risk prediction model specifically for Asian SLE populations\nto enhance personalized disease management and early intervention. Methods: A\nlongitudinal cohort study was conducted over 48 weeks, including 139 SLE\npatients monitored every 12 weeks. Patients were classified into flare (n = 53)\nand non-flare (n = 86) groups. Baseline plasma samples underwent\ndata-independent acquisition (DIA) proteomics analysis, and phenome-wide\nMendelian randomization (PheWAS) was performed to evaluate causal relationships\nbetween proteins and clinical predictors. Logistic regression (LR) and random\nforest (RF) models were used to integrate proteomic and clinical data for flare\nrisk prediction. Results: Five proteins (SAA1, B4GALT5, GIT2, NAA15, and RPIA)\nwere significantly associated with SLE Disease Activity Index-2K (SLEDAI-2K)\nscores and 1-year flare risk, implicating key pathways such as B-cell receptor\nsignaling and platelet degranulation. SAA1 demonstrated causal effects on\nflare-related clinical markers, including hemoglobin and red blood cell counts.\nA combined model integrating clinical and proteomic data achieved the highest\npredictive accuracy (AUC = 0.769), surpassing individual models. SAA1 was\nhighlighted as a priority biomarker for rapid flare discrimination. Conclusion:\nThe integration of proteomic and clinical data significantly improves flare\nprediction in Asian SLE patients. The identification of key proteins and their\ncausal relationships with flare-related clinical markers provides valuable\ninsights for proactive SLE management and personalized therapeutic approaches.\n", "date": "2024-11-18", "categories": []}
{"id": "2307.15051", "title": "Matching Patients to Clinical Trials with Large Language Models", "abstract": "  Patient recruitment is challenging for clinical trials. We introduce\nTrialGPT, an end-to-end framework for zero-shot patient-to-trial matching with\nlarge language models. TrialGPT comprises three modules: it first performs\nlarge-scale filtering to retrieve candidate trials (TrialGPT-Retrieval); then\npredicts criterion-level patient eligibility (TrialGPT-Matching); and finally\ngenerates trial-level scores (TrialGPT-Ranking). We evaluate TrialGPT on three\ncohorts of 183 synthetic patients with over 75,000 trial annotations.\nTrialGPT-Retrieval can recall over 90% of relevant trials using less than 6% of\nthe initial collection. Manual evaluations on 1,015 patient-criterion pairs\nshow that TrialGPT-Matching achieves an accuracy of 87.3% with faithful\nexplanations, close to the expert performance. The TrialGPT-Ranking scores are\nhighly correlated with human judgments and outperform the best-competing models\nby 43.8% in ranking and excluding trials. Furthermore, our user study reveals\nthat TrialGPT can reduce the screening time by 42.6% in patient recruitment.\nOverall, these results have demonstrated promising opportunities for\npatient-to-trial matching with TrialGPT.\n", "date": "2023-07-27", "categories": []}
{"id": "2411.06397", "title": "A Hybrid Approach for COVID-19 Detection: Combining Wasserstein GAN with\n  Transfer Learning", "abstract": "  COVID-19 is extremely contagious and its rapid growth has drawn attention\ntowards its early diagnosis. Early diagnosis of COVID-19 enables healthcare\nprofessionals and government authorities to break the chain of transition and\nflatten the epidemic curve. With the number of cases accelerating across the\ndeveloped world, COVID-19 induced Viral Pneumonia cases is a big challenge.\nOverlapping of COVID-19 cases with Viral Pneumonia and other lung infections\nwith limited dataset and long training hours is a serious problem to cater.\nLimited amount of data often results in over-fitting models and due to this\nreason, model does not predict generalized results. To fill this gap, we\nproposed GAN-based approach to synthesize images which later fed into the deep\nlearning models to classify images of COVID-19, Normal, and Viral Pneumonia.\nSpecifically, customized Wasserstein GAN is proposed to generate 19% more Chest\nX-ray images as compare to the real images. This expanded dataset is then used\nto train four proposed deep learning models: VGG-16, ResNet-50, GoogLeNet and\nMNAST. The result showed that expanded dataset utilized deep learning models to\ndeliver high classification accuracies. In particular, VGG-16 achieved highest\naccuracy of 99.17% among all four proposed schemes. Rest of the models like\nResNet-50, GoogLeNet and MNAST delivered 93.9%, 94.49% and 97.75% testing\naccuracies respectively. Later, the efficiency of these models is compared with\nthe state of art models on the basis of accuracy. Further, our proposed models\ncan be applied to address the issue of scant datasets for any problem of image\nanalysis.\n", "date": "2024-11-10", "categories": []}
{"id": "2411.10479", "title": "Challenges in the Differential Classification of Individual Diagnoses\n  from Co-Occurring Autism and ADHD Using Survey Data", "abstract": "  Autism and Attention-Deficit Hyperactivity Disorder (ADHD) are two of the\nmost commonly observed neurodevelopmental conditions in childhood. Providing a\nspecific computational assessment to distinguish between the two can prove\ndifficult and time intensive. Given the high prevalence of their co-occurrence,\nthere is a need for scalable and accessible methods for distinguishing the\nco-occurrence of autism and ADHD from individual diagnoses. The first step is\nto identify a core set of features that can serve as the basis for behavioral\nfeature extraction. We trained machine learning models on data from the\nNational Survey of Children's Health to identify behaviors to target as\nfeatures in automated clinical decision support systems. A model trained on the\nbinary task of distinguishing either developmental delay (autism or ADHD) vs.\nneither achieved sensitivity >92% and specificity >94%, while a model trained\non the 4-way classification task of autism vs. ADHD vs. both vs. none\ndemonstrated >65% sensitivity and >66% specificity. While the performance of\nthe binary model was respectable, the relatively low performance in the\ndifferential classification of autism and ADHD highlights the challenges that\npersist in achieving specificity within clinical decision support tools for\ndevelopmental delays. Nevertheless, this study demonstrates the potential of\napplying behavioral questionnaires not traditionally used for clinical purposes\ntowards supporting digital screening assessments for pediatric developmental\ndelays.\n", "date": "2024-11-13", "categories": []}
{"id": "2411.10645", "title": "Patient-Specific Models of Treatment Effects Explain Heterogeneity in\n  Tuberculosis", "abstract": "  Tuberculosis (TB) is a major global health challenge, and is compounded by\nco-morbidities such as HIV, diabetes, and anemia, which complicate treatment\noutcomes and contribute to heterogeneous patient responses. Traditional models\nof TB often overlook this heterogeneity by focusing on broad, pre-defined\npatient groups, thereby missing the nuanced effects of individual patient\ncontexts. We propose moving beyond coarse subgroup analyses by using\ncontextualized modeling, a multi-task learning approach that encodes patient\ncontext into personalized models of treatment effects, revealing\npatient-specific treatment benefits. Applied to the TB Portals dataset with\nmulti-modal measurements for over 3,000 TB patients, our model reveals\nstructured interactions between co-morbidities, treatments, and patient\noutcomes, identifying anemia, age of onset, and HIV as influential for\ntreatment efficacy. By enhancing predictive accuracy in heterogeneous\npopulations and providing patient-specific insights, contextualized models\npromise to enable new approaches to personalized treatment.\n", "date": "2024-11-16", "categories": []}
{"id": "2411.10746", "title": "LTCXNet: Advancing Chest X-Ray Analysis with Solutions for Long-Tailed\n  Multi-Label Classification and Fairness Challenges", "abstract": "  Chest X-rays (CXRs) often display various diseases with disparate class\nfrequencies, leading to a long-tailed, multi-label data distribution. In\nresponse to this challenge, we explore the Pruned MIMIC-CXR-LT dataset, a\ncurated collection derived from the MIMIC-CXR dataset, specifically designed to\nrepresent a long-tailed and multi-label data scenario. We introduce LTCXNet, a\nnovel framework that integrates the ConvNeXt model, ML-Decoder, and strategic\ndata augmentation, further enhanced by an ensemble approach. We demonstrate\nthat LTCXNet improves the performance of CXR interpretation across all classes,\nespecially enhancing detection in rarer classes like `Pneumoperitoneum' and\n`Pneumomediastinum' by 79\\% and 48\\%, respectively. Beyond performance metrics,\nour research extends into evaluating fairness, highlighting that some methods,\nwhile improving model accuracy, could inadvertently affect fairness across\ndifferent demographic groups negatively. This work contributes to advancing the\nunderstanding and management of long-tailed, multi-label data distributions in\nmedical imaging, paving the way for more equitable and effective diagnostic\ntools.\n", "date": "2024-11-16", "categories": []}
{"id": "2411.10819", "title": "An Oversampling-enhanced Multi-class Imbalanced Classification Framework\n  for Patient Health Status Prediction Using Patient-reported Outcomes", "abstract": "  Patient-reported outcomes (PROs) directly collected from cancer patients\nbeing treated with radiation therapy play a vital role in assisting clinicians\nin counseling patients regarding likely toxicities. Precise prediction and\nevaluation of symptoms or health status associated with PROs are fundamental to\nenhancing decision-making and planning for the required services and support as\npatients transition into survivorship. However, the raw PRO data collected from\nhospitals exhibits some intrinsic challenges such as incomplete item reports\nand imbalance patient toxicities. To the end, in this study, we explore various\nmachine learning techniques to predict patient outcomes related to health\nstatus such as pain levels and sleep discomfort using PRO datasets from a\ncancer photon/proton therapy center. Specifically, we deploy six advanced\nmachine learning classifiers -- Random Forest (RF), XGBoost, Gradient Boosting\n(GB), Support Vector Machine (SVM), Multi-Layer Perceptron with Bagging\n(MLP-Bagging), and Logistic Regression (LR) -- to tackle a multi-class\nimbalance classification problem across three prevalent cancer types: head and\nneck, prostate, and breast cancers. To address the class imbalance issue, we\nemploy an oversampling strategy, adjusting the training set sample sizes\nthrough interpolations of in-class neighboring samples, thereby augmenting\nminority classes without deviating from the original skewed class distribution.\nOur experimental findings across multiple PRO datasets indicate that the RF and\nXGB methods achieve robust generalization performance, evidenced by weighted\nAUC and detailed confusion matrices, in categorizing outcomes as mild,\nintermediate, and severe post-radiation therapy. These results underscore the\nmodels' effectiveness and potential utility in clinical settings.\n", "date": "2024-11-16", "categories": []}
{"id": "2411.11161", "title": "MPLite: Multi-Aspect Pretraining for Mining Clinical Health Records", "abstract": "  The adoption of digital systems in healthcare has resulted in the\naccumulation of vast electronic health records (EHRs), offering valuable data\nfor machine learning methods to predict patient health outcomes. However,\nsingle-visit records of patients are often neglected in the training process\ndue to the lack of annotations of next-visit information, thereby limiting the\npredictive and expressive power of machine learning models. In this paper, we\npresent a novel framework MPLite that utilizes Multi-aspect Pretraining with\nLab results through a light-weight neural network to enhance medical concept\nrepresentation and predict future health outcomes of individuals. By\nincorporating both structured medical data and additional information from lab\nresults, our approach fully leverages patient admission records. We design a\npretraining module that predicts medical codes based on lab results, ensuring\nrobust prediction by fusing multiple aspects of features. Our experimental\nevaluation using both MIMIC-III and MIMIC-IV datasets demonstrates improvements\nover existing models in diagnosis prediction and heart failure prediction\ntasks, achieving a higher weighted-F1 and recall with MPLite. This work reveals\nthe potential of integrating diverse aspects of data to advance predictive\nmodeling in healthcare.\n", "date": "2024-11-17", "categories": []}
{"id": "2405.14567", "title": "EHRMamba: Towards Generalizable and Scalable Foundation Models for\n  Electronic Health Records", "abstract": "  Transformers have significantly advanced the modeling of Electronic Health\nRecords (EHR), yet their deployment in real-world healthcare is limited by\nseveral key challenges. Firstly, the quadratic computational cost and\ninsufficient context length of these models hinder hospitals' ability in\nprocessing the extensive medical histories typical in EHR data. Additionally,\nexisting models employ separate finetuning for each clinical task, complicating\nmaintenance in healthcare environments. Moreover, these models focus\nexclusively on either clinical prediction or EHR forecasting, lacking\nproficiency in both tasks. To overcome these limitations, we introduce\nEHRMamba, a robust foundation model built on the Mamba architecture. EHRMamba\ncan process sequences up to 300% longer than previous models due to its linear\ncomputational cost. We also introduce a novel approach to Multitask Prompted\nFinetuning (MPF) for EHR data, which enables EHRMamba to simultaneously learn\nmultiple clinical tasks in a single finetuning phase, significantly enhancing\ndeployment and cross-task generalization. Furthermore, our model leverages the\nHL7 FHIR data standard to simplify integration into existing hospital systems.\nAlongside EHRMamba, we open-source Odyssey, a toolkit designed to support the\ndevelopment and deployment of EHR foundation models, with an emphasis on data\nstandardization and interpretability. Our evaluations on the MIMIC-IV dataset\ndemonstrate that EHRMamba advances state-of-the-art performance across 6 major\nclinical tasks and excels in EHR forecasting, marking a significant leap\nforward in the field.\n", "date": "2024-05-23", "categories": []}
{"id": "2410.18610", "title": "A Joint Representation Using Continuous and Discrete Features for\n  Cardiovascular Diseases Risk Prediction on Chest CT Scans", "abstract": "  Cardiovascular diseases (CVD) remain a leading health concern and contribute\nsignificantly to global mortality rates. While clinical advancements have led\nto a decline in CVD mortality, accurately identifying individuals who could\nbenefit from preventive interventions remains an unsolved challenge in\npreventive cardiology. Current CVD risk prediction models, recommended by\nguidelines, are based on limited traditional risk factors or use CT imaging to\nacquire quantitative biomarkers, and still have limitations in predictive\naccuracy and applicability. On the other hand, end-to-end trained CVD risk\nprediction methods leveraging deep learning on CT images often fail to provide\ntransparent and explainable decision grounds for assisting physicians. In this\nwork, we proposed a novel joint representation that integrates discrete\nquantitative biomarkers and continuous deep features extracted from chest CT\nscans. Our approach initiated with a deep CVD risk classification model by\ncapturing comprehensive continuous deep learning features while jointly\nobtaining currently clinical-established quantitative biomarkers via\nsegmentation models. In the feature joint representation stage, we use an\ninstance-wise feature-gated mechanism to align the continuous and discrete\nfeatures, followed by a soft instance-wise feature interaction mechanism\nfostering independent and effective feature interaction for the final CVD risk\nprediction. Our method substantially improves CVD risk predictive performance\nand offers individual contribution analysis of each biomarker, which is\nimportant in assisting physicians' decision-making processes. We validated our\nmethod on a public chest low-dose CT dataset and a private external chest\nstandard-dose CT patient cohort of 17,207 CT volumes from 6,393 unique\nsubjects, and demonstrated superior predictive performance, achieving AUCs of\n0.875 and 0.843, respectively.\n", "date": "2024-10-24", "categories": []}
{"id": "2411.05817", "title": "Demo: Multi-Modal Seizure Prediction System", "abstract": "  This demo presents SeizNet, an innovative system for predicting epileptic\nseizures benefiting from a multi-modal sensor network and utilizing Deep\nLearning (DL) techniques. Epilepsy affects approximately 65 million people\nworldwide, many of whom experience drug-resistant seizures. SeizNet aims at\nproviding highly accurate alerts, allowing individuals to take preventive\nmeasures without being disturbed by false alarms. SeizNet uses a combination of\ndata collected through either invasive (intracranial electroencephalogram\n(iEEG)) or non-invasive (electroencephalogram (EEG) and electrocardiogram\n(ECG)) sensors, and processed by advanced DL algorithms that are optimized for\nreal-time inference at the edge, ensuring privacy and minimizing data\ntransmission. SeizNet achieves > 97% accuracy in seizure prediction while\nkeeping the size and energy restrictions of an implantable device.\n", "date": "2024-11-01", "categories": []}
{"id": "2411.09767", "title": "Deep Learning for Fetal Inflammatory Response Diagnosis in the Umbilical\n  Cord", "abstract": "  Inflammation of the umbilical cord can be seen as a result of ascending\nintrauterine infection or other inflammatory stimuli. Acute fetal inflammatory\nresponse (FIR) is characterized by infiltration of the umbilical cord by fetal\nneutrophils, and can be associated with neonatal sepsis or fetal inflammatory\nresponse syndrome. Recent advances in deep learning in digital pathology have\ndemonstrated favorable performance across a wide range of clinical tasks, such\nas diagnosis and prognosis. In this study we classified FIR from whole slide\nimages (WSI). We digitized 4100 histological slides of umbilical cord stained\nwith hematoxylin and eosin(H&E) and extracted placental diagnoses from the\nelectronic health record. We build models using attention-based whole slide\nlearning models. We compared strategies between features extracted by a model\n(ConvNeXtXLarge) pretrained on non-medical images (ImageNet), and one\npretrained using histopathology images (UNI). We trained multiple iterations of\neach model and combined them into an ensemble. The predictions from the\nensemble of models trained using UNI achieved an overall balanced accuracy of\n0.836 on the test dataset. In comparison, the ensembled predictions using\nConvNeXtXLarge had a lower balanced accuracy of 0.7209. Heatmaps generated from\ntop accuracy model appropriately highlighted arteritis in cases of FIR 2. In\nFIR 1, the highest performing model assigned high attention to areas of\nactivated-appearing stroma in Wharton's Jelly. However, other high-performing\nmodels assigned attention to umbilical vessels. We developed models for\ndiagnosis of FIR from placental histology images, helping reduce interobserver\nvariability among pathologists. Future work may examine the utility of these\nmodels for identifying infants at risk of systemic inflammatory response or\nearly onset neonatal sepsis.\n", "date": "2024-11-14", "categories": []}
{"id": "2411.09874", "title": "A Hybrid Artificial Intelligence System for Automated EEG Background\n  Analysis and Report Generation", "abstract": "  Electroencephalography (EEG) plays a crucial role in the diagnosis of various\nneurological disorders. However, small hospitals and clinics often lack\nadvanced EEG signal analysis systems and are prone to misinterpretation in\nmanual EEG reading. This study proposes an innovative hybrid artificial\nintelligence (AI) system for automatic interpretation of EEG background\nactivity and report generation. The system combines deep learning models for\nposterior dominant rhythm (PDR) prediction, unsupervised artifact removal, and\nexpert-designed algorithms for abnormality detection. For PDR prediction, 1530\nlabeled EEGs were used, and the best ensemble model achieved a mean absolute\nerror (MAE) of 0.237, a root mean square error (RMSE) of 0.359, an accuracy of\n91.8% within a 0.6Hz error, and an accuracy of 99% within a 1.2Hz error. The AI\nsystem significantly outperformed neurologists in detecting generalized\nbackground slowing (p = 0.02; F1: AI 0.93, neurologists 0.82) and demonstrated\nimproved focal abnormality detection, although not statistically significant (p\n= 0.79; F1: AI 0.71, neurologists 0.55). Validation on both an internal dataset\nand the Temple University Abnormal EEG Corpus showed consistent performance\n(F1: 0.884 and 0.835, respectively; p = 0.66), demonstrating generalizability.\nThe use of large language models (LLMs) for report generation demonstrated 100%\naccuracy, verified by three other independent LLMs. This hybrid AI system\nprovides an easily scalable and accurate solution for EEG interpretation in\nresource-limited settings, assisting neurologists in improving diagnostic\naccuracy and reducing misdiagnosis rates.\n", "date": "2024-11-15", "categories": []}
{"id": "2411.10345", "title": "Comparative Analysis of Machine Learning Approaches for Bone Age\n  Assessment: A Comprehensive Study on Three Distinct Models", "abstract": "  Radiologists and doctors make use of X-ray images of the non-dominant hands\nof children and infants to assess the possibility of genetic conditions and\ngrowth abnormalities. This is done by assessing the difference between the\nactual extent of growth found using the X-rays and the chronological age of the\nsubject. The assessment was done conventionally using The Greulich Pyle (GP) or\nTanner Whitehouse (TW) approach. These approaches require a high level of\nexpertise and may often lead to observer bias. Hence, to automate the process\nof assessing the X-rays, and to increase its accuracy and efficiency, several\nmachine learning models have been developed. These machine-learning models have\nseveral differences in their accuracy and efficiencies, leading to an unclear\nchoice for the suitable model depending on their needs and available resources.\nMethods: In this study, we have analyzed the 3 most widely used models for the\nautomation of bone age prediction, which are the Xception model, VGG model and\nCNN model. These models were trained on the preprocessed dataset and the\naccuracy was measured using the MAE in terms of months for each model. Using\nthis, the comparison between the models was done. Results: The 3 models,\nXception, VGG, and CNN models have been tested for accuracy and other relevant\nfactors.\n", "date": "2024-11-15", "categories": []}
{"id": "2411.08935", "title": "Classification of Keratitis from Eye Corneal Photographs using Deep\n  Learning", "abstract": "  Keratitis is an inflammatory corneal condition responsible for 10% of visual\nimpairment in low- and middle-income countries (LMICs), with bacteria, fungi,\nor amoeba as the most common infection etiologies. While an accurate and timely\ndiagnosis is crucial for the selected treatment and the patients' sight\noutcomes, due to the high cost and limited availability of laboratory\ndiagnostics in LMICs, diagnosis is often made by clinical observation alone,\ndespite its lower accuracy. In this study, we investigate and compare different\ndeep learning approaches to diagnose the source of infection: 1) three separate\nbinary models for infection type predictions; 2) a multitask model with a\nshared backbone and three parallel classification layers (Multitask V1); and,\n3) a multitask model with a shared backbone and a multi-head classification\nlayer (Multitask V2). We used a private Brazilian cornea dataset to conduct the\nempirical evaluation. We achieved the best results with Multitask V2, with an\narea under the receiver operating characteristic curve (AUROC) confidence\nintervals of 0.7413-0.7740 (bacteria), 0.8395-0.8725 (fungi), and 0.9448-0.9616\n(amoeba). A statistical analysis of the impact of patient features on models'\nperformance revealed that sex significantly affects amoeba infection\nprediction, and age seems to affect fungi and bacteria predictions.\n", "date": "2024-11-13", "categories": []}
{"id": "2411.08048", "title": "Equitable Length of Stay Prediction for Patients with Learning\n  Disabilities and Multiple Long-term Conditions Using Machine Learning", "abstract": "  People with learning disabilities have a higher mortality rate and premature\ndeaths compared to the general public, as reported in published research in the\nUK and other countries. This study analyses hospitalisations of 9,618 patients\nidentified with learning disabilities and long-term conditions for the\npopulation of Wales using electronic health record (EHR) data sources from the\nSAIL Databank. We describe the demographic characteristics, prevalence of\nlong-term conditions, medication history, hospital visits, and lifestyle\nhistory for our study cohort, and apply machine learning models to predict the\nlength of hospital stays for this cohort. The random forest (RF) model achieved\nan Area Under the Curve (AUC) of 0.759 (males) and 0.756 (females), a false\nnegative rate of 0.224 (males) and 0.229 (females), and a balanced accuracy of\n0.690 (males) and 0.689 (females). After examining model performance across\nethnic groups, two bias mitigation algorithms (threshold optimization and the\nreductions algorithm using an exponentiated gradient) were applied to minimise\nperformance discrepancies. The threshold optimizer algorithm outperformed the\nreductions algorithm, achieving lower ranges in false positive rate and\nbalanced accuracy for the male cohort across the ethnic groups. This study\ndemonstrates the potential of applying machine learning models with effective\nbias mitigation approaches on EHR data sources to enable equitable prediction\nof hospital stays by addressing data imbalances across groups.\n", "date": "2024-11-03", "categories": []}
{"id": "2406.12199", "title": "Time Series Modeling for Heart Rate Prediction: From ARIMA to\n  Transformers", "abstract": "  Cardiovascular disease (CVD) is a leading cause of death globally,\nnecessitating precise forecasting models for monitoring vital signs like heart\nrate, blood pressure, and ECG. Traditional models, such as ARIMA and Prophet,\nare limited by their need for manual parameter tuning and challenges in\nhandling noisy, sparse, and highly variable medical data. This study\ninvestigates advanced deep learning models, including LSTM, and\ntransformer-based architectures, for predicting heart rate time series from the\nMIT-BIH Database. Results demonstrate that deep learning models, particularly\nPatchTST, significantly outperform traditional models across multiple metrics,\ncapturing complex patterns and dependencies more effectively. This research\nunderscores the potential of deep learning to enhance patient monitoring and\nCVD management, suggesting substantial clinical benefits. Future work should\nextend these findings to larger, more diverse datasets and real-world clinical\napplications to further validate and optimize model performance.\n", "date": "2024-06-18", "categories": []}
{"id": "2411.07252", "title": "High quality ECG dataset based on MIT-BIH recordings for improved\n  heartbeats classification", "abstract": "  Electrocardiogram (ECG) is a reliable tool for medical professionals to\ndetect and diagnose abnormal heart waves that may cause cardiovascular\ndiseases. This paper proposes a methodology to create a new high-quality\nheartbeat dataset from all 48 of the MIT-BIH recordings. The proposed approach\ncomputes an optimal heartbeat size, by eliminating outliers and calculating the\nmean value over 10-second windows. This results in independent QRS-centered\nheartbeats avoiding the mixing of successive heartbeats problem. The quality of\nthe newly constructed dataset has been evaluated and compared with existing\ndatasets. To this end, we built and trained a PyTorch 1-D Resnet architecture\nmodel that achieved 99.24\\% accuracy with a 5.7\\% improvement compared to other\nmethods. Additionally, downsampling the dataset has improved the model's\nexecution time by 33\\% and reduced 3x memory usage.\n", "date": "2024-10-27", "categories": []}
{"id": "2411.07643", "title": "xCG: Explainable Cell Graphs for Survival Prediction in Non-Small Cell\n  Lung Cancer", "abstract": "  Understanding how deep learning models predict oncology patient risk can\nprovide critical insights into disease progression, support clinical\ndecision-making, and pave the way for trustworthy and data-driven precision\nmedicine. Building on recent advances in the spatial modeling of the tumor\nmicroenvironment using graph neural networks, we present an explainable cell\ngraph (xCG) approach for survival prediction. We validate our model on a public\ncohort of imaging mass cytometry (IMC) data for 416 cases of lung\nadenocarcinoma. We explain survival predictions in terms of known phenotypes on\nthe cell level by computing risk attributions over cell graphs, for which we\npropose an efficient grid-based layer-wise relevance propagation (LRP) method.\nOur ablation studies highlight the importance of incorporating the cancer stage\nand model ensembling to improve the quality of risk estimates. Our xCG method,\ntogether with the IMC data, is made publicly available to support further\nresearch.\n", "date": "2024-11-12", "categories": []}
{"id": "2306.10805", "title": "Experts' cognition-driven ensemble deep learning for external validation\n  of predicting pathological complete response to neoadjuvant chemotherapy from\n  histological images in breast cancer", "abstract": "  In breast cancer, neoadjuvant chemotherapy (NAC) provides a standard\ntreatment option for patients who have locally advanced cancer and some large\noperable tumors. A patient will have better prognosis when he has achieved a\npathological complete response (pCR) with the treatment of NAC. There has been\na trend to directly predict pCR to NAC from histological images based on deep\nlearning (DL). However, the DL-based predictive models numerically have better\nperformances in internal validation than in external validation. In this paper,\nwe aim to alleviate this situation with an intrinsic approach. We propose an\nexperts' cognition-driven ensemble deep learning (ECDEDL) approach. Taking the\ncognition of both pathology and artificial intelligence experts into\nconsideration to improve the generalization of the predictive model to the\nexternal validation, ECDEDL can intrinsically approximate the working paradigm\nof a human being which will refer to his various working experiences to make\ndecisions. ECDEDL was validated with 695 WSIs collected from the same center as\nthe primary dataset to develop the predictive model and perform the internal\nvalidation, and was also validated with 340 WSIs collected from other three\ncenters as the external dataset to perform the external validation. In external\nvalidation, ECDEDL improves the AUCs of pCR prediction from 61.52(59.80-63.26)\nto 67.75(66.74-68.80) and the Accuracies of pCR prediction from\n56.09(49.39-62.79) to 71.01(69.44-72.58). ECDEDL was quite effective for\nexternal validation of predicting pCR to NAC from histological images in breast\ncancer, numerically approximating the internal validation.\n", "date": "2023-06-19", "categories": []}
{"id": "2405.10440", "title": "A Hybrid Framework with Large Language Models for Rare Disease\n  Phenotyping", "abstract": "  Rare diseases pose significant challenges in diagnosis and treatment due to\ntheir low prevalence and heterogeneous clinical presentations. Unstructured\nclinical notes contain valuable information for identifying rare diseases, but\nmanual curation is time-consuming and prone to subjectivity. This study aims to\ndevelop a hybrid approach combining dictionary-based natural language\nprocessing (NLP) tools with large language models (LLMs) to improve rare\ndisease identification from unstructured clinical reports. We propose a novel\nhybrid framework that integrates the Orphanet Rare Disease Ontology (ORDO) and\nthe Unified Medical Language System (UMLS) to create a comprehensive rare\ndisease vocabulary. The proposed hybrid approach demonstrates superior\nperformance compared to traditional NLP systems and standalone LLMs. Notably,\nthe approach uncovers a significant number of potential rare disease cases not\ndocumented in structured diagnostic records, highlighting its ability to\nidentify previously unrecognized patients.\n", "date": "2024-05-16", "categories": []}
{"id": "2411.07050", "title": "FedCVD: The First Real-World Federated Learning Benchmark on\n  Cardiovascular Disease Data", "abstract": "  Cardiovascular diseases (CVDs) are currently the leading cause of death\nworldwide, highlighting the critical need for early diagnosis and treatment.\nMachine learning (ML) methods can help diagnose CVDs early, but their\nperformance relies on access to substantial data with high quality. However,\nthe sensitive nature of healthcare data often restricts individual clinical\ninstitutions from sharing data to train sufficiently generalized and unbiased\nML models. Federated Learning (FL) is an emerging approach, which offers a\npromising solution by enabling collaborative model training across multiple\nparticipants without compromising the privacy of the individual data owners.\nHowever, to the best of our knowledge, there has been limited prior research\napplying FL to the cardiovascular disease domain. Moreover, existing FL\nbenchmarks and datasets are typically simulated and may fall short of\nreplicating the complexity of natural heterogeneity found in realistic datasets\nthat challenges current FL algorithms. To address these gaps, this paper\npresents the first real-world FL benchmark for cardiovascular disease\ndetection, named FedCVD. This benchmark comprises two major tasks:\nelectrocardiogram (ECG) classification and echocardiogram (ECHO) segmentation,\nbased on naturally scattered datasets constructed from the CVD data of seven\ninstitutions. Our extensive experiments on these datasets reveal that FL faces\nnew challenges with real-world non-IID and long-tail data. The code and\ndatasets of FedCVD are available https://github.com/SMILELab-FL/FedCVD.\n", "date": "2024-10-28", "categories": []}
{"id": "2407.06560", "title": "TCKAN:A Novel Integrated Network Model for Predicting Mortality Risk in\n  Sepsis Patients", "abstract": "  Sepsis poses a major global health threat, accounting for millions of deaths\nannually and significant economic costs. Accurately predicting the risk of\nmortality in sepsis patients enables early identification, promotes the\nefficient allocation of medical resources, and facilitates timely\ninterventions, thereby improving patient outcomes. Current methods typically\nutilize only one type of data--either constant, temporal, or ICD codes. This\nstudy introduces a novel approach, the Time-Constant Kolmogorov-Arnold Network\n(TCKAN), which uniquely integrates temporal data, constant data, and ICD codes\nwithin a single predictive model. Unlike existing methods that typically rely\non one type of data, TCKAN leverages a multi-modal data integration strategy,\nresulting in superior predictive accuracy and robustness in identifying\nhigh-risk sepsis patients. Validated against the MIMIC-III and MIMIC-IV\ndatasets, TCKAN surpasses existing machine learning and deep learning methods\nin accuracy, sensitivity, and specificity. Notably, TCKAN achieved AUCs of\n87.76% and 88.07%, demonstrating superior capability in identifying high-risk\npatients. Additionally, TCKAN effectively combats the prevalent issue of data\nimbalance in clinical settings, improving the detection of patients at elevated\nrisk of mortality and facilitating timely interventions. These results confirm\nthe model's effectiveness and its potential to transform patient management and\ntreatment optimization in clinical practice. Although the TCKAN model has\nalready incorporated temporal, constant, and ICD code data, future research\ncould include more diverse medical data types, such as imaging and laboratory\ntest results, to achieve a more comprehensive data integration and further\nimprove predictive accuracy.\n", "date": "2024-07-09", "categories": []}
{"id": "2411.00891", "title": "Deep Learning Predicts Mammographic Breast Density in Clinical Breast\n  Ultrasound Images", "abstract": "  Background: Breast density, as derived from mammographic images and defined\nby the American College of Radiology's Breast Imaging Reporting and Data System\n(BI-RADS), is one of the strongest risk factors for breast cancer. Breast\nultrasound (BUS) is an alternative breast cancer screening modality,\nparticularly useful for early detection in low-resource, rural contexts. The\npurpose of this study was to explore an artificial intelligence (AI) model to\npredict BI-RADS mammographic breast density category from clinical, handheld\nBUS imaging. Methods: All data are sourced from the Hawaii and Pacific Islands\nMammography Registry. We compared deep learning methods from BUS imaging, as\nwell as machine learning models from image statistics alone. The use of\nAI-derived BUS density as a risk factor for breast cancer was then compared to\nclinical BI-RADS breast density while adjusting for age. The BUS data were\nsplit by individual into 70/20/10% groups for training, validation, and\ntesting. Results: 405,120 clinical BUS images from 14.066 women were selected\nfor inclusion in this study, resulting in 9.846 women for training (302,574\nimages), 2,813 for validation (11,223 images), and 1,406 for testing (4,042\nimages). On the held-out testing set, the strongest AI model achieves AUROC\n0.854 predicting BI-RADS mammographic breast density from BUS imaging and\noutperforms all shallow machine learning methods based on image statistics. In\ncancer risk prediction, age-adjusted AI BUS breast density predicted 5-year\nbreast cancer risk with 0.633 AUROC, as compared to 0.637 AUROC from\nage-adjusted clinical breast density. Conclusions: BI-RADS mammographic breast\ndensity can be estimated from BUS imaging with high accuracy using a deep\nlearning model. Furthermore, we demonstrate that AI-derived BUS breast density\nis predictive of 5-year breast cancer risk in our population.\n", "date": "2024-10-31", "categories": []}
{"id": "2411.05188", "title": "AGE2HIE: Transfer Learning from Brain Age to Predicting Neurocognitive\n  Outcome for Infant Brain Injury", "abstract": "  Hypoxic-Ischemic Encephalopathy (HIE) affects 1 to 5 out of every 1,000\nnewborns, with 30% to 50% of cases resulting in adverse neurocognitive\noutcomes. However, these outcomes can only be reliably assessed as early as age\n2. Therefore, early and accurate prediction of HIE-related neurocognitive\noutcomes using deep learning models is critical for improving clinical\ndecision-making, guiding treatment decisions and assessing novel therapies.\nHowever, a major challenge in developing deep learning models for this purpose\nis the scarcity of large, annotated HIE datasets. We have assembled the first\nand largest public dataset, however it contains only 156 cases with 2-year\nneurocognitive outcome labels. In contrast, we have collected 8,859 normal\nbrain black Magnetic Resonance Imagings (MRIs) with 0-97 years of age that are\navailable for brain age estimation using deep learning models. In this paper,\nwe introduce AGE2HIE to transfer knowledge learned by deep learning models from\nhealthy controls brain MRIs to a diseased cohort, from structural to diffusion\nMRIs, from regression of continuous age estimation to prediction of the binary\nneurocognitive outcomes, and from lifespan age (0-97 years) to infant (0-2\nweeks). Compared to training from scratch, transfer learning from brain age\nestimation significantly improves not only the prediction accuracy (3% or 2%\nimprovement in same or multi-site), but also the model generalization across\ndifferent sites (5% improvement in cross-site validation).\n", "date": "2024-11-07", "categories": []}
{"id": "2401.00972", "title": "Robust Meta-Model for Predicting the Need for Blood Transfusion in\n  Non-traumatic ICU Patients", "abstract": "  Objective: Blood transfusions, crucial in managing anemia and coagulopathy in\nICU settings, require accurate prediction for effective resource allocation and\npatient risk assessment. However, existing clinical decision support systems\nhave primarily targeted a particular patient demographic with unique medical\nconditions and focused on a single type of blood transfusion. This study aims\nto develop an advanced machine learning-based model to predict the probability\nof transfusion necessity over the next 24 hours for a diverse range of\nnon-traumatic ICU patients.\n  Methods: We conducted a retrospective cohort study on 72,072 adult\nnon-traumatic ICU patients admitted to a high-volume US metropolitan academic\nhospital between 2016 and 2020. We developed a meta-learner and various machine\nlearning models to serve as predictors, training them annually with four-year\ndata and evaluating on the fifth, unseen year, iteratively over five years.\n  Results: The experimental results revealed that the meta-model surpasses the\nother models in different development scenarios. It achieved notable\nperformance metrics, including an Area Under the Receiver Operating\nCharacteristic (AUROC) curve of 0.97, an accuracy rate of 0.93, and an F1-score\nof 0.89 in the best scenario.\n  Conclusion: This study pioneers the use of machine learning models for\npredicting blood transfusion needs in a diverse cohort of critically ill\npatients. The findings of this evaluation confirm that our model not only\npredicts transfusion requirements effectively but also identifies key\nbiomarkers for making transfusion decisions.\n", "date": "2024-01-01", "categories": []}
{"id": "2411.04155", "title": "MINDSETS: Multi-omics Integration with Neuroimaging for Dementia\n  Subtyping and Effective Temporal Study", "abstract": "  In the complex realm of cognitive disorders, Alzheimer's disease (AD) and\nvascular dementia (VaD) are the two most prevalent dementia types, presenting\nentangled symptoms yet requiring distinct treatment approaches. The crux of\neffective treatment in slowing neurodegeneration lies in early, accurate\ndiagnosis, as this significantly assists doctors in determining the appropriate\ncourse of action. However, current diagnostic practices often delay VaD\ndiagnosis, impeding timely intervention and adversely affecting patient\nprognosis. This paper presents an innovative multi-omics approach to accurately\ndifferentiate AD from VaD, achieving a diagnostic accuracy of 89.25%. The\nproposed method segments the longitudinal MRI scans and extracts advanced\nradiomics features. Subsequently, it synergistically integrates the radiomics\nfeatures with an ensemble of clinical, cognitive, and genetic data to provide\nstate-of-the-art diagnostic accuracy, setting a new benchmark in classification\naccuracy on a large public dataset. The paper's primary contribution is\nproposing a comprehensive methodology utilizing multi-omics data to provide a\nnuanced understanding of dementia subtypes. Additionally, the paper introduces\nan interpretable model to enhance clinical decision-making coupled with a novel\nmodel architecture for evaluating treatment efficacy. These advancements lay\nthe groundwork for future work not only aimed at improving differential\ndiagnosis but also mitigating and preventing the progression of dementia.\n", "date": "2024-11-06", "categories": []}
{"id": "2411.04656", "title": "ICH-SCNet: Intracerebral Hemorrhage Segmentation and Prognosis\n  Classification Network Using CLIP-guided SAM mechanism", "abstract": "  Intracerebral hemorrhage (ICH) is the most fatal subtype of stroke and is\ncharacterized by a high incidence of disability. Accurate segmentation of the\nICH region and prognosis prediction are critically important for developing and\nrefining treatment plans for post-ICH patients. However, existing approaches\naddress these two tasks independently and predominantly focus on imaging data\nalone, thereby neglecting the intrinsic correlation between the tasks and\nmodalities. This paper introduces a multi-task network, ICH-SCNet, designed for\nboth ICH segmentation and prognosis classification. Specifically, we integrate\na SAM-CLIP cross-modal interaction mechanism that combines medical text and\nsegmentation auxiliary information with neuroimaging data to enhance\ncross-modal feature recognition. Additionally, we develop an effective feature\nfusion module and a multi-task loss function to improve performance further.\nExtensive experiments on an ICH dataset reveal that our approach surpasses\nother state-of-the-art methods. It excels in the overall performance of\nclassification tasks and outperforms competing models in all segmentation task\nmetrics.\n", "date": "2024-11-07", "categories": []}
{"id": "2411.04662", "title": "Enhancing Trust in Clinically Significant Prostate Cancer Prediction\n  with Multiple Magnetic Resonance Imaging Modalities", "abstract": "  In the United States, prostate cancer is the second leading cause of deaths\nin males with a predicted 35,250 deaths in 2024. However, most diagnoses are\nnon-lethal and deemed clinically insignificant which means that the patient\nwill likely not be impacted by the cancer over their lifetime. As a result,\nnumerous research studies have explored the accuracy of predicting clinical\nsignificance of prostate cancer based on magnetic resonance imaging (MRI)\nmodalities and deep neural networks. Despite their high performance, these\nmodels are not trusted by most clinical scientists as they are trained solely\non a single modality whereas clinical scientists often use multiple magnetic\nresonance imaging modalities during their diagnosis. In this paper, we\ninvestigate combining multiple MRI modalities to train a deep learning model to\nenhance trust in the models for clinically significant prostate cancer\nprediction. The promising performance and proposed training pipeline showcase\nthe benefits of incorporating multiple MRI modalities for enhanced trust and\naccuracy.\n", "date": "2024-11-07", "categories": []}
{"id": "2411.03497", "title": "Uncertainty Quantification for Clinical Outcome Predictions with (Large)\n  Language Models", "abstract": "  To facilitate healthcare delivery, language models (LMs) have significant\npotential for clinical prediction tasks using electronic health records (EHRs).\nHowever, in these high-stakes applications, unreliable decisions can result in\nhigh costs due to compromised patient safety and ethical concerns, thus\nincreasing the need for good uncertainty modeling of automated clinical\npredictions. To address this, we consider the uncertainty quantification of LMs\nfor EHR tasks in white- and black-box settings. We first quantify uncertainty\nin white-box models, where we can access model parameters and output logits. We\nshow that an effective reduction of model uncertainty can be achieved by using\nthe proposed multi-tasking and ensemble methods in EHRs. Continuing with this\nidea, we extend our approach to black-box settings, including popular\nproprietary LMs such as GPT-4. We validate our framework using longitudinal\nclinical data from more than 6,000 patients in ten clinical prediction tasks.\nResults show that ensembling methods and multi-task prediction prompts reduce\nuncertainty across different scenarios. These findings increase the\ntransparency of the model in white-box and black-box settings, thus advancing\nreliable AI healthcare.\n", "date": "2024-11-05", "categories": []}
{"id": "2411.03782", "title": "Navigating the landscape of multimodal AI in medicine: a scoping review\n  on technical challenges and clinical applications", "abstract": "  Recent technological advances in healthcare have led to unprecedented growth\nin patient data quantity and diversity. While artificial intelligence (AI)\nmodels have shown promising results in analyzing individual data modalities,\nthere is increasing recognition that models integrating multiple complementary\ndata sources, so-called multimodal AI, could enhance clinical decision-making.\nThis scoping review examines the landscape of deep learning-based multimodal AI\napplications across the medical domain, analyzing 432 papers published between\n2018 and 2024. We provide an extensive overview of multimodal AI development\nacross different medical disciplines, examining various architectural\napproaches, fusion strategies, and common application areas. Our analysis\nreveals that multimodal AI models consistently outperform their unimodal\ncounterparts, with an average improvement of 6.2 percentage points in AUC.\nHowever, several challenges persist, including cross-departmental coordination,\nheterogeneous data characteristics, and incomplete datasets. We critically\nassess the technical and practical challenges in developing multimodal AI\nsystems and discuss potential strategies for their clinical implementation,\nincluding a brief overview of commercially available multimodal AI models for\nclinical decision-making. Additionally, we identify key factors driving\nmultimodal AI development and propose recommendations to accelerate the field's\nmaturation. This review provides researchers and clinicians with a thorough\nunderstanding of the current state, challenges, and future directions of\nmultimodal AI in medicine.\n", "date": "2024-11-06", "categories": []}
{"id": "2411.03105", "title": "Evaluating Machine Learning Models against Clinical Protocols for\n  Enhanced Interpretability and Continuity of Care", "abstract": "  In clinical practice, decision-making relies heavily on established\nprotocols, often formalised as rules. Concurrently, Machine Learning (ML)\nmodels, trained on clinical data, aspire to integrate into medical\ndecision-making processes. However, despite the growing number of ML\napplications, their adoption into clinical practice remains limited. Two\ncritical concerns arise, relevant to the notions of consistency and continuity\nof care: (a) accuracy - the ML model, albeit more accurate, might introduce\nerrors that would not have occurred by applying the protocol; (b)\ninterpretability - ML models operating as black boxes might make predictions\nbased on relationships that contradict established clinical knowledge. In this\ncontext, the literature suggests using ML models integrating domain knowledge\nfor improved accuracy and interpretability. However, there is a lack of\nappropriate metrics for comparing ML models with clinical rules in addressing\nthese challenges. Accordingly, in this article, we first propose metrics to\nassess the accuracy of ML models with respect to the established protocol.\nSecondly, we propose an approach to measure the distance of explanations\nprovided by two rule sets, with the goal of comparing the explanation\nsimilarity between clinical rule-based systems and rules extracted from ML\nmodels. The approach is validated on the Pima Indians Diabetes dataset by\ntraining two neural networks - one exclusively on data, and the other\nintegrating a clinical protocol. Our findings demonstrate that the integrated\nML model achieves comparable performance to that of a fully data-driven model\nwhile exhibiting superior accuracy relative to the clinical protocol, ensuring\nenhanced continuity of care. Furthermore, we show that our integrated model\nprovides explanations for predictions that align more closely with the clinical\nprotocol compared to the data-driven model.\n", "date": "2024-11-05", "categories": []}
{"id": "2411.03131", "title": "Machine Learning Innovations in CPR: A Comprehensive Survey on Enhanced\n  Resuscitation Techniques", "abstract": "  This survey paper explores the transformative role of Machine Learning (ML)\nand Artificial Intelligence (AI) in Cardiopulmonary Resuscitation (CPR). It\nexamines the evolution from traditional CPR methods to innovative ML-driven\napproaches, highlighting the impact of predictive modeling, AI-enhanced\ndevices, and real-time data analysis in improving resuscitation outcomes. The\npaper provides a comprehensive overview, classification, and critical analysis\nof current applications, challenges, and future directions in this emerging\nfield.\n", "date": "2024-11-03", "categories": []}
{"id": "2403.11425", "title": "Narrative Feature or Structured Feature? A Study of Large Language\n  Models to Identify Cancer Patients at Risk of Heart Failure", "abstract": "  Cancer treatments are known to introduce cardiotoxicity, negatively impacting\noutcomes and survivorship. Identifying cancer patients at risk of heart failure\n(HF) is critical to improving cancer treatment outcomes and safety. This study\nexamined machine learning (ML) models to identify cancer patients at risk of HF\nusing electronic health records (EHRs), including traditional ML, Time-Aware\nlong short-term memory (T-LSTM), and large language models (LLMs) using novel\nnarrative features derived from the structured medical codes. We identified a\ncancer cohort of 12,806 patients from the University of Florida Health,\ndiagnosed with lung, breast, and colorectal cancers, among which 1,602\nindividuals developed HF after cancer. The LLM, GatorTron-3.9B, achieved the\nbest F1 scores, outperforming the traditional support vector machines by 39%,\nthe T-LSTM deep learning model by 7%, and a widely used transformer model,\nBERT, by 5.6%. The analysis shows that the proposed narrative features\nremarkably increased feature density and improved performance.\n", "date": "2024-03-18", "categories": []}
{"id": "2405.08226", "title": "Self-Normalizing Foundation Model for Enhanced Multi-Omics Data Analysis\n  in Oncology", "abstract": "  Multi-omics research has enhanced our understanding of cancer heterogeneity\nand progression. Investigating molecular data through multi-omics approaches is\ncrucial for unraveling the complex biological mechanisms underlying cancer,\nthereby enabling more effective diagnosis, treatment, and prevention\nstrategies. However, predicting patient outcomes through the integration of all\navailable multi-omics data is still an under-study research direction. Here, we\npresent SeNMo, a foundation model that has been trained on multi-omics data\nacross 33 cancer types. SeNMo is particularly efficient in handling multi-omics\ndata characterized by high-width and low-length attributes. We trained SeNMo\nfor the task of overall survival of patients using pan-cancer multi-omics data\ninvolving 33 cancer sites from the GDC. The training multi-omics data includes\ngene expression, DNA methylation, miRNA expression, DNA mutations, protein\nexpression modalities, and clinical data. SeNMo was validated on two\nindependent cohorts: Moffitt Cancer Center and CPTAC lung squamous cell\ncarcinoma. We evaluated the model's performance in predicting patient's overall\nsurvival using the C-Index. SeNMo performed consistently well in the training\nregime, reflected by the validation C-Index of 0.76 on GDC's public data. In\nthe testing regime, SeNMo performed with a C-Index of 0.758 on a held-out test\nset. The model showed an average accuracy of 99.8% on the task of classifying\nthe primary cancer type on the pan-cancer test cohort. SeNMo demonstrated\nrobust performance on the classification task of predicting the primary cancer\ntype of patients. SeNMo further demonstrated significant performance in\npredicting tertiary lymph structures from multi-omics data, showing\ngeneralizability across cancer types, molecular data types, and clinical\nendpoints.\n", "date": "2024-05-13", "categories": []}
{"id": "2411.00840", "title": "Peri-AIIMS: Perioperative Artificial Intelligence Driven Integrated\n  Modeling of Surgeries using Anesthetic, Physical and Cognitive Statuses for\n  Predicting Hospital Outcomes", "abstract": "  The association between preoperative cognitive status and surgical outcomes\nis a critical, yet scarcely explored area of research. Linking intraoperative\ndata with postoperative outcomes is a promising and low-cost way of evaluating\nlong-term impacts of surgical interventions. In this study, we evaluated how\npreoperative cognitive status as measured by the clock drawing test contributed\nto predicting length of hospital stay, hospital charges, average pain\nexperienced during follow-up, and 1-year mortality over and above\nintraoperative variables, demographics, preoperative physical status and\ncomorbidities. We expanded our analysis to 6 specific surgical groups where\nsufficient data was available for cross-validation. The clock drawing images\nwere represented by 10 constructional features discovered by a semi-supervised\ndeep learning algorithm, previously validated to differentiate between dementia\nand non-dementia patients. Different machine learning models were trained to\nclassify postoperative outcomes in hold-out test sets. The models were compared\nto their relative performance, time complexity, and interpretability. Shapley\nAdditive Explanations (SHAP) analysis was used to find the most predictive\nfeatures for classifying different outcomes in different surgical contexts.\nRelative classification performances achieved by different feature sets showed\nthat the perioperative cognitive dataset which included clock drawing features\nin addition to intraoperative variables, demographics, and comorbidities served\nas the best dataset for 12 of 18 possible surgery-outcome combinations...\n", "date": "2024-10-29", "categories": []}
{"id": "2411.00858", "title": "DiabML: AI-assisted diabetes diagnosis method with meta-heuristic-based\n  feature selection", "abstract": "  Diabetes is a chronic disorder identified by the high sugar level in the\nblood that can cause various different disorders such as kidney failure, heart\nattack, sightlessness, and stroke. Developments in the healthcare domain by\nfacilitating the early detection of diabetes risk can help not only caregivers\nbut also patients. AIoMT is a recent technology that integrates IoT and machine\nlearning methods to give services for medical purposes, which is a powerful\ntechnology for the early detection of diabetes. In this paper, we take\nadvantage of AIoMT and propose a hybrid diabetes risk detection method, DiabML,\nwhich uses the BWO algorithm and ML methods. BWO is utilized for feature\nselection and SMOTE for imbalance handling in the pre-processing procedure. The\nsimulation results prove the superiority of the proposed DiabML method compared\nto the existing works. DiabML achieves 86.1\\% classification accuracy by\nAdaBoost classifier outperforms the relevant existing methods.\n", "date": "2024-10-30", "categories": []}
{"id": "2411.02354", "title": "Machine learning identification of maternal inflammatory response and\n  histologic choroamnionitis from placental membrane whole slide images", "abstract": "  The placenta forms a critical barrier to infection through pregnancy, labor\nand, delivery. Inflammatory processes in the placenta have short-term, and\nlong-term consequences for offspring health. Digital pathology and machine\nlearning can play an important role in understanding placental inflammation,\nand there have been very few investigations into methods for predicting and\nunderstanding Maternal Inflammatory Response (MIR). This work intends to\ninvestigate the potential of using machine learning to understand MIR based on\nwhole slide images (WSI), and establish early benchmarks. To that end, we use\nMultiple Instance Learning framework with 3 feature extractors: ImageNet-based\nEfficientNet-v2s, and 2 histopathology foundation models, UNI and Phikon to\ninvestigate predictability of MIR stage from histopathology WSIs. We also\ninterpret predictions from these models using the learned attention maps from\nthese models. We also use the MIL framework for predicting white blood cells\ncount (WBC) and maximum fever temperature ($T_{max}$). Attention-based MIL\nmodels are able to classify MIR with a balanced accuracy of up to 88.5% with a\nCohen's Kappa ($\\kappa$) of up to 0.772. Furthermore, we found that the\npathology foundation models (UNI and Phikon) are both able to achieve higher\nperformance with balanced accuracy and $\\kappa$, compared to ImageNet-based\nfeature extractor (EfficientNet-v2s). For WBC and $T_{max}$ prediction, we\nfound mild correlation between actual values and those predicted from\nhistopathology WSIs. We used MIL framework for predicting MIR stage from WSIs,\nand compared effectiveness of foundation models as feature extractors, with\nthat of an ImageNet-based model. We further investigated model failure cases\nand found them to be either edge cases prone to interobserver variability,\nexamples of pathologist's overreach, or mislabeled due to processing errors.\n", "date": "2024-11-04", "categories": []}
{"id": "2411.00044", "title": "MIMIC-IV-Ext-PE: Using a large language model to predict pulmonary\n  embolism phenotype in the MIMIC-IV dataset", "abstract": "  Pulmonary embolism (PE) is a leading cause of preventable in-hospital\nmortality. Advances in diagnosis, risk stratification, and prevention can\nimprove outcomes. There are few large publicly available datasets that contain\nPE labels for research. Using the MIMIC-IV database, we extracted all available\nradiology reports of computed tomography pulmonary angiography (CTPA) scans and\ntwo physicians manually labeled the results as PE positive (acute PE) or PE\nnegative. We then applied a previously finetuned Bio_ClinicalBERT transformer\nlanguage model, VTE-BERT, to extract labels automatically. We verified\nVTE-BERT's reliability by measuring its performance against manual\nadjudication. We also compared the performance of VTE-BERT to diagnosis codes.\nWe found that VTE-BERT has a sensitivity of 92.4% and positive predictive value\n(PPV) of 87.8% on all 19,942 patients with CTPA radiology reports from the\nemergency room and/or hospital admission. In contrast, diagnosis codes have a\nsensitivity of 95.4% and PPV of 83.8% on the subset of 11,990 hospitalized\npatients with discharge diagnosis codes. We successfully add nearly 20,000\nlabels to CTPAs in a publicly available dataset and demonstrate the external\nvalidity of a semi-supervised language model in accelerating hematologic\nresearch.\n", "date": "2024-10-29", "categories": []}
{"id": "2411.00200", "title": "MEDS-Tab: Automated tabularization and baseline methods for MEDS\n  datasets", "abstract": "  Effective, reliable, and scalable development of machine learning (ML)\nsolutions for structured electronic health record (EHR) data requires the\nability to reliably generate high-quality baseline models for diverse\nsupervised learning tasks in an efficient and performant manner. Historically,\nproducing such baseline models has been a largely manual effort--individual\nresearchers would need to decide on the particular featurization and\ntabularization processes to apply to their individual raw, longitudinal data;\nand then train a supervised model over those data to produce a baseline result\nto compare novel methods against, all for just one task and one dataset. In\nthis work, powered by complementary advances in core data standardization\nthrough the MEDS framework, we dramatically simplify and accelerate this\nprocess of tabularizing irregularly sampled time-series data, providing\nresearchers the ability to automatically and scalably featurize and tabularize\ntheir longitudinal EHR data across tens of thousands of individual features,\nhundreds of millions of clinical events, and diverse windowing horizons and\naggregation strategies, all before ultimately leveraging these tabular data to\nautomatically produce high-caliber XGBoost baselines in a highly\ncomputationally efficient manner. This system scales to dramatically larger\ndatasets than tabularization tools currently available to the community and\nenables researchers with any MEDS format dataset to immediately begin producing\nreliable and performant baseline prediction results on various tasks, with\nminimal human effort required. This system will greatly enhance the\nreliability, reproducibility, and ease of development of powerful ML solutions\nfor health problems across diverse datasets and clinical settings.\n", "date": "2024-10-31", "categories": []}
{"id": "2411.00317", "title": "Forecasting Mortality in the Middle-Aged and Older Population of\n  England: A 1D-CNN Approach", "abstract": "  Convolutional Neural Networks (CNNs) are proven to be effective when data are\nhomogeneous such as images, or when there is a relationship between consecutive\ndata such as time series data. Although CNNs are not famous for tabular data,\nwe show that we can use them in longitudinal data, where individuals'\ninformation is recorded over a period and therefore there is a relationship\nbetween them. This study considers the English Longitudinal Study of Ageing\n(ELSA) survey, conducted every two years. We use one-dimensional convolutional\nneural networks (1D-CNNs) to forecast mortality using socio-demographics,\ndiseases, mobility impairment, Activities of Daily Living (ADLs), Instrumental\nActivities of Daily Living (IADLs), and lifestyle factors. As our dataset is\nhighly imbalanced, we try different over and undersampling methods and find\nthat over-representing the small class improves the results. We also try our\nmodel with different activation functions. Our results show that swish\nnonlinearity outperforms other functions.\n", "date": "2024-11-01", "categories": []}
{"id": "2410.23386", "title": "STIED: A deep learning model for the SpatioTemporal detection of focal\n  Interictal Epileptiform Discharges with MEG", "abstract": "  Magnetoencephalography (MEG) allows the non-invasive detection of interictal\nepileptiform discharges (IEDs). Clinical MEG analysis in epileptic patients\ntraditionally relies on the visual identification of IEDs, which is time\nconsuming and partially subjective. Automatic, data-driven detection methods\nexist but show limited performance. Still, the rise of deep learning (DL)-with\nits ability to reproduce human-like abilities-could revolutionize clinical MEG\npractice. Here, we developed and validated STIED, a simple yet powerful\nsupervised DL algorithm combining two convolutional neural networks with\ntemporal (1D time-course) and spatial (2D topography) features of MEG signals\ninspired from current clinical guidelines. Our DL model enabled both temporal\nand spatial localization of IEDs in patients suffering from focal epilepsy with\nfrequent and high amplitude spikes (FE group), with high-performance\nmetrics-accuracy, specificity, and sensitivity all exceeding 85%-when learning\nfrom spatiotemporal features of IEDs. This performance can be attributed to our\nhandling of input data, which mimics established clinical MEG practice. Reverse\nengineering further revealed that STIED encodes fine spatiotemporal features of\nIEDs rather than their mere amplitude. The model trained on the FE group also\nshowed promising results when applied to a separate group of presurgical\npatients with different types of refractory focal epilepsy, though further work\nis needed to distinguish IEDs from physiological transients. This study paves\nthe way of incorporating STIED and DL algorithms into the routine clinical MEG\nevaluation of epilepsy.\n", "date": "2024-10-30", "categories": []}
{"id": "2410.24002", "title": "Assessing the Efficacy of Classical and Deep Neuroimaging Biomarkers in\n  Early Alzheimer's Disease Diagnosis", "abstract": "  Alzheimer's disease (AD) is the leading cause of dementia, and its early\ndetection is crucial for effective intervention, yet current diagnostic methods\noften fall short in sensitivity and specificity. This study aims to detect\nsignificant indicators of early AD by extracting and integrating various\nimaging biomarkers, including radiomics, hippocampal texture descriptors,\ncortical thickness measurements, and deep learning features. We analyze\nstructural magnetic resonance imaging (MRI) scans from the Alzheimer's Disease\nNeuroimaging Initiative (ADNI) cohorts, utilizing comprehensive image analysis\nand machine learning techniques. Our results show that combining multiple\nbiomarkers significantly improves detection accuracy. Radiomics and texture\nfeatures emerged as the most effective predictors for early AD, achieving AUCs\nof 0.88 and 0.72 for AD and MCI detection, respectively. Although deep learning\nfeatures proved to be less effective than traditional approaches, incorporating\nage with other biomarkers notably enhanced MCI detection performance.\nAdditionally, our findings emphasize the continued importance of classical\nimaging biomarkers in the face of modern deep-learning approaches, providing a\nrobust framework for early AD diagnosis.\n", "date": "2024-10-31", "categories": []}
{"id": "2403.00880", "title": "CIDGMed: Causal Inference-Driven Medication Recommendation with Enhanced\n  Dual-Granularity Learning", "abstract": "  Medication recommendation aims to integrate patients' long-term health\nrecords to provide accurate and safe medication combinations for specific\nhealth states. Existing methods often fail to deeply explore the true causal\nrelationships between diseases/procedures and medications, resulting in biased\nrecommendations. Additionally, in medication representation learning, the\nrelationships between information at different granularities of medications,\ncoarse-grained (medication itself) and fine-grained (molecular level), are not\neffectively integrated, leading to biases in representation learning. To\naddress these limitations, we propose the Causal Inference-driven\nDual-Granularity Medication Recommendation method (CIDGMed). Our approach\nleverages causal inference to uncover the relationships between\ndiseases/procedures and medications, thereby enhancing the rationality and\ninterpretability of recommendations. By integrating coarse-grained medication\neffects with fine-grained molecular structure information, CIDGMed provides a\ncomprehensive representation of medications. Additionally, we employ a bias\ncorrection model during the prediction phase to further refine recommendations,\nensuring both accuracy and safety. Through extensive experiments, CIDGMed\nsignificantly outperforms current state-of-the-art models across multiple\nmetrics, achieving a 2.54% increase in accuracy, a 3.65% reduction in side\neffects, and a 39.42% improvement in time efficiency. Additionally, we\ndemonstrate the rationale of CIDGMed through a case study.\n", "date": "2024-03-01", "categories": []}
{"id": "2404.12132", "title": "Non-Invasive Suicide Risk Prediction Through Speech Analysis", "abstract": "  The delayed access to specialized psychiatric assessments and care for\npatients at risk of suicidal tendencies in emergency departments creates a\nnotable gap in timely intervention, hindering the provision of adequate mental\nhealth support during critical situations. To address this, we present a\nnon-invasive, speech-based approach for automatic suicide risk assessment. For\nour study, we collected a novel speech recording dataset from $20$ patients. We\nextract three sets of features, including wav2vec, interpretable speech and\nacoustic features, and deep learning-based spectral representations. We proceed\nby conducting a binary classification to assess suicide risk in a\nleave-one-subject-out fashion. Our most effective speech model achieves a\nbalanced accuracy of $66.2\\,\\%$. Moreover, we show that integrating our speech\nmodel with a series of patients' metadata, such as the history of suicide\nattempts or access to firearms, improves the overall result. The metadata\nintegration yields a balanced accuracy of $94.4\\,\\%$, marking an absolute\nimprovement of $28.2\\,\\%$, demonstrating the efficacy of our proposed\napproaches for automatic suicide risk assessment in emergency medicine.\n", "date": "2024-04-18", "categories": []}
{"id": "2410.22481", "title": "Bayesian Counterfactual Prediction Models for HIV Care Retention with\n  Incomplete Outcome and Covariate Information", "abstract": "  Like many chronic diseases, human immunodeficiency virus (HIV) is managed\nover time at regular clinic visits. At each visit, patient features are\nassessed, treatments are prescribed, and a subsequent visit is scheduled. There\nis a need for data-driven methods for both predicting retention and\nrecommending scheduling decisions that optimize retention. Prediction models\ncan be useful for estimating retention rates across a range of scheduling\noptions. However, training such models with electronic health records (EHR)\ninvolves several complexities. First, formal causal inference methods are\nneeded to adjust for observed confounding when estimating retention rates under\ncounterfactual scheduling decisions. Second, competing events such as death\npreclude retention, while censoring events render retention missing. Third,\ninconsistent monitoring of features such as viral load and CD4 count lead to\ncovariate missingness. This paper presents an all-in-one approach for both\npredicting HIV retention and optimizing scheduling while accounting for these\ncomplexities. We formulate and identify causal retention estimands in terms of\npotential return-time under a hypothetical scheduling decision. Flexible\nBayesian approaches are used to model the observed return-time distribution\nwhile accounting for competing and censoring events and form posterior point\nand uncertainty estimates for these estimands. We address the urgent need for\ndata-driven decision support in HIV care by applying our method to EHR from the\nAcademic Model Providing Access to Healthcare (AMPATH) - a consortium of\nclinics that treat HIV in Western Kenya.\n", "date": "2024-10-29", "categories": []}
{"id": "2410.22950", "title": "SpiroActive: Active Learning for Efficient Data Acquisition for\n  Spirometry", "abstract": "  Respiratory illnesses are a significant global health burden. Respiratory\nillnesses, primarily Chronic obstructive pulmonary disease (COPD), is the\nseventh leading cause of poor health worldwide and the third leading cause of\ndeath worldwide, causing 3.23 million deaths in 2019, necessitating early\nidentification and diagnosis for effective mitigation. Among the diagnostic\ntools employed, spirometry plays a crucial role in detecting respiratory\nabnormalities. However, conventional clinical spirometry methods often entail\nconsiderable costs and practical limitations like the need for specialized\nequipment, trained personnel, and a dedicated clinical setting, making them\nless accessible. To address these challenges, wearable spirometry technologies\nhave emerged as promising alternatives, offering accurate, cost-effective, and\nconvenient solutions. The development of machine learning models for wearable\nspirometry heavily relies on the availability of high-quality ground truth\nspirometry data, which is a laborious and expensive endeavor. In this research,\nwe propose using active learning, a sub-field of machine learning, to mitigate\nthe challenges associated with data collection and labeling. By strategically\nselecting samples from the ground truth spirometer, we can mitigate the need\nfor resource-intensive data collection. We present evidence that models trained\non small subsets obtained through active learning achieve comparable/better\nresults than models trained on the complete dataset.\n", "date": "2024-10-30", "categories": []}
{"id": "2402.09358", "title": "Integrating ChatGPT into Secure Hospital Networks: A Case Study on\n  Improving Radiology Report Analysis", "abstract": "  This study demonstrates the first in-hospital adaptation of a cloud-based AI,\nsimilar to ChatGPT, into a secure model for analyzing radiology reports,\nprioritizing patient data privacy. By employing a unique sentence-level\nknowledge distillation method through contrastive learning, we achieve over 95%\naccuracy in detecting anomalies. The model also accurately flags uncertainties\nin its predictions, enhancing its reliability and interpretability for\nphysicians with certainty indicators. These advancements represent significant\nprogress in developing secure and efficient AI tools for healthcare, suggesting\na promising future for in-hospital AI applications with minimal supervision.\n", "date": "2024-02-14", "categories": []}
{"id": "2410.21640", "title": "A Tutorial on Clinical Speech AI Development: From Data Collection to\n  Model Validation", "abstract": "  There has been a surge of interest in leveraging speech as a marker of health\nfor a wide spectrum of conditions. The underlying premise is that any\nneurological, mental, or physical deficits that impact speech production can be\nobjectively assessed via automated analysis of speech. Recent advances in\nspeech-based Artificial Intelligence (AI) models for diagnosing and tracking\nmental health, cognitive, and motor disorders often use supervised learning,\nsimilar to mainstream speech technologies like recognition and verification.\nHowever, clinical speech AI has distinct challenges, including the need for\nspecific elicitation tasks, small available datasets, diverse speech\nrepresentations, and uncertain diagnostic labels. As a result, application of\nthe standard supervised learning paradigm may lead to models that perform well\nin controlled settings but fail to generalize in real-world clinical\ndeployments. With translation into real-world clinical scenarios in mind, this\ntutorial paper provides an overview of the key components required for robust\ndevelopment of clinical speech AI. Specifically, this paper will cover the\ndesign of speech elicitation tasks and protocols most appropriate for different\nclinical conditions, collection of data and verification of hardware,\ndevelopment and validation of speech representations designed to measure\nclinical constructs of interest, development of reliable and robust clinical\nprediction models, and ethical and participant considerations for clinical\nspeech AI. The goal is to provide comprehensive guidance on building models\nwhose inputs and outputs link to the more interpretable and clinically\nmeaningful aspects of speech, that can be interrogated and clinically validated\non clinical datasets, and that adhere to ethical, privacy, and security\nconsiderations by design.\n", "date": "2024-10-29", "categories": []}
{"id": "2410.21836", "title": "Multi-aspect Depression Severity Assessment via Inductive Dialogue\n  System", "abstract": "  With the advancement of chatbots and the growing demand for automatic\ndepression detection, identifying depression in patient conversations has\ngained more attention. However, prior methods often assess depression in a\nbinary way or only a single score without diverse feedback and lack focus on\nenhancing dialogue responses. In this paper, we present a novel task of\nmulti-aspect depression severity assessment via an inductive dialogue system\n(MaDSA), evaluating a patient's depression level on multiple criteria by\nincorporating an assessment-aided response generation. Further, we propose a\nfoundational system for MaDSA, which induces psychological dialogue responses\nwith an auxiliary emotion classification task within a hierarchical severity\nassessment structure. We synthesize the conversational dataset annotated with\neight aspects of depression severity alongside emotion labels, proven robust\nvia human evaluations. Experimental results show potential for our preliminary\nwork on MaDSA.\n", "date": "2024-10-29", "categories": []}
{"id": "2303.14303", "title": "Unsupervised Feature Selection to Identify Important ICD-10 Codes for\n  Machine Learning: A Case Study on a Coronary Artery Disease Patient Cohort", "abstract": "  The use of International Classification of Diseases (ICD) codes in healthcare\npresents a challenge in selecting relevant codes as features for machine\nlearning models due to this system's large number of codes. In this study, we\ncompared several unsupervised feature selection methods for an ICD code\ndatabase of 49,075 coronary artery disease patients in Alberta, Canada.\nSpecifically, we employed Laplacian Score, Unsupervised Feature Selection for\nMulti-Cluster Data, Autoencoder Inspired Unsupervised Feature Selection,\nPrincipal Feature Analysis, and Concrete Autoencoders with and without ICD tree\nweight adjustment to select the 100 best features from over 9,000 codes. We\nassessed the selected features based on their ability to reconstruct the\ninitial feature space and predict 90-day mortality following discharge. Our\nfindings revealed that the Concrete Autoencoder methods outperformed all other\nmethods in both tasks. Furthermore, the weight adjustment in the Concrete\nAutoencoder method decreased the complexity of features.\n", "date": "2023-03-25", "categories": []}
{"id": "2410.19739", "title": "The Effect of Acute Stress on the Interpretability and Generalization of\n  Schizophrenia Predictive Machine Learning Models", "abstract": "  Introduction Schizophrenia is a severe mental disorder, and early diagnosis\nis key to improving outcomes. Its complexity makes predicting onset and\nprogression challenging. EEG has emerged as a valuable tool for studying\nschizophrenia, with machine learning increasingly applied for diagnosis. This\npaper assesses the accuracy of ML models for predicting schizophrenia and\nexamines the impact of stress during EEG recording on model performance. We\nintegrate acute stress prediction into the analysis, showing that overlapping\nconditions like stress during recording can negatively affect model accuracy.\n  Methods Four XGBoost models were built: one for stress prediction, two to\nclassify schizophrenia (at rest and task), and a model to predict schizophrenia\nfor both conditions. XAI techniques were applied to analyze results.\nExperiments tested the generalization of schizophrenia models using their\ndatasets' healthy controls and independent health-screened controls. The stress\nmodel identified high-stress subjects, who were excluded from further analysis.\nA novel method was used to adjust EEG frequency band power to remove stress\nartifacts, improving predictive model performance.\n  Results Our results show that acute stress vary across EEG sessions,\naffecting model performance and accuracy. Generalization improved once these\nvarying stress levels were considered and compensated for during model\ntraining. Our findings highlight the importance of thorough health screening\nand management of the patient's condition during the process. Stress induced\nduring or by the EEG recording can adversely affect model generalization. This\nmay require further preprocessing of data by treating stress as an additional\nphysiological artifact. Our proposed approach to compensate for stress\nartifacts in EEG data used for training models showed a significant improvement\nin predictive performance.\n", "date": "2024-10-04", "categories": []}
{"id": "2410.19887", "title": "TBBC: Predict True Bacteraemia in Blood Cultures via Deep Learning", "abstract": "  Bacteraemia, a bloodstream infection with high morbidity and mortality rates,\nposes significant diagnostic challenges. Accurate diagnosis through blood\ncultures is resource-intensive. Developing a machine learning model to predict\nblood culture outcomes in emergency departments offers potential for improved\ndiagnosis, reduced healthcare costs, and mitigated antibiotic use.This thesis\naims to identify optimal machine learning techniques for predicting bacteraemia\nand develop a predictive model using data from St. Antonius Hospital's\nemergency department. Based on current literature, CatBoost and Random Forest\nwere selected as the most promising techniques. Model optimization using Optuna\nprioritized sensitivity.The final Random Forest model achieved an ROC AUC of\n0.78 and demonstrated 0.92 sensitivity on the test set. Notably, it accurately\nidentified 36.02% of patients at low risk of bacteraemia, with only 0.85% false\nnegatives.Implementation of this model in St. Antonius Hospital's emergency\ndepartment could reduce blood cultures, healthcare costs, and antibiotic\ntreatments. Future studies should focus on external validation, exploring\nadvanced techniques, and addressing potential confounders to ensure model\ngeneralizability.\n", "date": "2024-10-25", "categories": []}
{"id": "2410.20300", "title": "Predicting Mortality and Functional Status Scores of Traumatic Brain\n  Injury Patients using Supervised Machine Learning", "abstract": "  Traumatic brain injury (TBI) presents a significant public health challenge,\noften resulting in mortality or lasting disability. Predicting outcomes such as\nmortality and Functional Status Scale (FSS) scores can enhance treatment\nstrategies and inform clinical decision-making. This study applies supervised\nmachine learning (ML) methods to predict mortality and FSS scores using a\nreal-world dataset of 300 pediatric TBI patients from the University of\nColorado School of Medicine. The dataset captures clinical features, including\ndemographics, injury mechanisms, and hospitalization outcomes. Eighteen ML\nmodels were evaluated for mortality prediction, and thirteen models were\nassessed for FSS score prediction. Performance was measured using accuracy, ROC\nAUC, F1-score, and mean squared error. Logistic regression and Extra Trees\nmodels achieved high precision in mortality prediction, while linear regression\ndemonstrated the best FSS score prediction. Feature selection reduced 103\nclinical variables to the most relevant, enhancing model efficiency and\ninterpretability. This research highlights the role of ML models in identifying\nhigh-risk patients and supporting personalized interventions, demonstrating the\npotential of data-driven analytics to improve TBI care and integrate into\nclinical workflows.\n", "date": "2024-10-27", "categories": []}
{"id": "2206.04548", "title": "Classification of COVID-19 in Chest X-ray Images Using Fusion of Deep\n  Features and LightGBM", "abstract": "  The COVID-19 disease was first discovered in Wuhan, China, and spread quickly\nworldwide. After the COVID-19 pandemic, many researchers have begun to identify\na way to diagnose the COVID-19 using chest X-ray images. The early diagnosis of\nthis disease can significantly impact the treatment process. In this article,\nwe propose a new technique that is faster and more accurate than the other\nmethods reported in the literature. The proposed method uses a combination of\nDenseNet169 and MobileNet Deep Neural Networks to extract the features of the\npatient's X-ray images. Using the univariate feature selection algorithm, we\nrefined the features for the most important ones. Then we applied the selected\nfeatures as input to the LightGBM (Light Gradient Boosting Machine) algorithm\nfor classification. To assess the effectiveness of the proposed method, the\nChestX-ray8 dataset, which includes 1125 X-ray images of the patient's chest,\nwas used. The proposed method achieved 98.54% and 91.11% accuracies in the\ntwo-class (COVID-19, Healthy) and multi-class (COVID-19, Healthy, Pneumonia)\nclassification problems, respectively. It is worth mentioning that we have used\nGradient-weighted Class Activation Mapping (Grad-CAM) for further analysis.\n", "date": "2022-06-09", "categories": []}
{"id": "2304.06016", "title": "PD-ADSV: An Automated Diagnosing System Using Voice Signals and Hard\n  Voting Ensemble Method for Parkinson's Disease", "abstract": "  Parkinson's disease (PD) is the most widespread movement condition and the\nsecond most common neurodegenerative disorder, following Alzheimer's. Movement\nsymptoms and imaging techniques are the most popular ways to diagnose this\ndisease. However, they are not accurate and fast and may only be accessible to\na few people. This study provides an autonomous system, i.e., PD-ADSV, for\ndiagnosing PD based on voice signals, which uses four machine learning\nclassifiers and the hard voting ensemble method to achieve the highest\naccuracy. PD-ADSV is developed using Python and the Gradio web framework.\n", "date": "2023-04-11", "categories": []}
{"id": "2305.05738", "title": "DOCTOR: A Multi-Disease Detection Continual Learning Framework Based on\n  Wearable Medical Sensors", "abstract": "  Modern advances in machine learning (ML) and wearable medical sensors (WMSs)\nin edge devices have enabled ML-driven disease detection for smart healthcare.\nConventional ML-driven methods for disease detection rely on customizing\nindividual models for each disease and its corresponding WMS data. However,\nsuch methods lack adaptability to distribution shifts and new task\nclassification classes. In addition, they need to be rearchitected and\nretrained from scratch for each new disease. Moreover, installing multiple ML\nmodels in an edge device consumes excessive memory, drains the battery faster,\nand complicates the detection process. To address these challenges, we propose\nDOCTOR, a multi-disease detection continual learning (CL) framework based on\nWMSs. It employs a multi-headed deep neural network (DNN) and a replay-style CL\nalgorithm. The CL algorithm enables the framework to continually learn new\nmissions where different data distributions, classification classes, and\ndisease detection tasks are introduced sequentially. It counteracts\ncatastrophic forgetting with a data preservation method and a synthetic data\ngeneration (SDG) module. The data preservation method preserves the most\ninformative subset of real training data from previous missions for exemplar\nreplay. The SDG module models the probability distribution of the real training\ndata and generates synthetic data for generative replay while retaining data\nprivacy. The multi-headed DNN enables DOCTOR to detect multiple diseases\nsimultaneously based on user WMS data. We demonstrate DOCTOR's efficacy in\nmaintaining high disease classification accuracy with a single DNN model in\nvarious CL experiments. In complex scenarios, DOCTOR achieves 1.43 times better\naverage test accuracy, 1.25 times better F1-score, and 0.41 higher backward\ntransfer than the naive fine-tuning framework with a small model size of less\nthan 350KB.\n", "date": "2023-05-09", "categories": []}
{"id": "2409.02046", "title": "Human-AI Collaborative Multi-modal Multi-rater Learning for\n  Endometriosis Diagnosis", "abstract": "  Endometriosis, affecting about 10% of individuals assigned female at birth,\nis challenging to diagnose and manage. Diagnosis typically involves the\nidentification of various signs of the disease using either laparoscopic\nsurgery or the analysis of T1/T2 MRI images, with the latter being quicker and\ncheaper but less accurate. A key diagnostic sign of endometriosis is the\nobliteration of the Pouch of Douglas (POD). However, even experienced\nclinicians struggle with accurately classifying POD obliteration from MRI\nimages, which complicates the training of reliable AI models. In this paper, we\nintroduce the Human-AI Collaborative Multi-modal Multi-rater Learning (HAICOMM)\nmethodology to address the challenge above. HAICOMM is the first method that\nexplores three important aspects of this problem: 1) multi-rater learning to\nextract a cleaner label from the multiple \"noisy\" labels available per training\nsample; 2) multi-modal learning to leverage the presence of T1/T2 MRI images\nfor training and testing; and 3) human-AI collaboration to build a system that\nleverages the predictions from clinicians and the AI model to provide more\naccurate classification than standalone clinicians and AI models. Presenting\nresults on the multi-rater T1/T2 MRI endometriosis dataset that we collected to\nvalidate our methodology, the proposed HAICOMM model outperforms an ensemble of\nclinicians, noisy-label learning models, and multi-rater learning methods.\n", "date": "2024-09-03", "categories": []}
{"id": "2409.12087", "title": "Towards Interpretable End-Stage Renal Disease (ESRD) Prediction:\n  Utilizing Administrative Claims Data with Explainable AI Techniques", "abstract": "  This study explores the potential of utilizing administrative claims data,\ncombined with advanced machine learning and deep learning techniques, to\npredict the progression of Chronic Kidney Disease (CKD) to End-Stage Renal\nDisease (ESRD). We analyze a comprehensive, 10-year dataset provided by a major\nhealth insurance organization to develop prediction models for multiple\nobservation windows using traditional machine learning methods such as Random\nForest and XGBoost as well as deep learning approaches such as Long Short-Term\nMemory (LSTM) networks. Our findings demonstrate that the LSTM model,\nparticularly with a 24-month observation window, exhibits superior performance\nin predicting ESRD progression, outperforming existing models in the\nliterature. We further apply SHapley Additive exPlanations (SHAP) analysis to\nenhance interpretability, providing insights into the impact of individual\nfeatures on predictions at the individual patient level. This study underscores\nthe value of leveraging administrative claims data for CKD management and\npredicting ESRD progression.\n", "date": "2024-09-18", "categories": []}
{"id": "2410.18985", "title": "rECGnition_v1.0: Arrhythmia detection using cardiologist-inspired\n  multi-modal architecture incorporating demographic attributes in ECG", "abstract": "  A substantial amount of variability in ECG manifested due to patient\ncharacteristics hinders the adoption of automated analysis algorithms in\nclinical practice. None of the ECG annotators developed till date consider the\ncharacteristics of the patients in a multi-modal architecture. We employed the\nXGBoost model to analyze the UCI Arrhythmia dataset, linking patient\ncharacteristics to ECG morphological changes. The model accurately classified\npatient gender using discriminative ECG features with 87.75% confidence. We\npropose a novel multi-modal methodology for ECG analysis and arrhythmia\nclassification that can help defy the variability in ECG related to\npatient-specific conditions. This deep learning algorithm, named\nrECGnition_v1.0 (robust ECG abnormality detection Version 1), fuses Beat\nMorphology with Patient Characteristics to create a discriminative feature map\nthat understands the internal correlation between both modalities. A Squeeze\nand Excitation based Patient characteristic Encoding Network (SEPcEnet) has\nbeen introduced, considering the patient's demographics. The trained model\noutperformed the various existing algorithms by achieving the overall F1-score\nof 0.986 for the ten arrhythmia class classification in the MITDB and achieved\nnear perfect prediction scores of ~0.99 for LBBB, RBBB, Premature ventricular\ncontraction beat, Atrial premature beat and Paced beat. Subsequently, the\nmethodology was validated across INCARTDB, EDB and different class groups of\nMITDB using transfer learning. The generalizability test provided F1-scores of\n0.980, 0.946, 0.977, and 0.980 for INCARTDB, EDB, MITDB AAMI, and MITDB Normal\nvs. Abnormal Classification, respectively. Therefore, with a more enhanced and\ncomprehensive understanding of the patient being examined and their ECG for\ndiverse CVD manifestations, the proposed rECGnition_v1.0 algorithm paves the\nway for its deployment in clinics.\n", "date": "2024-10-09", "categories": []}
{"id": "2410.19575", "title": "Considerations for Distribution Shift Robustness of Diagnostic Models in\n  Healthcare", "abstract": "  We consider robustness to distribution shifts in the context of diagnostic\nmodels in healthcare, where the prediction target $Y$, e.g., the presence of a\ndisease, is causally upstream of the observations $X$, e.g., a biomarker.\nDistribution shifts may occur, for instance, when the training data is\ncollected in a domain with patients having particular demographic\ncharacteristics while the model is deployed on patients from a different\ndemographic group. In the domain of applied ML for health, it is common to\npredict $Y$ from $X$ without considering further information about the patient.\nHowever, beyond the direct influence of the disease $Y$ on biomarker $X$, a\npredictive model may learn to exploit confounding dependencies (or shortcuts)\nbetween $X$ and $Y$ that are unstable under certain distribution shifts. In\nthis work, we highlight a data generating mechanism common to healthcare\nsettings and discuss how recent theoretical results from the causality\nliterature can be applied to build robust predictive models. We theoretically\nshow why ignoring covariates as well as common invariant learning approaches\nwill in general not yield robust predictors in the studied setting, while\nincluding certain covariates into the prediction model will. In an extensive\nsimulation study, we showcase the robustness (or lack thereof) of different\npredictors under various data generating processes. Lastly, we analyze the\nperformance of the different approaches using the PTB-XL dataset, a public\ndataset of annotated ECG recordings.\n", "date": "2024-10-25", "categories": []}
{"id": "2410.19690", "title": "Deep Learning for Classification of Inflammatory Bowel Disease Activity\n  in Whole Slide Images of Colonic Histopathology", "abstract": "  Grading inflammatory bowel disease (IBD) activity using standardized\nhistopathological scoring systems remains challenging due to resource\nconstraints and inter-observer variability. In this study, we developed a deep\nlearning model to classify activity grades in hematoxylin and eosin-stained\nwhole slide images (WSIs) from patients with IBD, offering a robust approach\nfor general pathologists. We utilized 2,077 WSIs from 636 patients treated at\nDartmouth-Hitchcock Medical Center in 2018 and 2019, scanned at 40x\nmagnification (0.25 micron/pixel). Board-certified gastrointestinal\npathologists categorized the WSIs into four activity classes: inactive, mildly\nactive, moderately active, and severely active. A transformer-based model was\ndeveloped and validated using five-fold cross-validation to classify IBD\nactivity. Using HoVerNet, we examined neutrophil distribution across activity\ngrades. Attention maps from our model highlighted areas contributing to its\nprediction. The model classified IBD activity with weighted averages of 0.871\n[95% Confidence Interval (CI): 0.860-0.883] for the area under the curve, 0.695\n[95% CI: 0.674-0.715] for precision, 0.697 [95% CI: 0.678-0.716] for recall,\nand 0.695 [95% CI: 0.674-0.714] for F1-score. Neutrophil distribution was\nsignificantly different across activity classes. Qualitative evaluation of\nattention maps by a gastrointestinal pathologist suggested their potential for\nimproved interpretability. Our model demonstrates robust diagnostic performance\nand could enhance consistency and efficiency in IBD activity assessment.\n", "date": "2024-10-25", "categories": []}
{"id": "2410.18091", "title": "Predicting Fine-grained Behavioral and Psychological Symptoms of\n  Dementia Based on Machine Learning and Smart Wearable Devices", "abstract": "  Behavioral and Psychological Symptoms of Dementia (BPSD) impact dementia care\nsubstantially, affecting both patients and caregivers. Effective management and\nearly detection of BPSD are crucial to reduce the stress and burden on\ncaregivers and healthcare systems. Despite the advancements in machine learning\nfor dementia prediction, there is a considerable gap in utilizing these methods\nfor BPSD prediction. This study aims to fill this gap by presenting a novel\npersonalized framework for BPSD prediction, utilizing physiological signals\nfrom smart wearable devices. Our personalized fine-grained BPSD prediction\nmethod accurately predicts BPSD occurrences by extracting individual behavioral\npatterns, while the generalized models identify diverse patterns and\ndifferentiate between various BPSD symptoms. Detailed comparisons between the\nproposed personalized method and conventional generalized methods reveals\nsubstantial improvements across all performance metrics, including a 16.0%\nincrease in AUC. These results demonstrate the potential of our proposed method\nin advancing dementia care by enabling proactive interventions and improving\npatient outcomes in real-world scenarios. To the best of our knowledge, this is\nthe first study that leverages physiological signals from smart wearable\ndevices to predict BPSD, marking a significant stride in dementia care\nresearch.\n", "date": "2024-10-08", "categories": []}
{"id": "2410.18094", "title": "Self-supervised inter-intra period-aware ECG representation learning for\n  detecting atrial fibrillation", "abstract": "  Atrial fibrillation is a commonly encountered clinical arrhythmia associated\nwith stroke and increased mortality. Since professional medical knowledge is\nrequired for annotation, exploiting a large corpus of ECGs to develop accurate\nsupervised learning-based atrial fibrillation algorithms remains challenging.\nSelf-supervised learning (SSL) is a promising recipe for generalized ECG\nrepresentation learning, eliminating the dependence on expensive labeling.\nHowever, without well-designed incorporations of knowledge related to atrial\nfibrillation, existing SSL approaches typically suffer from unsatisfactory\ncapture of robust ECG representations. In this paper, we propose an inter-intra\nperiod-aware ECG representation learning approach. Considering ECGs of atrial\nfibrillation patients exhibit the irregularity in RR intervals and the absence\nof P-waves, we develop specific pre-training tasks for interperiod and\nintraperiod representations, aiming to learn the single-period stable\nmorphology representation while retaining crucial interperiod features. After\nfurther fine-tuning, our approach demonstrates remarkable AUC performances on\nthe BTCH dataset, \\textit{i.e.}, 0.953/0.996 for paroxysmal/persistent atrial\nfibrillation detection. On commonly used benchmarks of CinC2017 and CPSC2021,\nthe generalization capability and effectiveness of our methodology are\nsubstantiated with competitive results.\n", "date": "2024-10-08", "categories": []}
{"id": "2410.18354", "title": "Assessing Alcohol Use Disorder: Insights from Lifestyle, Background, and\n  Family History with Machine Learning Techniques", "abstract": "  This study explored how lifestyle, personal background, and family history\ncontribute to the risk of developing Alcohol Use Disorder (AUD). Survey data\nfrom the All of Us Program was utilized to extract information on AUD status,\nlifestyle, personal background, and family history for 6,016 participants. Key\ndeterminants of AUD were identified using decision trees including annual\nincome, recreational drug use, length of residence, sex/gender, marital status,\neducation level, and family history of AUD. Data visualization and Chi-Square\nTests of Independence were then used to assess associations between identified\nfactors and AUD. Afterwards, machine learning techniques including decision\ntrees, random forests, and Naive Bayes were applied to predict an individual's\nlikelihood of developing AUD. Random forests were found to achieve the highest\naccuracy (82%), compared to Decision Trees and Naive Bayes. Findings from this\nstudy can offer insights that help parents, healthcare professionals, and\neducators develop strategies to reduce AUD risk, enabling early intervention\nand targeted prevention efforts.\n", "date": "2024-10-24", "categories": []}
{"id": "2410.18551", "title": "IMAN: An Adaptive Network for Robust NPC Mortality Prediction with\n  Missing Modalities", "abstract": "  Accurate prediction of mortality in nasopharyngeal carcinoma (NPC), a complex\nmalignancy particularly challenging in advanced stages, is crucial for\noptimizing treatment strategies and improving patient outcomes. However, this\npredictive process is often compromised by the high-dimensional and\nheterogeneous nature of NPC-related data, coupled with the pervasive issue of\nincomplete multi-modal data, manifesting as missing radiological images or\nincomplete diagnostic reports. Traditional machine learning approaches suffer\nsignificant performance degradation when faced with such incomplete data, as\nthey fail to effectively handle the high-dimensionality and intricate\ncorrelations across modalities. Even advanced multi-modal learning techniques\nlike Transformers struggle to maintain robust performance in the presence of\nmissing modalities, as they lack specialized mechanisms to adaptively integrate\nand align the diverse data types, while also capturing nuanced patterns and\ncontextual relationships within the complex NPC data. To address these problem,\nwe introduce IMAN: an adaptive network for robust NPC mortality prediction with\nmissing modalities.\n", "date": "2024-10-24", "categories": []}
{"id": "2403.06197", "title": "DrFuse: Learning Disentangled Representation for Clinical Multi-Modal\n  Fusion with Missing Modality and Modal Inconsistency", "abstract": "  The combination of electronic health records (EHR) and medical images is\ncrucial for clinicians in making diagnoses and forecasting prognosis.\nStrategically fusing these two data modalities has great potential to improve\nthe accuracy of machine learning models in clinical prediction tasks. However,\nthe asynchronous and complementary nature of EHR and medical images presents\nunique challenges. Missing modalities due to clinical and administrative\nfactors are inevitable in practice, and the significance of each data modality\nvaries depending on the patient and the prediction target, resulting in\ninconsistent predictions and suboptimal model performance. To address these\nchallenges, we propose DrFuse to achieve effective clinical multi-modal fusion.\nIt tackles the missing modality issue by disentangling the features shared\nacross modalities and those unique within each modality. Furthermore, we\naddress the modal inconsistency issue via a disease-wise attention layer that\nproduces the patient- and disease-wise weighting for each modality to make the\nfinal prediction. We validate the proposed method using real-world large-scale\ndatasets, MIMIC-IV and MIMIC-CXR. Experimental results show that the proposed\nmethod significantly outperforms the state-of-the-art models. Our\nimplementation is publicly available at https://github.com/dorothy-yao/drfuse.\n", "date": "2024-03-10", "categories": []}
{"id": "2403.15012", "title": "Empirical investigation of multi-source cross-validation in clinical ECG\n  classification", "abstract": "  Traditionally, machine learning-based clinical prediction models have been\ntrained and evaluated on patient data from a single source, such as a hospital.\nCross-validation methods can be used to estimate the accuracy of such models on\nnew patients originating from the same source, by repeated random splitting of\nthe data. However, such estimates tend to be highly overoptimistic when\ncompared to accuracy obtained from deploying models to sources not represented\nin the dataset, such as a new hospital. The increasing availability of\nmulti-source medical datasets provides new opportunities for obtaining more\ncomprehensive and realistic evaluations of expected accuracy through\nsource-level cross-validation designs.\n  In this study, we present a systematic empirical evaluation of standard\nK-fold cross-validation and leave-source-out cross-validation methods in a\nmulti-source setting. We consider the task of electrocardiogram based\ncardiovascular disease classification, combining and harmonizing the openly\navailable PhysioNet CinC Challenge 2021 and the Shandong Provincial Hospital\ndatasets for our study.\n  Our results show that K-fold cross-validation, both on single-source and\nmulti-source data, systemically overestimates prediction performance when the\nend goal is to generalize to new sources. Leave-source-out cross-validation\nprovides more reliable performance estimates, having close to zero bias though\nlarger variability. The evaluation highlights the dangers of obtaining\nmisleading cross-validation results on medical data and demonstrates how these\nissues can be mitigated when having access to multi-source data.\n", "date": "2024-03-22", "categories": []}
{"id": "2405.02485", "title": "A Survey of Few-Shot Learning for Biomedical Time Series", "abstract": "  Advancements in wearable sensor technologies and the digitization of medical\nrecords have contributed to the unprecedented ubiquity of biomedical time\nseries data. Data-driven models have tremendous potential to assist clinical\ndiagnosis and improve patient care by improving long-term monitoring\ncapabilities, facilitating early disease detection and intervention, as well as\npromoting personalized healthcare delivery. However, accessing extensively\nlabeled datasets to train data-hungry deep learning models encounters many\nbarriers, such as long-tail distribution of rare diseases, cost of annotation,\nprivacy and security concerns, data-sharing regulations, and ethical\nconsiderations. An emerging approach to overcome the scarcity of labeled data\nis to augment AI methods with human-like capabilities to leverage past\nexperiences to learn new tasks with limited examples, called few-shot learning.\nThis survey provides a comprehensive review and comparison of few-shot learning\nmethods for biomedical time series applications. The clinical benefits and\nlimitations of such methods are discussed in relation to traditional\ndata-driven approaches. This paper aims to provide insights into the current\nlandscape of few-shot learning for biomedical time series and its implications\nfor future research and applications.\n", "date": "2024-05-03", "categories": []}
{"id": "2410.15947", "title": "AI-Driven Approaches for Glaucoma Detection -- A Comprehensive Review", "abstract": "  The diagnosis of glaucoma plays a critical role in the management and\ntreatment of this vision-threatening disease. Glaucoma is a group of eye\ndiseases that cause blindness by damaging the optic nerve at the back of the\neye. Often called \"silent thief of sight\", it exhibits no symptoms during the\nearly stages. Therefore, early detection is crucial to prevent vision loss.\nWith the rise of Artificial Intelligence (AI), particularly Deep Learning (DL)\ntechniques, Computer-Aided Diagnosis (CADx) systems have emerged as promising\ntools to assist clinicians in accurately diagnosing glaucoma early. This paper\naims to provide a comprehensive overview of AI techniques utilized in CADx\nsystems for glaucoma diagnosis. Through a detailed analysis of current\nliterature, we identify key gaps and challenges in these systems, emphasizing\nthe need for improved safety, reliability, interpretability, and\nexplainability. By identifying research gaps, we aim to advance the field of\nCADx systems especially for the early diagnosis of glaucoma, in order to\nprevent any potential loss of vision.\n", "date": "2024-10-21", "categories": []}
{"id": "2410.16811", "title": "Masked Clinical Modelling: A Framework for Synthetic and Augmented\n  Survival Data Generation", "abstract": "  Access to real clinical data is often restricted due to privacy obligations,\ncreating significant barriers for healthcare research. Synthetic datasets\nprovide a promising solution, enabling secure data sharing and model\ndevelopment. However, most existing approaches focus on data realism rather\nthan utility -- ensuring that models trained on synthetic data yield clinically\nmeaningful insights comparable to those trained on real data. In this paper, we\npresent Masked Clinical Modelling (MCM), a framework inspired by masked\nlanguage modelling, designed for both data synthesis and conditional data\naugmentation. We evaluate this prototype on the WHAS500 dataset using Cox\nProportional Hazards models, focusing on the preservation of hazard ratios as\nkey clinical metrics. Our results show that data generated using the MCM\nframework improves both discrimination and calibration in survival analysis,\noutperforming existing methods. MCM demonstrates strong potential to support\nsurvival data analysis and broader healthcare applications.\n", "date": "2024-10-22", "categories": []}
{"id": "2410.17343", "title": "EEG-DIF: Early Warning of Epileptic Seizures through Generative\n  Diffusion Model-based Multi-channel EEG Signals Forecasting", "abstract": "  Multi-channel EEG signals are commonly used for the diagnosis and assessment\nof diseases such as epilepsy. Currently, various EEG diagnostic algorithms\nbased on deep learning have been developed. However, most research efforts\nfocus solely on diagnosing and classifying current signal data but do not\nconsider the prediction of future trends for early warning. Additionally, since\nmulti-channel EEG can be essentially regarded as the spatio-temporal signal\ndata received by detectors at different locations in the brain, how to\nconstruct spatio-temporal information representations of EEG signals to\nfacilitate future trend prediction for multi-channel EEG becomes an important\nproblem. This study proposes a multi-signal prediction algorithm based on\ngenerative diffusion models (EEG-DIF), which transforms the multi-signal\nforecasting task into an image completion task, allowing for comprehensive\nrepresentation and learning of the spatio-temporal correlations and future\ndevelopmental patterns of multi-channel EEG signals. Here, we employ a publicly\navailable epilepsy EEG dataset to construct and validate the EEG-DIF. The\nresults demonstrate that our method can accurately predict future trends for\nmulti-channel EEG signals simultaneously. Furthermore, the early warning\naccuracy for epilepsy seizures based on the generated EEG data reaches 0.89. In\ngeneral, EEG-DIF provides a novel approach for characterizing multi-channel EEG\nsignals and an innovative early warning algorithm for epilepsy seizures, aiding\nin optimizing and enhancing the clinical diagnosis process. The code is\navailable at https://github.com/JZK00/EEG-DIF.\n", "date": "2024-10-22", "categories": []}
{"id": "2410.17363", "title": "DeLLiriuM: A large language model for delirium prediction in the ICU\n  using structured EHR", "abstract": "  Delirium is an acute confusional state that has been shown to affect up to\n31% of patients in the intensive care unit (ICU). Early detection of this\ncondition could lead to more timely interventions and improved health outcomes.\nWhile artificial intelligence (AI) models have shown great potential for ICU\ndelirium prediction using structured electronic health records (EHR), most of\nthem have not explored the use of state-of-the-art AI models, have been limited\nto single hospitals, or have been developed and validated on small cohorts. The\nuse of large language models (LLM), models with hundreds of millions to\nbillions of parameters, with structured EHR data could potentially lead to\nimproved predictive performance. In this study, we propose DeLLiriuM, a novel\nLLM-based delirium prediction model using EHR data available in the first 24\nhours of ICU admission to predict the probability of a patient developing\ndelirium during the rest of their ICU admission. We develop and validate\nDeLLiriuM on ICU admissions from 104,303 patients pertaining to 195 hospitals\nacross three large databases: the eICU Collaborative Research Database, the\nMedical Information Mart for Intensive Care (MIMIC)-IV, and the University of\nFlorida Health's Integrated Data Repository. The performance measured by the\narea under the receiver operating characteristic curve (AUROC) showed that\nDeLLiriuM outperformed all baselines in two external validation sets, with 0.77\n(95% confidence interval 0.76-0.78) and 0.84 (95% confidence interval\n0.83-0.85) across 77,543 patients spanning 194 hospitals. To the best of our\nknowledge, DeLLiriuM is the first LLM-based delirium prediction tool for the\nICU based on structured EHR data, outperforming deep learning baselines which\nemploy structured features and can provide helpful information to clinicians\nfor timely interventions.\n", "date": "2024-10-22", "categories": []}
{"id": "2410.17545", "title": "Predicting 30-Day Hospital Readmission in Medicare Patients: Insights\n  from an LSTM Deep Learning Model", "abstract": "  Readmissions among Medicare beneficiaries are a major problem for the US\nhealthcare system from a perspective of both healthcare operations and patient\ncaregiving outcomes. Our study analyzes Medicare hospital readmissions using\nLSTM networks with feature engineering to assess feature contributions. We\nselected variables from admission-level data, inpatient medical history and\npatient demography. The LSTM model is designed to capture temporal dynamics\nfrom admission-level and patient-level data. On a case study on the MIMIC\ndataset, the LSTM model outperformed the logistic regression baseline,\naccurately leveraging temporal features to predict readmission. The major\nfeatures were the Charlson Comorbidity Index, hospital length of stay, the\nhospital admissions over the past 6 months, while demographic variables were\nless impactful. This work suggests that LSTM networks offers a more promising\napproach to improve Medicare patient readmission prediction. It captures\ntemporal interactions in patient databases, enhancing current prediction models\nfor healthcare providers. Adoption of predictive models into clinical practice\nmay be more effective in identifying Medicare patients to provide early and\ntargeted interventions to improve patient outcomes.\n", "date": "2024-10-23", "categories": []}
{"id": "2410.17865", "title": "Population stratification for prediction of mortality in post-AKI\n  patients", "abstract": "  Acute kidney injury (AKI) is a serious clinical condition that affects up to\n20% of hospitalised patients. AKI is associated with short term unplanned\nhospital readmission and post-discharge mortality risk. Patient risk and\nhealthcare expenditures can be minimised by followup planning grounded on\npredictive models and machine learning. Since AKI is multi-factorial,\npredictive models specialised in different categories of patients can increase\naccuracy of predictions. In the present article we present some results\nfollowing this approach.\n", "date": "2024-10-23", "categories": []}
{"id": "2410.17918", "title": "Addressing Asynchronicity in Clinical Multimodal Fusion via\n  Individualized Chest X-ray Generation", "abstract": "  Integrating multi-modal clinical data, such as electronic health records\n(EHR) and chest X-ray images (CXR), is particularly beneficial for clinical\nprediction tasks. However, in a temporal setting, multi-modal data are often\ninherently asynchronous. EHR can be continuously collected but CXR is generally\ntaken with a much longer interval due to its high cost and radiation dose. When\nclinical prediction is needed, the last available CXR image might have been\noutdated, leading to suboptimal predictions. To address this challenge, we\npropose DDL-CXR, a method that dynamically generates an up-to-date latent\nrepresentation of the individualized CXR images. Our approach leverages latent\ndiffusion models for patient-specific generation strategically conditioned on a\nprevious CXR image and EHR time series, providing information regarding\nanatomical structures and disease progressions, respectively. In this way, the\ninteraction across modalities could be better captured by the latent CXR\ngeneration process, ultimately improving the prediction performance.\nExperiments using MIMIC datasets show that the proposed model could effectively\naddress asynchronicity in multimodal fusion and consistently outperform\nexisting methods.\n", "date": "2024-10-23", "categories": []}
{"id": "2410.06412", "title": "Stochastic Sparse Sampling: A Framework for Variable-Length Medical Time\n  Series Classification", "abstract": "  While the majority of time series classification research has focused on\nmodeling fixed-length sequences, variable-length time series classification\n(VTSC) remains critical in healthcare, where sequence length may vary among\npatients and events. To address this challenge, we propose\n$\\textbf{S}$tochastic $\\textbf{S}$parse $\\textbf{S}$ampling (SSS), a novel VTSC\nframework developed for medical time series. SSS manages variable-length\nsequences by sparsely sampling fixed windows to compute local predictions,\nwhich are then aggregated and calibrated to form a global prediction. We apply\nSSS to the task of seizure onset zone (SOZ) localization, a critical VTSC\nproblem requiring identification of seizure-inducing brain regions from\nvariable-length electrophysiological time series. We evaluate our method on the\nEpilepsy iEEG Multicenter Dataset, a heterogeneous collection of intracranial\nelectroencephalography (iEEG) recordings obtained from four independent medical\ncenters. SSS demonstrates superior performance compared to state-of-the-art\n(SOTA) baselines across most medical centers, and superior performance on all\nout-of-distribution (OOD) unseen medical centers. Additionally, SSS naturally\nprovides post-hoc insights into local signal characteristics related to the\nSOZ, by visualizing temporally averaged local predictions throughout the\nsignal.\n", "date": "2024-10-08", "categories": []}
{"id": "2310.12677", "title": "Case-level Breast Cancer Prediction for Real Hospital Settings", "abstract": "  Breast cancer prediction models for mammography assume that annotations are\navailable for individual images or regions of interest (ROIs), and that there\nis a fixed number of images per patient. These assumptions do not hold in real\nhospital settings, where clinicians provide only a final diagnosis for the\nentire mammography exam (case). Since data in real hospital settings scales\nwith continuous patient intake, while manual annotation efforts do not, we\ndevelop a framework for case-level breast cancer prediction that does not\nrequire any manual annotation and can be trained with case labels readily\navailable at the hospital. Specifically, we propose a two-level multi-instance\nlearning (MIL) approach at patch and image level for case-level breast cancer\nprediction and evaluate it on two public and one private dataset. We propose a\nnovel domain-specific MIL pooling observing that breast cancer may or may not\noccur in both sides, while images of both breasts are taken as a precaution\nduring mammography. We propose a dynamic training procedure for training our\nMIL framework on a variable number of images per case. We show that our\ntwo-level MIL model can be applied in real hospital settings where only case\nlabels, and a variable number of images per case are available, without any\nloss in performance compared to models trained on image labels. Only trained\nwith weak (case-level) labels, it has the capability to point out in which\nbreast side, mammography view and view region the abnormality lies.\n", "date": "2023-10-19", "categories": []}
{"id": "2402.10940", "title": "Neural machine translation of clinical procedure codes for medical\n  diagnosis and uncertainty quantification", "abstract": "  A Clinical Decision Support System (CDSS) is designed to enhance clinician\ndecision-making by combining system-generated recommendations with medical\nexpertise. Given the high costs, intensive labor, and time-sensitive nature of\nmedical treatments, there is a pressing need for efficient decision support,\nespecially in complex emergency scenarios. In these scenarios, where\ninformation can be limited, an advanced CDSS framework that leverages AI\n(artificial intelligence) models to effectively reduce diagnostic uncertainty\nhas utility. Such an AI-enabled CDSS framework with quantified uncertainty\npromises to be practical and beneficial in the demanding context of real-world\nmedical care. In this study, we introduce the concept of Medical Entropy,\nquantifying uncertainties in patient outcomes predicted by neural machine\ntranslation based on the ICD-9 code of procedures. Our experimental results not\nonly show strong correlations between procedure and diagnosis sequences based\non the simple ICD-9 code but also demonstrate the promising capacity to model\ntrends of uncertainties during hospitalizations through a data-driven approach.\n", "date": "2024-02-07", "categories": []}
{"id": "2405.19363", "title": "Medformer: A Multi-Granularity Patching Transformer for Medical\n  Time-Series Classification", "abstract": "  Medical time series (MedTS) data, such as Electroencephalography (EEG) and\nElectrocardiography (ECG), play a crucial role in healthcare, such as\ndiagnosing brain and heart diseases. Existing methods for MedTS classification\nprimarily rely on handcrafted biomarkers extraction and CNN-based models, with\nlimited exploration of transformer-based models. In this paper, we introduce\nMedformer, a multi-granularity patching transformer tailored specifically for\nMedTS classification. Our method incorporates three novel mechanisms to\nleverage the unique characteristics of MedTS: cross-channel patching to\nleverage inter-channel correlations, multi-granularity embedding for capturing\nfeatures at different scales, and two-stage (intra- and inter-granularity)\nmulti-granularity self-attention for learning features and correlations within\nand among granularities. We conduct extensive experiments on five public\ndatasets under both subject-dependent and challenging subject-independent\nsetups. Results demonstrate Medformer's superiority over 10 baselines,\nachieving top averaged ranking across five datasets on all six evaluation\nmetrics. These findings underscore the significant impact of our method on\nhealthcare applications, such as diagnosing Myocardial Infarction, Alzheimer's,\nand Parkinson's disease. We release the source code at\nhttps://github.com/DL4mHealth/Medformer.\n", "date": "2024-05-24", "categories": []}
{"id": "2410.14738", "title": "Advancements In Heart Disease Prediction: A Machine Learning Approach\n  For Early Detection And Risk Assessment", "abstract": "  The primary aim of this paper is to comprehend, assess, and analyze the role,\nrelevance, and efficiency of machine learning models in predicting heart\ndisease risks using clinical data. While the importance of heart disease risk\nprediction cannot be overstated, the application of machine learning (ML) in\nidentifying and evaluating the impact of various features on the classification\nof patients with and without heart disease, as well as in generating a reliable\nclinical dataset, is equally significant. This study relies primarily on\ncross-sectional clinical data. The ML approach is designed to enhance the\nconsideration of various clinical features in the heart disease prognosis\nprocess. Some features emerge as strong predictors, adding significant value.\nThe paper evaluates seven ML classifiers: Logistic Regression, Random Forest,\nDecision Tree, Naive Bayes, k-Nearest Neighbors, Neural Networks, and Support\nVector Machine (SVM). The performance of each model is assessed based on\naccuracy metrics. Notably, the Support Vector Machine (SVM) demonstrates the\nhighest accuracy at 91.51%, confirming its superiority among the evaluated\nmodels in terms of predictive capability. The overall findings of this research\nhighlight the advantages of advanced computational methodologies in the\nevaluation, prediction, improvement, and management of cardiovascular risks. In\nother words, the strong performance of the SVM model illustrates its\napplicability and value in clinical settings, paving the way for further\nadvancements in personalized medicine and healthcare.\n", "date": "2024-10-16", "categories": []}
{"id": "2410.15581", "title": "Multimodal Learning for Embryo Viability Prediction in Clinical IVF", "abstract": "  In clinical In-Vitro Fertilization (IVF), identifying the most viable embryo\nfor transfer is important to increasing the likelihood of a successful\npregnancy. Traditionally, this process involves embryologists manually\nassessing embryos' static morphological features at specific intervals using\nlight microscopy. This manual evaluation is not only time-intensive and costly,\ndue to the need for expert analysis, but also inherently subjective, leading to\nvariability in the selection process. To address these challenges, we develop a\nmultimodal model that leverages both time-lapse video data and Electronic\nHealth Records (EHRs) to predict embryo viability. One of the primary\nchallenges of our research is to effectively combine time-lapse video and EHR\ndata, owing to their inherent differences in modality. We comprehensively\nanalyze our multimodal model with various modality inputs and integration\napproaches. Our approach will enable fast and automated embryo viability\npredictions in scale for clinical IVF.\n", "date": "2024-10-21", "categories": []}
{"id": "2410.16238", "title": "Deep Radiomics Detection of Clinically Significant Prostate Cancer on\n  Multicenter MRI: Initial Comparison to PI-RADS Assessment", "abstract": "  Objective: To develop and evaluate a deep radiomics model for clinically\nsignificant prostate cancer (csPCa, grade group >= 2) detection and compare its\nperformance to Prostate Imaging Reporting and Data System (PI-RADS) assessment\nin a multicenter cohort. Materials and Methods: This retrospective study\nanalyzed biparametric (T2W and DW) prostate MRI sequences of 615 patients (mean\nage, 63.1 +/- 7 years) from four datasets acquired between 2010 and 2020:\nPROSTATEx challenge, Prostate158 challenge, PCaMAP trial, and an in-house\n(NTNU/St. Olavs Hospital) dataset. With expert annotations as ground truth, a\ndeep radiomics model was trained, including nnU-Net segmentation of the\nprostate gland, voxel-wise radiomic feature extraction, extreme gradient boost\nclassification, and post-processing of tumor probability maps into csPCa\ndetection maps. Training involved 5-fold cross-validation using the PROSTATEx\n(n=199), Prostate158 (n=138), and PCaMAP (n=78) datasets, and testing on the\nin-house (n=200) dataset. Patient- and lesion-level performance were compared\nto PI-RADS using area under ROC curve (AUROC [95% CI]), sensitivity, and\nspecificity analysis. Results: On the test data, the radiologist achieved a\npatient-level AUROC of 0.94 [0.91-0.98] with 94% (75/80) sensitivity and 77%\n(92/120) specificity at PI-RADS >= 3. The deep radiomics model at a tumor\nprobability cut-off >= 0.76 achieved 0.91 [0.86-0.95] AUROC with 90% (72/80)\nsensitivity and 73% (87/120) specificity, not significantly different (p =\n0.068) from PI-RADS. On the lesion level, PI-RADS cut-off >= 3 had 84% (91/108)\nsensitivity at 0.2 (40/200) false positives per patient, while deep radiomics\nattained 68% (73/108) sensitivity at the same false positive rate. Conclusion:\nDeep radiomics machine learning model achieved comparable performance to\nPI-RADS assessment in csPCa detection at the patient-level but not at the\nlesion-level.\n", "date": "2024-10-21", "categories": []}
{"id": "2311.13852", "title": "A Cross Attention Approach to Diagnostic Explainability using Clinical\n  Practice Guidelines for Depression", "abstract": "  The lack of explainability using relevant clinical knowledge hinders the\nadoption of Artificial Intelligence-powered analysis of unstructured clinical\ndialogue. A wealth of relevant, untapped Mental Health (MH) data is available\nin online communities, providing the opportunity to address the explainability\nproblem with substantial potential impact as a screening tool for both online\nand offline applications. We develop a method to enhance attention in popular\ntransformer models and generate clinician-understandable explanations for\nclassification by incorporating external clinical knowledge. Inspired by how\nclinicians rely on their expertise when interacting with patients, we leverage\nrelevant clinical knowledge to model patient inputs, providing meaningful\nexplanations for classification. This will save manual review time and engender\ntrust. We develop such a system in the context of MH using clinical practice\nguidelines (CPG) for diagnosing depression, a mental health disorder of global\nconcern. We propose an application-specific language model called ProcesS\nknowledge-infused cross ATtention (PSAT), which incorporates CPGs when\ncomputing attention. Through rigorous evaluation on three expert-curated\ndatasets related to depression, we demonstrate application-relevant\nexplainability of PSAT. PSAT also surpasses the performance of nine baseline\nmodels and can provide explanations where other baselines fall short. We\ntransform a CPG resource focused on depression, such as the Patient Health\nQuestionnaire (e.g. PHQ-9) and related questions, into a machine-readable\nontology using SNOMED-CT. With this resource, PSAT enhances the ability of\nmodels like GPT-3.5 to generate application-relevant explanations.\n", "date": "2023-11-23", "categories": []}
{"id": "2410.13867", "title": "Self-Supervised Pre-Training with Joint-Embedding Predictive\n  Architecture Boosts ECG Classification Performance", "abstract": "  Accurate diagnosis of heart arrhythmias requires the interpretation of\nelectrocardiograms (ECG), which capture the electrical activity of the heart.\nAutomating this process through machine learning is challenging due to the need\nfor large annotated datasets, which are difficult and costly to collect. To\naddress this issue, transfer learning is often employed, where models are\npre-trained on large datasets and fine-tuned for specific ECG classification\ntasks with limited labeled data. Self-supervised learning has become a widely\nadopted pre-training method, enabling models to learn meaningful\nrepresentations from unlabeled datasets. In this work, we explore the\njoint-embedding predictive architecture (JEPA) for self-supervised learning\nfrom ECG data. Unlike invariance-based methods, JEPA does not rely on\nhand-crafted data augmentations, and unlike generative methods, it predicts\nlatent features rather than reconstructing input data. We create a large\nunsupervised pre-training dataset by combining ten public ECG databases,\namounting to over one million records. We pre-train Vision Transformers using\nJEPA on this dataset and fine-tune them on various PTB-XL benchmarks. Our\nresults show that JEPA outperforms existing invariance-based and generative\napproaches, achieving an AUC of 0.945 on the PTB-XL all statements task. JEPA\nconsistently learns the highest quality representations, as demonstrated in\nlinear evaluations, and proves advantageous for pre-training even in the\nabsence of additional data.\n", "date": "2024-10-02", "categories": []}
{"id": "2410.14625", "title": "Enhancing AI Accessibility in Veterinary Medicine: Linking Classifiers\n  and Electronic Health Records", "abstract": "  In the rapidly evolving landscape of veterinary healthcare, integrating\nmachine learning (ML) clinical decision-making tools with electronic health\nrecords (EHRs) promises to improve diagnostic accuracy and patient care.\nHowever, the seamless integration of ML classifiers into existing EHRs in\nveterinary medicine is frequently hindered by the rigidity of EHR systems or\nthe limited availability of IT resources. To address this shortcoming, we\npresent Anna, a freely-available software solution that provides ML classifier\nresults for EHR laboratory data in real-time.\n", "date": "2024-10-18", "categories": []}
{"id": "2410.12845", "title": "Toward Relieving Clinician Burden by Automatically Generating Progress\n  Notes using Interim Hospital Data", "abstract": "  Regular documentation of progress notes is one of the main contributors to\nclinician burden. The abundance of structured chart information in medical\nrecords further exacerbates the burden, however, it also presents an\nopportunity to automate the generation of progress notes. In this paper, we\npropose a task to automate progress note generation using structured or tabular\ninformation present in electronic health records. To this end, we present a\nnovel framework and a large dataset, ChartPNG, for the task which contains\n$7089$ annotation instances (each having a pair of progress notes and interim\nstructured chart data) across $1616$ patients. We establish baselines on the\ndataset using large language models from general and biomedical domains. We\nperform both automated (where the best performing Biomistral model achieved a\nBERTScore F1 of $80.53$ and MEDCON score of $19.61$) and manual (where we found\nthat the model was able to leverage relevant structured data with $76.9\\%$\naccuracy) analyses to identify the challenges with the proposed task and\nopportunities for future research.\n", "date": "2024-10-10", "categories": []}
{"id": "2410.12885", "title": "Exploiting Longitudinal Speech Sessions via Voice Assistant Systems for\n  Early Detection of Cognitive Decline", "abstract": "  Mild Cognitive Impairment (MCI) is an early stage of Alzheimer's disease\n(AD), a form of neurodegenerative disorder. Early identification of MCI is\ncrucial for delaying its progression through timely interventions. Existing\nresearch has demonstrated the feasibility of detecting MCI using speech\ncollected from clinical interviews or digital devices. However, these\napproaches typically analyze data collected at limited time points, limiting\ntheir ability to identify cognitive changes over time. This paper presents a\nlongitudinal study using voice assistant systems (VAS) to remotely collect\nseven-session speech data at three-month intervals across 18 months. We propose\ntwo methods to improve MCI detection and the prediction of cognitive changes.\nThe first method incorporates historical data, while the second predicts\ncognitive changes at two time points. Our results indicate improvements when\nincorporating historical data: the average F1-score for MCI detection improves\nfrom 58.6% to 71.2% (by 12.6%) in the case of acoustic features and from 62.1%\nto 75.1% (by 13.0%) in the case of linguistic features. Additionally, the\nprediction of cognitive changes achieves an F1-score of 73.7% in the case of\nacoustic features. These results confirm the potential of VAS-based speech\nsessions for early detection of cognitive decline.\n", "date": "2024-10-16", "categories": []}
{"id": "2410.13217", "title": "MixEHR-Nest: Identifying Subphenotypes within Electronic Health Records\n  through Hierarchical Guided-Topic Modeling", "abstract": "  Automatic subphenotyping from electronic health records (EHRs)provides\nnumerous opportunities to understand diseases with unique subgroups and enhance\npersonalized medicine for patients. However, existing machine learning\nalgorithms either focus on specific diseases for better interpretability or\nproduce coarse-grained phenotype topics without considering nuanced disease\npatterns. In this study, we propose a guided topic model, MixEHR-Nest, to infer\nsub-phenotype topics from thousands of disease using multi-modal EHR data.\nSpecifically, MixEHR-Nest detects multiple subtopics from each phenotype topic,\nwhose prior is guided by the expert-curated phenotype concepts such as\nPhenotype Codes (PheCodes) or Clinical Classification Software (CCS) codes. We\nevaluated MixEHR-Nest on two EHR datasets: (1) the MIMIC-III dataset consisting\nof over 38 thousand patients from intensive care unit (ICU) from Beth Israel\nDeaconess Medical Center (BIDMC) in Boston, USA; (2) the healthcare\nadministrative database PopHR, comprising 1.3 million patients from Montreal,\nCanada. Experimental results demonstrate that MixEHR-Nest can identify\nsubphenotypes with distinct patterns within each phenotype, which are\npredictive for disease progression and severity. Consequently, MixEHR-Nest\ndistinguishes between type 1 and type 2 diabetes by inferring subphenotypes\nusing CCS codes, which do not differentiate these two subtype concepts.\nAdditionally, MixEHR-Nest not only improved the prediction accuracy of\nshort-term mortality of ICU patients and initial insulin treatment in diabetic\npatients but also revealed the contributions of subphenotypes. For longitudinal\nanalysis, MixEHR-Nest identified subphenotypes of distinct age prevalence under\nthe same phenotypes, such as asthma, leukemia, epilepsy, and depression. The\nMixEHR-Nest software is available at GitHub:\nhttps://github.com/li-lab-mcgill/MixEHR-Nest.\n", "date": "2024-10-17", "categories": []}
{"id": "2410.13404", "title": "Predicting Breast Cancer Survival: A Survival Analysis Approach Using\n  Log Odds and Clinical Variables", "abstract": "  Breast cancer remains a significant global health challenge, with prognosis\nand treatment decisions largely dependent on clinical characteristics. Accurate\nprediction of patient outcomes is crucial for personalized treatment\nstrategies. This study employs survival analysis techniques, including Cox\nproportional hazards and parametric survival models, to enhance the prediction\nof the log odds of survival in breast cancer patients. Clinical variables such\nas tumor size, hormone receptor status, HER2 status, age, and treatment history\nwere analyzed to assess their impact on survival outcomes. Data from 1557\nbreast cancer patients were obtained from a publicly available dataset provided\nby the University College Hospital, Ibadan, Nigeria. This dataset was\npreprocessed and analyzed using both univariate and multivariate approaches to\nevaluate survival outcomes. Kaplan-Meier survival curves were generated to\nvisualize survival probabilities, while the Cox proportional hazards model\nidentified key risk factors influencing mortality. The results showed that\nolder age, larger tumor size, and HER2-positive status were significantly\nassociated with an increased risk of mortality. In contrast, estrogen receptor\npositivity and breast-conserving surgery were linked to better survival\noutcomes. The findings suggest that integrating these clinical variables into\npredictive models improvesthe accuracy of survival predictions, helping to\nidentify high-risk patients who may benefit from more aggressive interventions.\nThis study demonstrates the potential of survival analysis in optimizing breast\ncancer care, particularly in resource-limited settings. Future research should\nfocus on integrating genomic data and real-world clinical outcomes to further\nrefine these models.\n", "date": "2024-10-17", "categories": []}
{"id": "2410.12589", "title": "From Lab to Pocket: A Novel Continual Learning-based Mobile Application\n  for Screening COVID-19", "abstract": "  Artificial intelligence (AI) has emerged as a promising tool for predicting\nCOVID-19 from medical images. In this paper, we propose a novel continual\nlearning-based approach and present the design and implementation of a mobile\napplication for screening COVID-19. Our approach demonstrates the ability to\nadapt to evolving datasets, including data collected from different locations\nor hospitals, varying virus strains, and diverse clinical presentations,\nwithout retraining from scratch. We have evaluated state-of-the-art continual\nlearning methods for detecting COVID-19 from chest X-rays and selected the\nbest-performing model for our mobile app. We evaluated various deep learning\narchitectures to select the best-performing one as a foundation model for\ncontinual learning. Both regularization and memory-based methods for continual\nlearning were tested, using different memory sizes to develop the optimal\ncontinual learning model for our app. DenseNet161 emerged as the best\nfoundation model with 96.87\\% accuracy, and Learning without Forgetting (LwF)\nwas the top continual learning method with an overall performance of 71.99\\%.\nThe mobile app design considers both patient and doctor perspectives. It\nincorporates the continual learning DenseNet161 LwF model on a cloud server,\nenabling the model to learn from new instances of chest X-rays and their\nclassifications as they are submitted. The app is designed, implemented, and\nevaluated to ensure it provides an efficient tool for COVID-19 screening. The\napp is available to download from\nhttps://github.com/DannyFGitHub/COVID-19PneumoCheckApp.\n", "date": "2024-10-16", "categories": []}
{"id": "2410.12597", "title": "Personalized Prediction Models for Changes in Knee Pain among Patients\n  with Osteoarthritis Participating in Supervised Exercise and Education", "abstract": "  Knee osteoarthritis (OA) is a widespread chronic condition that impairs\nmobility and diminishes quality of life. Despite the proven benefits of\nexercise therapy and patient education in managing the OA symptoms pain and\nfunctional limitations, these strategies are often underutilized. Personalized\noutcome prediction models can help motivate and engage patients, but the\naccuracy of existing models in predicting changes in knee pain remains\ninsufficiently examined. To validate existing models and introduce a concise\npersonalized model predicting changes in knee pain before to after\nparticipating in a supervised education and exercise therapy program (GLA:D)\nfor knee OA patients. Our models use self-reported patient information and\nfunctional measures. To refine the number of variables, we evaluated the\nvariable importance and applied clinical reasoning. We trained random forest\nregression models and compared the rate of true predictions of our models with\nthose utilizing average values. We evaluated the performance of a full,\ncontinuous, and concise model including all 34, all 11 continuous, and the six\nmost predictive variables respectively. All three models performed similarly\nand were comparable to the existing model, with R-squares of 0.31-0.32 and\nRMSEs of 18.65-18.85 - despite our increased sample size. Allowing a deviation\nof 15 VAS points from the true change in pain, our concise model and utilizing\nthe average values estimated the change in pain at 58% and 51% correctly,\nrespectively. Our supplementary analysis led to similar outcomes. Our concise\npersonalized prediction model more accurately predicts changes in knee pain\nfollowing the GLA:D program compared to average pain improvement values.\nNeither the increase in sample size nor the inclusion of additional variables\nimproved previous models. To improve predictions, new variables beyond those in\nthe GLA:D are required.\n", "date": "2024-10-16", "categories": []}
{"id": "2410.11046", "title": "SGUQ: Staged Graph Convolution Neural Network for Alzheimer's Disease\n  Diagnosis using Multi-Omics Data", "abstract": "  Alzheimer's disease (AD) is a chronic neurodegenerative disorder and the\nleading cause of dementia, significantly impacting cost, mortality, and burden\nworldwide. The advent of high-throughput omics technologies, such as genomics,\ntranscriptomics, proteomics, and epigenomics, has revolutionized the molecular\nunderstanding of AD. Conventional AI approaches typically require the\ncompletion of all omics data at the outset to achieve optimal AD diagnosis,\nwhich are inefficient and may be unnecessary. To reduce the clinical cost and\nimprove the accuracy of AD diagnosis using multi-omics data, we propose a novel\nstaged graph convolutional network with uncertainty quantification (SGUQ). SGUQ\nbegins with mRNA and progressively incorporates DNA methylation and miRNA data\nonly when necessary, reducing overall costs and exposure to harmful tests.\nExperimental results indicate that 46.23% of the samples can be reliably\npredicted using only single-modal omics data (mRNA), while an additional 16.04%\nof the samples can achieve reliable predictions when combining two omics data\ntypes (mRNA + DNA methylation). In addition, the proposed staged SGUQ achieved\nan accuracy of 0.858 on ROSMAP dataset, which outperformed existing methods\nsignificantly. The proposed SGUQ can not only be applied to AD diagnosis using\nmulti-omics data but also has the potential for clinical decision-making using\nmulti-viewed data. Our implementation is publicly available at\nhttps://github.com/chenzhao2023/multiomicsuncertainty.\n", "date": "2024-10-14", "categories": []}
{"id": "2406.13305", "title": "Multimodal MRI Accurately Identifies Amyloid Status in Unbalanced\n  Cohorts in Alzheimer's Disease Continuum", "abstract": "  Amyloid-$\\beta$ (A$\\beta$) plaques in conjunction with hyperphosphorylated\ntau proteins in the form of neurofibrillary tangles are the two\nneuropathological hallmarks of Alzheimer's disease. It is well-known that the\nidentification of individuals with A$\\beta$ positivity could enable early\ndiagnosis. In this work, we aim at capturing the A$\\beta$ positivity status in\nan unbalanced cohort enclosing subjects at different disease stages, exploiting\nthe underlying structural and connectivity disease-induced modulations as\nrevealed by structural, functional, and diffusion MRI. Of note, due to the\nunbalanced cohort, the outcomes may be guided by those factors rather than\namyloid accumulation. The partial views provided by each modality are\nintegrated in the model allowing to take full advantage of their\ncomplementarity in encoding the effects of the A$\\beta$ accumulation, leading\nto an accuracy of $0.762\\pm0.04$. The specificity of the information brought by\neach modality is assessed by \\textit{post-hoc} explainability analysis (guided\nbackpropagation), highlighting the underlying structural and functional\nchanges. Noteworthy, well-established biomarker key regions related to A$\\beta$\ndeposition could be identified by all modalities, including the hippocampus,\nthalamus, precuneus, and cingulate gyrus, witnessing in favor of the\nreliability of the method as well as its potential in shading light on\nmodality-specific possibly unknown A$\\beta$ deposition signatures.\n", "date": "2024-06-19", "categories": []}
{"id": "2407.19340", "title": "Integrating Large Language Models into a Tri-Modal Architecture for\n  Automated Depression Classification on the DAIC-WOZ", "abstract": "  Major Depressive Disorder (MDD) is a pervasive mental health condition that\naffects 300 million people worldwide. This work presents a novel, BiLSTM-based\ntri-modal model-level fusion architecture for the binary classification of\ndepression from clinical interview recordings. The proposed architecture\nincorporates Mel Frequency Cepstral Coefficients, Facial Action Units, and uses\na two-shot learning based GPT-4 model to process text data. This is the first\nwork to incorporate large language models into a multi-modal architecture for\nthis task. It achieves impressive results on the DAIC-WOZ AVEC 2016 Challenge\ncross-validation split and Leave-One-Subject-Out cross-validation split,\nsurpassing all baseline models and multiple state-of-the-art models. In\nLeave-One-Subject-Out testing, it achieves an accuracy of 91.01%, an F1-Score\nof 85.95%, a precision of 80%, and a recall of 92.86%.\n", "date": "2024-07-27", "categories": []}
{"id": "2410.09116", "title": "Optimizing Hard-to-Place Kidney Allocation: A Machine Learning Approach\n  to Center Ranking", "abstract": "  Kidney transplantation is the preferred treatment for end-stage renal\ndisease, yet the scarcity of donors and inefficiencies in allocation systems\ncreate major bottlenecks, resulting in prolonged wait times and alarming\nmortality rates. Despite their severe scarcity, timely and effective\ninterventions to prevent non-utilization of life-saving organs remain\ninadequate. Expedited out-of-sequence placement of hard-to-place kidneys to\ncenters with the highest likelihood of utilizing them has been recommended in\nthe literature as an effective strategy to improve placement success.\nNevertheless, current attempts towards this practice is non-standardized and\nheavily rely on the subjective judgment of the decision-makers. This paper\nproposes a novel data-driven, machine learning-based ranking system for\nallocating hard-to-place kidneys to centers with a higher likelihood of\naccepting and successfully transplanting them. Using the national deceased\ndonor kidney offer and transplant datasets, we construct a unique dataset with\ndonor-, center-, and patient-specific features. We propose a data-driven\nout-of-sequence placement policy that utilizes machine learning models to\npredict the acceptance probability of a given kidney by a set of transplant\ncenters, ranking them accordingly based on their likelihood of acceptance. Our\nexperiments demonstrate that the proposed policy can reduce the average number\nof centers considered before placement by fourfold for all kidneys and tenfold\nfor hard-to-place kidneys. This significant reduction indicates that our method\ncan improve the utilization of hard-to-place kidneys and accelerate their\nacceptance, ultimately reducing patient mortality and the risk of graft\nfailure. Further, we utilize machine learning interpretability tools to provide\ninsights into factors influencing the kidney allocation decisions.\n", "date": "2024-10-10", "categories": []}
{"id": "2410.09199", "title": "An Efficient Contrastive Unimodal Pretraining Method for EHR Time Series\n  Data", "abstract": "  Machine learning has revolutionized the modeling of clinical timeseries data.\nUsing machine learning, a Deep Neural Network (DNN) can be automatically\ntrained to learn a complex mapping of its input features for a desired task.\nThis is particularly valuable in Electronic Health Record (EHR) databases,\nwhere patients often spend extended periods in intensive care units (ICUs).\nMachine learning serves as an efficient method for extract meaningful\ninformation.\n  However, many state-of-the-art (SOTA) methods for training DNNs demand\nsubstantial volumes of labeled data, posing significant challenges for clinics\nin terms of cost and time. Self-supervised learning offers an alternative by\nallowing practitioners to extract valuable insights from data without the need\nfor costly labels. Yet, current SOTA methods often necessitate large data\nbatches to achieve optimal performance, increasing computational demands. This\npresents a challenge when working with long clinical timeseries data.\n  To address this, we propose an efficient method of contrastive pretraining\ntailored for long clinical timeseries data. Our approach utilizes an estimator\nfor negative pair comparison, enabling effective feature extraction. We assess\nthe efficacy of our pretraining using standard self-supervised tasks such as\nlinear evaluation and semi-supervised learning. Additionally, our model\ndemonstrates the ability to impute missing measurements, providing clinicians\nwith deeper insights into patient conditions.\n  We demonstrate that our pretraining is capable of achieving better\nperformance as both the size of the model and the size of the measurement\nvocabulary scale. Finally, we externally validate our model, trained on the\nMIMIC-III dataset, using the eICU dataset. We demonstrate that our model is\ncapable of learning robust clinical information that is transferable to other\nclinics.\n", "date": "2024-10-11", "categories": []}
{"id": "2410.09643", "title": "Multimodal Physical Activity Forecasting in Free-Living Clinical\n  Settings: Hunting Opportunities for Just-in-Time Interventions", "abstract": "  Objective: This research aims to develop a lifestyle intervention system,\ncalled MoveSense, that forecasts a patient's activity behavior to allow for\nearly and personalized interventions in real-world clinical environments.\nMethods: We conducted two clinical studies involving 58 prediabetic veterans\nand 60 patients with obstructive sleep apnea to gather multimodal behavioral\ndata using wearable devices. We develop multimodal long short-term memory\n(LSTM) network models, which are capable of forecasting the number of step\ncounts of a patient up to 24 hours in advance by examining data from activity\nand engagement modalities. Furthermore, we design goal-based forecasting models\nto predict whether a person's next-day steps will be over a certain threshold.\nResults: Multimodal LSTM with early fusion achieves 33% and 37% lower mean\nabsolute errors than linear regression and ARIMA respectively on the\nprediabetes dataset. LSTM also outperforms linear regression and ARIMA with a\nmargin of 13% and 32% on the sleep dataset. Multimodal forecasting models also\nperform with 72% and 79% accuracy on the prediabetes dataset and sleep dataset\nrespectively on goal-based forecasting. Conclusion: Our experiments conclude\nthat multimodal LSTM models with early fusion are better than multimodal LSTM\nwith late fusion and unimodal LSTM models and also than ARIMA and linear\nregression models. Significance: We address an important and challenging task\nof time-series forecasting in uncontrolled environments. Effective forecasting\nof a person's physical activity can aid in designing adaptive behavioral\ninterventions to keep the user engaged and adherent to a prescribed routine.\n", "date": "2024-10-12", "categories": []}
{"id": "2410.10505", "title": "Comparison of deep learning and conventional methods for disease onset\n  prediction", "abstract": "  Background: Conventional prediction methods such as logistic regression and\ngradient boosting have been widely utilized for disease onset prediction for\ntheir reliability and interpretability. Deep learning methods promise enhanced\nprediction performance by extracting complex patterns from clinical data, but\nface challenges like data sparsity and high dimensionality.\n  Methods: This study compares conventional and deep learning approaches to\npredict lung cancer, dementia, and bipolar disorder using observational data\nfrom eleven databases from North America, Europe, and Asia. Models were\ndeveloped using logistic regression, gradient boosting, ResNet, and\nTransformer, and validated both internally and externally across the data\nsources. Discrimination performance was assessed using AUROC, and calibration\nwas evaluated using Eavg.\n  Findings: Across 11 datasets, conventional methods generally outperformed\ndeep learning methods in terms of discrimination performance, particularly\nduring external validation, highlighting their better transportability.\nLearning curves suggest that deep learning models require substantially larger\ndatasets to reach the same performance levels as conventional methods.\nCalibration performance was also better for conventional methods, with ResNet\nshowing the poorest calibration.\n  Interpretation: Despite the potential of deep learning models to capture\ncomplex patterns in structured observational healthcare data, conventional\nmodels remain highly competitive for disease onset prediction, especially in\nscenarios involving smaller datasets and if lengthy training times need to be\navoided. The study underscores the need for future research focused on\noptimizing deep learning models to handle the sparsity, high dimensionality,\nand heterogeneity inherent in healthcare datasets, and find new strategies to\nexploit the full capabilities of deep learning methods.\n", "date": "2024-10-14", "categories": []}
{"id": "2410.10509", "title": "Artificial Intelligence-Based Triaging of Cutaneous Melanocytic Lesions", "abstract": "  Pathologists are facing an increasing workload due to a growing volume of\ncases and the need for more comprehensive diagnoses. Aiming to facilitate\nworkload reduction and faster turnaround times, we developed an artificial\nintelligence (AI) model for triaging cutaneous melanocytic lesions based on\nwhole slide images. The AI model was developed and validated using a\nretrospective cohort from the UMC Utrecht. The dataset consisted of 52,202\nwhole slide images from 27,167 unique specimens, acquired from 20,707 patients.\nSpecimens with only common nevi were assigned to the low complexity category\n(86.6%). In contrast, specimens with any other melanocytic lesion subtype,\nincluding non-common nevi, melanocytomas, and melanomas, were assigned to the\nhigh complexity category (13.4%). The dataset was split on patient level into a\ndevelopment set (80%) and test sets (20%) for independent evaluation.\nPredictive performance was primarily measured using the area under the receiver\noperating characteristic curve (AUROC) and the area under the precision-recall\ncurve (AUPRC). A simulation experiment was performed to study the effect of\nimplementing AI-based triaging in the clinic. The AI model reached an AUROC of\n0.966 (95% CI, 0.960-0.972) and an AUPRC of 0.857 (95% CI, 0.836-0.877) on the\nin-distribution test set, and an AUROC of 0.899 (95% CI, 0.860-0.934) and an\nAUPRC of 0.498 (95% CI, 0.360-0.639) on the out-of-distribution test set. In\nthe simulation experiment, using random case assignment as baseline, AI-based\ntriaging prevented an average of 43.9 (95% CI, 36-55) initial examinations of\nhigh complexity cases by general pathologists for every 500 cases. In\nconclusion, the AI model achieved a strong predictive performance in\ndifferentiating between cutaneous melanocytic lesions of high and low\ncomplexity. The improvement in workflow efficiency due to AI-based triaging\ncould be substantial.\n", "date": "2024-10-14", "categories": []}
{"id": "2404.15201", "title": "CORE-BEHRT: A Carefully Optimized and Rigorously Evaluated BEHRT", "abstract": "  The widespread adoption of Electronic Health Records (EHR) has significantly\nincreased the amount of available healthcare data. This has allowed models\ninspired by Natural Language Processing (NLP) and Computer Vision, which scale\nexceptionally well, to be used in EHR research. Particularly, BERT-based models\nhave surged in popularity following the release of BEHRT and Med-BERT.\nSubsequent models have largely built on these foundations despite the\nfundamental design choices of these pioneering models remaining underexplored.\nThrough incremental optimization, we study BERT-based EHR modeling and isolate\nthe sources of improvement for key design choices, giving us insights into the\neffect of data representation, individual technical components, and training\nprocedure. Evaluating this across a set of generic tasks (death, pain\ntreatment, and general infection), we showed that improving data representation\ncan increase the average downstream performance from 0.785 to 0.797 AUROC\n($p<10^{-7}$), primarily when including medication and timestamps. Improving\nthe architecture and training protocol on top of this increased average\ndownstream performance to 0.801 AUROC ($p<10^{-7}$). We then demonstrated the\nconsistency of our optimization through a rigorous evaluation across 25 diverse\nclinical prediction tasks. We observed significant performance increases in 17\nout of 25 tasks and improvements in 24 tasks, highlighting the generalizability\nof our results. Our findings provide a strong foundation for future work and\naim to increase the trustworthiness of BERT-based EHR models.\n", "date": "2024-04-23", "categories": []}
{"id": "2410.08247", "title": "Forecasting mortality associated emergency department crowding", "abstract": "  Emergency department (ED) crowding is a global public health issue that has\nbeen repeatedly associated with increased mortality. Predicting future service\ndemand would enable preventative measures aiming to eliminate crowding along\nwith it's detrimental effects. Recent findings in our ED indicate that\noccupancy ratios exceeding 90% are associated with increased 10-day mortality.\nIn this paper, we aim to predict these crisis periods using retrospective data\nfrom a large Nordic ED with a LightGBM model. We provide predictions for the\nwhole ED and individually for it's different operational sections. We\ndemonstrate that afternoon crowding can be predicted at 11 a.m. with an AUC of\n0.82 (95% CI 0.78-0.86) and at 8 a.m. with an AUC up to 0.79 (95% CI\n0.75-0.83). Consequently we show that forecasting mortality-associated crowding\nusing anonymous administrative data is feasible.\n", "date": "2024-10-10", "categories": []}
{"id": "2410.08770", "title": "Causal machine learning for predicting treatment outcomes", "abstract": "  Causal machine learning (ML) offers flexible, data-driven methods for\npredicting treatment outcomes including efficacy and toxicity, thereby\nsupporting the assessment and safety of drugs. A key benefit of causal ML is\nthat it allows for estimating individualized treatment effects, so that\nclinical decision-making can be personalized to individual patient profiles.\nCausal ML can be used in combination with both clinical trial data and\nreal-world data, such as clinical registries and electronic health records, but\ncaution is needed to avoid biased or incorrect predictions. In this\nPerspective, we discuss the benefits of causal ML (relative to traditional\nstatistical or ML approaches) and outline the key components and steps.\nFinally, we provide recommendations for the reliable use of causal ML and\neffective translation into the clinic.\n", "date": "2024-10-11", "categories": []}
{"id": "2410.08816", "title": "Uncertainty-Aware Optimal Treatment Selection for Clinical Time Series", "abstract": "  In personalized medicine, the ability to predict and optimize treatment\noutcomes across various time frames is essential. Additionally, the ability to\nselect cost-effective treatments within specific budget constraints is\ncritical. Despite recent advancements in estimating counterfactual\ntrajectories, a direct link to optimal treatment selection based on these\nestimates is missing. This paper introduces a novel method integrating\ncounterfactual estimation techniques and uncertainty quantification to\nrecommend personalized treatment plans adhering to predefined cost constraints.\nOur approach is distinctive in its handling of continuous treatment variables\nand its incorporation of uncertainty quantification to improve prediction\nreliability. We validate our method using two simulated datasets, one focused\non the cardiovascular system and the other on COVID-19. Our findings indicate\nthat our method has robust performance across different counterfactual\nestimation baselines, showing that introducing uncertainty quantification in\nthese settings helps the current baselines in finding more reliable and\naccurate treatment selection. The robustness of our method across various\nsettings highlights its potential for broad applicability in personalized\nhealthcare solutions.\n", "date": "2024-10-11", "categories": []}
{"id": "2410.05350", "title": "GRU-D Characterizes Age-Specific Temporal Missingness in MIMIC-IV", "abstract": "  Temporal missingness, defined as unobserved patterns in time series, and its\npredictive potentials represent an emerging area in clinical machine learning.\nWe trained a gated recurrent unit with decay mechanisms, called GRU-D, for a\nbinary classification between elderly - and young patients. We extracted time\nseries for 5 vital signs from MIMIC-IV as model inputs. GRU-D was evaluated\nwith means of 0.780 AUROC and 0.810 AUPRC on bootstrapped data. Interpreting\ntrained model parameters, we found differences in blood pressure missingness\nand respiratory rate missingness as important predictors learned by\nparameterized hidden gated units. We successfully showed how GRU-D can be used\nto reveal patterns in temporal missingness building the basis of novel research\ndirections.\n", "date": "2024-10-07", "categories": []}
{"id": "2410.05491", "title": "Pre-Ictal Seizure Prediction Using Personalized Deep Learning", "abstract": "  Introduction: Approximately 23 million or 30% of epilepsy patients worldwide\nsuffer from drug-resistant epilepsy (DRE). The unpredictability of seizure\noccurrences, which causes safety issues as well as social concerns, restrict\nthe lifestyles of DRE patients. Surgical solutions and EEG-based solutions are\nvery expensive, unreliable, invasive or impractical. The goal of this research\nwas to employ improved technologies and methods to epilepsy patient\nphysiological data and predict seizures up to two hours before onset, enabling\nnon-invasive, affordable seizure prediction for DRE patients.\n  Methods: This research used a 1D Convolutional Neural Network-Based\nBidirectional Long Short-Term Memory network that was trained on a diverse set\nof epileptic patient physiological data to predict seizures. Transfer learning\nwas further utilized to personalize and optimize predictions for specific\npatients. Clinical data was retrospectively obtained for nine epilepsy patients\nvia wearable devices over a period of about three to five days from a\nprospectively maintained database. The physiological data included 54 seizure\noccurrences and included heart rate, blood volume pulse, accelerometry, body\ntemperature, and electrodermal activity.\n  Results and Conclusion: A general deep-learning model trained on the\nphysiological data with randomly sampled test data achieved an accuracy of\n91.94%. However, such a generalized deep learning model had varied performances\non data from unseen patients. When the general model was personalized (further\ntrained) with patient-specific data, the personalized model achieved\nsignificantly improved performance with accuracies as high as 97%. This\npreliminary research shows that patient-specific personalization may be a\nviable approach to achieve affordable, non-invasive seizure prediction that can\nimprove the quality of life for DRE patients.\n", "date": "2024-10-07", "categories": []}
{"id": "2410.05780", "title": "GlucoBench: Curated List of Continuous Glucose Monitoring Datasets with\n  Prediction Benchmarks", "abstract": "  The rising rates of diabetes necessitate innovative methods for its\nmanagement. Continuous glucose monitors (CGM) are small medical devices that\nmeasure blood glucose levels at regular intervals providing insights into daily\npatterns of glucose variation. Forecasting of glucose trajectories based on CGM\ndata holds the potential to substantially improve diabetes management, by both\nrefining artificial pancreas systems and enabling individuals to make\nadjustments based on predictions to maintain optimal glycemic range.Despite\nnumerous methods proposed for CGM-based glucose trajectory prediction, these\nmethods are typically evaluated on small, private datasets, impeding\nreproducibility, further research, and practical adoption. The absence of\nstandardized prediction tasks and systematic comparisons between methods has\nled to uncoordinated research efforts, obstructing the identification of\noptimal tools for tackling specific challenges. As a result, only a limited\nnumber of prediction methods have been implemented in clinical practice.\n  To address these challenges, we present a comprehensive resource that\nprovides (1) a consolidated repository of curated publicly available CGM\ndatasets to foster reproducibility and accessibility; (2) a standardized task\nlist to unify research objectives and facilitate coordinated efforts; (3) a set\nof benchmark models with established baseline performance, enabling the\nresearch community to objectively gauge new methods' efficacy; and (4) a\ndetailed analysis of performance-influencing factors for model development. We\nanticipate these resources to propel collaborative research endeavors in the\ncritical domain of CGM-based glucose predictions. {Our code is available online\nat github.com/IrinaStatsLab/GlucoBench.\n", "date": "2024-10-08", "categories": []}
{"id": "2305.19211", "title": "COVID-19 Detection from Exhaled Breath", "abstract": "  The SARS-CoV-2 coronavirus emerged in 2019, causing a COVID-19 pandemic that\nresulted in 7 million deaths out of 770 million reported cases over the next\nfour years. The global health emergency called for unprecedented efforts to\nmonitor and reduce the rate of infection, pushing the study of new diagnostic\nmethods. In this paper, we introduce a cheap, fast, and non-invasive detection\nsystem, which exploits only the exhaled breath. Specifically, provided an air\nsample, the mass spectra in the 10--351 mass-to-charge range are measured using\nan original nano-sampling device coupled with a high-precision spectrometer;\nthen, the raw spectra are processed by custom software algorithms; the clean\nand augmented data are eventually classified using state-of-the-art\nmachine-learning algorithms. An uncontrolled clinical trial was conducted\nbetween 2021 and 2022 on some 300 subjects who were concerned about being\ninfected, either due to exhibiting symptoms or having quite recently recovered\nfrom illness. Despite the simplicity of use, our system showed a performance\ncomparable to the traditional polymerase-chain-reaction and antigen testing in\nidentifying cases of COVID-19 (that is, 0.95 accuracy, 0.94 recall, 0.96\nspecificity, and 0.92 F1-score). In light of these outcomes, we think that the\nproposed system holds the potential for substantial contributions to routine\nscreenings and expedited responses during future epidemics, as it yields\nresults comparable to state-of-the-art methods, providing them in a more rapid\nand less invasive manner.\n", "date": "2023-05-30", "categories": []}
{"id": "2410.03725", "title": "Realtime, multimodal invasive ventilation risk monitoring using language\n  models and BoXHED", "abstract": "  Objective: realtime monitoring of invasive ventilation (iV) in intensive care\nunits (ICUs) plays a crucial role in ensuring prompt interventions and better\npatient outcomes. However, conventional methods often overlook valuable\ninsights embedded within clinical notes, relying solely on tabular data. In\nthis study, we propose an innovative approach to enhance iV risk monitoring by\nincorporating clinical notes into the monitoring pipeline through using\nlanguage models for text summarization. Results: We achieve superior\nperformance in all metrics reported by the state-of-the-art in iV risk\nmonitoring, namely: an AUROC of 0.86, an AUC-PR of 0.35, and an AUCt of up to\n0.86. We also demonstrate that our methodology allows for more lead time in\nflagging iV for certain time buckets. Conclusion: Our study underscores the\npotential of integrating clinical notes and language models into realtime iV\nrisk monitoring, paving the way for improved patient care and informed clinical\ndecision-making in ICU settings.\n", "date": "2024-09-29", "categories": []}
{"id": "2409.18878", "title": "Suicide Phenotyping from Clinical Notes in Safety-Net Psychiatric\n  Hospital Using Multi-Label Classification with Pre-Trained Language Models", "abstract": "  Accurate identification and categorization of suicidal events can yield\nbetter suicide precautions, reducing operational burden, and improving care\nquality in high-acuity psychiatric settings. Pre-trained language models offer\npromise for identifying suicidality from unstructured clinical narratives. We\nevaluated the performance of four BERT-based models using two fine-tuning\nstrategies (multiple single-label and single multi-label) for detecting\ncoexisting suicidal events from 500 annotated psychiatric evaluation notes. The\nnotes were labeled for suicidal ideation (SI), suicide attempts (SA), exposure\nto suicide (ES), and non-suicidal self-injury (NSSI). RoBERTa outperformed\nother models using multiple single-label classification strategy (acc=0.86,\nF1=0.78). MentalBERT (acc=0.83, F1=0.74) also exceeded BioClinicalBERT\n(acc=0.82, F1=0.72) which outperformed BERT (acc=0.80, F1=0.70). RoBERTa\nfine-tuned with single multi-label classification further improved the model\nperformance (acc=0.88, F1=0.81). The findings highlight that the model\noptimization, pretraining with domain-relevant data, and the single multi-label\nclassification strategy enhance the model performance of suicide phenotyping.\nKeywords: EHR-based Phenotyping; Natural Language Processing; Secondary Use of\nEHR Data; Suicide Classification; BERT-based Model; Psychiatry; Mental Health\n", "date": "2024-09-27", "categories": []}
{"id": "2402.17917", "title": "Collaborative learning of common latent representations in routinely\n  collected multivariate ICU physiological signals", "abstract": "  In Intensive Care Units (ICU), the abundance of multivariate time series\npresents an opportunity for machine learning (ML) to enhance patient\nphenotyping. In contrast to previous research focused on electronic health\nrecords (EHR), here we propose an ML approach for phenotyping using routinely\ncollected physiological time series data. Our new algorithm integrates Long\nShort-Term Memory (LSTM) networks with collaborative filtering concepts to\nidentify common physiological states across patients. Tested on real-world ICU\nclinical data for intracranial hypertension (IH) detection in patients with\nbrain injury, our method achieved an area under the curve (AUC) of 0.889 and\naverage precision (AP) of 0.725. Moreover, our algorithm outperforms\nautoencoders in learning more structured latent representations of the\nphysiological signals. These findings highlight the promise of our methodology\nfor patient phenotyping, leveraging routinely collected multivariate time\nseries to improve clinical care practices.\n", "date": "2024-02-27", "categories": []}
{"id": "2410.00034", "title": "Prediction and Detection of Terminal Diseases Using Internet of Medical\n  Things: A Review", "abstract": "  The integration of Artificial Intelligence (AI) and the Internet of Medical\nThings (IoMT) in healthcare, through Machine Learning (ML) and Deep Learning\n(DL) techniques, has advanced the prediction and diagnosis of chronic diseases.\nAI-driven models such as XGBoost, Random Forest, CNNs, and LSTM RNNs have\nachieved over 98\\% accuracy in predicting heart disease, chronic kidney disease\n(CKD), Alzheimer's disease, and lung cancer, using datasets from platforms like\nKaggle, UCI, private institutions, and real-time IoMT sources. However,\nchallenges persist due to variations in data quality, patient demographics, and\nformats from different hospitals and research sources. The incorporation of\nIoMT data, which is vast and heterogeneous, adds complexities in ensuring\ninteroperability and security to protect patient privacy. AI models often\nstruggle with overfitting, performing well in controlled environments but less\neffectively in real-world clinical settings. Moreover, multi-morbidity\nscenarios especially for rare diseases like dementia, stroke, and cancers\nremain insufficiently addressed. Future research should focus on data\nstandardization and advanced preprocessing techniques to improve data quality\nand interoperability. Transfer learning and ensemble methods are crucial for\nimproving model generalizability across clinical settings. Additionally, the\nexploration of disease interactions and the development of predictive models\nfor chronic illness intersections is needed. Creating standardized frameworks\nand open-source tools for integrating federated learning, blockchain, and\ndifferential privacy into IoMT systems will also ensure robust data privacy and\nsecurity.\n", "date": "2024-09-22", "categories": []}
{"id": "2410.00121", "title": "Using fractal dimension to predict the risk of intra cranial aneurysm\n  rupture with machine learning", "abstract": "  Intracranial aneurysms (IAs) that rupture result in significant morbidity and\nmortality. While traditional risk models such as the PHASES score are useful in\nclinical decision making, machine learning (ML) models offer the potential to\nprovide more accuracy. In this study, we compared the performance of four\ndifferent machine learning algorithms Random Forest (RF), XGBoost (XGB),\nSupport Vector Machine (SVM), and Multi Layer Perceptron (MLP) on clinical and\nradiographic features to predict rupture status of intracranial aneurysms.\nAmong the models, RF achieved the highest accuracy (85%) with balanced\nprecision and recall, while MLP had the lowest overall performance (accuracy of\n63%). Fractal dimension ranked as the most important feature for model\nperformance across all models.\n", "date": "2024-09-30", "categories": []}
{"id": "2410.00366", "title": "Easydiagnos: a framework for accurate feature selection for automatic\n  diagnosis in smart healthcare", "abstract": "  The rapid advancements in artificial intelligence (AI) have revolutionized\nsmart healthcare, driving innovations in wearable technologies, continuous\nmonitoring devices, and intelligent diagnostic systems. However, security,\nexplainability, robustness, and performance optimization challenges remain\ncritical barriers to widespread adoption in clinical environments. This\nresearch presents an innovative algorithmic method using the Adaptive Feature\nEvaluator (AFE) algorithm to improve feature selection in healthcare datasets\nand overcome problems. AFE integrating Genetic Algorithms (GA), Explainable\nArtificial Intelligence (XAI), and Permutation Combination Techniques (PCT),\nthe algorithm optimizes Clinical Decision Support Systems (CDSS), thereby\nenhancing predictive accuracy and interpretability. The proposed method is\nvalidated across three diverse healthcare datasets using six distinct machine\nlearning algorithms, demonstrating its robustness and superiority over\nconventional feature selection techniques. The results underscore the\ntransformative potential of AFE in smart healthcare, enabling personalized and\ntransparent patient care. Notably, the AFE algorithm, when combined with a\nMulti-layer Perceptron (MLP), achieved an accuracy of up to 98.5%, highlighting\nits capability to improve clinical decision-making processes in real-world\nhealthcare applications.\n", "date": "2024-10-01", "categories": []}
{"id": "2410.00536", "title": "Arges: Spatio-Temporal Transformer for Ulcerative Colitis Severity\n  Assessment in Endoscopy Videos", "abstract": "  Accurate assessment of disease severity from endoscopy videos in ulcerative\ncolitis (UC) is crucial for evaluating drug efficacy in clinical trials.\nSeverity is often measured by the Mayo Endoscopic Subscore (MES) and Ulcerative\nColitis Endoscopic Index of Severity (UCEIS) score. However, expert MES/UCEIS\nannotation is time-consuming and susceptible to inter-rater variability,\nfactors addressable by automation. Automation attempts with frame-level labels\nface challenges in fully-supervised solutions due to the prevalence of\nvideo-level labels in clinical trials. CNN-based weakly-supervised models (WSL)\nwith end-to-end (e2e) training lack generalization to new disease scores and\nignore spatio-temporal information crucial for accurate scoring. To address\nthese limitations, we propose \"Arges\", a deep learning framework that utilizes\na transformer with positional encoding to incorporate spatio-temporal\ninformation from frame features to estimate disease severity scores in\nendoscopy video. Extracted features are derived from a foundation model\n(ArgesFM), pre-trained on a large diverse dataset from multiple clinical trials\n(61M frames, 3927 videos). We evaluate four UC disease severity scores,\nincluding MES and three UCEIS component scores. Test set evaluation indicates\nsignificant improvements, with F1 scores increasing by 4.1% for MES and 18.8%,\n6.6%, 3.8% for the three UCEIS component scores compared to state-of-the-art\nmethods. Prospective validation on previously unseen clinical trial data\nfurther demonstrates the model's successful generalization.\n", "date": "2024-10-01", "categories": []}
{"id": "2409.19901", "title": "SurvCORN: Survival Analysis with Conditional Ordinal Ranking Neural\n  Network", "abstract": "  Survival analysis plays a crucial role in estimating the likelihood of future\nevents for patients by modeling time-to-event data, particularly in healthcare\nsettings where predictions about outcomes such as death and disease recurrence\nare essential. However, this analysis poses challenges due to the presence of\ncensored data, where time-to-event information is missing for certain data\npoints. Yet, censored data can offer valuable insights, provided we\nappropriately incorporate the censoring time during modeling. In this paper, we\npropose SurvCORN, a novel method utilizing conditional ordinal ranking networks\nto predict survival curves directly. Additionally, we introduce SurvMAE, a\nmetric designed to evaluate the accuracy of model predictions in estimating\ntime-to-event outcomes. Through empirical evaluation on two real-world cancer\ndatasets, we demonstrate SurvCORN's ability to maintain accurate ordering\nbetween patient outcomes while improving individual time-to-event predictions.\nOur contributions extend recent advancements in ordinal regression to survival\nanalysis, offering valuable insights into accurate prognosis in healthcare\nsettings.\n", "date": "2024-09-30", "categories": []}
{"id": "2409.18814", "title": "Early diagnosis of Alzheimer's disease from MRI images with deep\n  learning model", "abstract": "  It is acknowledged that the most common cause of dementia worldwide is\nAlzheimer's disease (AD). This condition progresses in severity from mild to\nsevere and interferes with people's everyday routines. Early diagnosis plays a\ncritical role in patient care and clinical trials. Convolutional neural\nnetworks (CNN) are used to create a framework for identifying specific disease\nfeatures from MRI scans Classification of dementia involves approaches such as\nmedical history review, neuropsychological tests, and magnetic resonance\nimaging (MRI). However, the image dataset obtained from Kaggle faces a\nsignificant issue of class imbalance, which requires equal distribution of\nsamples from each class to address. In this article, to address this imbalance,\nthe Synthetic Minority Oversampling Technique (SMOTE) is utilized. Furthermore,\na pre-trained convolutional neural network has been applied to the DEMNET\ndementia network to extract key features from AD images. The proposed model\nachieved an impressive accuracy of 98.67%.\n", "date": "2024-09-27", "categories": []}
{"id": "2409.17786", "title": "Predicting the Stay Length of Patients in Hospitals using Convolutional\n  Gated Recurrent Deep Learning Model", "abstract": "  Predicting hospital length of stay (LoS) stands as a critical factor in\nshaping public health strategies. This data serves as a cornerstone for\ngovernments to discern trends, patterns, and avenues for enhancing healthcare\ndelivery. In this study, we introduce a robust hybrid deep learning model, a\ncombination of Multi-layer Convolutional (CNNs) deep learning, Gated Recurrent\nUnits (GRU), and Dense neural networks, that outperforms 11 conventional and\nstate-of-the-art Machine Learning (ML) and Deep Learning (DL) methodologies in\naccurately forecasting inpatient hospital stay duration. Our investigation\ndelves into the implementation of this hybrid model, scrutinising variables\nlike geographic indicators tied to caregiving institutions, demographic markers\nencompassing patient ethnicity, race, and age, as well as medical attributes\nsuch as the CCS diagnosis code, APR DRG code, illness severity metrics, and\nhospital stay duration. Statistical evaluations reveal the pinnacle LoS\naccuracy achieved by our proposed model (CNN-GRU-DNN), which averages at 89%\nacross a 10-fold cross-validation test, surpassing LSTM, BiLSTM, GRU, and\nConvolutional Neural Networks (CNNs) by 19%, 18.2%, 18.6%, and 7%,\nrespectively. Accurate LoS predictions not only empower hospitals to optimise\nresource allocation and curb expenses associated with prolonged stays but also\npave the way for novel strategies in hospital stay management. This avenue\nholds promise for catalysing advancements in healthcare research and\ninnovation, inspiring a new era of precision-driven healthcare practices.\n", "date": "2024-09-26", "categories": []}
{"id": "2211.05207", "title": "Improving Clinician Performance in Classification of EEG Patterns on the\n  Ictal-Interictal-Injury Continuum using Interpretable Machine Learning", "abstract": "  In intensive care units (ICUs), critically ill patients are monitored with\nelectroencephalograms (EEGs) to prevent serious brain injury. The number of\npatients who can be monitored is constrained by the availability of trained\nphysicians to read EEGs, and EEG interpretation can be subjective and prone to\ninter-observer variability. Automated deep learning systems for EEG could\nreduce human bias and accelerate the diagnostic process. However, black box\ndeep learning models are untrustworthy, difficult to troubleshoot, and lack\naccountability in real-world applications, leading to a lack of trust and\nadoption by clinicians. To address these challenges, we propose a novel\ninterpretable deep learning model that not only predicts the presence of\nharmful brainwave patterns but also provides high-quality case-based\nexplanations of its decisions. Our model performs better than the corresponding\nblack box model, despite being constrained to be interpretable. The learned 2D\nembedded space provides the first global overview of the structure of\nictal-interictal-injury continuum brainwave patterns. The ability to understand\nhow our model arrived at its decisions will not only help clinicians to\ndiagnose and treat harmful brain activities more accurately but also increase\ntheir trust and adoption of machine learning models in clinical practice; this\ncould be an integral component of the ICU neurologists' standard workflow.\n", "date": "2022-11-09", "categories": []}
{"id": "2409.16339", "title": "Large-scale digital phenotyping: identifying depression and anxiety\n  indicators in a general UK population with over 10,000 participants", "abstract": "  Digital phenotyping offers a novel and cost-efficient approach for managing\ndepression and anxiety. Previous studies, often limited to small-to-medium or\nspecific populations, may lack generalizability. We conducted a cross-sectional\nanalysis of data from 10,129 participants recruited from a UK-based general\npopulation between June 2020 and August 2022. Participants shared wearable\n(Fitbit) data and self-reported questionnaires on depression (PHQ-8), anxiety\n(GAD-7), and mood via a study app. We first examined the correlations between\nPHQ-8/GAD-7 scores and wearable-derived features, demographics, health data,\nand mood assessments. Subsequently, unsupervised clustering was used to\nidentify behavioural patterns associated with depression or anxiety. Finally,\nwe employed separate XGBoost models to predict depression and anxiety and\ncompared the results using different subsets of features. We observed\nsignificant associations between the severity of depression and anxiety with\nseveral factors, including mood, age, gender, BMI, sleep patterns, physical\nactivity, and heart rate. Clustering analysis revealed that participants\nsimultaneously exhibiting lower physical activity levels and higher heart rates\nreported more severe symptoms. Prediction models incorporating all types of\nvariables achieved the best performance ($R^2$=0.41, MAE=3.42 for depression;\n$R^2$=0.31, MAE=3.50 for anxiety) compared to those using subsets of variables.\nThis study identified potential indicators for depression and anxiety,\nhighlighting the utility of digital phenotyping and machine learning\ntechnologies for rapid screening of mental disorders in general populations.\nThese findings provide robust real-world insights for future healthcare\napplications.\n", "date": "2024-09-24", "categories": []}
{"id": "2409.14638", "title": "Harmonising the Clinical Melody: Tuning Large Language Models for\n  Hospital Course Summarisation in Clinical Coding", "abstract": "  The increasing volume and complexity of clinical documentation in Electronic\nMedical Records systems pose significant challenges for clinical coders, who\nmust mentally process and summarise vast amounts of clinical text to extract\nessential information needed for coding tasks. While large language models have\nbeen successfully applied to shorter summarisation tasks in recent years, the\nchallenge of summarising a hospital course remains an open area for further\nresearch and development. In this study, we adapted three pre trained LLMs,\nLlama 3, BioMistral, Mistral Instruct v0.1 for the hospital course\nsummarisation task, using Quantized Low Rank Adaptation fine tuning. We created\na free text clinical dataset from MIMIC III data by concatenating various\nclinical notes as the input clinical text, paired with ground truth Brief\nHospital Course sections extracted from the discharge summaries for model\ntraining. The fine tuned models were evaluated using BERTScore and ROUGE\nmetrics to assess the effectiveness of clinical domain fine tuning.\nAdditionally, we validated their practical utility using a novel hospital\ncourse summary assessment metric specifically tailored for clinical coding. Our\nfindings indicate that fine tuning pre trained LLMs for the clinical domain can\nsignificantly enhance their performance in hospital course summarisation and\nsuggest their potential as assistive tools for clinical coding. Future work\nshould focus on refining data curation methods to create higher quality\nclinical datasets tailored for hospital course summary tasks and adapting more\nadvanced open source LLMs comparable to proprietary models to further advance\nthis research.\n", "date": "2024-09-23", "categories": []}
{"id": "2409.15491", "title": "Computational Pathology for Accurate Prediction of Breast Cancer\n  Recurrence: Development and Validation of a Deep Learning-based Tool", "abstract": "  Accurate recurrence risk stratification is crucial for optimizing treatment\nplans for breast cancer patients. Current prognostic tools like Oncotype DX\n(ODX) offer valuable genomic insights for HR+/HER2- patients but are limited by\ncost and accessibility, particularly in underserved populations. In this study,\nwe present Deep-BCR-Auto, a deep learning-based computational pathology\napproach that predicts breast cancer recurrence risk from routine H&E-stained\nwhole slide images (WSIs). Our methodology was validated on two independent\ncohorts: the TCGA-BRCA dataset and an in-house dataset from The Ohio State\nUniversity (OSU). Deep-BCR-Auto demonstrated robust performance in stratifying\npatients into low- and high-recurrence risk categories. On the TCGA-BRCA\ndataset, the model achieved an area under the receiver operating characteristic\ncurve (AUROC) of 0.827, significantly outperforming existing weakly supervised\nmodels (p=0.041). In the independent OSU dataset, Deep-BCR-Auto maintained\nstrong generalizability, achieving an AUROC of 0.832, along with 82.0%\naccuracy, 85.0% specificity, and 67.7% sensitivity. These findings highlight\nthe potential of computational pathology as a cost-effective alternative for\nrecurrence risk assessment, broadening access to personalized treatment\nstrategies. This study underscores the clinical utility of integrating deep\nlearning-based computational pathology into routine pathological assessment for\nbreast cancer prognosis across diverse clinical settings.\n", "date": "2024-09-23", "categories": []}
{"id": "2409.13743", "title": "Effect of Clinical History on Predictive Model Performance for Renal\n  Complications of Diabetes", "abstract": "  Diabetes is a chronic disease characterised by a high risk of developing\ndiabetic nephropathy, which, in turn, is the leading cause of end-stage chronic\nkidney disease. The early identification of individuals at heightened risk of\nsuch complications or their exacerbation can be of paramount importance to set\na correct course of treatment. In the present work, from the data collected in\nthe DARWIN-Renal (DApagliflozin Real-World evIdeNce-Renal) study, a nationwide\nmulticentre retrospective real-world study, we develop an array of logistic\nregression models to predict, over different prediction horizons, the crossing\nof clinically relevant glomerular filtration rate (eGFR) thresholds for\npatients with diabetes by means of variables associated with demographic,\nanthropometric, laboratory, pathology, and therapeutic data. In doing so, we\ninvestigate the impact of information coming from patient's past visits on the\nmodel's predictive performance, coupled with an analysis of feature importance\nthrough the Boruta algorithm. Our models yield very good performance (AUROC as\nhigh as 0.98). We also show that the introduction of information from patient's\npast visits leads to improved model performance of up to 4%. The usefulness of\npast information is further corroborated by a feature importance analysis.\n", "date": "2024-09-10", "categories": []}
{"id": "2409.13893", "title": "Transfer Learning with Clinical Concept Embeddings from Large Language\n  Models", "abstract": "  Knowledge sharing is crucial in healthcare, especially when leveraging data\nfrom multiple clinical sites to address data scarcity, reduce costs, and enable\ntimely interventions. Transfer learning can facilitate cross-site knowledge\ntransfer, but a major challenge is heterogeneity in clinical concepts across\ndifferent sites. Large Language Models (LLMs) show significant potential of\ncapturing the semantic meaning of clinical concepts and reducing heterogeneity.\nThis study analyzed electronic health records from two large healthcare systems\nto assess the impact of semantic embeddings from LLMs on local, shared, and\ntransfer learning models. Results indicate that domain-specific LLMs, such as\nMed-BERT, consistently outperform in local and direct transfer scenarios, while\ngeneric models like OpenAI embeddings require fine-tuning for optimal\nperformance. However, excessive tuning of models with biomedical embeddings may\nreduce effectiveness, emphasizing the need for balance. This study highlights\nthe importance of domain-specific embeddings and careful model tuning for\neffective knowledge transfer in healthcare.\n", "date": "2024-09-20", "categories": []}
{"id": "2409.14105", "title": "ESDS: AI-Powered Early Stunting Detection and Monitoring System using\n  Edited Radius-SMOTE Algorithm", "abstract": "  Stunting detection is a significant issue in Indonesian healthcare, causing\nlower cognitive function, lower productivity, a weakened immunity, delayed\nneuro-development, and degenerative diseases. In regions with a high prevalence\nof stunting and limited welfare resources, identifying children in need of\ntreatment is critical. The diagnostic process often raises challenges, such as\nthe lack of experience in medical workers, incompatible anthropometric\nequipment, and inefficient medical bureaucracy. To counteract the issues, the\nuse of load cell sensor and ultrasonic sensor can provide suitable\nanthropometric equipment and streamline the medical bureaucracy for stunting\ndetection. This paper also employs machine learning for stunting detection\nbased on sensor readings. The experiment results show that the sensitivity of\nthe load cell sensor and the ultrasonic sensor is 0.9919 and 0.9986,\nrespectively. Also, the machine learning test results have three classification\nclasses, which are normal, stunted, and stunting with an accuracy rate of 98\\%.\n", "date": "2024-09-21", "categories": []}
{"id": "2409.14231", "title": "Predicting Coronary Heart Disease Using a Suite of Machine Learning\n  Models", "abstract": "  Coronary Heart Disease affects millions of people worldwide and is a\nwell-studied area of healthcare. There are many viable and accurate methods for\nthe diagnosis and prediction of heart disease, but they have limiting points\nsuch as invasiveness, late detection, or cost. Supervised learning via machine\nlearning algorithms presents a low-cost (computationally speaking),\nnon-invasive solution that can be a precursor for early diagnosis. In this\nstudy, we applied several well-known methods and benchmarked their performance\nagainst each other. It was found that Random Forest with oversampling of the\npredictor variable produced the highest accuracy of 84%.\n", "date": "2024-09-21", "categories": []}
{"id": "2408.11854", "title": "When Raw Data Prevails: Are Large Language Model Embeddings Effective in\n  Numerical Data Representation for Medical Machine Learning Applications?", "abstract": "  The introduction of Large Language Models (LLMs) has advanced data\nrepresentation and analysis, bringing significant progress in their use for\nmedical questions and answering. Despite these advancements, integrating\ntabular data, especially numerical data pivotal in clinical contexts, into LLM\nparadigms has not been thoroughly explored. In this study, we examine the\neffectiveness of vector representations from last hidden states of LLMs for\nmedical diagnostics and prognostics using electronic health record (EHR) data.\nWe compare the performance of these embeddings with that of raw numerical EHR\ndata when used as feature inputs to traditional machine learning (ML)\nalgorithms that excel at tabular data learning, such as eXtreme Gradient\nBoosting. We focus on instruction-tuned LLMs in a zero-shot setting to\nrepresent abnormal physiological data and evaluating their utilities as feature\nextractors to enhance ML classifiers for predicting diagnoses, length of stay,\nand mortality. Furthermore, we examine prompt engineering techniques on\nzero-shot and few-shot LLM embeddings to measure their impact comprehensively.\nAlthough findings suggest the raw data features still prevails in medical ML\ntasks, zero-shot LLM embeddings demonstrate competitive results, suggesting a\npromising avenue for future research in medical applications.\n", "date": "2024-08-15", "categories": []}
{"id": "2409.13367", "title": "ALPEC: A Comprehensive Evaluation Framework and Dataset for Machine\n  Learning-Based Arousal Detection in Clinical Practice", "abstract": "  Detecting arousals in sleep is essential for diagnosing sleep disorders.\nHowever, using Machine Learning (ML) in clinical practice is impeded by\nfundamental issues, primarily due to mismatches between clinical protocols and\nML methods. Clinicians typically annotate only the onset of arousals, while ML\nmethods rely on annotations for both the beginning and end. Additionally, there\nis no standardized evaluation methodology tailored to clinical needs for\narousal detection models. This work addresses these issues by introducing a\nnovel post-processing and evaluation framework emphasizing approximate\nlocalization and precise event count (ALPEC) of arousals. We recommend that ML\npractitioners focus on detecting arousal onsets, aligning with clinical\npractice. We examine the impact of this shift on current training and\nevaluation schemes, addressing simplifications and challenges. We utilize a\nnovel comprehensive polysomnographic dataset (CPS) that reflects the\naforementioned clinical annotation constraints and includes modalities not\npresent in existing polysomnographic datasets. We release the dataset alongside\nthis paper, demonstrating the benefits of leveraging multimodal data for\narousal onset detection. Our findings significantly contribute to integrating\nML-based arousal detection in clinical settings, reducing the gap between\ntechnological advancements and clinical needs.\n", "date": "2024-09-20", "categories": []}
{"id": "2409.13606", "title": "Towards Child-Inclusive Clinical Video Understanding for Autism Spectrum\n  Disorder", "abstract": "  Clinical videos in the context of Autism Spectrum Disorder are often\nlong-form interactions between children and caregivers/clinical professionals,\nencompassing complex verbal and non-verbal behaviors. Objective analyses of\nthese videos could provide clinicians and researchers with nuanced insights\ninto the behavior of children with Autism Spectrum Disorder. Manually coding\nthese videos is a time-consuming task and requires a high level of domain\nexpertise. Hence, the ability to capture these interactions computationally can\naugment the manual effort and enable supporting the diagnostic procedure. In\nthis work, we investigate the use of foundation models across three modalities:\nspeech, video, and text, to analyse child-focused interaction sessions. We\npropose a unified methodology to combine multiple modalities by using large\nlanguage models as reasoning agents. We evaluate their performance on two tasks\nwith different information granularity: activity recognition and abnormal\nbehavior detection. We find that the proposed multimodal pipeline provides\nrobustness to modality-specific limitations and improves performance on the\nclinical video analysis compared to unimodal settings.\n", "date": "2024-09-20", "categories": []}
{"id": "2409.12116", "title": "Stronger Baseline Models -- A Key Requirement for Aligning Machine\n  Learning Research with Clinical Utility", "abstract": "  Machine Learning (ML) research has increased substantially in recent years,\ndue to the success of predictive modeling across diverse application domains.\nHowever, well-known barriers exist when attempting to deploy ML models in\nhigh-stakes, clinical settings, including lack of model transparency (or the\ninability to audit the inference process), large training data requirements\nwith siloed data sources, and complicated metrics for measuring model utility.\nIn this work, we show empirically that including stronger baseline models in\nhealthcare ML evaluations has important downstream effects that aid\npractitioners in addressing these challenges. Through a series of case studies,\nwe find that the common practice of omitting baselines or comparing against a\nweak baseline model (e.g. a linear model with no optimization) obscures the\nvalue of ML methods proposed in the research literature. Using these insights,\nwe propose some best practices that will enable practitioners to more\neffectively study and deploy ML models in clinical settings.\n", "date": "2024-09-18", "categories": []}
{"id": "2406.18212", "title": "Joint Stream: Malignant Region Learning for Breast Cancer Diagnosis", "abstract": "  Early diagnosis of breast cancer (BC) significantly contributes to reducing\nthe mortality rate worldwide. The detection of different factors and biomarkers\nsuch as Estrogen receptor (ER), Progesterone receptor (PR), Human epidermal\ngrowth factor receptor 2 (HER2) gene, Histological grade (HG), Auxiliary lymph\nnode (ALN) status, and Molecular subtype (MS) can play a significant role in\nimproved BC diagnosis. However, the existing methods predict only a single\nfactor which makes them less suitable to use in diagnosis and designing a\nstrategy for treatment. In this paper, we propose to classify the six essential\nindicating factors (ER, PR, HER2, ALN, HG, MS) for early BC diagnosis using\nH\\&E stained WSI's. To precisely capture local neighboring relationships, we\nuse spatial and frequency domain information from the large patch size of WSI's\nmalignant regions. Furthermore, to cater the variable number of regions of\ninterest sizes and give due attention to each region, we propose a malignant\nregion learning attention network. Our experimental results demonstrate that\ncombining spatial and frequency information using the malignant region learning\nmodule significantly improves multi-factor and single-factor classification\nperformance on publicly available datasets.\n", "date": "2024-06-26", "categories": []}
{"id": "2409.11302", "title": "Beyond LoRA: Exploring Efficient Fine-Tuning Techniques for Time Series\n  Foundational Models", "abstract": "  Time Series Foundation Models (TSFMs) have recently garnered attention for\ntheir ability to model complex, large-scale time series data across domains\nsuch as retail, finance, and transportation. However, their application to\nsensitive, domain-specific fields like healthcare remains challenging,\nprimarily due to the difficulty of fine-tuning these models for specialized,\nout-of-domain tasks with scarce publicly available datasets. In this work, we\nexplore the use of Parameter-Efficient Fine-Tuning (PEFT) techniques to address\nthese limitations, focusing on healthcare applications, particularly ICU vitals\nforecasting for sepsis patients. We introduce and evaluate two selective\n(BitFit and LayerNorm Tuning) and two additive (VeRA and FourierFT) PEFT\ntechniques on multiple configurations of the Chronos TSFM for forecasting vital\nsigns of sepsis patients. Our comparative analysis demonstrates that some of\nthese PEFT methods outperform LoRA in terms of parameter efficiency and domain\nadaptation, establishing state-of-the-art (SOTA) results in ICU vital\nforecasting tasks. Interestingly, FourierFT applied to the Chronos (Tiny)\nvariant surpasses the SOTA model while fine-tuning only 2,400 parameters\ncompared to the 700K parameters of the benchmark.\n", "date": "2024-09-17", "categories": []}
{"id": "2409.11350", "title": "Clinical Validation of a Real-Time Machine Learning-based System for the\n  Detection of Acute Myeloid Leukemia by Flow Cytometry", "abstract": "  Machine-learning (ML) models in flow cytometry have the potential to reduce\nerror rates, increase reproducibility, and boost the efficiency of clinical\nlabs. While numerous ML models for flow cytometry data have been proposed, few\nstudies have described the clinical deployment of such models. Realizing the\npotential gains of ML models in clinical labs requires not only an accurate\nmodel, but infrastructure for automated inference, error detection, analytics\nand monitoring, and structured data extraction. Here, we describe an ML model\nfor detection of Acute Myeloid Leukemia (AML), along with the infrastructure\nsupporting clinical implementation. Our infrastructure leverages the resilience\nand scalability of the cloud for model inference, a Kubernetes-based workflow\nsystem that provides model reproducibility and resource management, and a\nsystem for extracting structured diagnoses from full-text reports. We also\ndescribe our model monitoring and visualization platform, an essential element\nfor ensuring continued model accuracy. Finally, we present a post-deployment\nanalysis of impacts on turn-around time and compare production accuracy to the\noriginal validation statistics.\n", "date": "2024-09-17", "categories": []}
{"id": "2409.09968", "title": "Artificial Intelligence-Based Opportunistic Coronary Calcium Screening\n  in the Veterans Affairs National Healthcare System", "abstract": "  Coronary artery calcium (CAC) is highly predictive of cardiovascular events.\nWhile millions of chest CT scans are performed annually in the United States,\nCAC is not routinely quantified from scans done for non-cardiac purposes. A\ndeep learning algorithm was developed using 446 expert segmentations to\nautomatically quantify CAC on non-contrast, non-gated CT scans (AI-CAC). Our\nstudy differs from prior works as we leverage imaging data across the Veterans\nAffairs national healthcare system, from 98 medical centers, capturing\nextensive heterogeneity in imaging protocols, scanners, and patients. AI-CAC\nperformance on non-gated scans was compared against clinical standard ECG-gated\nCAC scoring. Non-gated AI-CAC differentiated zero vs. non-zero and less than\n100 vs. 100 or greater Agatston scores with accuracies of 89.4% (F1 0.93) and\n87.3% (F1 0.89), respectively, in 795 patients with paired gated scans within a\nyear of a non-gated CT scan. Non-gated AI-CAC was predictive of 10-year\nall-cause mortality (CAC 0 vs. >400 group: 25.4% vs. 60.2%, Cox HR 3.49, p <\n0.005), and composite first-time stroke, MI, or death (CAC 0 vs. >400 group:\n33.5% vs. 63.8%, Cox HR 3.00, p < 0.005). In a screening dataset of 8,052\npatients with low-dose lung cancer-screening CTs (LDCT), 3,091/8,052 (38.4%)\nindividuals had AI-CAC >400. Four cardiologists qualitatively reviewed LDCT\nimages from a random sample of >400 AI-CAC patients and verified that 527/531\n(99.2%) would benefit from lipid-lowering therapy. To the best of our\nknowledge, this is the first non-gated CT CAC algorithm developed across a\nnational healthcare system, on multiple imaging protocols, without filtering\nintra-cardiac hardware, and compared against a strong gated CT reference. We\nreport superior performance relative to previous CAC algorithms evaluated\nagainst paired gated scans that included patients with intra-cardiac hardware.\n", "date": "2024-09-16", "categories": []}
{"id": "2409.10191", "title": "LLMs for clinical risk prediction", "abstract": "  This study compares the efficacy of GPT-4 and clinalytix Medical AI in\npredicting the clinical risk of delirium development. Findings indicate that\nGPT-4 exhibited significant deficiencies in identifying positive cases and\nstruggled to provide reliable probability estimates for delirium risk, while\nclinalytix Medical AI demonstrated superior accuracy. A thorough analysis of\nthe large language model's (LLM) outputs elucidated potential causes for these\ndiscrepancies, consistent with limitations reported in extant literature. These\nresults underscore the challenges LLMs face in accurately diagnosing conditions\nand interpreting complex clinical data. While LLMs hold substantial potential\nin healthcare, they are currently unsuitable for independent clinical\ndecision-making. Instead, they should be employed in assistive roles,\ncomplementing clinical expertise. Continued human oversight remains essential\nto ensure optimal outcomes for both patients and healthcare providers.\n", "date": "2024-09-16", "categories": []}
{"id": "2309.13781", "title": "Explainable Machine Learning for ICU Readmission Prediction", "abstract": "  The intensive care unit (ICU) comprises a complex hospital environment, where\ndecisions made by clinicians have a high level of risk for the patients' lives.\nA comprehensive care pathway must then be followed to reduce p complications.\nUncertain, competing and unplanned aspects within this environment increase the\ndifficulty in uniformly implementing the care pathway. Readmission contributes\nto this pathway's difficulty, occurring when patients are admitted again to the\nICU in a short timeframe, resulting in high mortality rates and high resource\nutilisation. Several works have tried to predict readmission through patients'\nmedical information. Although they have some level of success while predicting\nreadmission, those works do not properly assess, characterise and understand\nreadmission prediction. This work proposes a standardised and explainable\nmachine learning pipeline to model patient readmission on a multicentric\ndatabase (i.e., the eICU cohort with 166,355 patients, 200,859 admissions and\n6,021 readmissions) while validating it on monocentric (i.e., the MIMIC IV\ncohort with 382,278 patients, 523,740 admissions and 5,984 readmissions) and\nmulticentric settings. Our machine learning pipeline achieved predictive\nperformance in terms of the area of the receiver operating characteristic curve\n(AUC) up to 0.7 with a Random Forest classification model, yielding an overall\ngood calibration and consistency on validation sets. From explanations provided\nby the constructed models, we could also derive a set of insightful\nconclusions, primarily on variables related to vital signs and blood tests\n(e.g., albumin, blood urea nitrogen and hemoglobin levels), demographics (e.g.,\nage, and admission height and weight), and ICU-associated variables (e.g., unit\ntype). These insights provide an invaluable source of information during\nclinicians' decision-making while discharging ICU patients.\n", "date": "2023-09-25", "categories": []}
{"id": "2409.07997", "title": "Privacy-preserving federated prediction of pain intensity change based\n  on multi-center survey data", "abstract": "  Background: Patient-reported survey data are used to train prognostic models\naimed at improving healthcare. However, such data are typically available\nmulti-centric and, for privacy reasons, cannot easily be centralized in one\ndata repository. Models trained locally are less accurate, robust, and\ngeneralizable. We present and apply privacy-preserving federated machine\nlearning techniques for prognostic model building, where local survey data\nnever leaves the legally safe harbors of the medical centers. Methods: We used\ncentralized, local, and federated learning techniques on two healthcare\ndatasets (GLA:D data from the five health regions of Denmark and international\nSHARE data of 27 countries) to predict two different health outcomes. We\ncompared linear regression, random forest regression, and random forest\nclassification models trained on local data with those trained on the entire\ndata in a centralized and in a federated fashion. Results: In GLA:D data,\nfederated linear regression (R2 0.34, RMSE 18.2) and federated random forest\nregression (R2 0.34, RMSE 18.3) models outperform their local counterparts\n(i.e., R2 0.32, RMSE 18.6, R2 0.30, RMSE 18.8) with statistical significance.\nWe also found that centralized models (R2 0.34, RMSE 18.2, R2 0.32, RMSE 18.5,\nrespectively) did not perform significantly better than the federated models.\nIn SHARE, the federated model (AC 0.78, AUROC: 0.71) and centralized model (AC\n0.84, AUROC: 0.66) perform significantly better than the local models (AC:\n0.74, AUROC: 0.69). Conclusion: Federated learning enables the training of\nprognostic models from multi-center surveys without compromising privacy and\nwith only minimal or no compromise regarding model performance.\n", "date": "2024-09-12", "categories": []}
{"id": "2408.09410", "title": "BernGraph: Probabilistic Graph Neural Networks for EHR-based Medication\n  Recommendations", "abstract": "  The medical community believes binary medical event outcomes in EHR data\ncontain sufficient information for making a sensible recommendation. However,\nthere are two challenges to effectively utilizing such data: (1) modeling the\nrelationship between massive 0,1 event outcomes is difficult, even with expert\nknowledge; (2) in practice, learning can be stalled by the binary values since\nthe equally important 0 entries propagate no learning signals. Currently, there\nis a large gap between the assumed sufficient information and the reality that\nno promising results have been shown by utilizing solely the binary data:\nvisiting or secondary information is often necessary to reach acceptable\nperformance. In this paper, we attempt to build the first successful binary EHR\ndata-oriented drug recommendation system by tackling the two difficulties,\nmaking sensible drug recommendations solely using the binary EHR medical\nrecords. To this end, we take a statistical perspective to view the EHR data as\na sample from its cohorts and transform them into continuous Bernoulli\nprobabilities. The transformed entries not only model a deterministic binary\nevent with a distribution but also allow reflecting \\emph{event-event}\nrelationship by conditional probability. A graph neural network is learned on\ntop of the transformation. It captures event-event correlations while\nemphasizing \\emph{event-to-patient} features. Extensive results demonstrate\nthat the proposed method achieves state-of-the-art performance on large-scale\ndatabases, outperforming baseline methods that use secondary information by a\nlarge margin. The source code is available at\n\\url{https://github.com/chenzRG/BEHRMecom}\n", "date": "2024-08-18", "categories": []}
{"id": "2409.05989", "title": "A Comprehensive Comparison Between ANNs and KANs For Classifying EEG\n  Alzheimer's Data", "abstract": "  Alzheimer's Disease is an incurable cognitive condition that affects\nthousands of people globally. While some diagnostic methods exist for\nAlzheimer's Disease, many of these methods cannot detect Alzheimer's in its\nearlier stages. Recently, researchers have explored the use of\nElectroencephalogram (EEG) technology for diagnosing Alzheimer's. EEG is a\nnoninvasive method of recording the brain's electrical signals, and EEG data\nhas shown distinct differences between patients with and without Alzheimer's.\nIn the past, Artificial Neural Networks (ANNs) have been used to predict\nAlzheimer's from EEG data, but these models sometimes produce false positive\ndiagnoses. This study aims to compare losses between ANNs and Kolmogorov-Arnold\nNetworks (KANs) across multiple types of epochs, learning rates, and nodes. The\nresults show that across these different parameters, ANNs are more accurate in\npredicting Alzheimer's Disease from EEG signals.\n", "date": "2024-09-09", "categories": []}
{"id": "2409.06887", "title": "Ordinal Learning: Longitudinal Attention Alignment Model for Predicting\n  Time to Future Breast Cancer Events from Mammograms", "abstract": "  Precision breast cancer (BC) risk assessment is crucial for developing\nindividualized screening and prevention. Despite the promising potential of\nrecent mammogram (MG) based deep learning models in predicting BC risk, they\nmostly overlook the 'time-to-future-event' ordering among patients and exhibit\nlimited explorations into how they track history changes in breast tissue,\nthereby limiting their clinical application. In this work, we propose a novel\nmethod, named OA-BreaCR, to precisely model the ordinal relationship of the\ntime to and between BC events while incorporating longitudinal breast tissue\nchanges in a more explainable manner. We validate our method on public EMBED\nand inhouse datasets, comparing with existing BC risk prediction and time\nprediction methods. Our ordinal learning method OA-BreaCR outperforms existing\nmethods in both BC risk and time-to-future-event prediction tasks.\nAdditionally, ordinal heatmap visualizations show the model's attention over\ntime. Our findings underscore the importance of interpretable and precise risk\nassessment for enhancing BC screening and prevention efforts. The code will be\naccessible to the public.\n", "date": "2024-09-10", "categories": []}
{"id": "2409.07308", "title": "Non-Invasive Glucose Prediction System Enhanced by Mixed Linear Models\n  and Meta-Forests for Domain Generalization", "abstract": "  In this study, we present a non-invasive glucose prediction system that\nintegrates Near-Infrared (NIR) spectroscopy and millimeter-wave (mm-wave)\nsensing. We employ a Mixed Linear Model (MixedLM) to analyze the association\nbetween mm-wave frequency S_21 parameters and blood glucose levels within a\nheterogeneous dataset. The MixedLM method considers inter-subject variability\nand integrates multiple predictors, offering a more comprehensive analysis than\ntraditional correlation analysis. Additionally, we incorporate a Domain\nGeneralization (DG) model, Meta-forests, to effectively handle domain variance\nin the dataset, enhancing the model's adaptability to individual differences.\nOur results demonstrate promising accuracy in glucose prediction for unseen\nsubjects, with a mean absolute error (MAE) of 17.47 mg/dL, a root mean square\nerror (RMSE) of 31.83 mg/dL, and a mean absolute percentage error (MAPE) of\n10.88%, highlighting its potential for clinical application. This study marks a\nsignificant step towards developing accurate, personalized, and non-invasive\nglucose monitoring systems, contributing to improved diabetes management.\n", "date": "2024-09-11", "categories": []}
{"id": "2409.06585", "title": "Developing the Temporal Graph Convolutional Neural Network Model to\n  Predict Hip Replacement using Electronic Health Records", "abstract": "  Background: Hip replacement procedures improve patient lives by relieving\npain and restoring mobility. Predicting hip replacement in advance could reduce\npain by enabling timely interventions, prioritising individuals for surgery or\nrehabilitation, and utilising physiotherapy to potentially delay the need for\njoint replacement. This study predicts hip replacement a year in advance to\nenhance quality of life and health service efficiency. Methods: Adapting\nprevious work using Temporal Graph Convolutional Neural Network (TG-CNN)\nmodels, we construct temporal graphs from primary care medical event codes,\nsourced from ResearchOne EHRs of 40-75-year-old patients, to predict hip\nreplacement risk. We match hip replacement cases to controls by age, sex, and\nIndex of Multiple Deprivation. The model, trained on 9,187 cases and 9,187\ncontrols, predicts hip replacement one year in advance. We validate the model\non two unseen datasets, recalibrating for class imbalance. Additionally, we\nconduct an ablation study and compare against four baseline models. Results:\nOur best model predicts hip replacement risk one year in advance with an AUROC\nof 0.724 (95% CI: 0.715-0.733) and an AUPRC of 0.185 (95% CI: 0.160-0.209),\nachieving a calibration slope of 1.107 (95% CI: 1.074-1.139) after\nrecalibration. Conclusions: The TG-CNN model effectively predicts hip\nreplacement risk by identifying patterns in patient trajectories, potentially\nimproving understanding and management of hip-related conditions.\n", "date": "2024-09-10", "categories": []}
{"id": "2312.00817", "title": "TimelyGPT: Extrapolatable Transformer Pre-training for Long-term\n  Time-Series Forecasting in Healthcare", "abstract": "  Large-scale pre-trained models (PTMs) such as BERT and GPT have recently\nachieved great success in Natural Language Processing and Computer Vision\ndomains. However, the development of PTMs on healthcare time-series data is\nlagging behind.This underscores the limitations of the existing\ntransformer-based architectures, particularly their scalability to handle\nlarge-scale time series and ability to capture long-term temporal dependencies.\nIn this study, we present Timely Generative Pre-trained Transformer\n(TimelyGPT). TimelyGPT employs an extrapolatable position (xPos) embedding to\nencode trend and periodic patterns into time-series representations. It also\nintegrates recurrent attention and temporal convolution modules to effectively\ncapture global-local temporal dependencies. We evaluated TimelyGPT on two\nlarge-scale healthcare time series datasets corresponding to continuous\nbiosignals and irregularly-sampled time series, respectively. Our experiments\nshow that during pre-training, TimelyGPT excels in learning time-series\nrepresentations from continuously monitored biosignals and irregularly-sampled\ntime series data commonly observed in longitudinal electronic health records\n(EHRs). In forecasting continuous biosignals, TimelyGPT achieves accurate\nextrapolation up to 6,000 timesteps of body temperature during the sleep stage\ntransition, given a short look-up window (i.e., prompt) containing only 2,000\ntimesteps. For irregularly-sampled time series, TimelyGPT with a proposed\ntime-specific inference demonstrates high top recall scores in predicting\nfuture diagnoses using early diagnostic records, effectively handling irregular\nintervals between clinical records. Together, we envision TimelyGPT to be\nuseful in a broad spectrum of health domains, including long-term patient\nhealth state forecasting and patient risk trajectory prediction.\n", "date": "2023-11-29", "categories": []}
{"id": "2403.08947", "title": "Robust COVID-19 Detection in CT Images with CLIP", "abstract": "  In the realm of medical imaging, particularly for COVID-19 detection, deep\nlearning models face substantial challenges such as the necessity for extensive\ncomputational resources, the paucity of well-annotated datasets, and a\nsignificant amount of unlabeled data. In this work, we introduce the first\nlightweight detector designed to overcome these obstacles, leveraging a frozen\nCLIP image encoder and a trainable multilayer perception (MLP). Enhanced with\nConditional Value at Risk (CVaR) for robustness and a loss landscape flattening\nstrategy for improved generalization, our model is tailored for high efficacy\nin COVID-19 detection. Furthermore, we integrate a teacher-student framework to\ncapitalize on the vast amounts of unlabeled data, enabling our model to achieve\nsuperior performance despite the inherent data limitations. Experimental\nresults on the COV19-CT-DB dataset demonstrate the effectiveness of our\napproach, surpassing baseline by up to 10.6% in `macro' F1 score in supervised\nlearning. The code is available at\nhttps://github.com/Purdue-M2/COVID-19_Detection_M2_PURDUE.\n", "date": "2024-03-13", "categories": []}
{"id": "2407.19984", "title": "Confidence Estimation for Automatic Detection of Depression and\n  Alzheimer's Disease Based on Clinical Interviews", "abstract": "  Speech-based automatic detection of Alzheimer's disease (AD) and depression\nhas attracted increased attention. Confidence estimation is crucial for a\ntrust-worthy automatic diagnostic system which informs the clinician about the\nconfidence of model predictions and helps reduce the risk of misdiagnosis. This\npaper investigates confidence estimation for automatic detection of AD and\ndepression based on clinical interviews. A novel Bayesian approach is proposed\nwhich uses a dynamic Dirichlet prior distribution to model the second-order\nprobability of the predictive distribution. Experimental results on the\npublicly available ADReSS and DAIC-WOZ datasets demonstrate that the proposed\nmethod outperforms a range of baselines for both classification accuracy and\nconfidence estimation.\n", "date": "2024-07-29", "categories": []}
{"id": "2409.03697", "title": "Classification and Prediction of Heart Diseases using Machine Learning\n  Algorithms", "abstract": "  Heart disease is a serious worldwide health issue because it claims the lives\nof many people who might have been treated if the disease had been identified\nearlier. The leading cause of death in the world is cardiovascular disease,\nusually referred to as heart disease. Creating reliable, effective, and precise\npredictions for these diseases is one of the biggest issues facing the medical\nworld today. Although there are tools for predicting heart diseases, they are\neither expensive or challenging to apply for determining a patient's risk. The\nbest classifier for foretelling and spotting heart disease was the aim of this\nresearch. This experiment examined a range of machine learning approaches,\nincluding Logistic Regression, K-Nearest Neighbor, Support Vector Machine, and\nArtificial Neural Networks, to determine which machine learning algorithm was\nmost effective at predicting heart diseases. One of the most often utilized\ndata sets for this purpose, the UCI heart disease repository provided the data\nset for this study. The K-Nearest Neighbor technique was shown to be the most\neffective machine learning algorithm for determining whether a patient has\nheart disease. It will be beneficial to conduct further studies on the\napplication of additional machine learning algorithms for heart disease\nprediction.\n", "date": "2024-09-05", "categories": []}
{"id": "2409.04704", "title": "A Multi-scenario Attention-based Generative Model for Personalized Blood\n  Pressure Time Series Forecasting", "abstract": "  Continuous blood pressure (BP) monitoring is essential for timely diagnosis\nand intervention in critical care settings. However, BP varies significantly\nacross individuals, this inter-patient variability motivates the development of\npersonalized models tailored to each patient's physiology. In this work, we\npropose a personalized BP forecasting model mainly using electrocardiogram\n(ECG) and photoplethysmogram (PPG) signals. This time-series model incorporates\n2D representation learning to capture complex physiological relationships.\nExperiments are conducted on datasets collected from three diverse scenarios\nwith BP measurements from 60 subjects total. Results demonstrate that the model\nachieves accurate and robust BP forecasts across scenarios within the\nAssociation for the Advancement of Medical Instrumentation (AAMI) standard\ncriteria. This reliable early detection of abnormal fluctuations in BP is\ncrucial for at-risk patients undergoing surgery or intensive care. The proposed\nmodel provides a valuable addition for continuous BP tracking to reduce\nmortality and improve prognosis.\n", "date": "2024-09-07", "categories": []}
{"id": "2409.04888", "title": "A Quantitative Approach for Evaluating Disease Focus and\n  Interpretability of Deep Learning Models for Alzheimer's Disease\n  Classification", "abstract": "  Deep learning (DL) models have shown significant potential in Alzheimer's\nDisease (AD) classification. However, understanding and interpreting these\nmodels remains challenging, which hinders the adoption of these models in\nclinical practice. Techniques such as saliency maps have been proven effective\nin providing visual and empirical clues about how these models work, but there\nstill remains a gap in understanding which specific brain regions DL models\nfocus on and whether these brain regions are pathologically associated with AD.\n  To bridge such gap, in this study, we developed a quantitative\ndisease-focusing strategy to first enhance the interpretability of DL models\nusing saliency maps and brain segmentations; then we propose a disease-focus\n(DF) score that quantifies how much a DL model focuses on brain areas relevant\nto AD pathology based on clinically known MRI-based pathological regions of AD.\nUsing this strategy, we compared several state-of-the-art DL models, including\na baseline 3D ResNet model, a pretrained MedicalNet model, and a MedicalNet\nwith data augmentation to classify patients with AD vs. cognitive normal\npatients using MRI data; then we evaluated these models in terms of their\nabilities to focus on disease-relevant regions. Our results show interesting\ndisease-focusing patterns with different models, particularly characteristic\npatterns with the pretrained models and data augmentation, and also provide\ninsight into their classification performance. These results suggest that the\napproach we developed for quantitatively assessing the abilities of DL models\nto focus on disease-relevant regions may help improve interpretability of these\nmodels for AD classification and facilitate their adoption for AD diagnosis in\nclinical practice. The code is publicly available at\nhttps://github.com/Liang-lt/ADNI.\n", "date": "2024-09-07", "categories": []}
{"id": "2310.00505", "title": "Unveiling the Unborn: Advancing Fetal Health Classification through\n  Machine Learning", "abstract": "  Fetal health classification is a critical task in obstetrics, enabling early\nidentification and management of potential health problems. However, it remains\nchallenging due to data complexity and limited labeled samples. This research\npaper presents a novel machine-learning approach for fetal health\nclassification, leveraging a LightGBM classifier trained on a comprehensive\ndataset. The proposed model achieves an impressive accuracy of 98.31% on a test\nset. Our findings demonstrate the potential of machine learning in enhancing\nfetal health classification, offering a more objective and accurate assessment.\nNotably, our approach combines various features, such as fetal heart rate,\nuterine contractions, and maternal blood pressure, to provide a comprehensive\nevaluation. This methodology holds promise for improving early detection and\ntreatment of fetal health issues, ensuring better outcomes for both mothers and\nbabies. Beyond the high accuracy achieved, the novelty of our approach lies in\nits comprehensive feature selection and assessment methodology. By\nincorporating multiple data points, our model offers a more holistic and\nreliable evaluation compared to traditional methods. This research has\nsignificant implications in the field of obstetrics, paving the way for\nadvancements in early detection and intervention of fetal health concerns.\nFuture work involves validating the model on a larger dataset and developing a\nclinical application. Ultimately, we anticipate that our research will\nrevolutionize the assessment and management of fetal health, contributing to\nimproved healthcare outcomes for expectant mothers and their babies.\n", "date": "2023-09-30", "categories": []}
{"id": "2408.13958", "title": "Prediction of COPD Using Machine Learning, Clinical Summary Notes, and\n  Vital Signs", "abstract": "  Chronic obstructive pulmonary disease (COPD) is a chronic inflammatory lung\ndisease that causes obstructed airflow from the lungs. In the United States,\nmore than 15.7 million Americans have been diagnosed with COPD, with 96% of\nindividuals living with at least one other chronic health condition. It is the\n4th leading cause of death in the country. Over 2.2 million patients are\nadmitted to hospitals annually due to COPD exacerbations. Monitoring and\npredicting patient exacerbations on-time could save their life. This paper\npresents two different predictive models to predict COPD exacerbation using AI\nand natural language processing (NLP) approaches. These models use respiration\nsummary notes, symptoms, and vital signs. To train and test these models, data\nrecords containing physiologic signals and vital signs time series were used.\nThese records were captured from patient monitors and comprehensive clinical\ndata obtained from hospital medical information systems for tens of thousands\nof Intensive Care Unit (ICU) patients. We achieved an area under the Receiver\noperating characteristic (ROC) curve of 0.82 in detection and prediction of\nCOPD exacerbation.\n", "date": "2024-08-25", "categories": []}
{"id": "2409.02957", "title": "Comparison of Epilepsy Induced by Ischemic Hypoxic Brain Injury and\n  Hypoglycemic Brain Injury using Multilevel Fusion of Data Features", "abstract": "  The study aims to investigate the similarities and differences in the brain\ndamage caused by Hypoxia-Ischemia (HI), Hypoglycemia, and Epilepsy.\nHypoglycemia poses a significant challenge in improving glycemic regulation for\ninsulin-treated patients, while HI brain disease in neonates is associated with\nlow oxygen levels. The study examines the possibility of using a combination of\nmedical data and Electroencephalography (EEG) measurements to predict outcomes\nover a two-year period. The study employs a multilevel fusion of data features\nto enhance the accuracy of the predictions. Therefore this paper suggests a\nhybridized classification model for Hypoxia-Ischemia and Hypoglycemia, Epilepsy\nbrain injury (HCM-BI). A Support Vector Machine is applied with clinical\ndetails to define the Hypoxia-Ischemia outcomes of each infant. The newborn\nbabies are assessed every two years again to know the neural development\nresults. A selection of four attributes is derived from the\nElectroencephalography records, and SVM does not get conclusions regarding the\nclassification of diseases. The final feature extraction of the EEG signal is\noptimized by the Bayesian Neural Network (BNN) to get the clear health\ncondition of Hypoglycemia and Epilepsy patients. Through monitoring and\nassessing physical effects resulting from Electroencephalography, The Bayesian\nNeural Network (BNN) is used to extract the test samples with the most log data\nand to report hypoglycemia and epilepsy Keywords- Hypoxia-Ischemia ,\nHypoglycemia , Epilepsy , Multilevel Fusion of Data Features , Bayesian Neural\nNetwork (BNN) , Support Vector Machine (SVM)\n", "date": "2024-09-03", "categories": []}
{"id": "2409.03147", "title": "Addressing the Gaps in Early Dementia Detection: A Path Towards Enhanced\n  Diagnostic Models through Machine Learning", "abstract": "  The rapid global aging trend has led to an increase in dementia cases,\nincluding Alzheimer's disease, underscoring the urgent need for early and\naccurate diagnostic methods. Traditional diagnostic techniques, such as\ncognitive tests, neuroimaging, and biomarker analysis, face significant\nlimitations in sensitivity, accessibility, and cost, particularly in the early\nstages. This study explores the potential of machine learning (ML) as a\ntransformative approach to enhance early dementia detection by leveraging ML\nmodels to analyze and integrate complex multimodal datasets, including\ncognitive assessments, neuroimaging, and genetic information. A comprehensive\nreview of existing literature was conducted to evaluate various ML models,\nincluding supervised learning, deep learning, and advanced techniques such as\nensemble learning and transformer models, assessing their accuracy,\ninterpretability, and potential for clinical integration. The findings indicate\nthat while ML models show significant promise in improving diagnostic precision\nand enabling earlier interventions, challenges remain in their\ngeneralizability, interpretability, and ethical deployment. This research\nconcludes by outlining future directions aimed at enhancing the clinical\nutility of ML models in dementia detection, emphasizing interdisciplinary\ncollaboration and ethically sound frameworks to improve early detection and\nintervention strategies for Alzheimer's disease and other forms of dementia.\n", "date": "2024-09-05", "categories": []}
{"id": "2409.03375", "title": "Leveraging Large Language Models through Natural Language Processing to\n  provide interpretable Machine Learning predictions of mental deterioration in\n  real time", "abstract": "  Based on official estimates, 50 million people worldwide are affected by\ndementia, and this number increases by 10 million new patients every year.\nWithout a cure, clinical prognostication and early intervention represent the\nmost effective ways to delay its progression. To this end, Artificial\nIntelligence and computational linguistics can be exploited for natural\nlanguage analysis, personalized assessment, monitoring, and treatment. However,\ntraditional approaches need more semantic knowledge management and\nexplicability capabilities. Moreover, using Large Language Models (LLMs) for\ncognitive decline diagnosis is still scarce, even though these models represent\nthe most advanced way for clinical-patient communication using intelligent\nsystems. Consequently, we leverage an LLM using the latest Natural Language\nProcessing (NLP) techniques in a chatbot solution to provide interpretable\nMachine Learning prediction of cognitive decline in real-time.\nLinguistic-conceptual features are exploited for appropriate natural language\nanalysis. Through explainability, we aim to fight potential biases of the\nmodels and improve their potential to help clinical workers in their diagnosis\ndecisions. More in detail, the proposed pipeline is composed of (i) data\nextraction employing NLP-based prompt engineering; (ii) stream-based data\nprocessing including feature engineering, analysis, and selection; (iii)\nreal-time classification; and (iv) the explainability dashboard to provide\nvisual and natural language descriptions of the prediction outcome.\nClassification results exceed 80 % in all evaluation metrics, with a recall\nvalue for the mental deterioration class about 85 %. To sum up, we contribute\nwith an affordable, flexible, non-invasive, personalized diagnostic system to\nthis work.\n", "date": "2024-09-05", "categories": []}
{"id": "2409.02530", "title": "Understanding eGFR Trajectories and Kidney Function Decline via Large\n  Multimodal Models", "abstract": "  The estimated Glomerular Filtration Rate (eGFR) is an essential indicator of\nkidney function in clinical practice. Although traditional equations and\nMachine Learning (ML) models using clinical and laboratory data can estimate\neGFR, accurately predicting future eGFR levels remains a significant challenge\nfor nephrologists and ML researchers. Recent advances demonstrate that Large\nLanguage Models (LLMs) and Large Multimodal Models (LMMs) can serve as robust\nfoundation models for diverse applications. This study investigates the\npotential of LMMs to predict future eGFR levels with a dataset consisting of\nlaboratory and clinical values from 50 patients. By integrating various\nprompting techniques and ensembles of LMMs, our findings suggest that these\nmodels, when combined with precise prompts and visual representations of eGFR\ntrajectories, offer predictive performance comparable to existing ML models.\nThis research extends the application of foundation models and suggests avenues\nfor future studies to harness these models in addressing complex medical\nforecasting challenges.\n", "date": "2024-09-04", "categories": []}
{"id": "2211.08082", "title": "UniHPF : Universal Healthcare Predictive Framework with Zero Domain\n  Knowledge", "abstract": "  Despite the abundance of Electronic Healthcare Records (EHR), its\nheterogeneity restricts the utilization of medical data in building predictive\nmodels. To address this challenge, we propose Universal Healthcare Predictive\nFramework (UniHPF), which requires no medical domain knowledge and minimal\npre-processing for multiple prediction tasks. Experimental results demonstrate\nthat UniHPF is capable of building large-scale EHR models that can process any\nform of medical data from distinct EHR systems. We believe that our findings\ncan provide helpful insights for further research on the multi-source learning\nof EHRs.\n", "date": "2022-11-15", "categories": []}
{"id": "2404.01589", "title": "Classifying Cancer Stage with Open-Source Clinical Large Language Models", "abstract": "  Cancer stage classification is important for making treatment and care\nmanagement plans for oncology patients. Information on staging is often\nincluded in unstructured form in clinical, pathology, radiology and other\nfree-text reports in the electronic health record system, requiring extensive\nwork to parse and obtain. To facilitate the extraction of this information,\nprevious NLP approaches rely on labeled training datasets, which are\nlabor-intensive to prepare. In this study, we demonstrate that without any\nlabeled training data, open-source clinical large language models (LLMs) can\nextract pathologic tumor-node-metastasis (pTNM) staging information from\nreal-world pathology reports. Our experiments compare LLMs and a BERT-based\nmodel fine-tuned using the labeled data. Our findings suggest that while LLMs\nstill exhibit subpar performance in Tumor (T) classification, with the\nappropriate adoption of prompting strategies, they can achieve comparable\nperformance on Metastasis (M) classification and improved performance on Node\n(N) classification.\n", "date": "2024-04-02", "categories": []}
{"id": "2407.14211", "title": "Advanced Predictive Modeling for Enhanced Mortality Prediction in ICU\n  Stroke Patients Using Clinical Data", "abstract": "  Background: Stroke is second-leading cause of disability and death among\nadults. Approximately 17 million people suffer from a stroke annually, with\nabout 85% being ischemic strokes. Predicting mortality of ischemic stroke\npatients in intensive care unit (ICU) is crucial for optimizing treatment\nstrategies, allocating resources, and improving survival rates. Methods: We\nacquired data on ICU ischemic stroke patients from MIMIC-IV database, including\ndiagnoses, vital signs, laboratory tests, medications, procedures, treatments,\nand clinical notes. Stroke patients were randomly divided into training (70%,\nn=2441), test (15%, n=523), and validation (15%, n=523) sets. To address data\nimbalances, we applied Synthetic Minority Over-sampling Technique (SMOTE). We\nselected 30 features for model development, significantly reducing feature\nnumber from 1095 used in the best study. We developed a deep learning model to\nassess mortality risk and implemented several baseline machine learning models\nfor comparison. Results: XGB-DL model, combining XGBoost for feature selection\nand deep learning, effectively minimized false positives. Model's AUROC\nimproved from 0.865 (95% CI: 0.821 - 0.905) on first day to 0.903 (95% CI:\n0.868 - 0.936) by fourth day using data from 3,646 ICU mortality patients in\nthe MIMIC-IV database with 0.945 AUROC (95% CI: 0.944 - 0.947) during training.\nAlthough other ML models also performed well in terms of AUROC, we chose Deep\nLearning for its higher specificity. Conclusions: Through enhanced feature\nselection and data cleaning, proposed model demonstrates a 13% AUROC\nimprovement compared to existing models while reducing feature number from 1095\nin previous studies to 30.\n", "date": "2024-07-19", "categories": []}
{"id": "2408.15667", "title": "Towards reliable respiratory disease diagnosis based on cough sounds and\n  vision transformers", "abstract": "  Recent advancements in deep learning techniques have sparked performance\nboosts in various real-world applications including disease diagnosis based on\nmulti-modal medical data. Cough sound data-based respiratory disease (e.g.,\nCOVID-19 and Chronic Obstructive Pulmonary Disease) diagnosis has also\nattracted much attention. However, existing works usually utilise traditional\nmachine learning or deep models of moderate scales. On the other hand, the\ndeveloped approaches are trained and evaluated on small-scale data due to the\ndifficulty of curating and annotating clinical data on scale. To address these\nissues in prior works, we create a unified framework to evaluate various deep\nmodels from lightweight Convolutional Neural Networks (e.g., ResNet18) to\nmodern vision transformers and compare their performance in respiratory disease\nclassification. Based on the observations from such an extensive empirical\nstudy, we propose a novel approach to cough-based disease classification based\non both self-supervised and supervised learning on a large-scale cough data\nset. Experimental results demonstrate our proposed approach outperforms prior\narts consistently on two benchmark datasets for COVID-19 diagnosis and a\nproprietary dataset for COPD/non-COPD classification with an AUROC of 92.5%.\n", "date": "2024-08-28", "categories": []}
{"id": "2409.00155", "title": "Common Steps in Machine Learning Might Hinder The Explainability Aims in\n  Medicine", "abstract": "  Data pre-processing is a significant step in machine learning to improve the\nperformance of the model and decreases the running time. This might include\ndealing with missing values, outliers detection and removing, data\naugmentation, dimensionality reduction, data normalization and handling the\nimpact of confounding variables. Although it is found the steps improve the\naccuracy of the model, but they might hinder the explainability of the model if\nthey are not carefully considered especially in medicine. They might block new\nfindings when missing values and outliers removal are implemented\ninappropriately. In addition, they might make the model unfair against all the\ngroups in the model when making the decision. Moreover, they turn the features\ninto unitless and clinically meaningless and consequently not explainable. This\npaper discusses the common steps of the data preprocessing in machine learning\nand their impacts on the explainability and interpretability of the model.\nFinally, the paper discusses some possible solutions that improve the\nperformance of the model while not decreasing its explainability.\n", "date": "2024-08-30", "categories": []}
{"id": "2409.00163", "title": "Deep Neural Networks for Predicting Recurrence and Survival in Patients\n  with Esophageal Cancer After Surgery", "abstract": "  Esophageal cancer is a major cause of cancer-related mortality\ninternationally, with high recurrence rates and poor survival even among\npatients treated with curative-intent surgery. Investigating relevant\nprognostic factors and predicting prognosis can enhance post-operative clinical\ndecision-making and potentially improve patients' outcomes. In this work, we\nassessed prognostic factor identification and discriminative performances of\nthree models for Disease-Free Survival (DFS) and Overall Survival (OS) using a\nlarge multicenter international dataset from ENSURE study. We first employed\nCox Proportional Hazards (CoxPH) model to assess the impact of each feature on\noutcomes. Subsequently, we utilised CoxPH and two deep neural network\n(DNN)-based models, DeepSurv and DeepHit, to predict DFS and OS. The\nsignificant prognostic factors identified by our models were consistent with\nclinical literature, with post-operative pathologic features showing higher\nsignificance than clinical stage features. DeepSurv and DeepHit demonstrated\ncomparable discriminative accuracy to CoxPH, with DeepSurv slightly\noutperforming in both DFS and OS prediction tasks, achieving C-index of 0.735\nand 0.74, respectively. While these results suggested the potential of DNNs as\nprognostic tools for improving predictive accuracy and providing personalised\nguidance with respect to risk stratification, CoxPH still remains an adequately\ngood prediction model, with the data used in this study.\n", "date": "2024-08-30", "categories": []}
{"id": "2409.00724", "title": "BUET Multi-disease Heart Sound Dataset: A Comprehensive Auscultation\n  Dataset for Developing Computer-Aided Diagnostic Systems", "abstract": "  Cardiac auscultation, an integral tool in diagnosing cardiovascular diseases\n(CVDs), often relies on the subjective interpretation of clinicians, presenting\na limitation in consistency and accuracy. Addressing this, we introduce the\nBUET Multi-disease Heart Sound (BMD-HS) dataset - a comprehensive and\nmeticulously curated collection of heart sound recordings. This dataset,\nencompassing 864 recordings across five distinct classes of common heart\nsounds, represents a broad spectrum of valvular heart diseases, with a focus on\ndiagnostically challenging cases. The standout feature of the BMD-HS dataset is\nits innovative multi-label annotation system, which captures a diverse range of\ndiseases and unique disease states. This system significantly enhances the\ndataset's utility for developing advanced machine learning models in automated\nheart sound classification and diagnosis. By bridging the gap between\ntraditional auscultation practices and contemporary data-driven diagnostic\nmethods, the BMD-HS dataset is poised to revolutionize CVD diagnosis and\nmanagement, providing an invaluable resource for the advancement of cardiac\nhealth research. The dataset is publicly available at this link:\nhttps://github.com/mHealthBuet/BMD-HS-Dataset.\n", "date": "2024-09-01", "categories": []}
{"id": "2409.01235", "title": "MRI-based and metabolomics-based age scores act synergetically for\n  mortality prediction shown by multi-cohort federated learning", "abstract": "  Biological age scores are an emerging tool to characterize aging by\nestimating chronological age based on physiological biomarkers. Various scores\nhave shown associations with aging-related outcomes. This study assessed the\nrelation between an age score based on brain MRI images (BrainAge) and an age\nscore based on metabolomic biomarkers (MetaboAge). We trained a federated deep\nlearning model to estimate BrainAge in three cohorts. The federated BrainAge\nmodel yielded significantly lower error for age prediction across the cohorts\nthan locally trained models. Harmonizing the age interval between cohorts\nfurther improved BrainAge accuracy. Subsequently, we compared BrainAge with\nMetaboAge using federated association and survival analyses. The results showed\na small association between BrainAge and MetaboAge as well as a higher\npredictive value for the time to mortality of both scores combined than for the\nindividual scores. Hence, our study suggests that both aging scores capture\ndifferent aspects of the aging process.\n", "date": "2024-09-02", "categories": []}
{"id": "2409.01685", "title": "Optimizing Mortality Prediction for ICU Heart Failure Patients:\n  Leveraging XGBoost and Advanced Machine Learning with the MIMIC-III Database", "abstract": "  Heart failure affects millions of people worldwide, significantly reducing\nquality of life and leading to high mortality rates. Despite extensive\nresearch, the relationship between heart failure and mortality rates among ICU\npatients is not fully understood, indicating the need for more accurate\nprediction models. This study analyzed data from 1,177 patients over 18 years\nold from the MIMIC-III database, identified using ICD-9 codes. Preprocessing\nsteps included handling missing data, removing duplicates, treating skewness,\nand using oversampling techniques to address data imbalances.\n  Through rigorous feature selection using Variance Inflation Factor (VIF),\nexpert clinical input, and ablation studies, 46 key features were identified to\nenhance model performance. Our analysis compared several machine learning\nmodels, including Logistic Regression, Support Vector Machine (SVM), Random\nForest, LightGBM, and XGBoost. XGBoost emerged as the superior model, achieving\na test AUC-ROC of 0.9228 (95\\% CI 0.8748 - 0.9613), significantly outperforming\nour previous work (AUC-ROC of 0.8766) and the best results reported in existing\nliterature (AUC-ROC of 0.824).\n  The improved model's success is attributed to advanced feature selection\nmethods, robust preprocessing techniques, and comprehensive hyperparameter\noptimization through Grid-Search. SHAP analysis and feature importance\nevaluations based on XGBoost highlighted key variables like leucocyte count and\nRDW, providing valuable insights into the clinical factors influencing\nmortality risk. This framework offers significant support for clinicians,\nenabling them to identify high-risk ICU heart failure patients and improve\npatient outcomes through timely and informed interventions.\n", "date": "2024-09-03", "categories": []}
{"id": "2408.16800", "title": "CNN Based Detection of Cardiovascular Diseases from ECG Images", "abstract": "  This study develops a Convolutional Neural Network (CNN) model for detecting\nmyocardial infarction (MI) from Electrocardiogram (ECG) images. The model,\nbuilt using the InceptionV3 architecture and optimized through transfer\nlearning, was trained using ECG data obtained from the Ch. Pervaiz Elahi\nInstitute of Cardiology in Pakistan. The dataset includes ECG images\nrepresenting four different cardiac conditions: myocardial infarction, abnormal\nheartbeat, history of myocardial infarction, and normal heart activity. The\ndeveloped model successfully detects MI and other cardiovascular conditions\nwith an accuracy of 93.27%. This study demonstrates that deep learning-based\nmodels can provide significant support to clinicians in the early detection and\nprevention of heart attacks.\n", "date": "2024-08-29", "categories": []}
{"id": "2408.17115", "title": "Multi-centric AI Model for Unruptured Intracranial Aneurysm Detection\n  and Volumetric Segmentation in 3D TOF-MRI", "abstract": "  Purpose: To develop an open-source nnU-Net-based AI model for combined\ndetection and segmentation of unruptured intracranial aneurysms (UICA) in 3D\nTOF-MRI, and compare models trained on datasets with aneurysm-like differential\ndiagnoses. Methods: This retrospective study (2020-2023) included 385\nanonymized 3D TOF-MRI images from 364 patients (mean age 59 years, 60% female)\nat multiple centers plus 113 subjects from the ADAM challenge. Images featured\nuntreated or possible UICAs and differential diagnoses. Four distinct training\ndatasets were created, and the nnU-Net framework was used for model\ndevelopment. Performance was assessed on a separate test set using sensitivity\nand False Positive (FP)/case rate for detection, and DICE score and NSD\n(Normalized Surface Distance) with a 0.5mm threshold for segmentation.\nStatistical analysis included chi-square, Mann-Whitney-U, and Kruskal-Wallis\ntests, with significance set at p < 0.05. Results: Models achieved overall\nsensitivity between 82% and 85% and a FP/case rate of 0.20 to 0.31, with no\nsignificant differences (p = 0.90 and p = 0.16). The primary model showed 85%\nsensitivity and 0.23 FP/case rate, outperforming the ADAM-challenge winner\n(61%) and a nnU-Net trained on ADAM data (51%) in sensitivity (p < 0.05). It\nachieved a mean DICE score of 0.73 and an NSD of 0.84 for correctly detected\nUICA. Conclusions: Our open-source, nnU-Net-based AI model (available at\n10.5281/zenodo.13386859) demonstrates high sensitivity, low false positive\nrates, and consistent segmentation accuracy for UICA detection and segmentation\nin 3D TOF-MRI, suggesting its potential to improve clinical diagnosis and for\nmonitoring of UICA.\n", "date": "2024-08-30", "categories": []}
{"id": "2408.17376", "title": "Exploring the Impact of Environmental Pollutants on Multiple Sclerosis\n  Progression", "abstract": "  Multiple Sclerosis (MS) is a chronic autoimmune and inflammatory neurological\ndisorder characterised by episodes of symptom exacerbation, known as relapses.\nIn this study, we investigate the role of environmental factors in relapse\noccurrence among MS patients, using data from the H2020 BRAINTEASER project. We\nemployed predictive models, including Random Forest (RF) and Logistic\nRegression (LR), with varying sets of input features to predict the occurrence\nof relapses based on clinical and pollutant data collected over a week. The RF\nyielded the best result, with an AUC-ROC score of 0.713. Environmental\nvariables, such as precipitation, NO2, PM2.5, humidity, and temperature, were\nfound to be relevant to the prediction.\n", "date": "2024-08-30", "categories": []}
{"id": "2404.13149", "title": "Beyond Self-Consistency: Ensemble Reasoning Boosts Consistency and\n  Accuracy of LLMs in Cancer Staging", "abstract": "  Advances in large language models (LLMs) have encouraged their adoption in\nthe healthcare domain where vital clinical information is often contained in\nunstructured notes. Cancer staging status is available in clinical reports, but\nit requires natural language processing to extract the status from the\nunstructured text. With the advance in clinical-oriented LLMs, it is promising\nto extract such status without extensive efforts in training the algorithms.\nPrompting approaches of the pre-trained LLMs that elicit a model's reasoning\nprocess, such as chain-of-thought, may help to improve the trustworthiness of\nthe generated responses. Using self-consistency further improves model\nperformance, but often results in inconsistent generations across the multiple\nreasoning paths. In this study, we propose an ensemble reasoning approach with\nthe aim of improving the consistency of the model generations. Using an open\naccess clinical large language model to determine the pathologic cancer stage\nfrom real-world pathology reports, we show that the ensemble reasoning approach\nis able to improve both the consistency and performance of the LLM in\ndetermining cancer stage, thereby demonstrating the potential to use these\nmodels in clinical or other domains where reliability and trustworthiness are\ncritical.\n", "date": "2024-04-19", "categories": []}
{"id": "2408.16256", "title": "Coalitions of AI-based Methods Predict 15-Year Risks of Breast Cancer\n  Metastasis Using Real-World Clinical Data with AUC up to 0.9", "abstract": "  Breast cancer is one of the two cancers responsible for the most deaths in\nwomen, with about 42,000 deaths each year in the US. That there are over\n300,000 breast cancers newly diagnosed each year suggests that only a fraction\nof the cancers result in mortality. Thus, most of the women undergo seemingly\ncurative treatment for localized cancers, but a significant later succumb to\nmetastatic disease for which current treatments are only temporizing for the\nvast majority. The current prognostic metrics are of little actionable value\nfor 4 of the 5 women seemingly cured after local treatment, and many women are\nexposed to morbid and even mortal adjuvant therapies unnecessarily, with these\nadjuvant therapies reducing metastatic recurrence by only a third. Thus, there\nis a need for better prognostics to target aggressive treatment at those who\nare likely to relapse and spare those who were actually cured. While there is a\nplethora of molecular and tumor-marker assays in use and under-development to\ndetect recurrence early, these are time consuming, expensive and still often\nun-validated as to actionable prognostic utility. A different approach would\nuse large data techniques to determine clinical and histopathological\nparameters that would provide accurate prognostics using existing data. Herein,\nwe report on machine learning, together with grid search and Bayesian Networks\nto develop algorithms that present a AUC of up to 0.9 in ROC analyses, using\nonly extant data. Such algorithms could be rapidly translated to clinical\nmanagement as they do not require testing beyond routine tumor evaluations.\n", "date": "2024-08-29", "categories": []}
{"id": "2408.16463", "title": "An Exploratory Deep Learning Approach for Predicting Subsequent Suicidal\n  Acts in Chinese Psychological Support Hotlines", "abstract": "  Psychological support hotlines are an effective suicide prevention measure\nthat typically relies on professionals using suicide risk assessment scales to\npredict individual risk scores. However, the accuracy of scale-based predictive\nmethods for suicide risk assessment can vary widely depending on the expertise\nof the operator. This limitation underscores the need for more reliable\nmethods, prompting this research's innovative exploration of the use of\nartificial intelligence to improve the accuracy and efficiency of suicide risk\nprediction within the context of psychological support hotlines. The study\nincluded data from 1,549 subjects from 2015-2017 in China who contacted a\npsychological support hotline. Each participant was followed for 12 months to\nidentify instances of suicidal behavior. We proposed a novel multi-task\nlearning method that uses the large-scale pre-trained model Whisper for feature\nextraction and fits psychological scales while predicting the risk of suicide.\nThe proposed method yields a 2.4\\% points improvement in F1-score compared to\nthe traditional manual approach based on the psychological scales. Our model\ndemonstrated superior performance compared to the other eight popular models.\nTo our knowledge, this study is the first to apply deep learning to long-term\nspeech data to predict suicide risk in China, indicating grate potential for\nclinical applications. The source code is publicly available at:\n\\url{https://github.com/songchangwei/Suicide-Risk-Prediction}.\n", "date": "2024-08-29", "categories": []}
{"id": "2408.15272", "title": "Estimating ECG Intervals from Lead-I Alone: External Validation of\n  Supervised Models", "abstract": "  The diagnosis, prognosis, and treatment of a number of cardiovascular\ndisorders rely on ECG interval measurements, including the PR, QRS, and QT\nintervals. These quantities are measured from the 12-lead ECG, either manually\nor using automated algorithms, which are readily available in clinical\nsettings. A number of wearable devices, however, can acquire the lead-I ECG in\nan outpatient setting, thereby raising the potential for out-of-hospital\nmonitoring for disorders that involve clinically significant changes in ECG\nintervals. In this work, we therefore developed a series of deep learning\nmodels for estimating the PR, QRS, and QT intervals using lead-I ECG. From a\ncorpus of 4.2 million ECGs from patients at the Massachusetts General Hospital,\nwe train and validate each of the models. At internal holdout validation, we\nachieve mean absolute errors (MAE) of 6.3 ms for QRS durations and 11.9 ms for\nQT intervals, and an MAE of 9.2 ms for estimating PR intervals. Moreover, as a\nwell-defined P-wave does not always exist in ECG tracings - for example, when\nthere is atrial fibrillation - we trained a model that can identify when there\nis a P-wave, and consequently, a measurable PR interval. We validate our models\non three large external healthcare datasets without any finetuning or\nretraining - 3.2 million ECG from the Brigham and Womens Hospital, 668 thousand\nfrom MIMIC-IV, and 20 thousand from PTB-XL - and achieve similar performance.\nAlso, our models significantly outperform two publicly available baseline\nalgorithms. This work demonstrates that ECG intervals can be tracked from only\nlead-I ECG using deep learning, and highlights the potential for\nout-of-hospital applications.\n", "date": "2024-08-12", "categories": []}
{"id": "2408.15357", "title": "On the effectiveness of smartphone IMU sensors and Deep Learning in the\n  detection of cardiorespiratory conditions", "abstract": "  This research introduces an innovative method for the early screening of\ncardiorespiratory diseases based on an acquisition protocol, which leverages\ncommodity smartphone's Inertial Measurement Units (IMUs) and deep learning\ntechniques. We collected, in a clinical setting, a dataset featuring recordings\nof breathing kinematics obtained by accelerometer and gyroscope readings from\nfive distinct body regions. We propose an end-to-end deep learning pipeline for\nearly cardiorespiratory disease screening, incorporating a preprocessing step\nsegmenting the data into individual breathing cycles, and a recurrent\nbidirectional module capturing features from diverse body regions. We employed\nLeave-one-out-cross-validation with Bayesian optimization for hyperparameter\ntuning and model selection. The experimental results consistently demonstrated\nthe superior performance of a bidirectional Long-Short Term Memory (Bi-LSTM) as\na feature encoder architecture, yielding an average sensitivity of $0.81 \\pm\n0.02$, specificity of $0.82 \\pm 0.05$, F1 score of $0.81 \\pm 0.02$, and\naccuracy of $80.2\\% \\pm 3.9$ across diverse seed variations. We also assessed\ngeneralization capabilities on a skewed distribution, comprising exclusively\nhealthy patients not used in training, revealing a true negative rate of $74.8\n\\% \\pm 4.5$. The sustained accuracy of predictions over time during breathing\ncycles within a single patient underscores the efficacy of the preprocessing\nstrategy, highlighting the model's ability to discern significant patterns\nthroughout distinct phases of the respiratory cycle. This investigation\nunderscores the potential usefulness of widely available smartphones as devices\nfor timely cardiorespiratory disease screening in the general population, in\nat-home settings, offering crucial assistance to public health efforts\n(especially during a pandemic outbreaks, such as the recent COVID-19).\n", "date": "2024-08-27", "categories": []}
{"id": "2408.15498", "title": "Deep Learning to Predict Late-Onset Breast Cancer Metastasis: the Single\n  Hyperparameter Grid Search (SHGS) Strategy for Meta Tuning Concerning Deep\n  Feed-forward Neural Network", "abstract": "  While machine learning has advanced in medicine, its widespread use in\nclinical applications, especially in predicting breast cancer metastasis, is\nstill limited. We have been dedicated to constructing a DFNN model to predict\nbreast cancer metastasis n years in advance. However, the challenge lies in\nefficiently identifying optimal hyperparameter values through grid search,\ngiven the constraints of time and resources. Issues such as the infinite\npossibilities for continuous hyperparameters like l1 and l2, as well as the\ntime-consuming and costly process, further complicate the task. To address\nthese challenges, we developed Single Hyperparameter Grid Search (SHGS)\nstrategy, serving as a preselection method before grid search. Our experiments\nwith SHGS applied to DFNN models for breast cancer metastasis prediction focus\non analyzing eight target hyperparameters: epochs, batch size, dropout, L1, L2,\nlearning rate, decay, and momentum. We created three figures, each depicting\nthe experiment results obtained from three LSM-I-10-Plus-year datasets. These\nfigures illustrate the relationship between model performance and the target\nhyperparameter values. For each hyperparameter, we analyzed whether changes in\nthis hyperparameter would affect model performance, examined if there were\nspecific patterns, and explored how to choose values for the particular\nhyperparameter. Our experimental findings reveal that the optimal value of a\nhyperparameter is not only dependent on the dataset but is also significantly\ninfluenced by the settings of other hyperparameters. Additionally, our\nexperiments suggested some reduced range of values for a target hyperparameter,\nwhich may be helpful for low-budget grid search. This approach serves as a\nprior experience and foundation for subsequent use of grid search to enhance\nmodel performance.\n", "date": "2024-08-28", "categories": []}
{"id": "2408.15827", "title": "Automatic Differential Diagnosis using Transformer-Based Multi-Label\n  Sequence Classification", "abstract": "  As the field of artificial intelligence progresses, assistive technologies\nare becoming more widely used across all industries. The healthcare industry is\nno different, with numerous studies being done to develop assistive tools for\nhealthcare professionals. Automatic diagnostic systems are one such beneficial\ntool that can assist with a variety of tasks, including collecting patient\ninformation, analyzing test results, and diagnosing patients. However, the idea\nof developing systems that can provide a differential diagnosis has been\nlargely overlooked in most of these research studies. In this study, we propose\na transformer-based approach for providing differential diagnoses based on a\npatient's age, sex, medical history, and symptoms. We use the DDXPlus dataset,\nwhich provides differential diagnosis information for patients based on 49\ndisease types. Firstly, we propose a method to process the tabular patient data\nfrom the dataset and engineer them into patient reports to make them suitable\nfor our research. In addition, we introduce two data modification modules to\ndiversify the training data and consequently improve the robustness of the\nmodels. We approach the task as a multi-label classification problem and\nconduct extensive experiments using four transformer models. All the models\ndisplayed promising results by achieving over 97% F1 score on the held-out test\nset. Moreover, we design additional behavioral tests to get a broader\nunderstanding of the models. In particular, for one of our test cases, we\nprepared a custom test set of 100 samples with the assistance of a doctor. The\nresults on the custom set showed that our proposed data modification modules\nimproved the model's generalization capabilities. We hope our findings will\nprovide future researchers with valuable insights and inspire them to develop\nreliable systems for automatic differential diagnosis.\n", "date": "2024-08-28", "categories": []}
{"id": "2405.18723", "title": "Conformal Depression Prediction", "abstract": "  While existing depression prediction methods based on deep learning show\npromise, their practical application is hindered by the lack of\ntrustworthiness, as these deep models are often deployed as black box models,\nleaving us uncertain on the confidence of their predictions. For high-risk\nclinical applications like depression prediction, uncertainty quantification is\nessential in decision-making. In this paper, we introduce conformal depression\nprediction (CDP), a depression prediction method with uncertainty\nquantification based on conformal prediction (CP), giving valid confidence\nintervals with theoretical coverage guarantees for the model predictions. CDP\nis a plug-and-play module that requires neither model retraining nor an\nassumption about the depression data distribution. As CDP provides only an\naverage coverage guarantee across all inputs rather than per-input performance\nguarantee, we further propose CDP-ACC, an improved conformal prediction with\napproximate conditional coverage. CDP-ACC firstly estimates the prediction\ndistribution through neighborhood relaxation, and then introduces a conformal\nscore function by constructing nested sequences, so as to provide a tighter\nprediction interval adaptive to specific input. We empirically demonstrate the\napplication of CDP in uncertainty-aware facial depression prediction, as well\nas the effectiveness and superiority of CDP-ACC on the AVEC 2013 and AVEC 2014\ndatasets. Our code is publicly available at https://github.com/PushineLee/CDP.\n", "date": "2024-05-29", "categories": []}
{"id": "2408.14053", "title": "Enhancing Depression Diagnosis with Chain-of-Thought Prompting", "abstract": "  When using AI to detect signs of depressive disorder, AI models habitually\ndraw preemptive conclusions. We theorize that using chain-of-thought (CoT)\nprompting to evaluate Patient Health Questionnaire-8 (PHQ-8) scores will\nimprove the accuracy of the scores determined by AI models. In our findings,\nwhen the models reasoned with CoT, the estimated PHQ-8 scores were consistently\ncloser on average to the accepted true scores reported by each participant\ncompared to when not using CoT. Our goal is to expand upon AI models'\nunderstanding of the intricacies of human conversation, allowing them to more\neffectively assess a patient's feelings and tone, therefore being able to more\naccurately discern mental disorder symptoms; ultimately, we hope to augment AI\nmodels' abilities, so that they can be widely accessible and used in the\nmedical field.\n", "date": "2024-08-26", "categories": []}
{"id": "2408.14927", "title": "Automatic Detection of COVID-19 from Chest X-ray Images Using Deep\n  Learning Model", "abstract": "  The infectious disease caused by novel corona virus (2019-nCoV) has been\nwidely spreading since last year and has shaken the entire world. It has caused\nan unprecedented effect on daily life, global economy and public health. Hence\nthis disease detection has life-saving importance for both patients as well as\ndoctors. Due to limited test kits, it is also a daunting task to test every\npatient with severe respiratory problems using conventional techniques\n(RT-PCR). Thus implementing an automatic diagnosis system is urgently required\nto overcome the scarcity problem of Covid-19 test kits at hospital, health care\nsystems. The diagnostic approach is mainly classified into two\ncategories-laboratory based and Chest radiography approach. In this paper, a\nnovel approach for computerized corona virus (2019-nCoV) detection from lung\nx-ray images is presented. Here, we propose models using deep learning to show\nthe effectiveness of diagnostic systems. In the experimental result, we\nevaluate proposed models on publicly available data-set which exhibit\nsatisfactory performance and promising results compared with other previous\nexisting methods.\n", "date": "2024-08-27", "categories": []}
{"id": "2312.01210", "title": "When accurate prediction models yield harmful self-fulfilling prophecies", "abstract": "  Prediction models are popular in medical research and practice. By predicting\nan outcome of interest for specific patients, these models may help inform\ndifficult treatment decisions, and are often hailed as the poster children for\npersonalized, data-driven healthcare. We show however, that using prediction\nmodels for decision making can lead to harmful decisions, even when the\npredictions exhibit good discrimination after deployment. These models are\nharmful self-fulfilling prophecies: their deployment harms a group of patients\nbut the worse outcome of these patients does not invalidate the predictive\npower of the model. Our main result is a formal characterization of a set of\nsuch prediction models. Next we show that models that are well calibrated\nbefore and after deployment are useless for decision making as they made no\nchange in the data distribution. These results point to the need to revise\nstandard practices for validation, deployment and evaluation of prediction\nmodels that are used in medical decisions.\n", "date": "2023-12-02", "categories": []}
{"id": "2402.09558", "title": "Bidirectional Generative Pre-training for Improving Healthcare\n  Time-series Representation Learning", "abstract": "  Learning time-series representations for discriminative tasks, such as\nclassification and regression, has been a long-standing challenge in the\nhealthcare domain. Current pre-training methods are limited in either\nunidirectional next-token prediction or randomly masked token prediction. We\npropose a novel architecture called Bidirectional Timely Generative Pre-trained\nTransformer (BiTimelyGPT), which pre-trains on biosignals and longitudinal\nclinical records by both next-token and previous-token prediction in\nalternating transformer layers. This pre-training task preserves original\ndistribution and data shapes of the time-series. Additionally, the full-rank\nforward and backward attention matrices exhibit more expressive representation\ncapabilities. Using biosignals and longitudinal clinical records, BiTimelyGPT\ndemonstrates superior performance in predicting neurological functionality,\ndisease diagnosis, and physiological signs. By visualizing the attention\nheatmap, we observe that the pre-trained BiTimelyGPT can identify\ndiscriminative segments from biosignal time-series sequences, even more so\nafter fine-tuning on the task.\n", "date": "2024-02-14", "categories": []}
{"id": "2408.03816", "title": "Early Prediction of Causes (not Effects) in Healthcare by Long-Term\n  Clinical Time Series Forecasting", "abstract": "  Machine learning for early syndrome diagnosis aims to solve the intricate\ntask of predicting a ground truth label that most often is the outcome (effect)\nof a medical consensus definition applied to observed clinical measurements\n(causes), given clinical measurements observed several hours before. Instead of\nfocusing on the prediction of the future effect, we propose to directly predict\nthe causes via time series forecasting (TSF) of clinical variables and\ndetermine the effect by applying the gold standard consensus definition to the\nforecasted values. This method has the invaluable advantage of being\nstraightforwardly interpretable to clinical practitioners, and because model\ntraining does not rely on a particular label anymore, the forecasted data can\nbe used to predict any consensus-based label. We exemplify our method by means\nof long-term TSF with Transformer models, with a focus on accurate prediction\nof sparse clinical variables involved in the SOFA-based Sepsis-3 definition and\nthe new Simplified Acute Physiology Score (SAPS-II) definition. Our experiments\nare conducted on two datasets and show that contrary to recent proposals which\nadvocate set function encoders for time series and direct multi-step decoders,\nbest results are achieved by a combination of standard dense encoders with\niterative multi-step decoders. The key for success of iterative multi-step\ndecoding can be attributed to its ability to capture cross-variate dependencies\nand to a student forcing training strategy that teaches the model to rely on\nits own previous time step predictions for the next time step prediction.\n", "date": "2024-08-07", "categories": []}
{"id": "2408.13315", "title": "A systematic review: Deep learning-based methods for pneumonia region\n  detection", "abstract": "  Pneumonia disease is one of the leading causes of death among children and\nadults worldwide. In the last ten years, computer-aided pneumonia detection\nmethods have been developed to improve the efficiency and accuracy of the\ndiagnosis process. Among those methods, the effects of deep learning approaches\nsurpassed that of other traditional machine learning methods. This review paper\nsearched and examined existing mainstream deep-learning approaches in the\ndetection of pneumonia regions. This paper focuses on key aspects of the\ncollected research, including their datasets, data processing techniques,\ngeneral workflow, outcomes, advantages, and limitations. This paper also\ndiscusses current challenges in the field and proposes future work that can be\ndone to enhance research procedures and the overall performance of deep\nlearning models in detecting, classifying, and localizing infected regions.\nThis review aims to offer an insightful summary and analysis of current\nresearch, facilitating the development of deep learning approaches in\naddressing treatable diseases.\n", "date": "2024-08-23", "categories": []}
{"id": "2408.13626", "title": "Towards Case-based Interpretability for Medical Federated Learning", "abstract": "  We explore deep generative models to generate case-based explanations in a\nmedical federated learning setting. Explaining AI model decisions through\ncase-based interpretability is paramount to increasing trust and allowing\nwidespread adoption of AI in clinical practice. However, medical AI training\nparadigms are shifting towards federated learning settings in order to comply\nwith data protection regulations. In a federated scenario, past data is\ninaccessible to the current user. Thus, we use a deep generative model to\ngenerate synthetic examples that protect privacy and explain decisions. Our\nproof-of-concept focuses on pleural effusion diagnosis and uses publicly\navailable Chest X-ray data.\n", "date": "2024-08-24", "categories": []}
{"id": "2408.12682", "title": "MultiMed: Massively Multimodal and Multitask Medical Understanding", "abstract": "  Biomedical data is inherently multimodal, consisting of electronic health\nrecords, medical imaging, digital pathology, genome sequencing, wearable\nsensors, and more. The application of artificial intelligence tools to these\nmultifaceted sensing technologies has the potential to revolutionize the\nprognosis, diagnosis, and management of human health and disease. However,\ncurrent approaches to biomedical AI typically only train and evaluate with one\nor a small set of medical modalities and tasks. This limitation hampers the\ndevelopment of comprehensive tools that can leverage the rich interconnected\ninformation across many heterogeneous biomedical sensors. To address this\nchallenge, we present MultiMed, a benchmark designed to evaluate and enable\nlarge-scale learning across a wide spectrum of medical modalities and tasks.\nMultiMed consists of 2.56 million samples across ten medical modalities such as\nmedical reports, pathology, genomics, and protein data, and is structured into\neleven challenging tasks, including disease prognosis, protein structure\nprediction, and medical question answering. Using MultiMed, we conduct\ncomprehensive experiments benchmarking state-of-the-art unimodal, multimodal,\nand multitask models. Our analysis highlights the advantages of training\nlarge-scale medical models across many related modalities and tasks. Moreover,\nMultiMed enables studies of generalization across related medical concepts,\nrobustness to real-world noisy data and distribution shifts, and novel modality\ncombinations to improve prediction performance. MultiMed will be publicly\navailable and regularly updated and welcomes inputs from the community.\n", "date": "2024-08-22", "categories": []}
{"id": "2309.08560", "title": "Deep Reinforcement Learning for Efficient and Fair Allocation of Health\n  Care Resources", "abstract": "  Scarcity of health care resources could result in the unavoidable consequence\nof rationing. For example, ventilators are often limited in supply, especially\nduring public health emergencies or in resource-constrained health care\nsettings, such as amid the pandemic of COVID-19. Currently, there is no\nuniversally accepted standard for health care resource allocation protocols,\nresulting in different governments prioritizing patients based on various\ncriteria and heuristic-based protocols. In this study, we investigate the use\nof reinforcement learning for critical care resource allocation policy\noptimization to fairly and effectively ration resources. We propose a\ntransformer-based deep Q-network to integrate the disease progression of\nindividual patients and the interaction effects among patients during the\ncritical care resource allocation. We aim to improve both fairness of\nallocation and overall patient outcomes. Our experiments demonstrate that our\nmethod significantly reduces excess deaths and achieves a more equitable\ndistribution under different levels of ventilator shortage, when compared to\nexisting severity-based and comorbidity-based methods in use by different\ngovernments. Our source code is included in the supplement and will be released\non Github upon publication.\n", "date": "2023-09-15", "categories": []}
{"id": "2401.08002", "title": "Discovery of Generalizable TBI Phenotypes Using Multivariate Time-Series\n  Clustering", "abstract": "  Traumatic Brain Injury (TBI) presents a broad spectrum of clinical\npresentations and outcomes due to its inherent heterogeneity, leading to\ndiverse recovery trajectories and varied therapeutic responses. While many\nstudies have delved into TBI phenotyping for distinct patient populations,\nidentifying TBI phenotypes that consistently generalize across various settings\nand populations remains a critical research gap. Our research addresses this by\nemploying multivariate time-series clustering to unveil TBI's dynamic\nintricates. Utilizing a self-supervised learning-based approach to clustering\nmultivariate time-Series data with missing values (SLAC-Time), we analyzed both\nthe research-centric TRACK-TBI and the real-world MIMIC-IV datasets.\nRemarkably, the optimal hyperparameters of SLAC-Time and the ideal number of\nclusters remained consistent across these datasets, underscoring SLAC-Time's\nstability across heterogeneous datasets. Our analysis revealed three\ngeneralizable TBI phenotypes ({\\alpha}, \\b{eta}, and {\\gamma}), each exhibiting\ndistinct non-temporal features during emergency department visits, and temporal\nfeature profiles throughout ICU stays. Specifically, phenotype {\\alpha}\nrepresents mild TBI with a remarkably consistent clinical presentation. In\ncontrast, phenotype \\b{eta} signifies severe TBI with diverse clinical\nmanifestations, and phenotype {\\gamma} represents a moderate TBI profile in\nterms of severity and clinical diversity. Age is a significant determinant of\nTBI outcomes, with older cohorts recording higher mortality rates. Importantly,\nwhile certain features varied by age, the core characteristics of TBI\nmanifestations tied to each phenotype remain consistent across diverse\npopulations.\n", "date": "2024-01-15", "categories": []}
{"id": "2408.11493", "title": "XDT-CXR: Investigating Cross-Disease Transferability in Zero-Shot Binary\n  Classification of Chest X-Rays", "abstract": "  This study explores the concept of cross-disease transferability (XDT) in\nmedical imaging, focusing on the potential of binary classifiers trained on one\ndisease to perform zero-shot classification on another disease affecting the\nsame organ. Utilizing chest X-rays (CXR) as the primary modality, we\ninvestigate whether a model trained on one pulmonary disease can make\npredictions about another novel pulmonary disease, a scenario with significant\nimplications for medical settings with limited data on emerging diseases. The\nXDT framework leverages the embedding space of a vision encoder, which, through\nkernel transformation, aids in distinguishing between diseased and non-diseased\nclasses in the latent space. This capability is especially beneficial in\nresource-limited environments or in regions with low prevalence of certain\ndiseases, where conventional diagnostic practices may fail. However, the XDT\nframework is currently limited to binary classification, determining only the\npresence or absence of a disease rather than differentiating among multiple\ndiseases. This limitation underscores the supplementary role of XDT to\ntraditional diagnostic tests in clinical settings. Furthermore, results show\nthat XDT-CXR as a framework is able to make better predictions compared to\nother zero-shot learning (ZSL) baselines.\n", "date": "2024-08-21", "categories": []}
{"id": "2403.00868", "title": "SoftTiger: A Clinical Foundation Model for Healthcare Workflows", "abstract": "  We introduce SoftTiger, a clinical large language model (CLaM) designed as a\nfoundation model for healthcare workflows. The narrative and unstructured\nnature of clinical notes is a major obstacle for healthcare intelligentization.\nWe address a critical problem of structuring clinical notes into clinical data,\naccording to international interoperability standards. We collect and annotate\ndata for three subtasks, namely, international patient summary, clinical\nimpression and medical encounter. We then supervised fine-tuned a\nstate-of-the-art LLM using public and credentialed clinical data. The training\nis orchestrated in a way that the target model can first support basic clinical\ntasks such as abbreviation expansion and temporal information extraction, and\nthen learn to perform more complex downstream clinical tasks. Moreover, we\naddress several modeling challenges in the healthcare context, e.g., extra long\ncontext window. Our blind pairwise evaluation shows that SoftTiger outperforms\nother popular open-source models and GPT-3.5, comparable to Gemini-pro, with a\nmild gap from GPT-4. We believe that LLMs may become a step-stone towards\nhealthcare digitalization and democratization. Therefore, we publicly release\nSoftTiger models at scales of 13 billion and 70 billion parameters, as well as\ndatasets and code for our innovative scalable evaluation, hopefully, making a\nsignificant contribution to the healthcare industry.\n", "date": "2024-03-01", "categories": []}
{"id": "2407.00731", "title": "Large Language Models Struggle in Token-Level Clinical Named Entity\n  Recognition", "abstract": "  Large Language Models (LLMs) have revolutionized various sectors, including\nhealthcare where they are employed in diverse applications. Their utility is\nparticularly significant in the context of rare diseases, where data scarcity,\ncomplexity, and specificity pose considerable challenges. In the clinical\ndomain, Named Entity Recognition (NER) stands out as an essential task and it\nplays a crucial role in extracting relevant information from clinical texts.\nDespite the promise of LLMs, current research mostly concentrates on\ndocument-level NER, identifying entities in a more general context across\nentire documents, without extracting their precise location. Additionally,\nefforts have been directed towards adapting ChatGPT for token-level NER.\nHowever, there is a significant research gap when it comes to employing\ntoken-level NER for clinical texts, especially with the use of local\nopen-source LLMs. This study aims to bridge this gap by investigating the\neffectiveness of both proprietary and local LLMs in token-level clinical NER.\nEssentially, we delve into the capabilities of these models through a series of\nexperiments involving zero-shot prompting, few-shot prompting,\nretrieval-augmented generation (RAG), and instruction-fine-tuning. Our\nexploration reveals the inherent challenges LLMs face in token-level NER,\nparticularly in the context of rare diseases, and suggests possible\nimprovements for their application in healthcare. This research contributes to\nnarrowing a significant gap in healthcare informatics and offers insights that\ncould lead to a more refined application of LLMs in the healthcare sector.\n", "date": "2024-06-30", "categories": []}
{"id": "2408.09005", "title": "Comparative Performance Analysis of Transformer-Based Pre-Trained Models\n  for Detecting Keratoconus Disease", "abstract": "  This study compares eight pre-trained CNNs for diagnosing keratoconus, a\ndegenerative eye disease. A carefully selected dataset of keratoconus, normal,\nand suspicious cases was used. The models tested include DenseNet121,\nEfficientNetB0, InceptionResNetV2, InceptionV3, MobileNetV2, ResNet50, VGG16,\nand VGG19. To maximize model training, bad sample removal, resizing, rescaling,\nand augmentation were used. The models were trained with similar parameters,\nactivation function, classification function, and optimizer to compare\nperformance. To determine class separation effectiveness, each model was\nevaluated on accuracy, precision, recall, and F1-score. MobileNetV2 was the\nbest accurate model in identifying keratoconus and normal cases with few\nmisclassifications. InceptionV3 and DenseNet121 both performed well in\nkeratoconus detection, but they had trouble with questionable cases. In\ncontrast, EfficientNetB0, ResNet50, and VGG19 had more difficulty\ndistinguishing dubious cases from regular ones, indicating the need for model\nrefining and development. A detailed comparison of state-of-the-art CNN\narchitectures for automated keratoconus identification reveals each model's\nbenefits and weaknesses. This study shows that advanced deep learning models\ncan enhance keratoconus diagnosis and treatment planning. Future research\nshould explore hybrid models and integrate clinical parameters to improve\ndiagnostic accuracy and robustness in real-world clinical applications, paving\nthe way for more effective AI-driven ophthalmology tools.\n", "date": "2024-08-16", "categories": []}
{"id": "2408.09635", "title": "Meta-Learning on Augmented Gene Expression Profiles for Enhanced Lung\n  Cancer Detection", "abstract": "  Gene expression profiles obtained through DNA microarray have proven\nsuccessful in providing critical information for cancer detection classifiers.\nHowever, the limited number of samples in these datasets poses a challenge to\nemploy complex methodologies such as deep neural networks for sophisticated\nanalysis. To address this \"small data\" dilemma, Meta-Learning has been\nintroduced as a solution to enhance the optimization of machine learning models\nby utilizing similar datasets, thereby facilitating a quicker adaptation to\ntarget datasets without the requirement of sufficient samples. In this study,\nwe present a meta-learning-based approach for predicting lung cancer from gene\nexpression profiles. We apply this framework to well-established deep learning\nmethodologies and employ four distinct datasets for the meta-learning tasks,\nwhere one as the target dataset and the rest as source datasets. Our approach\nis evaluated against both traditional and deep learning methodologies, and the\nresults show the superior performance of meta-learning on augmented source data\ncompared to the baselines trained on single datasets. Moreover, we conduct the\ncomparative analysis between meta-learning and transfer learning methodologies\nto highlight the efficiency of the proposed approach in addressing the\nchallenges associated with limited sample sizes. Finally, we incorporate the\nexplainability study to illustrate the distinctiveness of decisions made by\nmeta-learning.\n", "date": "2024-08-19", "categories": []}
{"id": "2408.08316", "title": "SepAl: Sepsis Alerts On Low Power Wearables With Digital Biomarkers and\n  On-Device Tiny Machine Learning", "abstract": "  Sepsis is a lethal syndrome of organ dysfunction that is triggered by an\ninfection and claims 11 million lives per year globally. Prognostic algorithms\nbased on deep learning have shown promise in detecting the onset of sepsis\nhours before the actual event but use a large number of bio-markers, including\nvital signs and laboratory tests. The latter makes the deployment of such\nsystems outside hospitals or in resource-limited environments extremely\nchallenging. This paper introduces SepAl, an energy-efficient and lightweight\nneural network, using only data from low-power wearable sensors, such as\nphotoplethysmography (PPG), inertial measurement units (IMU), and body\ntemperature sensors, designed to deliver alerts in real-time. SepAl leverages\nonly six digitally acquirable vital signs and tiny machine learning algorithms,\nenabling on-device real-time sepsis prediction.\n  SepAl uses a lightweight temporal convolution neural network capable of\nproviding sepsis alerts with a median predicted time to sepsis of 9.8 hours.\nThe model has been fully quantized, being able to be deployed on any low-power\nprocessors, and evaluated on an ARM Cortex-M33 core. Experimental evaluations\nshow an inference efficiency of 0.11MAC/Cycle and a latency of 143ms, with an\nenergy per inference of 2.68mJ. This work aims at paving the way toward\naccurate disease prediction, deployable in a long-lasting multi-vital sign\nwearable device, suitable for providing sepsis onset alerts at the point of\ncare.\n  The code used in this work has been open-sourced and is available at\nhttps://github.com/mgiordy/sepsis-prediction\n", "date": "2024-07-31", "categories": []}
{"id": "2407.17900", "title": "The Power of Combining Data and Knowledge: GPT-4o is an Effective\n  Interpreter of Machine Learning Models in Predicting Lymph Node Metastasis of\n  Lung Cancer", "abstract": "  Lymph node metastasis (LNM) is a crucial factor in determining the initial\ntreatment for patients with lung cancer, yet accurate preoperative diagnosis of\nLNM remains challenging. Recently, large language models (LLMs) have garnered\nsignificant attention due to their remarkable text generation capabilities.\nLeveraging the extensive medical knowledge learned from vast corpora, LLMs can\nestimate probabilities for clinical problems, though their performance has\nhistorically been inferior to data-driven machine learning models. In this\npaper, we propose a novel ensemble method that combines the medical knowledge\nacquired by LLMs with the latent patterns identified by machine learning models\nto enhance LNM prediction performance. Initially, we developed machine learning\nmodels using patient data. We then designed a prompt template to integrate the\npatient data with the predicted probability from the machine learning model.\nSubsequently, we instructed GPT-4o, the most advanced LLM developed by OpenAI,\nto estimate the likelihood of LNM based on patient data and then adjust the\nestimate using the machine learning output. Finally, we collected three outputs\nfrom the GPT-4o using the same prompt and ensembled these results as the final\nprediction. Using the proposed method, our models achieved an AUC value of\n0.778 and an AP value of 0.426 for LNM prediction, significantly improving\npredictive performance compared to baseline machine learning models. The\nexperimental results indicate that GPT-4o can effectively leverage its medical\nknowledge and the probabilities predicted by machine learning models to achieve\nmore accurate LNM predictions. These findings demonstrate that LLMs can perform\nwell in clinical risk prediction tasks, offering a new paradigm for integrating\nmedical knowledge and patient data in clinical predictions.\n", "date": "2024-07-25", "categories": []}
{"id": "2408.08109", "title": "Hearing Your Blood Sugar: Non-Invasive Glucose Measurement Through\n  Simple Vocal Signals, Transforming any Speech into a Sensor with Machine\n  Learning", "abstract": "  Effective diabetes management relies heavily on the continuous monitoring of\nblood glucose levels, traditionally achieved through invasive and uncomfortable\nmethods. While various non-invasive techniques have been explored, such as\noptical, microwave, and electrochemical approaches, none have effectively\nsupplanted these invasive technologies due to issues related to complexity,\naccuracy, and cost. In this study, we present a transformative and\nstraightforward method that utilizes voice analysis to predict blood glucose\nlevels. Our research investigates the relationship between fluctuations in\nblood glucose and vocal characteristics, highlighting the influence of blood\nvessel dynamics during voice production. By applying advanced machine learning\nalgorithms, we analyzed vocal signal variations and established a significant\ncorrelation with blood glucose levels. We developed a predictive model using\nartificial intelligence, based on voice recordings and corresponding glucose\nmeasurements from participants, utilizing logistic regression and Ridge\nregularization. Our findings indicate that voice analysis may serve as a viable\nnon-invasive alternative for glucose monitoring. This innovative approach not\nonly has the potential to streamline and reduce the costs associated with\ndiabetes management but also aims to enhance the quality of life for\nindividuals living with diabetes by providing a painless and user-friendly\nmethod for monitoring blood sugar levels.\n", "date": "2024-08-15", "categories": []}
{"id": "2404.15332", "title": "Clinical translation of machine learning algorithms for seizure\n  detection in scalp electroencephalography: systematic review", "abstract": "  Machine learning algorithms for seizure detection have shown considerable\ndiagnostic potential, with recent reported accuracies reaching 100%. Yet, only\nfew published algorithms have fully addressed the requirements for successful\nclinical translation. This is, for example, because the properties of training\ndata may limit the generalisability of algorithms, algorithm performance may\nvary depending on which electroencephalogram (EEG) acquisition hardware was\nused, or run-time processing costs may be prohibitive to real-time clinical use\ncases. To address these issues in a critical manner, we systematically review\nmachine learning algorithms for seizure detection with a focus on clinical\ntranslatability, assessed by criteria including generalisability, run-time\ncosts, explainability, and clinically-relevant performance metrics. For\nnon-specialists, the domain-specific knowledge necessary to contextualise model\ndevelopment and evaluation is provided. It is our hope that such critical\nevaluation of machine learning algorithms with respect to their potential\nreal-world effectiveness can help accelerate clinical translation and identify\ngaps in the current seizure detection literature.\n", "date": "2024-04-08", "categories": []}
{"id": "2408.07569", "title": "Multi-task Heterogeneous Graph Learning on Electronic Health Records", "abstract": "  Learning electronic health records (EHRs) has received emerging attention\nbecause of its capability to facilitate accurate medical diagnosis. Since the\nEHRs contain enriched information specifying complex interactions between\nentities, modeling EHRs with graphs is shown to be effective in practice. The\nEHRs, however, present a great degree of heterogeneity, sparsity, and\ncomplexity, which hamper the performance of most of the models applied to them.\nMoreover, existing approaches modeling EHRs often focus on learning the\nrepresentations for a single task, overlooking the multi-task nature of EHR\nanalysis problems and resulting in limited generalizability across different\ntasks. In view of these limitations, we propose a novel framework for EHR\nmodeling, namely MulT-EHR (Multi-Task EHR), which leverages a heterogeneous\ngraph to mine the complex relations and model the heterogeneity in the EHRs. To\nmitigate the large degree of noise, we introduce a denoising module based on\nthe causal inference framework to adjust for severe confounding effects and\nreduce noise in the EHR data. Additionally, since our model adopts a single\ngraph neural network for simultaneous multi-task prediction, we design a\nmulti-task learning module to leverage the inter-task knowledge to regularize\nthe training process. Extensive empirical studies on MIMIC-III and MIMIC-IV\ndatasets validate that the proposed method consistently outperforms the\nstate-of-the-art designs in four popular EHR analysis tasks -- drug\nrecommendation, and predictions of the length of stay, mortality, and\nreadmission. Thorough ablation studies demonstrate the robustness of our method\nupon variations to key components and hyperparameters.\n", "date": "2024-08-14", "categories": []}
{"id": "2402.01077", "title": "Recent Advances in Predictive Modeling with Electronic Health Records", "abstract": "  The development of electronic health records (EHR) systems has enabled the\ncollection of a vast amount of digitized patient data. However, utilizing EHR\ndata for predictive modeling presents several challenges due to its unique\ncharacteristics. With the advancements in machine learning techniques, deep\nlearning has demonstrated its superiority in various applications, including\nhealthcare. This survey systematically reviews recent advances in deep\nlearning-based predictive models using EHR data. Specifically, we begin by\nintroducing the background of EHR data and providing a mathematical definition\nof the predictive modeling task. We then categorize and summarize predictive\ndeep models from multiple perspectives. Furthermore, we present benchmarks and\ntoolkits relevant to predictive modeling in healthcare. Finally, we conclude\nthis survey by discussing open challenges and suggesting promising directions\nfor future research.\n", "date": "2024-02-02", "categories": []}
{"id": "2408.05754", "title": "PRECISe : Prototype-Reservation for Explainable Classification under\n  Imbalanced and Scarce-Data Settings", "abstract": "  Deep learning models used for medical image classification tasks are often\nconstrained by the limited amount of training data along with severe class\nimbalance. Despite these problems, models should be explainable to enable human\ntrust in the models' decisions to ensure wider adoption in high-risk\nsituations. In this paper, we propose PRECISe, an explainable-by-design model\nmeticulously constructed to concurrently address all three challenges.\nEvaluation on 2 imbalanced medical image datasets reveals that PRECISe\noutperforms the current state-of-the-art methods on data efficient\ngeneralization to minority classes, achieving an accuracy of ~87% in detecting\npneumonia in chest x-rays upon training on <60 images only. Additionally, a\ncase study is presented to highlight the model's ability to produce easily\ninterpretable predictions, reinforcing its practical utility and reliability\nfor medical imaging tasks.\n", "date": "2024-08-11", "categories": []}
{"id": "2408.06142", "title": "Med42-v2: A Suite of Clinical LLMs", "abstract": "  Med42-v2 introduces a suite of clinical large language models (LLMs) designed\nto address the limitations of generic models in healthcare settings. These\nmodels are built on Llama3 architecture and fine-tuned using specialized\nclinical data. They underwent multi-stage preference alignment to effectively\nrespond to natural prompts. While generic models are often preference-aligned\nto avoid answering clinical queries as a precaution, Med42-v2 is specifically\ntrained to overcome this limitation, enabling its use in clinical settings.\nMed42-v2 models demonstrate superior performance compared to the original\nLlama3 models in both 8B and 70B parameter configurations and GPT-4 across\nvarious medical benchmarks. These LLMs are developed to understand clinical\nqueries, perform reasoning tasks, and provide valuable assistance in clinical\nenvironments. The models are now publicly available at\n\\href{https://huggingface.co/m42-health}{https://huggingface.co/m42-health}.\n", "date": "2024-08-12", "categories": []}
{"id": "2407.00040", "title": "A Machine Learning Approach for Identifying Anatomical Biomarkers of\n  Early Mild Cognitive Impairment", "abstract": "  Alzheimer Disease poses a significant challenge, necessitating early\ndetection for effective intervention. MRI is a key neuroimaging tool due to its\nease of use and cost effectiveness. This study analyzes machine learning\nmethods for MRI based biomarker selection and classification to distinguish\nbetween healthy controls and those who develop mild cognitive impairment within\nfive years. Using 3 Tesla MRI data from ADNI and OASIS 3, we applied various\nmachine learning techniques, including MATLAB Classification Learner app,\nnested cross validation, and Bayesian optimization. Data harmonization with\npolynomial regression improved performance. Consistent features identified were\nthe entorhinal, hippocampus, lateral ventricle, and lateral orbitofrontal\nregions. For balanced ADNI data, Naive Bayes with z score harmonization\nperformed best. For balanced OASIS 3, SVM with z score correction excelled. In\nimbalanced data, RUSBoost showed strong performance on ADNI and OASIS 3. Z\nscore harmonization highlighted the potential of a semi automatic pipeline for\nearly AD detection using MRI.\n", "date": "2024-05-29", "categories": []}
{"id": "2408.04775", "title": "Hybrid Student-Teacher Large Language Model Refinement for Cancer\n  Toxicity Symptom Extraction", "abstract": "  Large Language Models (LLMs) offer significant potential for clinical symptom\nextraction, but their deployment in healthcare settings is constrained by\nprivacy concerns, computational limitations, and operational costs. This study\ninvestigates the optimization of compact LLMs for cancer toxicity symptom\nextraction using a novel iterative refinement approach. We employ a\nstudent-teacher architecture, utilizing Zephyr-7b-beta and Phi3-mini-128 as\nstudent models and GPT-4o as the teacher, to dynamically select between prompt\nrefinement, Retrieval-Augmented Generation (RAG), and fine-tuning strategies.\nOur experiments on 294 clinical notes covering 12 post-radiotherapy toxicity\nsymptoms demonstrate the effectiveness of this approach. The RAG method proved\nmost efficient, improving average accuracy scores from 0.32 to 0.73 for\nZephyr-7b-beta and from 0.40 to 0.87 for Phi3-mini-128 during refinement. In\nthe test set, both models showed an approximate 0.20 increase in accuracy\nacross symptoms. Notably, this improvement was achieved at a cost 45 times\nlower than GPT-4o for Zephyr and 79 times lower for Phi-3. These results\nhighlight the potential of iterative refinement techniques in enhancing the\ncapabilities of compact LLMs for clinical applications, offering a balance\nbetween performance, cost-effectiveness, and privacy preservation in healthcare\nsettings.\n", "date": "2024-08-08", "categories": []}
{"id": "2408.04815", "title": "Towards improving Alzheimer's intervention: a machine learning approach\n  for biomarker detection through combining MEG and MRI pipelines", "abstract": "  MEG are non invasive neuroimaging techniques with excellent temporal and\nspatial resolution, crucial for studying brain function in dementia and\nAlzheimer Disease. They identify changes in brain activity at various Alzheimer\nstages, including preclinical and prodromal phases. MEG may detect pathological\nchanges before clinical symptoms, offering potential biomarkers for\nintervention. This study evaluates classification techniques using MEG features\nto distinguish between healthy controls and mild cognitive impairment\nparticipants from the BioFIND study. We compare MEG based biomarkers with MRI\nbased anatomical features, both independently and combined. We used 3 Tesla MRI\nand MEG data from 324 BioFIND participants;158 MCI and 166 HC. Analyses were\nperformed using MATLAB with SPM12 and OSL toolboxes. Machine learning analyses,\nincluding 100 Monte Carlo replications of 10 fold cross validation, were\nconducted on sensor and source spaces. Combining MRI with MEG features achieved\nthe best performance; 0.76 accuracy and AUC of 0.82 for GLMNET using LCMV\nsource based MEG. MEG only analyses using LCMV and eLORETA also performed well,\nsuggesting that combining uncorrected MEG with z-score-corrected MRI features\nis optimal.\n", "date": "2024-08-09", "categories": []}
{"id": "2210.17473", "title": "Chronic pain patient narratives allow for the estimation of current pain\n  intensity", "abstract": "  Chronic pain is a multi-dimensional experience, and pain intensity plays an\nimportant part, impacting the patients emotional balance, psychology, and\nbehaviour. Standard self-reporting tools, such as the Visual Analogue Scale for\npain, fail to capture this burden. Moreover, this type of tools is susceptible\nto a degree of subjectivity, dependent on the patients clear understanding of\nhow to use it, social biases, and their ability to translate a complex\nexperience to a scale. To overcome these and other self-reporting challenges,\npain intensity estimation has been previously studied based on facial\nexpressions, electroencephalograms, brain imaging, and autonomic features.\nHowever, to the best of our knowledge, it has never been attempted to base this\nestimation on the patient narratives of the personal experience of chronic\npain, which is what we propose in this work. Indeed, in the clinical assessment\nand management of chronic pain, verbal communication is essential to convey\ninformation to physicians that would otherwise not be easily accessible through\nstandard reporting tools, since language, sociocultural, and psychosocial\nvariables are intertwined. We show that language features from patient\nnarratives indeed convey information relevant for pain intensity estimation,\nand that our computational models can take advantage of that. Specifically, our\nresults show that patients with mild pain focus more on the use of verbs,\nwhilst moderate and severe pain patients focus on adverbs, and nouns and\nadjectives, respectively, and that these differences allow for the distinction\nbetween these three pain classes.\n", "date": "2022-10-31", "categories": []}
{"id": "2312.10308", "title": "Event-Based Contrastive Learning for Medical Time Series", "abstract": "  In clinical practice, one often needs to identify whether a patient is at\nhigh risk of adverse outcomes after some key medical event. For example,\nquantifying the risk of adverse outcomes after an acute cardiovascular event\nhelps healthcare providers identify those patients at the highest risk of poor\noutcomes; i.e., patients who benefit from invasive therapies that can lower\ntheir risk. Assessing the risk of adverse outcomes, however, is challenging due\nto the complexity, variability, and heterogeneity of longitudinal medical data,\nespecially for individuals suffering from chronic diseases like heart failure.\nIn this paper, we introduce Event-Based Contrastive Learning (EBCL) - a method\nfor learning embeddings of heterogeneous patient data that preserves temporal\ninformation before and after key index events. We demonstrate that EBCL can be\nused to construct models that yield improved performance on important\ndownstream tasks relative to other pretraining methods. We develop and test the\nmethod using a cohort of heart failure patients obtained from a large hospital\nnetwork and the publicly available MIMIC-IV dataset consisting of patients in\nan intensive care unit at a large tertiary care center. On both cohorts, EBCL\npretraining yields models that are performant with respect to a number of\ndownstream tasks, including mortality, hospital readmission, and length of\nstay. In addition, unsupervised EBCL embeddings effectively cluster heart\nfailure patients into subgroups with distinct outcomes, thereby providing\ninformation that helps identify new heart failure phenotypes. The contrastive\nframework around the index event can be adapted to a wide array of time-series\ndatasets and provides information that can be used to guide personalized care.\n", "date": "2023-12-16", "categories": []}
{"id": "2407.14631", "title": "Two new feature selection methods based on learn-heuristic techniques\n  for breast cancer prediction: A comprehensive analysis", "abstract": "  Breast cancer is not preventable because of its unknown causes. However, its\nearly diagnosis increases patients' recovery chances. Machine learning (ML) can\nbe utilized to improve treatment outcomes in healthcare operations while\ndiminishing costs and time. In this research, we suggest two novel feature\nselection (FS) methods based upon an imperialist competitive algorithm (ICA)\nand a bat algorithm (BA) and their combination with ML algorithms. This study\naims to enhance diagnostic models' efficiency and present a comprehensive\nanalysis to help clinical physicians make much more precise and reliable\ndecisions than before. K-nearest neighbors, support vector machine, decision\ntree, Naive Bayes, AdaBoost, linear discriminant analysis, random forest,\nlogistic regression, and artificial neural network are some of the methods\nemployed. This paper applied a distinctive integration of evaluation measures\nand ML algorithms using the wrapper feature selection based on ICA (WFSIC) and\nBA (WFSB) separately. We compared two proposed approaches for the performance\nof the classifiers. Also, we compared our best diagnostic model with previous\nworks reported in the literature survey. Experimentations were performed on the\nWisconsin diagnostic breast cancer dataset. Results reveal that the proposed\nframework that uses the BA with an accuracy of 99.12\\%, surpasses the framework\nusing the ICA and most previous works. Additionally, the RF classifier in the\napproach of FS based on BA emerges as the best model and outperforms others\nregarding its criteria. Besides, the results illustrate the role of our\ntechniques in reducing the dataset dimensions up to 90\\% and increasing the\nperformance of diagnostic models by over 99\\%. Moreover, the result\ndemonstrates that there are more critical features than the optimum dataset\nobtained by proposed FS approaches that have been selected by most ML models.\n", "date": "2024-07-19", "categories": []}
{"id": "2408.04276", "title": "Early Risk Assessment Model for ICA Timing Strategy in Unstable Angina\n  Patients Using Multi-Modal Machine Learning", "abstract": "  Background: Invasive coronary arteriography (ICA) is recognized as the gold\nstandard for diagnosing cardiovascular diseases, including unstable angina\n(UA). The challenge lies in determining the optimal timing for ICA in UA\npatients, balancing the need for revascularization in high-risk patients\nagainst the potential complications in low-risk ones. Unlike myocardial\ninfarction, UA does not have specific indicators like ST-segment deviation or\ncardiac enzymes, making risk assessment complex. Objectives: Our study aims to\nenhance the early risk assessment for UA patients by utilizing machine learning\nalgorithms. These algorithms can potentially identify patients who would\nbenefit most from ICA by analyzing less specific yet related indicators that\nare challenging for human physicians to interpret. Methods: We collected data\nfrom 640 UA patients at Shanghai General Hospital, including medical history\nand electrocardiograms (ECG). Machine learning algorithms were trained using\nmulti-modal demographic characteristics including clinical risk factors,\nsymptoms, biomarker levels, and ECG features extracted by pre-trained neural\nnetworks. The goal was to stratify patients based on their revascularization\nrisk. Additionally, we translated our models into applicable and explainable\nlook-up tables through discretization for practical clinical use. Results: The\nstudy achieved an Area Under the Curve (AUC) of $0.719 \\pm 0.065$ in risk\nstratification, significantly surpassing the widely adopted GRACE score's AUC\nof $0.579 \\pm 0.044$. Conclusions: The results suggest that machine learning\ncan provide superior risk stratification for UA patients. This improved\nstratification could help in balancing the risks, costs, and complications\nassociated with ICA, indicating a potential shift in clinical assessment\npractices for unstable angina.\n", "date": "2024-08-08", "categories": []}
{"id": "2408.04396", "title": "Evaluating the Impact of Pulse Oximetry Bias in Machine Learning under\n  Counterfactual Thinking", "abstract": "  Algorithmic bias in healthcare mirrors existing data biases. However, the\nfactors driving unfairness are not always known. Medical devices capture\nsignificant amounts of data but are prone to errors; for instance, pulse\noximeters overestimate the arterial oxygen saturation of darker-skinned\nindividuals, leading to worse outcomes. The impact of this bias in machine\nlearning (ML) models remains unclear. This study addresses the technical\nchallenges of quantifying the impact of medical device bias in downstream ML.\nOur experiments compare a \"perfect world\", without pulse oximetry bias, using\nSaO2 (blood-gas), to the \"actual world\", with biased measurements, using SpO2\n(pulse oximetry). Under this counterfactual design, two models are trained with\nidentical data, features, and settings, except for the method of measuring\noxygen saturation: models using SaO2 are a \"control\" and models using SpO2 a\n\"treatment\". The blood-gas oximetry linked dataset was a suitable test-bed,\ncontaining 163,396 nearly-simultaneous SpO2 - SaO2 paired measurements, aligned\nwith a wide array of clinical features and outcomes. We studied three\nclassification tasks: in-hospital mortality, respiratory SOFA score in the next\n24 hours, and SOFA score increase by two points. Models using SaO2 instead of\nSpO2 generally showed better performance. Patients with overestimation of O2 by\npulse oximetry of > 3% had significant decreases in mortality prediction\nrecall, from 0.63 to 0.59, P < 0.001. This mirrors clinical processes where\nbiased pulse oximetry readings provide clinicians with false reassurance of\npatients' oxygen levels. A similar degradation happened in ML models, with\npulse oximetry biases leading to more false negatives in predicting adverse\noutcomes.\n", "date": "2024-08-08", "categories": []}
{"id": "2404.02348", "title": "COVID-19 Detection Based on Blood Test Parameters using Various\n  Artificial Intelligence Methods", "abstract": "  In 2019, the world faced a new challenge: a COVID-19 disease caused by the\nnovel coronavirus, SARS-CoV-2. The virus rapidly spread across the globe,\nleading to a high rate of mortality, which prompted health organizations to\ntake measures to control its transmission. Early disease detection is crucial\nin the treatment process, and computer-based automatic detection systems have\nbeen developed to aid in this effort. These systems often rely on artificial\nintelligence (AI) approaches such as machine learning, neural networks, fuzzy\nsystems, and deep learning to classify diseases. This study aimed to\ndifferentiate COVID-19 patients from others using self-categorizing classifiers\nand employing various AI methods. This study used two datasets: the blood test\nsamples and radiography images. The best results for the blood test samples\nobtained from San Raphael Hospital, which include two classes of individuals,\nthose with COVID-19 and those with non-COVID diseases, were achieved through\nthe use of the Ensemble method (a combination of a neural network and two\nmachines learning methods). The results showed that this approach for COVID-19\ndiagnosis is cost-effective and provides results in a shorter amount of time\nthan other methods. The proposed model achieved an accuracy of 94.09% on the\ndataset used. Secondly, the radiographic images were divided into four classes:\nnormal, viral pneumonia, ground glass opacity, and COVID-19 infection. These\nwere used for segmentation and classification. The lung lobes were extracted\nfrom the images and then categorized into specific classes. We achieved an\naccuracy of 91.1% on the image dataset. Generally, this study highlights the\npotential of AI in detecting and managing COVID-19 and underscores the\nimportance of continued research and development in this field.\n", "date": "2024-04-02", "categories": []}
{"id": "2406.14068", "title": "Classifying Dry Eye Disease Patients from Healthy Controls Using Machine\n  Learning and Metabolomics Data", "abstract": "  Dry eye disease is a common disorder of the ocular surface, leading patients\nto seek eye care. Clinical signs and symptoms are currently used to diagnose\ndry eye disease. Metabolomics, a method for analyzing biological systems, has\nbeen found helpful in identifying distinct metabolites in patients and in\ndetecting metabolic profiles that may indicate dry eye disease at early stages.\nIn this study, we explored using machine learning and metabolomics information\nto identify which cataract patients suffered from dry eye disease. As there is\nno one-size-fits-all machine learning model for metabolomics data, choosing the\nmost suitable model can significantly affect the quality of predictions and\nsubsequent metabolomics analyses. To address this challenge, we conducted a\ncomparative analysis of nine machine learning models on three metabolomics data\nsets from cataract patients with and without dry eye disease. The models were\nevaluated and optimized using nested k-fold cross-validation. To assess the\nperformance of these models, we selected a set of suitable evaluation metrics\ntailored to the data set's challenges. The logistic regression model overall\nperformed the best, achieving the highest area under the curve score of 0.8378,\nbalanced accuracy of 0.735, Matthew's correlation coefficient of 0.5147, an\nF1-score of 0.8513, and a specificity of 0.5667. Additionally, following the\nlogistic regression, the XGBoost and Random Forest models also demonstrated\ngood performance.\n", "date": "2024-06-20", "categories": []}
{"id": "2408.03648", "title": "HiQuE: Hierarchical Question Embedding Network for Multimodal Depression\n  Detection", "abstract": "  The utilization of automated depression detection significantly enhances\nearly intervention for individuals experiencing depression. Despite numerous\nproposals on automated depression detection using recorded clinical interview\nvideos, limited attention has been paid to considering the hierarchical\nstructure of the interview questions. In clinical interviews for diagnosing\ndepression, clinicians use a structured questionnaire that includes routine\nbaseline questions and follow-up questions to assess the interviewee's\ncondition. This paper introduces HiQuE (Hierarchical Question Embedding\nnetwork), a novel depression detection framework that leverages the\nhierarchical relationship between primary and follow-up questions in clinical\ninterviews. HiQuE can effectively capture the importance of each question in\ndiagnosing depression by learning mutual information across multiple\nmodalities. We conduct extensive experiments on the widely-used clinical\ninterview data, DAIC-WOZ, where our model outperforms other state-of-the-art\nmultimodal depression detection models and emotion recognition models,\nshowcasing its clinical utility in depression detection.\n", "date": "2024-08-07", "categories": []}
{"id": "2408.03771", "title": "Methodological Explainability Evaluation of an Interpretable Deep\n  Learning Model for Post-Hepatectomy Liver Failure Prediction Incorporating\n  Counterfactual Explanations and Layerwise Relevance Propagation: A\n  Prospective In Silico Trial", "abstract": "  Artificial intelligence (AI)-based decision support systems have demonstrated\nvalue in predicting post-hepatectomy liver failure (PHLF) in hepatocellular\ncarcinoma (HCC). However, they often lack transparency, and the impact of model\nexplanations on clinicians' decisions has not been thoroughly evaluated.\nBuilding on prior research, we developed a variational autoencoder-multilayer\nperceptron (VAE-MLP) model for preoperative PHLF prediction. This model\nintegrated counterfactuals and layerwise relevance propagation (LRP) to provide\ninsights into its decision-making mechanism. Additionally, we proposed a\nmethodological framework for evaluating the explainability of AI systems. This\nframework includes qualitative and quantitative assessments of explanations\nagainst recognized biomarkers, usability evaluations, and an in silico clinical\ntrial. Our evaluations demonstrated that the model's explanation correlated\nwith established biomarkers and exhibited high usability at both the case and\nsystem levels. Furthermore, results from the three-track in silico clinical\ntrial showed that clinicians' prediction accuracy and confidence increased when\nAI explanations were provided.\n", "date": "2024-08-07", "categories": []}
{"id": "2407.21317", "title": "Pathology Foundation Models", "abstract": "  Pathology has played a crucial role in the diagnosis and evaluation of\npatient tissue samples obtained from surgeries and biopsies for many years. The\nadvent of Whole Slide Scanners and the development of deep learning\ntechnologies have significantly advanced the field, leading to extensive\nresearch and development in pathology AI (Artificial Intelligence). These\nadvancements have contributed to reducing the workload of pathologists and\nsupporting decision-making in treatment plans. Recently, large-scale AI models\nknown as Foundation Models (FMs), which are more accurate and applicable to a\nwide range of tasks compared to traditional AI, have emerged, and expanded\ntheir application scope in the healthcare field. Numerous FMs have been\ndeveloped in pathology, and there are reported cases of their application in\nvarious tasks, such as disease diagnosis, rare cancer diagnosis, patient\nsurvival prognosis prediction, biomarker expression prediction, and the scoring\nof immunohistochemical expression intensity. However, several challenges remain\nfor the clinical application of FMs, which healthcare professionals, as users,\nmust be aware of. Research is ongoing to address these challenges. In the\nfuture, it is expected that the development of Generalist Medical AI, which\nintegrates pathology FMs with FMs from other medical domains, will progress,\nleading to the effective utilization of AI in real clinical settings to promote\nprecision and personalized medicine.\n", "date": "2024-07-31", "categories": []}
{"id": "2408.02677", "title": "Patient-centered data science: an integrative framework for evaluating\n  and predicting clinical outcomes in the digital health era", "abstract": "  This study proposes a novel, integrative framework for patient-centered data\nscience in the digital health era. We developed a multidimensional model that\ncombines traditional clinical data with patient-reported outcomes, social\ndeterminants of health, and multi-omic data to create comprehensive digital\npatient representations. Our framework employs a multi-agent artificial\nintelligence approach, utilizing various machine learning techniques including\nlarge language models, to analyze complex, longitudinal datasets. The model\naims to optimize multiple patient outcomes simultaneously while addressing\nbiases and ensuring generalizability. We demonstrate how this framework can be\nimplemented to create a learning healthcare system that continuously refines\nstrategies for optimal patient care. This approach has the potential to\nsignificantly improve the translation of digital health innovations into\nreal-world clinical benefits, addressing current limitations in AI-driven\nhealthcare models.\n", "date": "2024-07-31", "categories": []}
{"id": "2408.02683", "title": "Improving Machine Learning Based Sepsis Diagnosis Using Heart Rate\n  Variability", "abstract": "  The early and accurate diagnosis of sepsis is critical for enhancing patient\noutcomes. This study aims to use heart rate variability (HRV) features to\ndevelop an effective predictive model for sepsis detection. Critical HRV\nfeatures are identified through feature engineering methods, including\nstatistical bootstrapping and the Boruta algorithm, after which XGBoost and\nRandom Forest classifiers are trained with differential hyperparameter\nsettings. In addition, ensemble models are constructed to pool the prediction\nprobabilities of high-recall and high-precision classifiers and improve model\nperformance. Finally, a neural network model is trained on the HRV features,\nachieving an F1 score of 0.805, a precision of 0.851, and a recall of 0.763.\nThe best-performing machine learning model is compared to this neural network\nthrough an interpretability analysis, where Local Interpretable Model-agnostic\nExplanations are implemented to determine decision-making criterion based on\nnumerical ranges and thresholds for specific features. This study not only\nhighlights the efficacy of HRV in automated sepsis diagnosis but also increases\nthe transparency of black box outputs, maximizing clinical applicability.\n", "date": "2024-08-01", "categories": []}
{"id": "2408.02706", "title": "Bayesian Kolmogorov Arnold Networks (Bayesian_KANs): A Probabilistic\n  Approach to Enhance Accuracy and Interpretability", "abstract": "  Because of its strong predictive skills, deep learning has emerged as an\nessential tool in many industries, including healthcare. Traditional deep\nlearning models, on the other hand, frequently lack interpretability and omit\nto take prediction uncertainty into account two crucial components of clinical\ndecision making. In order to produce explainable and uncertainty aware\npredictions, this study presents a novel framework called Bayesian Kolmogorov\nArnold Networks (BKANs), which combines the expressive capacity of Kolmogorov\nArnold Networks with Bayesian inference. We employ BKANs on two medical\ndatasets, which are widely used benchmarks for assessing machine learning\nmodels in medical diagnostics: the Pima Indians Diabetes dataset and the\nCleveland Heart Disease dataset. Our method provides useful insights into\nprediction confidence and decision boundaries and outperforms traditional deep\nlearning models in terms of prediction accuracy. Moreover, BKANs' capacity to\nrepresent aleatoric and epistemic uncertainty guarantees doctors receive more\nsolid and trustworthy decision support. Our Bayesian strategy improves the\ninterpretability of the model and considerably minimises overfitting, which is\nimportant for tiny and imbalanced medical datasets, according to experimental\nresults. We present possible expansions to further use BKANs in more\ncomplicated multimodal datasets and address the significance of these\ndiscoveries for future research in building reliable AI systems for healthcare.\nThis work paves the way for a new paradigm in deep learning model deployment in\nvital sectors where transparency and reliability are crucial.\n", "date": "2024-08-05", "categories": []}
{"id": "2408.03151", "title": "Optimizing Disease Prediction with Artificial Intelligence Driven\n  Feature Selection and Attention Networks", "abstract": "  The rapid integration of machine learning methodologies in healthcare has\nignited innovative strategies for disease prediction, particularly with the\nvast repositories of Electronic Health Records (EHR) data. This article delves\ninto the realm of multi-disease prediction, presenting a comprehensive study\nthat introduces a pioneering ensemble feature selection model. This model,\ndesigned to optimize learning systems, combines statistical, deep, and\noptimally selected features through the innovative Stabilized Energy Valley\nOptimization with Enhanced Bounds (SEV-EB) algorithm. The objective is to\nachieve unparalleled accuracy and stability in predicting various disorders.\nThis work proposes an advanced ensemble model that synergistically integrates\nstatistical, deep, and optimally selected features. This combination aims to\nenhance the predictive power of the model by capturing diverse aspects of the\nhealth data. At the heart of the proposed model lies the SEV-EB algorithm, a\nnovel approach to optimal feature selection. The algorithm introduces enhanced\nbounds and stabilization techniques, contributing to the robustness and\naccuracy of the overall prediction model. To further elevate the predictive\ncapabilities, an HSC-AttentionNet is introduced. This network architecture\ncombines deep temporal convolution capabilities with LSTM, allowing the model\nto capture both short-term patterns and long-term dependencies in health data.\nRigorous evaluations showcase the remarkable performance of the proposed model.\nAchieving a 95% accuracy and 94% F1-score in predicting various disorders, the\nmodel surpasses traditional methods, signifying a significant advancement in\ndisease prediction accuracy. The implications of this research extend beyond\nthe confines of academia.\n", "date": "2024-07-31", "categories": []}
{"id": "2312.05959", "title": "VAE-IF: Deep feature extraction with averaging for fully unsupervised\n  artifact detection in routinely acquired ICU time-series", "abstract": "  Artifacts are a common problem in physiological time series collected from\nintensive care units (ICU) and other settings. They affect the quality and\nreliability of clinical research and patient care. Manual annotation of\nartifacts is costly and time-consuming, rendering it impractical. Automated\nmethods are desired. Here, we propose a novel fully unsupervised approach to\ndetect artifacts in clinical-standard, minute-by-minute resolution ICU data\nwithout any prior labeling or signal-specific knowledge. Our approach combines\na variational autoencoder (VAE) and an isolation forest (IF) into a hybrid\nmodel to learn features and identify anomalies in different types of vital\nsigns, such as blood pressure, heart rate, and intracranial pressure. We\nevaluate our approach on a real-world ICU dataset and compare it with\nsupervised benchmark models based on long short-term memory (LSTM) and XGBoost\nand statistical methods such as ARIMA. We show that our unsupervised approach\nachieves comparable sensitivity to fully supervised methods and generalizes\nwell to an external dataset. We also visualize the latent space learned by the\nVAE and demonstrate its ability to disentangle clean and noisy samples. Our\napproach offers a promising solution for cleaning ICU data in clinical research\nand practice without the need for any labels whatsoever.\n", "date": "2023-12-10", "categories": []}
{"id": "2402.00955", "title": "FairEHR-CLP: Towards Fairness-Aware Clinical Predictions with\n  Contrastive Learning in Multimodal Electronic Health Records", "abstract": "  In the high-stakes realm of healthcare, ensuring fairness in predictive\nmodels is crucial. Electronic Health Records (EHRs) have become integral to\nmedical decision-making, yet existing methods for enhancing model fairness\nrestrict themselves to unimodal data and fail to address the multifaceted\nsocial biases intertwined with demographic factors in EHRs. To mitigate these\nbiases, we present FairEHR-CLP: a general framework for Fairness-aware Clinical\nPredictions with Contrastive Learning in EHRs. FairEHR-CLP operates through a\ntwo-stage process, utilizing patient demographics, longitudinal data, and\nclinical notes. First, synthetic counterparts are generated for each patient,\nallowing for diverse demographic identities while preserving essential health\ninformation. Second, fairness-aware predictions employ contrastive learning to\nalign patient representations across sensitive attributes, jointly optimized\nwith an MLP classifier with a softmax layer for clinical classification tasks.\nAcknowledging the unique challenges in EHRs, such as varying group sizes and\nclass imbalance, we introduce a novel fairness metric to effectively measure\nerror rate disparities across subgroups. Extensive experiments on three diverse\nEHR datasets on three tasks demonstrate the effectiveness of FairEHR-CLP in\nterms of fairness and utility compared with competitive baselines. FairEHR-CLP\nrepresents an advancement towards ensuring both accuracy and equity in\npredictive healthcare models.\n", "date": "2024-02-01", "categories": []}
{"id": "2408.00938", "title": "CIResDiff: A Clinically-Informed Residual Diffusion Model for Predicting\n  Idiopathic Pulmonary Fibrosis Progression", "abstract": "  The progression of Idiopathic Pulmonary Fibrosis (IPF) significantly\ncorrelates with higher patient mortality rates. Early detection of IPF\nprogression is critical for initiating timely treatment, which can effectively\nslow down the advancement of the disease. However, the current clinical\ncriteria define disease progression requiring two CT scans with a one-year\ninterval, presenting a dilemma: a disease progression is identified only after\nthe disease has already progressed. To this end, in this paper, we develop a\nnovel diffusion model to accurately predict the progression of IPF by\ngenerating patient's follow-up CT scan from the initial CT scan. Specifically,\nfrom the clinical prior knowledge, we tailor improvements to the traditional\ndiffusion model and propose a Clinically-Informed Residual Diffusion model,\ncalled CIResDiff. The key innovations of CIResDiff include 1) performing the\ntarget region pre-registration to align the lung regions of two CT scans at\ndifferent time points for reducing the generation difficulty, 2) adopting the\nresidual diffusion instead of traditional diffusion to enable the model focus\nmore on differences (i.e., lesions) between the two CT scans rather than the\nlargely identical anatomical content, and 3) designing the clinically-informed\nprocess based on CLIP technology to integrate lung function information which\nis highly relevant to diagnosis into the reverse process for assisting\ngeneration. Extensive experiments on clinical data demonstrate that our\napproach can outperform state-of-the-art methods and effectively predict the\nprogression of IPF.\n", "date": "2024-08-01", "categories": []}
{"id": "2408.01445", "title": "MiranDa: Mimicking the Learning Processes of Human Doctors to Achieve\n  Causal Inference for Medication Recommendation", "abstract": "  To enhance therapeutic outcomes from a pharmacological perspective, we\npropose MiranDa, designed for medication recommendation, which is the first\nactionable model capable of providing the estimated length of stay in hospitals\n(ELOS) as counterfactual outcomes that guide clinical practice and model\ntraining. In detail, MiranDa emulates the educational trajectory of doctors\nthrough two gradient-scaling phases shifted by ELOS: an Evidence-based Training\nPhase that utilizes supervised learning and a Therapeutic Optimization Phase\ngrounds in reinforcement learning within the gradient space, explores optimal\nmedications by perturbations from ELOS. Evaluation of the Medical Information\nMart for Intensive Care III dataset and IV dataset, showcased the superior\nresults of our model across five metrics, particularly in reducing the ELOS.\nSurprisingly, our model provides structural attributes of medication\ncombinations proved in hyperbolic space and advocated \"procedure-specific\"\nmedication combinations. These findings posit that MiranDa enhanced medication\nefficacy. Notably, our paradigm can be applied to nearly all medical tasks and\nthose with information to evaluate predicted outcomes. The source code of the\nMiranDa model is available at https://github.com/azusakou/MiranDa.\n", "date": "2024-07-23", "categories": []}
{"id": "2408.01988", "title": "MetaWearS: A Shortcut in Wearable Systems Lifecycle with Only a Few\n  Shots", "abstract": "  Wearable systems provide continuous health monitoring and can lead to early\ndetection of potential health issues. However, the lifecycle of wearable\nsystems faces several challenges. First, effective model training for new\nwearable devices requires substantial labeled data from various subjects\ncollected directly by the wearable. Second, subsequent model updates require\nfurther extensive labeled data for retraining. Finally, frequent model updating\non the wearable device can decrease the battery life in long-term data\nmonitoring. Addressing these challenges, in this paper, we propose MetaWearS, a\nmeta-learning method to reduce the amount of initial data collection required.\nMoreover, our approach incorporates a prototypical updating mechanism,\nsimplifying the update process by modifying the class prototype rather than\nretraining the entire model. We explore the performance of MetaWearS in two\ncase studies, namely, the detection of epileptic seizures and the detection of\natrial fibrillation. We show that by fine-tuning with just a few samples, we\nachieve 70% and 82% AUC for the detection of epileptic seizures and the\ndetection of atrial fibrillation, respectively. Compared to a conventional\napproach, our proposed method performs better with up to 45% AUC. Furthermore,\nupdating the model with only 16 minutes of additional labeled data increases\nthe AUC by up to 5.3%. Finally, MetaWearS reduces the energy consumption for\nmodel updates by 456x and 418x for epileptic seizure and AF detection,\nrespectively.\n", "date": "2024-08-04", "categories": []}
{"id": "2408.02018", "title": "Individualized multi-horizon MRI trajectory prediction for Alzheimer's\n  Disease", "abstract": "  Neurodegeneration as measured through magnetic resonance imaging (MRI) is\nrecognized as a potential biomarker for diagnosing Alzheimer's disease (AD),\nbut is generally considered less specific than amyloid or tau based biomarkers.\nDue to a large amount of variability in brain anatomy between different\nindividuals, we hypothesize that leveraging MRI time series can help improve\nspecificity, by treating each patient as their own baseline. Here we turn to\nconditional variational autoencoders to generate individualized MRI predictions\ngiven the subject's age, disease status and one previous scan. Using serial\nimaging data from the Alzheimer's Disease Neuroimaging Initiative, we train a\nnovel architecture to build a latent space distribution which can be sampled\nfrom to generate future predictions of changing anatomy. This enables us to\nextrapolate beyond the dataset and predict MRIs up to 10 years. We evaluated\nthe model on a held-out set from ADNI and an independent dataset (from Open\nAccess Series of Imaging Studies). By comparing to several alternatives, we\nshow that our model produces more individualized images with higher resolution.\nFurther, if an individual already has a follow-up MRI, we demonstrate a usage\nof our model to compute a likelihood ratio classifier for disease status. In\npractice, the model may be able to assist in early diagnosis of AD and provide\na counterfactual baseline trajectory for treatment effect estimation.\nFurthermore, it generates a synthetic dataset that can potentially be used for\ndownstream tasks such as anomaly detection and classification.\n", "date": "2024-08-04", "categories": []}
{"id": "2408.02073", "title": "Case-based reasoning approach for diagnostic screening of children with\n  developmental delays", "abstract": "  According to the World Health Organization, the population of children with\ndevelopmental delays constitutes approximately 6% to 9% of the total\npopulation. Based on the number of newborns in Huaibei, Anhui Province, China,\nin 2023 (94,420), it is estimated that there are about 7,500 cases (suspected\ncases of developmental delays) of suspicious cases annually. Early\nidentification and appropriate early intervention for these children can\nsignificantly reduce the wastage of medical resources and societal costs.\nInternational research indicates that the optimal period for intervention in\nchildren with developmental delays is before the age of six, with the golden\ntreatment period being before three and a half years of age. Studies have shown\nthat children with developmental delays who receive early intervention exhibit\nsignificant improvement in symptoms; some may even fully recover. This research\nadopts a hybrid model combining a CNN-Transformer model with Case-Based\nReasoning (CBR) to enhance the screening efficiency for children with\ndevelopmental delays. The CNN-Transformer model is an excellent model for image\nfeature extraction and recognition, effectively identifying features in bone\nage images to determine bone age. CBR is a technique for solving problems based\non similar cases; it solves current problems based on past experiences, similar\nto how humans solve problems through learning from experience. Given CBR's\nmemory capability to judge and compare new cases based on previously stored old\ncases, it is suitable for application in support systems with latent and\nvariable characteristics. Therefore, this study utilizes the\nCNN-Transformer-CBR to establish a screening system for children with\ndevelopmental delays, aiming to improve screening efficiency.\n", "date": "2024-07-18", "categories": []}
{"id": "2408.02214", "title": "More Than Positive and Negative: Communicating Fine Granularity in\n  Medical Diagnosis", "abstract": "  With the advance of deep learning, much progress has been made in building\npowerful artificial intelligence (AI) systems for automatic Chest X-ray (CXR)\nanalysis. Most existing AI models are trained to be a binary classifier with\nthe aim of distinguishing positive and negative cases. However, a large gap\nexists between the simple binary setting and complicated real-world medical\nscenarios. In this work, we reinvestigate the problem of automatic radiology\ndiagnosis. We first observe that there is considerable diversity among cases\nwithin the positive class, which means simply classifying them as positive\nloses many important details. This motivates us to build AI models that can\ncommunicate fine-grained knowledge from medical images like human experts. To\nthis end, we first propose a new benchmark on fine granularity learning from\nmedical images. Specifically, we devise a division rule based on medical\nknowledge to divide positive cases into two subcategories, namely atypical\npositive and typical positive. Then, we propose a new metric termed\nAUC$^\\text{FG}$ on the two subcategories for evaluation of the ability to\nseparate them apart. With the proposed benchmark, we encourage the community to\ndevelop AI diagnosis systems that could better learn fine granularity from\nmedical images. Last, we propose a simple risk modulation approach to this\nproblem by only using coarse labels in training. Empirical results show that\ndespite its simplicity, the proposed method achieves superior performance and\nthus serves as a strong baseline.\n", "date": "2024-08-05", "categories": []}
{"id": "2408.02296", "title": "Heart Rate and its Variability from Short-term ECG Recordings as\n  Biomarkers for Detecting Mild Cognitive Impairment in Indian Population", "abstract": "  Alterations in Heart Rate (HR) and Heart Rate Variability (HRV) can reflect\nautonomic dysfunction associated with neurodegeneration. We investigate the\ninfluence of Mild Cognitive Impairment (MCI) on HR and its variability measures\nin the Indian population by designing a complete signal processing pipeline to\ndetect the R-wave peaks and compute HR and HRV features from ECG recordings of\n10 seconds, for point-of-care applications. The study cohort involves 297 urban\nparticipants, among which 48.48% are male and 51.51% are female. From the\nAddenbrooke's Cognitive Examination-III (ACE-III), MCI is detected in 19.19% of\nparticipants and the rest, 80.8% of them are cognitively healthy. Statistical\nfeatures like central tendency (mean and root mean square (RMS) of the\nNormal-to-Normal (NN) intervals) and dispersion (standard deviation (SD) of all\nNN intervals (SDNN) and root mean square of successive differences of NN\nintervals (RMSSD)) of beat-to-beat intervals are computed. The Wilcoxon rank\nsum test reveals that mean of NN intervals (p = 0.0021), the RMS of NN\nintervals (p = 0.0014), the SDNN (p = 0.0192) and the RMSSD (p = 0.0206) values\ndiffer significantly between MCI and non-MCI classes, for a level of\nsignificance, 0.05. Machine learning classifiers like, Support Vector Machine\n(SVM), Discriminant Analysis (DA) and Naive Bayes (NB) driven by mean NN\nintervals, RMS, SDNN and RMSSD, show a high accuracy of 80.80% on each\nindividual feature input. Individuals with MCI are observed to have\ncomparatively higher HR than healthy subjects. HR and its variability can be\nconsidered as potential biomarkers for detecting MCI.\n", "date": "2024-08-05", "categories": []}
{"id": "2408.00940", "title": "A dual-task mutual learning framework for predicting post-thrombectomy\n  cerebral hemorrhage", "abstract": "  Ischemic stroke is a severe condition caused by the blockage of brain blood\nvessels, and can lead to the death of brain tissue due to oxygen deprivation.\nThrombectomy has become a common treatment choice for ischemic stroke due to\nits immediate effectiveness. But, it carries the risk of postoperative cerebral\nhemorrhage. Clinically, multiple CT scans within 0-72 hours post-surgery are\nused to monitor for hemorrhage. However, this approach exposes radiation dose\nto patients, and may delay the detection of cerebral hemorrhage. To address\nthis dilemma, we propose a novel prediction framework for measuring\npostoperative cerebral hemorrhage using only the patient's initial CT scan.\nSpecifically, we introduce a dual-task mutual learning framework to takes the\ninitial CT scan as input and simultaneously estimates both the follow-up CT\nscan and prognostic label to predict the occurrence of postoperative cerebral\nhemorrhage. Our proposed framework incorporates two attention mechanisms, i.e.,\nself-attention and interactive attention. Specifically, the self-attention\nmechanism allows the model to focus more on high-density areas in the image,\nwhich are critical for diagnosis (i.e., potential hemorrhage areas). The\ninteractive attention mechanism further models the dependencies between the\ninterrelated generation and classification tasks, enabling both tasks to\nperform better than the case when conducted individually. Validated on clinical\ndata, our method can generate follow-up CT scans better than state-of-the-art\nmethods, and achieves an accuracy of 86.37% in predicting follow-up prognostic\nlabels. Thus, our work thus contributes to the timely screening of\npost-thrombectomy cerebral hemorrhage, and could significantly reform the\nclinical process of thrombectomy and other similar operations related to\nstroke.\n", "date": "2024-08-01", "categories": []}
{"id": "2408.01144", "title": "Enhanced Prediction of Ventilator-Associated Pneumonia in Patients with\n  Traumatic Brain Injury Using Advanced Machine Learning Techniques", "abstract": "  Background: Ventilator-associated pneumonia (VAP) in traumatic brain injury\n(TBI) patients poses a significant mortality risk and imposes a considerable\nfinancial burden on patients and healthcare systems. Timely detection and\nprognostication of VAP in TBI patients are crucial to improve patient outcomes\nand alleviate the strain on healthcare resources.\n  Methods: We implemented six machine learning models using the MIMIC-III\ndatabase. Our methodology included preprocessing steps, such as feature\nselection with CatBoost and expert opinion, addressing class imbalance with the\nSynthetic Minority Oversampling Technique (SMOTE), and rigorous model tuning\nthrough 5-fold cross-validation to optimize hyperparameters. Key models\nevaluated included SVM, Logistic Regression, Random Forest, XGBoost, ANN, and\nAdaBoost. Additionally, we conducted SHAP analysis to determine feature\nimportance and performed an ablation study to assess feature impacts on model\nperformance.\n  Results: XGBoost outperformed the baseline models and the best existing\nliterature. We used metrics, including AUC, Accuracy, Specificity, Sensitivity,\nF1 Score, PPV, and NPV. XGBoost demonstrated the highest performance with an\nAUC of 0.940 and an Accuracy of 0.875, which are 23.4% and 23.5% higher than\nthe best results in the existing literature, with an AUC of 0.706 and an\nAccuracy of 0.640, respectively. This enhanced performance underscores the\nmodels' effectiveness in clinical settings.\n  Conclusions: This study enhances the predictive modeling of VAP in TBI\npatients, improving early detection and intervention potential. Refined feature\nselection and advanced ensemble techniques significantly boosted model accuracy\nand reliability, offering promising directions for future clinical applications\nand medical diagnostics research.\n", "date": "2024-08-02", "categories": []}
{"id": "2408.00711", "title": "Investigating Brain Connectivity and Regional Statistics from EEG for\n  early stage Parkinson's Classification", "abstract": "  We evaluate the effectiveness of combining brain connectivity metrics with\nsignal statistics for early stage Parkinson's Disease (PD) classification using\nelectroencephalogram data (EEG). The data is from 5 arousal states - wakeful\nand four sleep stages (N1, N2, N3 and REM). Our pipeline uses an Ada Boost\nmodel for classification on a challenging early stage PD classification task\nwith with only 30 participants (11 PD , 19 Healthy Control). Evaluating 9 brain\nconnectivity metrics we find the best connectivity metric to be different for\neach arousal state with Phase Lag Index achieving the highest individual\nclassification accuracy of 86\\% on N1 data. Further to this our pipeline using\nregional signal statistics achieves an accuracy of 78\\%, using brain\nconnectivity only achieves an accuracy of 86\\% whereas combining the two\nachieves a best accuracy of 91\\%. This best performance is achieved on N1 data\nusing Phase Lag Index (PLI) combined with statistics derived from the frequency\ncharacteristics of the EEG signal. This model also achieves a recall of 80 \\%\nand precision of 96\\%. Furthermore we find that on data from each arousal\nstate, combining PLI with regional signal statistics improves classification\naccuracy versus using signal statistics or brain connectivity alone. Thus we\nconclude that combining brain connectivity statistics with regional EEG\nstatistics is optimal for classifier performance on early stage Parkinson's.\nAdditionally, we find outperformance of N1 EEG for classification of\nParkinson's and expect this could be due to disrupted N1 sleep in PD. This\nshould be explored in future work.\n", "date": "2024-08-01", "categories": []}
{"id": "2310.19967", "title": "Early detection of inflammatory arthritis to improve referrals using\n  multimodal machine learning from blood testing, semi-structured and\n  unstructured patient records", "abstract": "  Early detection of inflammatory arthritis (IA) is critical to efficient and\naccurate hospital referral triage for timely treatment and preventing the\ndeterioration of the IA disease course, especially under limited healthcare\nresources. The manual assessment process is the most common approach in\npractice for the early detection of IA, but it is extremely labor-intensive and\ninefficient. A large amount of clinical information needs to be assessed for\nevery referral from General Practice (GP) to the hospitals. Machine learning\nshows great potential in automating repetitive assessment tasks and providing\ndecision support for the early detection of IA. However, most machine\nlearning-based methods for IA detection rely on blood testing results. But in\npractice, blood testing data is not always available at the point of referrals,\nso we need methods to leverage multimodal data such as semi-structured and\nunstructured data for early detection of IA. In this research, we present\nfusion and ensemble learning-based methods using multimodal data to assist\ndecision-making in the early detection of IA, and a conformal prediction-based\nmethod to quantify the uncertainty of the prediction and detect any unreliable\npredictions. To the best of our knowledge, our study is the first attempt to\nutilize multimodal data to support the early detection of IA from GP referrals.\n", "date": "2023-10-30", "categories": []}
{"id": "2407.20249", "title": "Revisiting the Disequilibrium Issues in Tackling Heart Disease\n  Classification Tasks", "abstract": "  In the field of heart disease classification, two primary obstacles arise.\nFirstly, existing Electrocardiogram (ECG) datasets consistently demonstrate\nimbalances and biases across various modalities. Secondly, these time-series\ndata consist of diverse lead signals, causing Convolutional Neural Networks\n(CNNs) to become overfitting to the one with higher power, hence diminishing\nthe performance of the Deep Learning (DL) process. In addition, when facing an\nimbalanced dataset, performance from such high-dimensional data may be\nsusceptible to overfitting. Current efforts predominantly focus on enhancing DL\nmodels by designing novel architectures, despite these evident challenges,\nseemingly overlooking the core issues, therefore hindering advancements in\nheart disease classification. To address these obstacles, our proposed approach\nintroduces two straightforward and direct methods to enhance the classification\ntasks. To address the high dimensionality issue, we employ a Channel-wise\nMagnitude Equalizer (CME) on signal-encoded images. This approach reduces\nredundancy in the feature data range, highlighting changes in the dataset.\nSimultaneously, to counteract data imbalance, we propose the Inverted Weight\nLogarithmic Loss (IWL) to alleviate imbalances among the data. When applying\nIWL loss, the accuracy of state-of-the-art models (SOTA) increases up to 5% in\nthe CPSC2018 dataset. CME in combination with IWL also surpasses the\nclassification results of other baseline models from 5% to 10%.\n", "date": "2024-07-19", "categories": []}
{"id": "2407.20284", "title": "MLtoGAI: Semantic Web based with Machine Learning for Enhanced Disease\n  Prediction and Personalized Recommendations using Generative AI", "abstract": "  In modern healthcare, addressing the complexities of accurate disease\nprediction and personalized recommendations is both crucial and challenging.\nThis research introduces MLtoGAI, which integrates Semantic Web technology with\nMachine Learning (ML) to enhance disease prediction and offer user-friendly\nexplanations through ChatGPT. The system comprises three key components: a\nreusable disease ontology that incorporates detailed knowledge about various\ndiseases, a diagnostic classification model that uses patient symptoms to\ndetect specific diseases accurately, and the integration of Semantic Web Rule\nLanguage (SWRL) with ontology and ChatGPT to generate clear, personalized\nhealth advice. This approach significantly improves prediction accuracy and\nensures results that are easy to understand, addressing the complexity of\ndiseases and diverse symptoms. The MLtoGAI system demonstrates substantial\nadvancements in accuracy and user satisfaction, contributing to developing more\nintelligent and accessible healthcare solutions. This innovative approach\ncombines the strengths of ML algorithms with the ability to provide\ntransparent, human-understandable explanations through ChatGPT, achieving\nsignificant improvements in prediction accuracy and user comprehension. By\nleveraging semantic technology and explainable AI, the system enhances the\naccuracy of disease prediction and ensures that the recommendations are\nrelevant and easily understood by individual patients. Our research highlights\nthe potential of integrating advanced technologies to overcome existing\nchallenges in medical diagnostics, paving the way for future developments in\nintelligent healthcare systems. Additionally, the system is validated using 200\nsynthetic patient data records, ensuring robust performance and reliability.\n", "date": "2024-07-26", "categories": []}
{"id": "2407.20893", "title": "MambaCapsule: Towards Transparent Cardiac Disease Diagnosis with\n  Electrocardiography Using Mamba Capsule Network", "abstract": "  Cardiac arrhythmia, a condition characterized by irregular heartbeats, often\nserves as an early indication of various heart ailments. With the advent of\ndeep learning, numerous innovative models have been introduced for diagnosing\narrhythmias using Electrocardiogram (ECG) signals. However, recent studies\nsolely focus on the performance of models, neglecting the interpretation of\ntheir results. This leads to a considerable lack of transparency, posing a\nsignificant risk in the actual diagnostic process. To solve this problem, this\npaper introduces MambaCapsule, a deep neural networks for ECG arrhythmias\nclassification, which increases the explainability of the model while enhancing\nthe accuracy.Our model utilizes Mamba for feature extraction and Capsule\nnetworks for prediction, providing not only a confidence score but also signal\nfeatures. Akin to the processing mechanism of human brain, the model learns\nsignal features and their relationship between them by reconstructing ECG\nsignals in the predicted selection. The model evaluation was conducted on\nMIT-BIH and PTB dataset, following the AAMI standard. MambaCapsule has achieved\na total accuracy of 99.54% and 99.59% on the test sets respectively. These\nresults demonstrate the promising performance of under the standard test\nprotocol.\n", "date": "2024-07-30", "categories": []}
{"id": "2309.00237", "title": "Publicly Shareable Clinical Large Language Model Built on Synthetic\n  Clinical Notes", "abstract": "  The development of large language models tailored for handling patients'\nclinical notes is often hindered by the limited accessibility and usability of\nthese notes due to strict privacy regulations. To address these challenges, we\nfirst create synthetic large-scale clinical notes using publicly available case\nreports extracted from biomedical literature. We then use these synthetic notes\nto train our specialized clinical large language model, Asclepius. While\nAsclepius is trained on synthetic data, we assess its potential performance in\nreal-world applications by evaluating it using real clinical notes. We\nbenchmark Asclepius against several other large language models, including\nGPT-3.5-turbo and other open-source alternatives. To further validate our\napproach using synthetic notes, we also compare Asclepius with its variants\ntrained on real clinical notes. Our findings convincingly demonstrate that\nsynthetic clinical notes can serve as viable substitutes for real ones when\nconstructing high-performing clinical language models. This conclusion is\nsupported by detailed evaluations conducted by both GPT-4 and medical\nprofessionals. All resources including weights, codes, and data used in the\ndevelopment of Asclepius are made publicly accessible for future research.\n(https://github.com/starmpcc/Asclepius)\n", "date": "2023-09-01", "categories": []}
{"id": "2403.00815", "title": "RAM-EHR: Retrieval Augmentation Meets Clinical Predictions on Electronic\n  Health Records", "abstract": "  We present RAM-EHR, a Retrieval AugMentation pipeline to improve clinical\npredictions on Electronic Health Records (EHRs). RAM-EHR first collects\nmultiple knowledge sources, converts them into text format, and uses dense\nretrieval to obtain information related to medical concepts. This strategy\naddresses the difficulties associated with complex names for the concepts.\nRAM-EHR then augments the local EHR predictive model co-trained with\nconsistency regularization to capture complementary information from patient\nvisits and summarized knowledge. Experiments on two EHR datasets show the\nefficacy of RAM-EHR over previous knowledge-enhanced baselines (3.4% gain in\nAUROC and 7.2% gain in AUPR), emphasizing the effectiveness of the summarized\nknowledge from RAM-EHR for clinical prediction tasks. The code will be\npublished at \\url{https://github.com/ritaranx/RAM-EHR}.\n", "date": "2024-02-25", "categories": []}
{"id": "2407.08554", "title": "Establishing Rigorous and Cost-effective Clinical Trials for Artificial\n  Intelligence Models", "abstract": "  A profound gap persists between artificial intelligence (AI) and clinical\npractice in medicine, primarily due to the lack of rigorous and cost-effective\nevaluation methodologies. State-of-the-art and state-of-the-practice AI model\nevaluations are limited to laboratory studies on medical datasets or direct\nclinical trials with no or solely patient-centered controls. Moreover, the\ncrucial role of clinicians in collaborating with AI, pivotal for determining\nits impact on clinical practice, is often overlooked. For the first time, we\nemphasize the critical necessity for rigorous and cost-effective evaluation\nmethodologies for AI models in clinical practice, featuring\npatient/clinician-centered (dual-centered) AI randomized controlled trials\n(DC-AI RCTs) and virtual clinician-based in-silico trials (VC-MedAI) as an\neffective proxy for DC-AI RCTs. Leveraging 7500 diagnosis records from two-step\ninaugural DC-AI RCTs across 14 medical centers with 125 clinicians, our results\ndemonstrate the necessity of DC-AI RCTs and the effectiveness of VC-MedAI.\nNotably, VC-MedAI performs comparably to human clinicians, replicating insights\nand conclusions from prospective DC-AI RCTs. We envision DC-AI RCTs and\nVC-MedAI as pivotal advancements, presenting innovative and transformative\nevaluation methodologies for AI models in clinical practice, offering a\npreclinical-like setting mirroring conventional medicine, and reshaping\ndevelopment paradigms in a cost-effective and fast-iterative manner. Chinese\nClinical Trial Registration: ChiCTR2400086816.\n", "date": "2024-07-11", "categories": []}
{"id": "2407.18606", "title": "A data balancing approach towards design of an expert system for Heart\n  Disease Prediction", "abstract": "  Heart disease is a serious global health issue that claims millions of lives\nevery year. Early detection and precise prediction are critical to the\nprevention and successful treatment of heart related issues. A lot of research\nutilizes machine learning (ML) models to forecast cardiac disease and obtain\nearly detection. In order to do predictive analysis on \"Heart disease health\nindicators \" dataset. We employed five machine learning methods in this paper:\nDecision Tree (DT), Random Forest (RF), Linear Discriminant Analysis, Extra\nTree Classifier, and AdaBoost. The model is further examined using various\nfeature selection (FS) techniques. To enhance the baseline model, we have\nseparately applied four FS techniques: Sequential Forward FS, Sequential\nBackward FS, Correlation Matrix, and Chi2. Lastly, K means SMOTE oversampling\nis applied to the models to enable additional analysis. The findings show that\nwhen it came to predicting heart disease, ensemble approaches in particular,\nrandom forests performed better than individual classifiers. The presence of\nsmoking, blood pressure, cholesterol, and physical inactivity were among the\nmajor predictors that were found. The accuracy of the Random Forest and\nDecision Tree model was 99.83%. This paper demonstrates how machine learning\nmodels can improve the accuracy of heart disease prediction, especially when\nusing ensemble methodologies. The models provide a more accurate risk\nassessment than traditional methods since they incorporate a large number of\nfactors and complex algorithms.\n", "date": "2024-07-26", "categories": []}
{"id": "2407.19359", "title": "Learning to Select the Best Forecasting Tasks for Clinical Outcome\n  Prediction", "abstract": "  We propose to meta-learn an a self-supervised patient trajectory forecast\nlearning rule by meta-training on a meta-objective that directly optimizes the\nutility of the patient representation over the subsequent clinical outcome\nprediction. This meta-objective directly targets the usefulness of a\nrepresentation generated from unlabeled clinical measurement forecast for later\nsupervised tasks.\n  The meta-learned can then be directly used in target risk prediction, and the\nlimited available samples can be used for further fine-tuning the model\nperformance. The effectiveness of our approach is tested on a real open source\npatient EHR dataset MIMIC-III. We are able to demonstrate that our\nattention-based patient state representation approach can achieve much better\nperformance for predicting target risk with low resources comparing with both\ndirect supervised learning and pretraining with all-observation trajectory\nforecast.\n", "date": "2024-07-28", "categories": []}
{"id": "2407.19371", "title": "Deep State-Space Generative Model For Correlated Time-to-Event\n  Predictions", "abstract": "  Capturing the inter-dependencies among multiple types of clinically-critical\nevents is critical not only to accurate future event prediction, but also to\nbetter treatment planning. In this work, we propose a deep latent state-space\ngenerative model to capture the interactions among different types of\ncorrelated clinical events (e.g., kidney failure, mortality) by explicitly\nmodeling the temporal dynamics of patients' latent states. Based on these\nlearned patient states, we further develop a new general discrete-time\nformulation of the hazard rate function to estimate the survival distribution\nof patients with significantly improved accuracy. Extensive evaluations over\nreal EMR data show that our proposed model compares favorably to various\nstate-of-the-art baselines. Furthermore, our method also uncovers meaningful\ninsights about the latent correlations among mortality and different types of\norgan failures.\n", "date": "2024-07-28", "categories": []}
{"id": "2407.19380", "title": "Empowering Clinicians with Medical Decision Transformers: A Framework\n  for Sepsis Treatment", "abstract": "  Offline reinforcement learning has shown promise for solving tasks in\nsafety-critical settings, such as clinical decision support. Its application,\nhowever, has been limited by the lack of interpretability and interactivity for\nclinicians. To address these challenges, we propose the medical decision\ntransformer (MeDT), a novel and versatile framework based on the\ngoal-conditioned reinforcement learning paradigm for sepsis treatment\nrecommendation. MeDT uses the decision transformer architecture to learn a\npolicy for drug dosage recommendation. During offline training, MeDT utilizes\ncollected treatment trajectories to predict administered treatments for each\ntime step, incorporating known treatment outcomes, target acuity scores, past\ntreatment decisions, and current and past medical states. This analysis enables\nMeDT to capture complex dependencies among a patient's medical history,\ntreatment decisions, outcomes, and short-term effects on stability. Our\nproposed conditioning uses acuity scores to address sparse reward issues and to\nfacilitate clinician-model interactions, enhancing decision-making. Following\ntraining, MeDT can generate tailored treatment recommendations by conditioning\non the desired positive outcome (survival) and user-specified short-term\nstability improvements. We carry out rigorous experiments on data from the\nMIMIC-III dataset and use off-policy evaluation to demonstrate that MeDT\nrecommends interventions that outperform or are competitive with existing\noffline reinforcement learning methods while enabling a more interpretable,\npersonalized and clinician-directed approach.\n", "date": "2024-07-28", "categories": []}
{"id": "2407.19990", "title": "Classification of Alzheimer's Dementia vs. Healthy subjects by studying\n  structural disparities in fMRI Time-Series of DMN", "abstract": "  Time series from different regions of interest (ROI) of default mode network\n(DMN) from Functional Magnetic Resonance Imaging (fMRI) can reveal significant\ndifferences between healthy and unhealthy people. Here, we propose the utility\nof an existing metric quantifying the lack/presence of structure in a signal\ncalled, \"deviation from stochasticity\" (DS) measure to characterize\nresting-state fMRI time series. The hypothesis is that differences in the level\nof structure in the time series can lead to discrimination between the subject\ngroups. In this work, an autoencoder-based model is utilized to learn efficient\nrepresentations of data by training the network to reconstruct its input data.\nThe proposed methodology is applied on fMRI time series of 50 healthy\nindividuals and 50 subjects with Alzheimer's Disease (AD), obtained from\npublicly available ADNI database. DS measure for healthy fMRI as expected turns\nout to be different compared to that of AD. Peak classification accuracy of 95%\nwas obtained using Gradient Boosting classifier, using the DS measure applied\non 100 subjects.\n", "date": "2024-07-29", "categories": []}
{"id": "2407.18424", "title": "Model-driven Heart Rate Estimation and Heart Murmur Detection based on\n  Phonocardiogram", "abstract": "  Acoustic signals are crucial for health monitoring, particularly heart sounds\nwhich provide essential data like heart rate and detect cardiac anomalies such\nas murmurs. This study utilizes a publicly available phonocardiogram (PCG)\ndataset to estimate heart rate using model-driven methods and extends the\nbest-performing model to a multi-task learning (MTL) framework for simultaneous\nheart rate estimation and murmur detection. Heart rate estimates are derived\nusing a sliding window technique on heart sound snippets, analyzed with a\ncombination of acoustic features (Mel spectrogram, cepstral coefficients, power\nspectral density, root mean square energy). Our findings indicate that a 2D\nconvolutional neural network (\\textbf{\\texttt{2dCNN}}) is most effective for\nheart rate estimation, achieving a mean absolute error (MAE) of 1.312 bpm. We\nsystematically investigate the impact of different feature combinations and\nfind that utilizing all four features yields the best results. The MTL model\n(\\textbf{\\texttt{2dCNN-MTL}}) achieves accuracy over 95% in murmur detection,\nsurpassing existing models, while maintaining an MAE of 1.636 bpm in heart rate\nestimation, satisfying the requirements stated by Association for the\nAdvancement of Medical Instrumentation (AAMI).\n", "date": "2024-07-25", "categories": []}
{"id": "2407.17494", "title": "AI in Remote Patient Monitoring", "abstract": "  The rapid evolution of Artificial Intelligence (AI) has significantly\ntransformed healthcare, particularly in the domain of Remote Patient Monitoring\n(RPM). This chapter explores the integration of AI in RPM, highlighting\nreal-life applications, system architectures, and the benefits it brings to\npatient care and healthcare systems. Through a comprehensive analysis of\ncurrent technologies, methodologies, and case studies, I present a detailed\noverview of how AI enhances monitoring accuracy, predictive analytics, and\npersonalized treatment plans. The chapter also discusses the challenges and\nfuture directions in this field, providing a comprehensive view of AI's role in\nrevolutionizing remote patient care.\n", "date": "2024-07-04", "categories": []}
{"id": "2407.16721", "title": "Machine Learning Models for the Identification of Cardiovascular\n  Diseases Using UK Biobank Data", "abstract": "  Machine learning models have the potential to identify cardiovascular\ndiseases (CVDs) early and accurately in primary healthcare settings, which is\ncrucial for delivering timely treatment and management. Although\npopulation-based CVD risk models have been used traditionally, these models\noften do not consider variations in lifestyles, socioeconomic conditions, or\ngenetic predispositions. Therefore, we aimed to develop machine learning models\nfor CVD detection using primary healthcare data, compare the performance of\ndifferent models, and identify the best models. We used data from the UK\nBiobank study, which included over 500,000 middle-aged participants from\ndifferent primary healthcare centers in the UK. Data collected at baseline\n(2006--2010) and during imaging visits after 2014 were used in this study.\nBaseline characteristics, including sex, age, and the Townsend Deprivation\nIndex, were included. Participants were classified as having CVD if they\nreported at least one of the following conditions: heart attack, angina,\nstroke, or high blood pressure. Cardiac imaging data such as electrocardiogram\nand echocardiography data, including left ventricular size and function,\ncardiac output, and stroke volume, were also used. We used 9 machine learning\nmodels (LSVM, RBFSVM, GP, DT, RF, NN, AdaBoost, NB, and QDA), which are\nexplainable and easily interpretable. We reported the accuracy, precision,\nrecall, and F-1 scores; confusion matrices; and area under the curve (AUC)\ncurves.\n", "date": "2024-07-23", "categories": []}
{"id": "2407.16999", "title": "SepsisLab: Early Sepsis Prediction with Uncertainty Quantification and\n  Active Sensing", "abstract": "  Sepsis is the leading cause of in-hospital mortality in the USA. Early sepsis\nonset prediction and diagnosis could significantly improve the survival of\nsepsis patients. Existing predictive models are usually trained on high-quality\ndata with few missing information, while missing values widely exist in\nreal-world clinical scenarios (especially in the first hours of admissions to\nthe hospital), which causes a significant decrease in accuracy and an increase\nin uncertainty for the predictive models. The common method to handle missing\nvalues is imputation, which replaces the unavailable variables with estimates\nfrom the observed data. The uncertainty of imputation results can be propagated\nto the sepsis prediction outputs, which have not been studied in existing works\non either sepsis prediction or uncertainty quantification. In this study, we\nfirst define such propagated uncertainty as the variance of prediction output\nand then introduce uncertainty propagation methods to quantify the propagated\nuncertainty. Moreover, for the potential high-risk patients with low confidence\ndue to limited observations, we propose a robust active sensing algorithm to\nincrease confidence by actively recommending clinicians to observe the most\ninformative variables. We validate the proposed models in both publicly\navailable data (i.e., MIMIC-III and AmsterdamUMCdb) and proprietary data in The\nOhio State University Wexner Medical Center (OSUWMC). The experimental results\nshow that the propagated uncertainty is dominant at the beginning of admissions\nto hospitals and the proposed algorithm outperforms state-of-the-art active\nsensing methods. Finally, we implement a SepsisLab system for early sepsis\nprediction and active sensing based on our pre-trained models. Clinicians and\npotential sepsis patients can benefit from the system in early prediction and\ndiagnosis of sepsis.\n", "date": "2024-07-24", "categories": []}
{"id": "2407.17230", "title": "Improving ICD coding using Chapter based Named Entities and Attentional\n  Models", "abstract": "  Recent advancements in natural language processing (NLP) have led to\nautomation in various domains. However, clinical NLP often relies on benchmark\ndatasets that may not reflect real-world scenarios accurately. Automatic ICD\ncoding, a vital NLP task, typically uses outdated and imbalanced datasets like\nMIMIC-III, with existing methods yielding micro-averaged F1 scores between 0.4\nand 0.7 due to many false positives. Our research introduces an enhanced\napproach to ICD coding that improves F1 scores by using chapter-based named\nentities and attentional models. This method categorizes discharge summaries\ninto ICD-9 Chapters and develops attentional models with chapter-specific data,\neliminating the need to consider external data for code identification. For\ncategorization, we use Chapter-IV to de-bias and influence key entities and\nweights without neural networks, creating accurate thresholds and providing\ninterpretability for human validation. Post-validation, we develop attentional\nmodels for three frequent and three non-frequent codes from Chapter-IV using\nBidirectional-Gated Recurrent Units (GRUs) with Attention and Transformer with\nMulti-head Attention architectures. The average Micro-F1 scores of 0.79 and\n0.81 from these models demonstrate significant performance improvements in ICD\ncoding.\n", "date": "2024-07-24", "categories": []}
{"id": "2401.14694", "title": "TA-RNN: an Attention-based Time-aware Recurrent Neural Network\n  Architecture for Electronic Health Records", "abstract": "  Motivation: Electronic Health Records (EHR) represent a comprehensive\nresource of a patient's medical history. EHR are essential for utilizing\nadvanced technologies such as deep learning (DL), enabling healthcare providers\nto analyze extensive data, extract valuable insights, and make precise and\ndata-driven clinical decisions. DL methods such as Recurrent Neural Networks\n(RNN) have been utilized to analyze EHR to model disease progression and\npredict diagnosis. However, these methods do not address some inherent\nirregularities in EHR data such as irregular time intervals between clinical\nvisits. Furthermore, most DL models are not interpretable. In this study, we\npropose two interpretable DL architectures based on RNN, namely Time-Aware RNN\n(TA-RNN) and TA-RNN-Autoencoder (TA-RNN-AE) to predict patient's clinical\noutcome in EHR at next visit and multiple visits ahead, respectively. To\nmitigate the impact of irregular time intervals, we propose incorporating time\nembedding of the elapsed times between visits. For interpretability, we propose\nemploying a dual-level attention mechanism that operates between visits and\nfeatures within each visit.\n  Results: The results of the experiments conducted on Alzheimer's Disease\nNeuroimaging Initiative (ADNI) and National Alzheimer's Coordinating Center\n(NACC) datasets indicated superior performance of proposed models for\npredicting Alzheimer's Disease (AD) compared to state-of-the-art and baseline\napproaches based on F2 and sensitivity. Additionally, TA-RNN showed superior\nperformance on Medical Information Mart for Intensive Care (MIMIC-III) dataset\nfor mortality prediction. In our ablation study, we observed enhanced\npredictive performance by incorporating time embedding and attention\nmechanisms. Finally, investigating attention weights helped identify\ninfluential visits and features in predictions.\n", "date": "2024-01-26", "categories": []}
{"id": "2407.15885", "title": "Improving Prediction of Need for Mechanical Ventilation using\n  Cross-Attention", "abstract": "  In the intensive care unit, the capability to predict the need for mechanical\nventilation (MV) facilitates more timely interventions to improve patient\noutcomes. Recent works have demonstrated good performance in this task\nutilizing machine learning models. This paper explores the novel application of\na deep learning model with multi-head attention (FFNN-MHA) to make more\naccurate MV predictions and reduce false positives by learning personalized\ncontextual information of individual patients. Utilizing the publicly available\nMIMIC-IV dataset, FFNN-MHA demonstrates an improvement of 0.0379 in AUC and a\n17.8\\% decrease in false positives compared to baseline models such as\nfeed-forward neural networks. Our results highlight the potential of the\nFFNN-MHA model as an effective tool for accurate prediction of the need for\nmechanical ventilation in critical care settings.\n", "date": "2024-07-21", "categories": []}
{"id": "2407.16091", "title": "Early Recognition of Parkinson's Disease Through Acoustic Analysis and\n  Machine Learning", "abstract": "  Parkinson's Disease (PD) is a progressive neurodegenerative disorder that\nsignificantly impacts both motor and non-motor functions, including speech.\nEarly and accurate recognition of PD through speech analysis can greatly\nenhance patient outcomes by enabling timely intervention. This paper provides a\ncomprehensive review of methods for PD recognition using speech data,\nhighlighting advances in machine learning and data-driven approaches. We\ndiscuss the process of data wrangling, including data collection, cleaning,\ntransformation, and exploratory data analysis, to prepare the dataset for\nmachine learning applications. Various classification algorithms are explored,\nincluding logistic regression, SVM, and neural networks, with and without\nfeature selection. Each method is evaluated based on accuracy, precision, and\ntraining time. Our findings indicate that specific acoustic features and\nadvanced machine-learning techniques can effectively differentiate between\nindividuals with PD and healthy controls. The study concludes with a comparison\nof the different models, identifying the most effective approaches for PD\nrecognition, and suggesting potential directions for future research.\n", "date": "2024-07-22", "categories": []}
{"id": "2407.16634", "title": "Knowledge-driven AI-generated data for accurate and interpretable breast\n  ultrasound diagnoses", "abstract": "  Data-driven deep learning models have shown great capabilities to assist\nradiologists in breast ultrasound (US) diagnoses. However, their effectiveness\nis limited by the long-tail distribution of training data, which leads to\ninaccuracies in rare cases. In this study, we address a long-standing challenge\nof improving the diagnostic model performance on rare cases using long-tailed\ndata. Specifically, we introduce a pipeline, TAILOR, that builds a\nknowledge-driven generative model to produce tailored synthetic data. The\ngenerative model, using 3,749 lesions as source data, can generate millions of\nbreast-US images, especially for error-prone rare cases. The generated data can\nbe further used to build a diagnostic model for accurate and interpretable\ndiagnoses. In the prospective external evaluation, our diagnostic model\noutperforms the average performance of nine radiologists by 33.5% in\nspecificity with the same sensitivity, improving their performance by providing\npredictions with an interpretable decision-making process. Moreover, on ductal\ncarcinoma in situ (DCIS), our diagnostic model outperforms all radiologists by\na large margin, with only 34 DCIS lesions in the source data. We believe that\nTAILOR can potentially be extended to various diseases and imaging modalities.\n", "date": "2024-07-23", "categories": []}
{"id": "2304.07679", "title": "Using Geographic Location-based Public Health Features in Survival\n  Analysis", "abstract": "  Time elapsed till an event of interest is often modeled using the survival\nanalysis methodology, which estimates a survival score based on the input\nfeatures. There is a resurgence of interest in developing more accurate\nprediction models for time-to-event prediction in personalized healthcare using\nmodern tools such as neural networks. Higher quality features and more frequent\nobservations improve the predictions for a patient, however, the impact of\nincluding a patient's geographic location-based public health statistics on\nindividual predictions has not been studied. This paper proposes a\ncomplementary improvement to survival analysis models by incorporating public\nhealth statistics in the input features. We show that including geographic\nlocation-based public health information results in a statistically significant\nimprovement in the concordance index evaluated on the Surveillance,\nEpidemiology, and End Results (SEER) dataset containing nationwide cancer\nincidence data. The improvement holds for both the standard Cox proportional\nhazards model and the state-of-the-art Deep Survival Machines model. Our\nresults indicate the utility of geographic location-based public health\nfeatures in survival analysis.\n", "date": "2023-04-16", "categories": []}
{"id": "2310.20204", "title": "General-Purpose Retrieval-Enhanced Medical Prediction Model Using\n  Near-Infinite History", "abstract": "  Machine learning (ML) has recently shown promising results in medical\npredictions using electronic health records (EHRs). However, since ML models\ntypically have a limited capability in terms of input sizes, selecting specific\nmedical events from EHRs for use as input is necessary. This selection process,\noften relying on expert opinion, can cause bottlenecks in development. We\npropose Retrieval-Enhanced Medical prediction model (REMed) to address such\nchallenges. REMed can essentially evaluate unlimited medical events, select the\nrelevant ones, and make predictions. This allows for an unrestricted input\nsize, eliminating the need for manual event selection. We verified these\nproperties through experiments involving 27 clinical prediction tasks across\nfour independent cohorts, where REMed outperformed the baselines. Notably, we\nfound that the preferences of REMed align closely with those of medical\nexperts. We expect our approach to significantly expedite the development of\nEHR prediction models by minimizing clinicians' need for manual involvement.\n", "date": "2023-10-31", "categories": []}
{"id": "2407.15006", "title": "Fair Machine Learning for Healthcare Requires Recognizing the\n  Intersectionality of Sociodemographic Factors, a Case Study", "abstract": "  As interest in implementing artificial intelligence (AI) in medical systems\ngrows, discussion continues on how to evaluate the fairness of these systems,\nor the disparities they may perpetuate. Socioeconomic status (SES) is commonly\nincluded in machine learning models to control for health inequities, with the\nunderlying assumption that increased SES is associated with better health. In\nthis work, we considered a large cohort of patients from the Mount Sinai Health\nSystem in New York City to investigate the effect of patient SES, race, and sex\non schizophrenia (SCZ) diagnosis rates via a logistic regression model. Within\nan intersectional framework, patient SES, race, and sex were found to have\nsignificant interactions. Our findings showed that increased SES is associated\nwith a higher probability of obtaining a SCZ diagnosis in Black Americans\n($\\beta=4.1\\times10^{-8}$, $SE=4.5\\times10^{-9}$, $p < 0.001$). Whereas high\nSES acts as a protective factor for SCZ diagnosis in White Americans\n($\\beta=-4.1\\times10^{-8}$, $SE=6.7\\times10^{-9}$, $p < 0.001$). Further\ninvestigation is needed to reliably explain and quantify health disparities.\nNevertheless, we advocate that building fair AI tools for the health care space\nrequires recognizing the intersectionality of sociodemographic factors.\n", "date": "2024-05-30", "categories": []}
{"id": "2407.15359", "title": "UF-HOBI at \"Discharge Me!\": A Hybrid Solution for Discharge Summary\n  Generation Through Prompt-based Tuning of GatorTronGPT Models", "abstract": "  Automatic generation of discharge summaries presents significant challenges\ndue to the length of clinical documentation, the dispersed nature of patient\ninformation, and the diverse terminology used in healthcare. This paper\npresents a hybrid solution for generating discharge summary sections as part of\nour participation in the \"Discharge Me!\" Challenge at the BioNLP 2024 Shared\nTask. We developed a two-stage generation method using both extractive and\nabstractive techniques, in which we first apply name entity recognition (NER)\nto extract key clinical concepts, which are then used as input for a\nprompt-tuning-based GatorTronGPT model to generate coherent text for two\nimportant sections including \"Brief Hospital Course\" and \"Discharge\nInstructions\". Our system was ranked 5th in this challenge, achieving an\noverall score of 0.284. The results demonstrate the effectiveness of our hybrid\nsolution in improving the quality of automated discharge section generation.\n", "date": "2024-07-22", "categories": []}
{"id": "2401.11447", "title": "Sequential Model for Predicting Patient Adherence in Subcutaneous\n  Immunotherapy for Allergic Rhinitis", "abstract": "  Objective: Subcutaneous Immunotherapy (SCIT) is the long-lasting causal\ntreatment of allergic rhinitis (AR). How to enhance the adherence of patients\nto maximize the benefit of allergen immunotherapy (AIT) plays a crucial role in\nthe management of AIT. This study aims to leverage novel machine learning\nmodels to precisely predict the risk of non-adherence of AR patients and\nrelated local symptom scores in three years SCIT.\n  Methods: The research develops and analyzes two models, sequential\nlatent-variable model (SLVM) of Stochastic Latent Actor-Critic (SLAC) and Long\nShort-Term Memory (LSTM) evaluating them based on scoring and adherence\nprediction capabilities.\n  Results: Excluding the biased samples at the first time step, the predictive\nadherence accuracy of the SLAC models is from 60\\% to 72\\%, and for LSTM\nmodels, it is 66\\% to 84\\%, varying according to the time steps. The range of\nRoot Mean Square Error (RMSE) for SLAC models is between 0.93 and 2.22, while\nfor LSTM models it is between 1.09 and 1.77. Notably, these RMSEs are\nsignificantly lower than the random prediction error of 4.55.\n  Conclusion: We creatively apply sequential models in the long-term management\nof SCIT with promising accuracy in the prediction of SCIT nonadherence in AR\npatients. While LSTM outperforms SLAC in adherence prediction, SLAC excels in\nscore prediction for patients undergoing SCIT for AR. The state-action-based\nSLAC adds flexibility, presenting a novel and effective approach for managing\nlong-term AIT.\n", "date": "2024-01-21", "categories": []}
{"id": "2403.20260", "title": "Prototype-based Interpretable Breast Cancer Prediction Models: Analysis\n  and Challenges", "abstract": "  Deep learning models have achieved high performance in medical applications,\nhowever, their adoption in clinical practice is hindered due to their black-box\nnature. Self-explainable models, like prototype-based models, can be especially\nbeneficial as they are interpretable by design. However, if the learnt\nprototypes are of low quality then the prototype-based models are as good as\nblack-box. Having high quality prototypes is a pre-requisite for a truly\ninterpretable model. In this work, we propose a prototype evaluation framework\nfor coherence (PEF-C) for quantitatively evaluating the quality of the\nprototypes based on domain knowledge. We show the use of PEF-C in the context\nof breast cancer prediction using mammography. Existing works on\nprototype-based models on breast cancer prediction using mammography have\nfocused on improving the classification performance of prototype-based models\ncompared to black-box models and have evaluated prototype quality through\nanecdotal evidence. We are the first to go beyond anecdotal evidence and\nevaluate the quality of the mammography prototypes systematically using our\nPEF-C. Specifically, we apply three state-of-the-art prototype-based models,\nProtoPNet, BRAIxProtoPNet++ and PIP-Net on mammography images for breast cancer\nprediction and evaluate these models w.r.t. i) classification performance, and\nii) quality of the prototypes, on three public datasets. Our results show that\nprototype-based models are competitive with black-box models in terms of\nclassification performance, and achieve a higher score in detecting ROIs.\nHowever, the quality of the prototypes are not yet sufficient and can be\nimproved in aspects of relevance, purity and learning a variety of prototypes.\nWe call the XAI community to systematically evaluate the quality of the\nprototypes to check their true usability in high stake decisions and improve\nsuch models further.\n", "date": "2024-03-29", "categories": []}
{"id": "2405.18518", "title": "Modeling Long Sequences in Bladder Cancer Recurrence: A Comparative\n  Evaluation of LSTM,Transformer,and Mamba", "abstract": "  Traditional survival analysis methods often struggle with complex\ntime-dependent data,failing to capture and interpret dynamic characteristics\nadequately.This study aims to evaluate the performance of three long-sequence\nmodels,LSTM,Transformer,and Mamba,in analyzing recurrence event data and\nintegrating them with the Cox proportional hazards model.This study integrates\nthe advantages of deep learning models for handling long-sequence data with the\nCox proportional hazards model to enhance the performance in analyzing\nrecurrent events with dynamic time information.Additionally,this study compares\nthe ability of different models to extract and utilize features from\ntime-dependent clinical recurrence data.The LSTM-Cox model outperformed both\nthe Transformer-Cox and Mamba-Cox models in prediction accuracy and model\nfit,achieving a Concordance index of up to 0.90 on the test set.Significant\npredictors of bladder cancer recurrence,such as treatment stop time,maximum\ntumor size at recurrence and recurrence frequency,were identified.The LSTM-Cox\nmodel aligned well with clinical outcomes,effectively distinguishing between\nhigh-risk and low-risk patient groups.This study demonstrates that the LSTM-Cox\nmodel is a robust and efficient method for recurrent data analysis and feature\nextraction,surpassing newer models like Transformer and Mamba.It offers a\npractical approach for integrating deep learning technologies into clinical\nrisk prediction systems,thereby improving patient management and treatment\noutcomes.\n", "date": "2024-05-28", "categories": []}
{"id": "2311.04215", "title": "Wearable data from subjects playing Super Mario, sitting university\n  exams, or performing physical exercise help detect acute mood episodes via\n  self-supervised learning", "abstract": "  Personal sensing, leveraging data passively and near-continuously collected\nwith wearables from patients in their ecological environment, is a promising\nparadigm to monitor mood disorders (MDs), a major determinant of worldwide\ndisease burden. However, collecting and annotating wearable data is very\nresource-intensive. Studies of this kind can thus typically afford to recruit\nonly a couple dozens of patients. This constitutes one of the major obstacles\nto applying modern supervised machine learning techniques to MDs detection. In\nthis paper, we overcome this data bottleneck and advance the detection of MDs\nacute episode vs stable state from wearables data on the back of recent\nadvances in self-supervised learning (SSL). This leverages unlabelled data to\nlearn representations during pre-training, subsequently exploited for a\nsupervised task. First, we collected open-access datasets recording with an\nEmpatica E4 spanning different, unrelated to MD monitoring, personal sensing\ntasks -- from emotion recognition in Super Mario players to stress detection in\nundergraduates -- and devised a pre-processing pipeline performing on-/off-body\ndetection, sleep-wake detection, segmentation, and (optionally) feature\nextraction. With 161 E4-recorded subjects, we introduce E4SelfLearning, the\nlargest to date open access collection, and its pre-processing pipeline.\nSecond, we show that SSL confidently outperforms fully-supervised pipelines\nusing either our novel E4-tailored Transformer architecture (E4mer) or\nclassical baseline XGBoost: 81.23% against 75.35% (E4mer) and 72.02% (XGBoost)\ncorrectly classified recording segments from 64 (half acute, half stable)\npatients. Lastly, we illustrate that SSL performance is strongly associated\nwith the specific surrogate task employed for pre-training as well as with\nunlabelled data availability.\n", "date": "2023-11-07", "categories": []}
{"id": "2407.13047", "title": "A Novel GAN Approach to Augment Limited Tabular Data for Short-Term\n  Substance Use Prediction", "abstract": "  Substance use is a global issue that negatively impacts millions of persons\nwho use drugs (PWUDs). In practice, identifying vulnerable PWUDs for efficient\nallocation of appropriate resources is challenging due to their complex use\npatterns (e.g., their tendency to change usage within months) and the high\nacquisition costs for collecting PWUD-focused substance use data. Thus, there\nhas been a paucity of machine learning models for accurately predicting\nshort-term substance use behaviors of PWUDs. In this paper, using longitudinal\nsurvey data of 258 PWUDs in the U.S. Great Plains collected by our team, we\ndesign a novel GAN that deals with high-dimensional low-sample-size tabular\ndata and survey skip logic to augment existing data to improve classification\nmodels' prediction on (A) whether the PWUDs would increase usage and (B) at\nwhich ordinal frequency they would use a particular drug within the next 12\nmonths. Our evaluation results show that, when trained on augmented data from\nour proposed GAN, the classification models improve their predictive\nperformance (AUROC) by up to 13.4% in Problem (A) and 15.8% in Problem (B) for\nusage of marijuana, meth, amphetamines, and cocaine, which outperform\nstate-of-the-art generative models.\n", "date": "2024-07-17", "categories": []}
{"id": "2311.11862", "title": "Establishing Central Sensitization Inventory Cut-off Values in patients\n  with Chronic Low Back Pain by Unsupervised Machine Learning", "abstract": "  Human Assumed Central Sensitization is involved in the development and\nmaintenance of chronic low back pain (CLBP). The Central Sensitization\nInventory (CSI) was developed to evaluate the presence of HACS, with a cut-off\nvalue of 40/100 based on patients with chronic pain. However, various factors\nincluding pain conditions (e.g., CLBP), and gender may influence this cut-off\nvalue. For chronic pain condition such as CLBP, unsupervised clustering\napproaches can take these factors into consideration and automatically learn\nthe HACS-related patterns. Therefore, this study aimed to determine the cut-off\nvalues for a Dutch-speaking population with CLBP, considering the total group\nand stratified by gender based on unsupervised machine learning. In this study,\nquestionnaire data covering pain, physical, and psychological aspects were\ncollected from patients with CLBP and aged-matched pain-free adults (referred\nto as healthy controls, HC). Four clustering approaches were applied to\nidentify HACS-related clusters based on the questionnaire data and gender. The\nclustering performance was assessed using internal and external indicators.\nSubsequently, receiver operating characteristic analysis was conducted on the\nbest clustering results to determine the optimal cut-off values. The study\nincluded 151 subjects, consisting of 63 HCs and 88 patients with CLBP.\nHierarchical clustering yielded the best results, identifying three clusters:\nhealthy group, CLBP with low HACS level, and CLBP with high HACS level groups.\nBased on the low HACS levels group (including HC and CLBP with low HACS level)\nand high HACS level group, the cut-off value for the overall groups were 35, 34\nfor females, and 35 for. The findings suggest that the optimal cut-off values\nfor CLBP is 35. The gender-related cut-off values should be interpreted with\ncaution due to the unbalanced gender distribution in the sample.\n", "date": "2023-11-20", "categories": []}
{"id": "2407.12012", "title": "Specific language impairment (SLI) detection pipeline from\n  transcriptions of spontaneous narratives", "abstract": "  Specific Language Impairment (SLI) is a disorder that affects communication\nand can affect both comprehension and expression. This study focuses on\neffectively detecting SLI in children using transcripts of spontaneous\nnarratives from 1063 interviews. A three-stage cascading pipeline was proposed\nf. In the first stage, feature extraction and dimensionality reduction of the\ndata are performed using the Random Forest (RF) and Spearman correlation\nmethods. In the second stage, the most predictive variables from the first\nstage are estimated using logistic regression, which is used in the last stage\nto detect SLI in children from transcripts of spontaneous narratives using a\nnearest neighbor classifier. The results revealed an accuracy of 97.13% in\nidentifying SLI, highlighting aspects such as the length of the responses, the\nquality of their utterances, and the complexity of the language. This new\napproach, framed in natural language processing, offers significant benefits to\nthe field of SLI detection by avoiding complex subjective variables and\nfocusing on quantitative metrics directly related to the child's performance.\n", "date": "2024-06-25", "categories": []}
{"id": "2407.12309", "title": "MEDFuse: Multimodal EHR Data Fusion with Masked Lab-Test Modeling and\n  Large Language Models", "abstract": "  Electronic health records (EHRs) are multimodal by nature, consisting of\nstructured tabular features like lab tests and unstructured clinical notes. In\nreal-life clinical practice, doctors use complementary multimodal EHR data\nsources to get a clearer picture of patients' health and support clinical\ndecision-making. However, most EHR predictive models do not reflect these\nprocedures, as they either focus on a single modality or overlook the\ninter-modality interactions/redundancy. In this work, we propose MEDFuse, a\nMultimodal EHR Data Fusion framework that incorporates masked lab-test modeling\nand large language models (LLMs) to effectively integrate structured and\nunstructured medical data. MEDFuse leverages multimodal embeddings extracted\nfrom two sources: LLMs fine-tuned on free clinical text and masked tabular\ntransformers trained on structured lab test results. We design a disentangled\ntransformer module, optimized by a mutual information loss to 1) decouple\nmodality-specific and modality-shared information and 2) extract useful joint\nrepresentation from the noise and redundancy present in clinical notes. Through\ncomprehensive validation on the public MIMIC-III dataset and the in-house FEMH\ndataset, MEDFuse demonstrates great potential in advancing clinical\npredictions, achieving over 90% F1 score in the 10-disease multi-label\nclassification task.\n", "date": "2024-07-17", "categories": []}
{"id": "2407.11012", "title": "Exploring Gender-Specific Speech Patterns in Automatic Suicide Risk\n  Assessment", "abstract": "  In emergency medicine, timely intervention for patients at risk of suicide is\noften hindered by delayed access to specialised psychiatric care. To bridge\nthis gap, we introduce a speech-based approach for automatic suicide risk\nassessment. Our study involves a novel dataset comprising speech recordings of\n20 patients who read neutral texts. We extract four speech representations\nencompassing interpretable and deep features. Further, we explore the impact of\ngender-based modelling and phrase-level normalisation. By applying\ngender-exclusive modelling, features extracted from an emotion fine-tuned\nwav2vec2.0 model can be utilised to discriminate high- from low- suicide risk\nwith a balanced accuracy of 81%. Finally, our analysis reveals a discrepancy in\nthe relationship of speech characteristics and suicide risk between female and\nmale subjects. For men in our dataset, suicide risk increases together with\nagitation while voice characteristics of female subjects point the other way.\n", "date": "2024-06-26", "categories": []}
{"id": "2407.11096", "title": "Static and multivariate-temporal attentive fusion transformer for\n  readmission risk prediction", "abstract": "  Background: Accurate short-term readmission prediction of ICU patients is\nsignificant in improving the efficiency of resource assignment by assisting\nphysicians in making discharge decisions. Clinically, both individual static\nstatic and multivariate temporal data collected from ICU monitors play critical\nroles in short-term readmission prediction. Informative static and multivariate\ntemporal feature representation capturing and fusion present challenges for\naccurate readmission prediction. Methods:We propose a novel static and\nmultivariate-temporal attentive fusion transformer (SMTAFormer) to predict\nshort-term readmission of ICU patients by fully leveraging the potential of\ndemographic and dynamic temporal data. In SMTAFormer, we first apply an MLP\nnetwork and a temporal transformer network to learn useful static and temporal\nfeature representations, respectively. Then, the well-designed static and\nmultivariate temporal feature fusion module is applied to fuse static and\ntemporal feature representations by modeling intra-correlation among\nmultivariate temporal features and constructing inter-correlation between\nstatic and multivariate temporal features. Results: We construct a readmission\nrisk assessment (RRA) dataset based on the MIMIC-III dataset. The extensive\nexperiments show that SMTAFormer outperforms advanced methods, in which the\naccuracy of our proposed method is up to 86.6%, and the area under the receiver\noperating characteristic curve (AUC) is up to 0.717. Conclusion: Our proposed\nSMTAFormer can efficiently capture and fuse static and multivariate temporal\nfeature representations. The results show that SMTAFormer significantly\nimproves the short-term readmission prediction performance of ICU patients\nthrough comparisons to strong baselines.\n", "date": "2024-07-15", "categories": []}
{"id": "2407.11529", "title": "Cross-Phase Mutual Learning Framework for Pulmonary Embolism\n  Identification on Non-Contrast CT Scans", "abstract": "  Pulmonary embolism (PE) is a life-threatening condition where rapid and\naccurate diagnosis is imperative yet difficult due to predominantly atypical\nsymptomatology. Computed tomography pulmonary angiography (CTPA) is\nacknowledged as the gold standard imaging tool in clinics, yet it can be\ncontraindicated for emergency department (ED) patients and represents an\nonerous procedure, thus necessitating PE identification through non-contrast CT\n(NCT) scans. In this work, we explore the feasibility of applying a\ndeep-learning approach to NCT scans for PE identification. We propose a novel\nCross-Phase Mutual learNing framework (CPMN) that fosters knowledge transfer\nfrom CTPA to NCT, while concurrently conducting embolism segmentation and\nabnormality classification in a multi-task manner. The proposed CPMN leverages\nthe Inter-Feature Alignment (IFA) strategy that enhances spatial contiguity and\nmutual learning between the dual-pathway network, while the Intra-Feature\nDiscrepancy (IFD) strategy can facilitate precise segmentation of PE against\ncomplex backgrounds for single-pathway networks. For a comprehensive assessment\nof the proposed approach, a large-scale dual-phase dataset containing 334 PE\npatients and 1,105 normal subjects has been established. Experimental results\ndemonstrate that CPMN achieves the leading identification performance, which is\n95.4\\% and 99.6\\% in patient-level sensitivity and specificity on NCT scans,\nindicating the potential of our approach as an economical, accessible, and\nprecise tool for PE identification in clinical practice.\n", "date": "2024-07-16", "categories": []}
{"id": "2407.09515", "title": "Rethinking Knee Osteoarthritis Severity Grading: A Few Shot\n  Self-Supervised Contrastive Learning Approach", "abstract": "  Knee Osteoarthritis (OA) is a debilitating disease affecting over 250 million\npeople worldwide. Currently, radiologists grade the severity of OA on an\nordinal scale from zero to four using the Kellgren-Lawrence (KL) system. Recent\nstudies have raised concern in relation to the subjectivity of the KL grading\nsystem, highlighting the requirement for an automated system, while also\nindicating that five ordinal classes may not be the most appropriate approach\nfor assessing OA severity. This work presents preliminary results of an\nautomated system with a continuous grading scale. This system, namely\nSS-FewSOME, uses self-supervised pre-training to learn robust representations\nof the features of healthy knee X-rays. It then assesses the OA severity by the\nX-rays' distance to the normal representation space. SS-FewSOME initially\ntrains on only 'few' examples of healthy knee X-rays, thus reducing the\nbarriers to clinical implementation by eliminating the need for large training\nsets and costly expert annotations that existing automated systems require. The\nwork reports promising initial results, obtaining a positive Spearman Rank\nCorrelation Coefficient of 0.43, having had access to only 30 ground truth\nlabels at training time.\n", "date": "2024-06-18", "categories": []}
{"id": "2407.09688", "title": "Large Language Models for Integrating Social Determinant of Health Data:\n  A Case Study on Heart Failure 30-Day Readmission Prediction", "abstract": "  Social determinants of health (SDOH) $-$ the myriad of circumstances in which\npeople live, grow, and age $-$ play an important role in health outcomes.\nHowever, existing outcome prediction models often only use proxies of SDOH as\nfeatures. Recent open data initiatives present an opportunity to construct a\nmore comprehensive view of SDOH, but manually integrating the most relevant\ndata for individual patients becomes increasingly challenging as the volume and\ndiversity of public SDOH data grows. Large language models (LLMs) have shown\npromise at automatically annotating structured data. Here, we conduct an\nend-to-end case study evaluating the feasibility of using LLMs to integrate\nSDOH data, and the utility of these SDOH features for clinical prediction. We\nfirst manually label 700+ variables from two publicly-accessible SDOH data\nsources to one of five semantic SDOH categories. Then, we benchmark performance\nof 9 open-source LLMs on this classification task. Finally, we train ML models\nto predict 30-day hospital readmission among 39k heart failure (HF) patients,\nand we compare the prediction performance of the categorized SDOH variables\nwith standard clinical variables. Additionally, we investigate the impact of\nfew-shot LLM prompting on LLM annotation performance, and perform a metadata\nablation study on prompts to evaluate which information helps LLMs accurately\nannotate these variables. We find that some open-source LLMs can effectively,\naccurately annotate SDOH variables with zero-shot prompting without the need\nfor fine-tuning. Crucially, when combined with standard clinical features, the\nLLM-annotated Neighborhood and Built Environment subset of the SDOH variables\nshows the best performance predicting 30-day readmission of HF patients.\n", "date": "2024-07-12", "categories": []}
{"id": "2407.10630", "title": "Brain Tumor Classification From MRI Images Using Machine Learning", "abstract": "  Brain tumor is a life-threatening problem and hampers the normal functioning\nof the human body. The average five-year relative survival rate for malignant\nbrain tumors is 35.6 percent. For proper diagnosis and efficient treatment\nplanning, it is necessary to detect the brain tumor in early stages. Due to\nadvancement in medical imaging technology, the brain images are taken in\ndifferent modalities. The ability to extract relevant characteristics from\nmagnetic resonance imaging (MRI) scans is a crucial step for brain tumor\nclassifiers. Several studies have proposed various strategies to extract\nrelevant features from different modalities of MRI to predict the growth of\nabnormal tumors. Most techniques used conventional methods of image processing\nfor feature extraction and machine learning for classification. More recently,\nthe use of deep learning algorithms in medical imaging has resulted in\nsignificant improvements in the classification and diagnosis of brain tumors.\nSince tumors are located at different regions of the brain, localizing the\ntumor and classifying it to a particular category is a challenging task. The\nobjective of this project is to develop a predictive system for brain tumor\ndetection using machine learning(ensembling).\n", "date": "2024-07-15", "categories": []}
{"id": "2311.00565", "title": "Detecting Visual Cues in the Intensive Care Unit and Association with\n  Patient Clinical Status", "abstract": "  Intensive Care Units (ICU) provide close supervision and continuous care to\npatients with life-threatening conditions. However, continuous patient\nassessment in the ICU is still limited due to time constraints and the workload\non healthcare providers. Existing patient assessments in the ICU such as pain\nor mobility assessment are mostly sporadic and administered manually, thus\nintroducing the potential for human errors. Developing Artificial intelligence\n(AI) tools that can augment human assessments in the ICU can be beneficial for\nproviding more objective and granular monitoring capabilities. For example,\ncapturing the variations in a patient's facial cues related to pain or\nagitation can help in adjusting pain-related medications or detecting\nagitation-inducing conditions such as delirium. Additionally, subtle changes in\nvisual cues during or prior to adverse clinical events could potentially aid in\ncontinuous patient monitoring when combined with high-resolution physiological\nsignals and Electronic Health Record (EHR) data. In this paper, we examined the\nassociation between visual cues and patient condition including acuity status,\nacute brain dysfunction, and pain. We leveraged our AU-ICU dataset with 107,064\nframes collected in the ICU annotated with facial action units (AUs) labels by\ntrained annotators. We developed a new \"masked loss computation\" technique that\naddresses the data imbalance problem by maximizing data resource utilization.\nWe trained the model using our AU-ICU dataset in conjunction with three\nexternal datasets to detect 18 AUs. The SWIN Transformer model achieved 0.57\nmean F1-score and 0.89 mean accuracy on the test set. Additionally, we\nperformed AU inference on 634,054 frames to evaluate the association between\nfacial AUs and clinically important patient conditions such as acuity status,\nacute brain dysfunction, and pain.\n", "date": "2023-11-01", "categories": []}
{"id": "2407.08902", "title": "Application of Artificial Intelligence in Supporting Healthcare\n  Professionals and Caregivers in Treatment of Autistic Children", "abstract": "  Autism Spectrum Disorder (ASD) represents a multifaceted neurodevelopmental\ncondition marked by difficulties in social interaction, communication\nimpediments, and repetitive behaviors. Despite progress in understanding ASD,\nits diagnosis and treatment continue to pose significant challenges due to the\nvariability in symptomatology and the necessity for multidisciplinary care\napproaches. This paper investigates the potential of Artificial Intelligence\n(AI) to augment the capabilities of healthcare professionals and caregivers in\nmanaging ASD. We have developed a sophisticated algorithm designed to analyze\nfacial and bodily expressions during daily activities of both autistic and\nnon-autistic children, leading to the development of a powerful deep\nlearning-based autism detection system. Our study demonstrated that AI models,\nspecifically the Xception and ResNet50V2 architectures, achieved high accuracy\nin diagnosing Autism Spectrum Disorder (ASD). This research highlights the\ntransformative potential of AI in improving the diagnosis, treatment, and\ncomprehensive management of ASD. Our study revealed that AI models, notably the\nXception and ResNet50V2 architectures, demonstrated high accuracy in diagnosing\nASD.\n", "date": "2024-07-12", "categories": []}
{"id": "2407.09373", "title": "Towards Personalised Patient Risk Prediction Using Temporal Hospital\n  Data Trajectories", "abstract": "  Quantifying a patient's health status provides clinicians with insight into\npatient risk, and the ability to better triage and manage resources. Early\nWarning Scores (EWS) are widely deployed to measure overall health status, and\nrisk of adverse outcomes, in hospital patients. However, current EWS are\nlimited both by their lack of personalisation and use of static observations.\nWe propose a pipeline that groups intensive care unit patients by the\ntrajectories of observations data throughout their stay as a basis for the\ndevelopment of personalised risk predictions. Feature importance is considered\nto provide model explainability. Using the MIMIC-IV dataset, six clusters were\nidentified, capturing differences in disease codes, observations, lengths of\nadmissions and outcomes. Applying the pipeline to data from just the first four\nhours of each ICU stay assigns the majority of patients to the same cluster as\nwhen the entire stay duration is considered. In-hospital mortality prediction\nmodels trained on individual clusters had higher F1 score performance in five\nof the six clusters when compared against the unclustered patient cohort. The\npipeline could form the basis of a clinical decision support tool, working to\nimprove the clinical characterisation of risk groups and the early detection of\npatient deterioration.\n", "date": "2024-07-12", "categories": []}
{"id": "2407.07997", "title": "ICD Codes are Insufficient to Create Datasets for Machine Learning: An\n  Evaluation Using All of Us Data for Coccidioidomycosis and Myocardial\n  Infarction", "abstract": "  In medicine, machine learning (ML) datasets are often built using the\nInternational Classification of Diseases (ICD) codes. As new models are being\ndeveloped, there is a need for larger datasets. However, ICD codes are intended\nfor billing. We aim to determine how suitable ICD codes are for creating\ndatasets to train ML models. We focused on a rare and common disease using the\nAll of Us database. First, we compared the patient cohort created using ICD\ncodes for Valley fever (coccidioidomycosis, CM) with that identified via\nserological confirmation. Second, we compared two similarly created patient\ncohorts for myocardial infarction (MI) patients. We identified significant\ndiscrepancies between these two groups, and the patient overlap was small. The\nCM cohort had 811 patients in the ICD-10 group, 619 patients in the\npositive-serology group, and 24 with both. The MI cohort had 14,875 patients in\nthe ICD-10 group, 23,598 in the MI laboratory-confirmed group, and 6,531 in\nboth. Demographics, rates of disease symptoms, and other clinical data varied\nacross our case study cohorts.\n", "date": "2024-07-10", "categories": []}
{"id": "2407.08003", "title": "Machine Learning for ALSFRS-R Score Prediction: Making Sense of the\n  Sensor Data", "abstract": "  Amyotrophic Lateral Sclerosis (ALS) is characterized as a rapidly progressive\nneurodegenerative disease that presents individuals with limited treatment\noptions in the realm of medical interventions and therapies. The disease\nshowcases a diverse range of onset patterns and progression trajectories,\nemphasizing the critical importance of early detection of functional decline to\nenable tailored care strategies and timely therapeutic interventions. The\npresent investigation, spearheaded by the iDPP@CLEF 2024 challenge, focuses on\nutilizing sensor-derived data obtained through an app. This data is used to\nconstruct various machine learning models specifically designed to forecast the\nadvancement of the ALS Functional Rating Scale-Revised (ALSFRS-R) score,\nleveraging the dataset provided by the organizers. In our analysis, multiple\npredictive models were evaluated to determine their efficacy in handling ALS\nsensor data. The temporal aspect of the sensor data was compressed and\namalgamated using statistical methods, thereby augmenting the interpretability\nand applicability of the gathered information for predictive modeling\nobjectives. The models that demonstrated optimal performance were a naive\nbaseline and ElasticNet regression. The naive model achieved a Mean Absolute\nError (MAE) of 0.20 and a Root Mean Square Error (RMSE) of 0.49, slightly\noutperforming the ElasticNet model, which recorded an MAE of 0.22 and an RMSE\nof 0.50. Our comparative analysis suggests that while the naive approach\nyielded marginally better predictive accuracy, the ElasticNet model provides a\nrobust framework for understanding feature contributions.\n", "date": "2024-07-10", "categories": []}
{"id": "2407.08107", "title": "Advanced Meta-Ensemble Machine Learning Models for Early and Accurate\n  Sepsis Prediction to Improve Patient Outcomes", "abstract": "  Sepsis, a critical condition from the body's response to infection, poses a\nmajor global health crisis affecting all age groups. Timely detection and\nintervention are crucial for reducing healthcare expenses and improving patient\noutcomes. This paper examines the limitations of traditional sepsis screening\ntools like Systemic Inflammatory Response Syndrome, Modified Early Warning\nScore, and Quick Sequential Organ Failure Assessment, highlighting the need for\nadvanced approaches. We propose using machine learning techniques - Random\nForest, Extreme Gradient Boosting, and Decision Tree models - to predict sepsis\nonset. Our study evaluates these models individually and in a combined\nmeta-ensemble approach using key metrics such as Accuracy, Precision, Recall,\nF1 score, and Area Under the Receiver Operating Characteristic Curve. Results\nshow that the meta-ensemble model outperforms individual models, achieving an\nAUC-ROC score of 0.96, indicating superior predictive accuracy for early sepsis\ndetection. The Random Forest model also performs well with an AUC-ROC score of\n0.95, while Extreme Gradient Boosting and Decision Tree models score 0.94 and\n0.90, respectively.\n", "date": "2024-07-11", "categories": []}
{"id": "2407.08289", "title": "Predicting Heart Failure with Attention Learning Techniques Utilizing\n  Cardiovascular Data", "abstract": "  Cardiovascular diseases (CVDs) encompass a group of disorders affecting the\nheart and blood vessels, including conditions such as coronary artery disease,\nheart failure, stroke, and hypertension. In cardiovascular diseases, heart\nfailure is one of the main causes of death and also long-term suffering in\npatients worldwide. Prediction is one of the risk factors that is highly\nvaluable for treatment and intervention to minimize heart failure. In this\nwork, an attention learning-based heart failure prediction approach is proposed\non EHR(electronic health record) cardiovascular data such as ejection fraction\nand serum creatinine. Moreover, different optimizers with various learning rate\napproaches are applied to fine-tune the proposed approach. Serum creatinine and\nejection fraction are the two most important features to predict the patient's\nheart failure. The computational result shows that the RMSProp optimizer with\n0.001 learning rate has a better prediction based on serum creatinine. On the\nother hand, the combination of SGD optimizer with 0.01 learning rate exhibits\noptimum performance based on ejection fraction features. Overall, the proposed\nattention learning-based approach performs very efficiently in predicting heart\nfailure compared to the existing state-of-the-art such as LSTM approach.\n", "date": "2024-07-11", "categories": []}
{"id": "2403.13078", "title": "HuLP: Human-in-the-Loop for Prognosis", "abstract": "  This paper introduces HuLP, a Human-in-the-Loop for Prognosis model designed\nto enhance the reliability and interpretability of prognostic models in\nclinical contexts, especially when faced with the complexities of missing\ncovariates and outcomes. HuLP offers an innovative approach that enables human\nexpert intervention, empowering clinicians to interact with and correct models'\npredictions, thus fostering collaboration between humans and AI models to\nproduce more accurate prognosis. Additionally, HuLP addresses the challenges of\nmissing data by utilizing neural networks and providing a tailored methodology\nthat effectively handles missing data. Traditional methods often struggle to\ncapture the nuanced variations within patient populations, leading to\ncompromised prognostic predictions. HuLP imputes missing covariates based on\nimaging features, aligning more closely with clinician workflows and enhancing\nreliability. We conduct our experiments on two real-world, publicly available\nmedical datasets to demonstrate the superiority and competitiveness of HuLP.\n", "date": "2024-03-19", "categories": []}
{"id": "2405.11622", "title": "Continuous Predictive Modeling of Clinical Notes and ICD Codes in\n  Patient Health Records", "abstract": "  Electronic Health Records (EHR) serve as a valuable source of patient\ninformation, offering insights into medical histories, treatments, and\noutcomes. Previous research has developed systems for detecting applicable ICD\ncodes that should be assigned while writing a given EHR document, mainly\nfocusing on discharge summaries written at the end of a hospital stay. In this\nwork, we investigate the potential of predicting these codes for the whole\npatient stay at different time points during their stay, even before they are\nofficially assigned by clinicians. The development of methods to predict\ndiagnoses and treatments earlier in advance could open opportunities for\npredictive medicine, such as identifying disease risks sooner, suggesting\ntreatments, and optimizing resource allocation. Our experiments show that\npredictions regarding final ICD codes can be made already two days after\nadmission and we propose a custom model that improves performance on this early\nprediction task.\n", "date": "2024-05-19", "categories": []}
{"id": "2407.04953", "title": "Effective-LDAM: An Effective Loss Function To Mitigate Data Imbalance\n  for Robust Chest X-Ray Disease Classification", "abstract": "  Deep Learning (DL) approaches have gained prominence in medical imaging for\ndisease diagnosis. Chest X-ray (CXR) classification has emerged as an effective\nmethod for detecting various diseases. Among these methodologies, Chest X-ray\n(CXR) classification has proven to be an effective approach for detecting and\nanalyzing various diseases. However, the reliable performance of DL\nclassification algorithms is dependent upon access to large and balanced\ndatasets, which pose challenges in medical imaging due to the impracticality of\nacquiring sufficient data for every disease category. To tackle this problem,\nwe propose an algorithmic-centric approach called Effective-Label Distribution\nAware Margin (E-LDAM), which modifies the margin of the widely adopted Label\nDistribution Aware Margin (LDAM) loss function using an effective number of\nsamples in each class. Experimental evaluations on the COVIDx CXR dataset focus\non Normal, Pneumonia, and COVID-19 classification. The experimental results\ndemonstrate the effectiveness of the proposed E-LDAM approach, achieving a\nremarkable recall score of 97.81% for the minority class (COVID-19) in CXR\nimage prediction. Furthermore, the overall accuracy of the three-class\nclassification task attains an impressive level of 95.26%.\n", "date": "2024-07-06", "categories": []}
{"id": "2310.12574", "title": "A reproducible 3D convolutional neural network with dual attention\n  module (3D-DAM) for Alzheimer's disease classification", "abstract": "  Alzheimer's disease is one of the most common types of neurodegenerative\ndisease, characterized by the accumulation of amyloid-beta plaque and tau\ntangles. Recently, deep learning approaches have shown promise in Alzheimer's\ndisease diagnosis. In this study, we propose a reproducible model that utilizes\na 3D convolutional neural network with a dual attention module for Alzheimer's\ndisease classification. We trained the model in the ADNI database and verified\nthe generalizability of our method in two independent datasets (AIBL and\nOASIS1). Our method achieved state-of-the-art classification performance, with\nan accuracy of 91.94% for MCI progression classification and 96.30% for\nAlzheimer's disease classification on the ADNI dataset. Furthermore, the model\ndemonstrated good generalizability, achieving an accuracy of 86.37% on the AIBL\ndataset and 83.42% on the OASIS1 dataset. These results indicate that our\nproposed approach has competitive performance and generalizability when\ncompared to recent studies in the field.\n", "date": "2023-10-19", "categories": []}
{"id": "2311.12560", "title": "Benchmarking bias: Expanding clinical AI model card to incorporate bias\n  reporting of social and non-social factors", "abstract": "  Clinical AI model reporting cards should be expanded to incorporate a broad\nbias reporting of both social and non-social factors. Non-social factors\nconsider the role of other factors, such as disease dependent, anatomic, or\ninstrument factors on AI model bias, which are essential to ensure safe\ndeployment.\n", "date": "2023-11-21", "categories": []}
{"id": "2401.10733", "title": "Dynamic Q&A of Clinical Documents with Large Language Models", "abstract": "  Electronic health records (EHRs) house crucial patient data in clinical\nnotes. As these notes grow in volume and complexity, manual extraction becomes\nchallenging. This work introduces a natural language interface using large\nlanguage models (LLMs) for dynamic question-answering on clinical notes. Our\nchatbot, powered by Langchain and transformer-based LLMs, allows users to query\nin natural language, receiving relevant answers from clinical notes.\nExperiments, utilizing various embedding models and advanced LLMs, show Wizard\nVicuna's superior accuracy, albeit with high compute demands. Model\noptimization, including weight quantization, improves latency by approximately\n48 times. Promising results indicate potential, yet challenges such as model\nhallucinations and limited diverse medical case evaluations remain. Addressing\nthese gaps is crucial for unlocking the value in clinical notes and advancing\nAI-driven clinical decision-making.\n", "date": "2024-01-19", "categories": []}
{"id": "2403.06659", "title": "Zero-Shot ECG Classification with Multimodal Learning and Test-time\n  Clinical Knowledge Enhancement", "abstract": "  Electrocardiograms (ECGs) are non-invasive diagnostic tools crucial for\ndetecting cardiac arrhythmic diseases in clinical practice. While ECG\nSelf-supervised Learning (eSSL) methods show promise in representation learning\nfrom unannotated ECG data, they often overlook the clinical knowledge that can\nbe found in reports. This oversight and the requirement for annotated samples\nfor downstream tasks limit eSSL's versatility. In this work, we address these\nissues with the Multimodal ECG Representation Learning (MERL}) framework.\nThrough multimodal learning on ECG records and associated reports, MERL is\ncapable of performing zero-shot ECG classification with text prompts,\neliminating the need for training data in downstream tasks. At test time, we\npropose the Clinical Knowledge Enhanced Prompt Engineering (CKEPE) approach,\nwhich uses Large Language Models (LLMs) to exploit external expert-verified\nclinical knowledge databases, generating more descriptive prompts and reducing\nhallucinations in LLM-generated content to boost zero-shot classification.\nBased on MERL, we perform the first benchmark across six public ECG datasets,\nshowing the superior performance of MERL compared against eSSL methods.\nNotably, MERL achieves an average AUC score of 75.2% in zero-shot\nclassification (without training data), 3.2% higher than linear probed eSSL\nmethods with 10\\% annotated training data, averaged across all six datasets.\nCode and models are available at https://github.com/cheliu-computation/MERL\n", "date": "2024-03-11", "categories": []}
{"id": "2407.00438", "title": "AI Age Discrepancy: A Novel Parameter for Frailty Assessment in Kidney\n  Tumor Patients", "abstract": "  Kidney cancer is a global health concern, and accurate assessment of patient\nfrailty is crucial for optimizing surgical outcomes. This paper introduces AI\nAge Discrepancy, a novel metric derived from machine learning analysis of\npreoperative abdominal CT scans, as a potential indicator of frailty and\npostoperative risk in kidney cancer patients. This retrospective study of 599\npatients from the 2023 Kidney Tumor Segmentation (KiTS) challenge dataset found\nthat a higher AI Age Discrepancy is significantly associated with longer\nhospital stays and lower overall survival rates, independent of established\nfactors. This suggests that AI Age Discrepancy may provide valuable insights\ninto patient frailty and could thus inform clinical decision-making in kidney\ncancer treatment.\n", "date": "2024-06-29", "categories": []}
{"id": "2403.18631", "title": "First Experiences with the Identification of People at Risk for Diabetes\n  in Argentina using Machine Learning Techniques", "abstract": "  Detecting Type 2 Diabetes (T2D) and Prediabetes (PD) is a real challenge for\nmedicine due to the absence of pathogenic symptoms and the lack of known\nassociated risk factors. Even though some proposals for machine learning models\nenable the identification of people at risk, the nature of the condition makes\nit so that a model suitable for one population may not necessarily be suitable\nfor another. In this article, the development and assessment of predictive\nmodels to identify people at risk for T2D and PD specifically in Argentina are\ndiscussed. First, the database was thoroughly preprocessed and three specific\ndatasets were generated considering a compromise between the number of records\nand the amount of available variables. After applying 5 different\nclassification models, the results obtained show that a very good performance\nwas observed for two datasets with some of these models. In particular, RF, DT,\nand ANN demonstrated great classification power, with good values for the\nmetrics under consideration. Given the lack of this type of tool in Argentina,\nthis work represents the first step towards the development of more\nsophisticated models.\n", "date": "2024-03-27", "categories": []}
{"id": "2404.01273", "title": "TWIN-GPT: Digital Twins for Clinical Trials via Large Language Model", "abstract": "  Clinical trials are indispensable for medical research and the development of\nnew treatments. However, clinical trials often involve thousands of\nparticipants and can span several years to complete, with a high probability of\nfailure during the process. Recently, there has been a burgeoning interest in\nvirtual clinical trials, which simulate real-world scenarios and hold the\npotential to significantly enhance patient safety, expedite development, reduce\ncosts, and contribute to the broader scientific knowledge in healthcare.\nExisting research often focuses on leveraging electronic health records (EHRs)\nto support clinical trial outcome prediction. Yet, trained with limited\nclinical trial outcome data, existing approaches frequently struggle to perform\naccurate predictions. Some research has attempted to generate EHRs to augment\nmodel development but has fallen short in personalizing the generation for\nindividual patient profiles. Recently, the emergence of large language models\nhas illuminated new possibilities, as their embedded comprehensive clinical\nknowledge has proven beneficial in addressing medical issues. In this paper, we\npropose a large language model-based digital twin creation approach, called\nTWIN-GPT. TWIN-GPT can establish cross-dataset associations of medical\ninformation given limited data, generating unique personalized digital twins\nfor different patients, thereby preserving individual patient characteristics.\nComprehensive experiments show that using digital twins created by TWIN-GPT can\nboost the clinical trial outcome prediction, exceeding various previous\nprediction approaches.\n", "date": "2024-04-01", "categories": []}
{"id": "2406.18602", "title": "Multi-level Phenotypic Models of Cardiovascular Disease and Obstructive\n  Sleep Apnea Comorbidities: A Longitudinal Wisconsin Sleep Cohort Study", "abstract": "  Cardiovascular diseases (CVDs) are notably prevalent among patients with\nobstructive sleep apnea (OSA), posing unique challenges in predicting CVD\nprogression due to the intricate interactions of comorbidities. Traditional\nmodels typically lack the necessary dynamic and longitudinal scope to\naccurately forecast CVD trajectories in OSA patients. This study introduces a\nnovel multi-level phenotypic model to analyze the progression and interplay of\nthese conditions over time, utilizing data from the Wisconsin Sleep Cohort,\nwhich includes 1,123 participants followed for decades. Our methodology\ncomprises three advanced steps: (1) Conducting feature importance analysis\nthrough tree-based models to underscore critical predictive variables like\ntotal cholesterol, low-density lipoprotein (LDL), and diabetes. (2) Developing\na logistic mixed-effects model (LGMM) to track longitudinal transitions and\npinpoint significant factors, which displayed a diagnostic accuracy of 0.9556.\n(3) Implementing t-distributed Stochastic Neighbor Embedding (t-SNE) alongside\nGaussian Mixture Models (GMM) to segment patient data into distinct phenotypic\nclusters that reflect varied risk profiles and disease progression pathways.\nThis phenotypic clustering revealed two main groups, with one showing a\nmarkedly increased risk of major adverse cardiovascular events (MACEs),\nunderscored by the significant predictive role of nocturnal hypoxia and\nsympathetic nervous system activity from sleep data. Analysis of transitions\nand trajectories with t-SNE and GMM highlighted different progression rates\nwithin the cohort, with one cluster progressing more slowly towards severe CVD\nstates than the other. This study offers a comprehensive understanding of the\ndynamic relationship between CVD and OSA, providing valuable tools for\npredicting disease onset and tailoring treatment approaches.\n", "date": "2024-06-19", "categories": []}
{"id": "2407.00028", "title": "Harnessing XGBoost for Robust Biomarker Selection of\n  Obsessive-Compulsive Disorder (OCD) from Adolescent Brain Cognitive\n  Development (ABCD) data", "abstract": "  This study evaluates the performance of various supervised machine learning\nmodels in analyzing highly correlated neural signaling data from the Adolescent\nBrain Cognitive Development (ABCD) Study, with a focus on predicting\nobsessive-compulsive disorder scales. We simulated a dataset to mimic the\ncorrelation structures commonly found in imaging data and evaluated logistic\nregression, elastic networks, random forests, and XGBoost on their ability to\nhandle multicollinearity and accurately identify predictive features. Our study\naims to guide the selection of appropriate machine learning methods for\nprocessing neuroimaging data, highlighting models that best capture underlying\nsignals in high feature correlations and prioritize clinically relevant\nfeatures associated with Obsessive-Compulsive Disorder (OCD).\n", "date": "2024-05-14", "categories": []}
{"id": "2407.00147", "title": "Predicting Elevated Risk of Hospitalization Following Emergency\n  Department Discharges", "abstract": "  Hospitalizations that follow closely on the heels of one or more emergency\ndepartment visits are often symptoms of missed opportunities to form a proper\ndiagnosis. These diagnostic errors imply a failure to recognize the need for\nhospitalization and deliver appropriate care, and thus also bear important\nconnotations for patient safety. In this paper, we show how data mining\ntechniques can be applied to a large existing hospitalization data set to learn\nuseful models that predict these upcoming hospitalizations with high accuracy.\nSpecifically, we use an ensemble of logistics regression, na\\\"ive Bayes and\nassociation rule classifiers to successfully predict hospitalization within 3,\n7 and 14 days of an emergency department discharge. Aside from high accuracy,\none of the advantages of the techniques proposed here is that the resulting\nclassifier is easily inspected and interpreted by humans so that the learned\nrules can be readily operationalized. These rules can then be easily\ndistributed and applied directly by physicians in emergency department settings\nto predict the risk of early admission prior to discharging their emergency\ndepartment patients.\n", "date": "2024-06-28", "categories": []}
{"id": "2407.00541", "title": "Answering real-world clinical questions using large language model based\n  systems", "abstract": "  Evidence to guide healthcare decisions is often limited by a lack of relevant\nand trustworthy literature as well as difficulty in contextualizing existing\nresearch for a specific patient. Large language models (LLMs) could potentially\naddress both challenges by either summarizing published literature or\ngenerating new studies based on real-world data (RWD). We evaluated the ability\nof five LLM-based systems in answering 50 clinical questions and had nine\nindependent physicians review the responses for relevance, reliability, and\nactionability. As it stands, general-purpose LLMs (ChatGPT-4, Claude 3 Opus,\nGemini Pro 1.5) rarely produced answers that were deemed relevant and\nevidence-based (2% - 10%). In contrast, retrieval augmented generation\n(RAG)-based and agentic LLM systems produced relevant and evidence-based\nanswers for 24% (OpenEvidence) to 58% (ChatRWD) of questions. Only the agentic\nChatRWD was able to answer novel questions compared to other LLMs (65% vs.\n0-9%). These results suggest that while general-purpose LLMs should not be used\nas-is, a purpose-built system for evidence summarization based on RAG and one\nfor generating novel evidence working synergistically would improve\navailability of pertinent evidence for patient care.\n", "date": "2024-06-29", "categories": []}
{"id": "2406.19980", "title": "Comparative Analysis of LSTM Neural Networks and Traditional Machine\n  Learning Models for Predicting Diabetes Patient Readmission", "abstract": "  Diabetes mellitus is a chronic metabolic disorder that has emerged as one of\nthe major health problems worldwide due to its high prevalence and serious\ncomplications, which are pricey to manage. Effective management requires good\nglycemic control and regular follow-up in the clinic; however, non-adherence to\nscheduled follow-ups is very common. This study uses the Diabetes 130-US\nHospitals dataset for analysis and prediction of readmission patients by\nvarious traditional machine learning models, such as XGBoost, LightGBM,\nCatBoost, Decision Tree, and Random Forest, and also uses an in-house LSTM\nneural network for comparison. The quality of the data was assured by\npreprocessing it, and the performance evaluation for all these models was based\non accuracy, precision, recall, and F1-score. LightGBM turned out to be the\nbest traditional model, while XGBoost was the runner-up. The LSTM model\nsuffered from overfitting despite high training accuracy. A major strength of\nLSTM is capturing temporal dependencies among the patient data. Further, SHAP\nvalues were used, which improved model interpretability, whereby key factors\namong them number of lab procedures and discharge disposition were identified\nas critical in the prediction of readmissions. This study demonstrates that\nmodel selection, validation, and interpretability are key steps in predictive\nhealthcare modeling. This will help health providers design interventions for\nimproved follow-up adherence and better management of diabetes.\n", "date": "2024-06-28", "categories": []}
{"id": "2402.02021", "title": "Transfer Learning in ECG Diagnosis: Is It Effective?", "abstract": "  The adoption of deep learning in ECG diagnosis is often hindered by the\nscarcity of large, well-labeled datasets in real-world scenarios, leading to\nthe use of transfer learning to leverage features learned from larger datasets.\nYet the prevailing assumption that transfer learning consistently outperforms\ntraining from scratch has never been systematically validated. In this study,\nwe conduct the first extensive empirical study on the effectiveness of transfer\nlearning in multi-label ECG classification, by investigating comparing the\nfine-tuning performance with that of training from scratch, covering a variety\nof ECG datasets and deep neural networks. We confirm that fine-tuning is the\npreferable choice for small downstream datasets; however, when the dataset is\nsufficiently large, training from scratch can achieve comparable performance,\nalbeit requiring a longer training time to catch up. Furthermore, we find that\ntransfer learning exhibits better compatibility with convolutional neural\nnetworks than with recurrent neural networks, which are the two most prevalent\narchitectures for time-series ECG applications. Our results underscore the\nimportance of transfer learning in ECG diagnosis, yet depending on the amount\nof available data, researchers may opt not to use it, considering the\nnon-negligible cost associated with pre-training.\n", "date": "2024-02-03", "categories": []}
{"id": "2402.06530", "title": "Refining Myocardial Infarction Detection: A Novel Multi-Modal Composite\n  Kernel Strategy in One-Class Classification", "abstract": "  Early detection of myocardial infarction (MI), a critical condition arising\nfrom coronary artery disease (CAD), is vital to prevent further myocardial\ndamage. This study introduces a novel method for early MI detection using a\none-class classification (OCC) algorithm in echocardiography. Our study\novercomes the challenge of limited echocardiography data availability by\nadopting a novel approach based on Multi-modal Subspace Support Vector Data\nDescription. The proposed technique involves a specialized MI detection\nframework employing multi-view echocardiography incorporating a composite\nkernel in the non-linear projection trick, fusing Gaussian and Laplacian\nsigmoid functions. Additionally, we enhance the update strategy of the\nprojection matrices by adapting maximization for both or one of the modalities\nin the optimization process. Our method boosts MI detection capability by\nefficiently transforming features extracted from echocardiography data into an\noptimized lower-dimensional subspace. The OCC model trained specifically on\ntarget class instances from the comprehensive HMC-QU dataset that includes\nmultiple echocardiography views indicates a marked improvement in MI detection\naccuracy. Our findings reveal that our proposed multi-view approach achieves a\ngeometric mean of 71.24%, signifying a substantial advancement in\nechocardiography-based MI diagnosis and offering more precise and efficient\ndiagnostic tools.\n", "date": "2024-02-09", "categories": []}
{"id": "2406.19189", "title": "BISeizuRe: BERT-Inspired Seizure Data Representation to Improve Epilepsy\n  Monitoring", "abstract": "  This study presents a novel approach for EEG-based seizure detection\nleveraging a BERT-based model. The model, BENDR, undergoes a two-phase training\nprocess. Initially, it is pre-trained on the extensive Temple University\nHospital EEG Corpus (TUEG), a 1.5 TB dataset comprising over 10,000 subjects,\nto extract common EEG data patterns. Subsequently, the model is fine-tuned on\nthe CHB-MIT Scalp EEG Database, consisting of 664 EEG recordings from 24\npediatric patients, of which 198 contain seizure events. Key contributions\ninclude optimizing fine-tuning on the CHB-MIT dataset, where the impact of\nmodel architecture, pre-processing, and post-processing techniques are\nthoroughly examined to enhance sensitivity and reduce false positives per hour\n(FP/h). We also explored custom training strategies to ascertain the most\neffective setup. The model undergoes a novel second pre-training phase before\nsubject-specific fine-tuning, enhancing its generalization capabilities. The\noptimized model demonstrates substantial performance enhancements, achieving as\nlow as 0.23 FP/h, 2.5$\\times$ lower than the baseline model, with a lower but\nstill acceptable sensitivity rate, showcasing the effectiveness of applying a\nBERT-based approach on EEG-based seizure detection.\n", "date": "2024-06-27", "categories": []}
{"id": "2406.17822", "title": "AI for the prediction of early stages of Alzheimer's disease from\n  neuroimaging biomarkers -- A narrative review of a growing field", "abstract": "  Objectives: The objectives of this narrative review are to summarize the\ncurrent state of AI applications in neuroimaging for early Alzheimer's disease\n(AD) prediction and to highlight the potential of AI techniques in improving\nearly AD diagnosis, prognosis, and management.\n  Methods: We conducted a narrative review of studies using AI techniques\napplied to neuroimaging data for early AD prediction. We examined\nsingle-modality studies using structural MRI and PET imaging, as well as\nmulti-modality studies integrating multiple neuroimaging techniques and\nbiomarkers. Furthermore, they reviewed longitudinal studies that model AD\nprogression and identify individuals at risk of rapid decline.\n  Results: Single-modality studies using structural MRI and PET imaging have\ndemonstrated high accuracy in classifying AD and predicting progression from\nmild cognitive impairment (MCI) to AD. Multi-modality studies, integrating\nmultiple neuroimaging techniques and biomarkers, have shown improved\nperformance and robustness compared to single-modality approaches. Longitudinal\nstudies have highlighted the value of AI in modeling AD progression and\nidentifying individuals at risk of rapid decline. However, challenges remain in\ndata standardization, model interpretability, generalizability, clinical\nintegration, and ethical considerations.\n  Conclusion: AI techniques applied to neuroimaging data have the potential to\nimprove early AD diagnosis, prognosis, and management. Addressing challenges\nrelated to data standardization, model interpretability, generalizability,\nclinical integration, and ethical considerations is crucial for realizing the\nfull potential of AI in AD research and clinical practice. Collaborative\nefforts among researchers, clinicians, and regulatory agencies are needed to\ndevelop reliable, robust, and ethical AI tools that can benefit AD patients and\nsociety.\n", "date": "2024-06-25", "categories": []}
{"id": "2406.18087", "title": "EHR-Based Mobile and Web Platform for Chronic Disease Risk Prediction\n  Using Large Language Multimodal Models", "abstract": "  Traditional diagnosis of chronic diseases involves in-person consultations\nwith physicians to identify the disease. However, there is a lack of research\nfocused on predicting and developing application systems using clinical notes\nand blood test values. We collected five years of Electronic Health Records\n(EHRs) from Taiwan's hospital database between 2017 and 2021 as an AI database.\nFurthermore, we developed an EHR-based chronic disease prediction platform\nutilizing Large Language Multimodal Models (LLMMs), successfully integrating\nwith frontend web and mobile applications for prediction. This prediction\nplatform can also connect to the hospital's backend database, providing\nphysicians with real-time risk assessment diagnostics. The demonstration link\ncan be found at https://www.youtube.com/watch?v=oqmL9DEDFgA.\n", "date": "2024-06-26", "categories": []}
{"id": "2406.16915", "title": "Unlocking Telemetry Potential: Self-Supervised Learning for Continuous\n  Clinical Electrocardiogram Monitoring", "abstract": "  Machine learning (ML) applied to routine patient monitoring within intensive\ncare units (ICUs) has the potential to improve care by providing clinicians\nwith novel insights into each patient's health and expected response to\ninterventions. This paper applies deep learning to a large volume of unlabeled\nelectrocardiogram (ECG) telemetry signals, which are commonly used for\ncontinuous patient monitoring in hospitals but have important differences from\nthe standard, single time-point 12-lead ECG used in many prior machine learning\nstudies. We applied self-supervised learning to pretrain a spectrum of deep\nnetworks on approximately 147,000 hours of ECG telemetry data. Our approach\nleverages this dataset to train models that significantly improve performance\non four distinct downstream tasks compared with direct supervised learning\nusing labeled data. These pretrained models enable medically useful predictions\nand estimates in smaller patient cohorts that are typically limited by the\nscarcity of labels. Notably, we demonstrate that our pretrained networks can\ncontinuously annotate ECG telemetry signals, thereby providing monitoring\ncapabilities that are often unavailable due to the requirement for specialized\nexpertise and time-consuming professional annotations.\n", "date": "2024-06-07", "categories": []}
{"id": "2406.16982", "title": "Research on Disease Prediction Model Construction Based on Computer AI\n  deep Learning Technology", "abstract": "  The prediction of disease risk factors can screen vulnerable groups for\neffective prevention and treatment, so as to reduce their morbidity and\nmortality. Machine learning has a great demand for high-quality labeling\ninformation, and labeling noise in medical big data poses a great challenge to\nefficient disease risk warning methods. Therefore, this project intends to\nstudy the robust learning algorithm and apply it to the early warning of\ninfectious disease risk. A dynamic truncated loss model is proposed, which\ncombines the traditional mutual entropy implicit weight feature with the mean\nvariation feature. It is robust to label noise. A lower bound on training loss\nis constructed, and a method based on sampling rate is proposed to reduce the\ngradient of suspected samples to reduce the influence of noise on training\nresults. The effectiveness of this method under different types of noise was\nverified by using a stroke screening data set as an example. This method\nenables robust learning of data containing label noise.\n", "date": "2024-06-23", "categories": []}
{"id": "2311.04846", "title": "Incorporating temporal dynamics of mutations to enhance the prediction\n  capability of antiretroviral therapy's outcome for HIV-1", "abstract": "  Motivation: In predicting HIV therapy outcomes, a critical clinical question\nis whether using historical information can enhance predictive capabilities\ncompared with current or latest available data analysis. This study analyses\nwhether historical knowledge, which includes viral mutations detected in all\ngenotypic tests before therapy, their temporal occurrence, and concomitant\nviral load measurements, can bring improvements. We introduce a method to weigh\nmutations, considering the previously enumerated factors and the reference\nmutation-drug Stanford resistance tables. We compare a model encompassing\nhistory (H) with one not using it (NH). Results: The H-model demonstrates\nsuperior discriminative ability, with a higher ROC-AUC score (76.34%) than the\nNH-model (74.98%). Significant Wilcoxon test results confirm that incorporating\nhistorical information improves consistently predictive accuracy for treatment\noutcomes. The better performance of the H-model might be attributed to its\nconsideration of latent HIV reservoirs, probably obtained when leveraging\nhistorical information. The findings emphasize the importance of temporal\ndynamics in mutations, offering insights into HIV infection complexities.\nHowever, our result also shows that prediction accuracy remains relatively high\neven when no historical information is available. Supplementary information:\nSupplementary material is available.\n", "date": "2023-11-08", "categories": []}
{"id": "2402.00593", "title": "Coronary Artery Disease Classification with Different Lesion Degree\n  Ranges based on Deep Learning", "abstract": "  Invasive Coronary Angiography (ICA) images are considered the gold standard\nfor assessing the state of the coronary arteries. Deep learning classification\nmethods are widely used and well-developed in different areas where medical\nimaging evaluation has an essential impact due to the development of\ncomputer-aided diagnosis systems that can support physicians in their clinical\nprocedures. In this paper, a new performance analysis of deep learning methods\nfor binary ICA classification with different lesion degrees is reported. To\nreach this goal, an annotated dataset of ICA images that contains the ground\ntruth, the location of lesions and seven possible severity degrees ranging\nbetween 0% and 100% was employed. The ICA images were divided into 'lesion' or\n'non-lesion' patches. We aim to study how binary classification performance is\naffected by the different lesion degrees considered in the positive class.\nTherefore, five known convolutional neural network architectures were trained\nwith different input images where different lesion degree ranges were gradually\nincorporated until considering the seven lesion degrees. Besides, four types of\nexperiments with and without data augmentation were designed, whose F-measure\nand Area Under Curve (AUC) were computed. Reported results achieved an\nF-measure and AUC of 92.7% and 98.1%, respectively. However, lesion\nclassification is highly affected by the degree of the lesion intended to\nclassify, with 15% less accuracy when <99% lesion patches are present.\n", "date": "2024-02-01", "categories": []}
{"id": "2406.15962", "title": "Privacy Preserving Machine Learning for Electronic Health Records using\n  Federated Learning and Differential Privacy", "abstract": "  An Electronic Health Record (EHR) is an electronic database used by\nhealthcare providers to store patients' medical records which may include\ndiagnoses, treatments, costs, and other personal information. Machine learning\n(ML) algorithms can be used to extract and analyze patient data to improve\npatient care. Patient records contain highly sensitive information, such as\nsocial security numbers (SSNs) and residential addresses, which introduces a\nneed to apply privacy-preserving techniques for these ML models using federated\nlearning and differential privacy.\n", "date": "2024-06-23", "categories": []}
{"id": "2406.16000", "title": "Predicting Individual Depression Symptoms from Acoustic Features During\n  Speech", "abstract": "  Current automatic depression detection systems provide predictions directly\nwithout relying on the individual symptoms/items of depression as denoted in\nthe clinical depression rating scales. In contrast, clinicians assess each item\nin the depression rating scale in a clinical setting, thus implicitly providing\na more detailed rationale for a depression diagnosis. In this work, we make a\nfirst step towards using the acoustic features of speech to predict individual\nitems of the depression rating scale before obtaining the final depression\nprediction. For this, we use convolutional (CNN) and recurrent (long short-term\nmemory (LSTM)) neural networks. We consider different approaches to learning\nthe temporal context of speech. Further, we analyze two variants of voting\nschemes for individual item prediction and depression detection. We also\ninclude an animated visualization that shows an example of item prediction over\ntime as the speech progresses.\n", "date": "2024-06-23", "categories": []}
{"id": "2402.10748", "title": "A Tiny Transformer for Low-Power Arrhythmia Classification on\n  Microcontrollers", "abstract": "  Wearable systems for the continuous and real-time monitoring of\ncardiovascular diseases are becoming widespread and valuable assets in\ndiagnosis and therapy. A promising approach for real-time analysis of the\nelectrocardiographic (ECG) signal and the detection of heart conditions, such\nas arrhythmia, is represented by the transformer machine learning model.\nTransformers are powerful models for the classification of time series,\nalthough efficient implementation in the wearable domain raises significant\ndesign challenges, to combine adequate accuracy and a suitable complexity. In\nthis work, we present a tiny transformer model for the analysis of the ECG\nsignal, requiring only 6k parameters and reaching 98.97% accuracy in the\nrecognition of the 5 most common arrhythmia classes from the MIT-BIH Arrhythmia\ndatabase, assessed considering 8-bit integer inference as required for\nefficient execution on low-power microcontroller-based devices. We explored an\naugmentation-based training approach for improving the robustness against\nelectrode motion artifacts noise, resulting in a worst-case post-deployment\nperformance assessment of 98.36% accuracy. Suitability for wearable monitoring\nsolutions is finally demonstrated through efficient deployment on the parallel\nultra-low-power GAP9 processor, where inference execution requires 4.28ms and\n0.09mJ.\n", "date": "2024-02-16", "categories": []}
{"id": "2406.14579", "title": "Attention Networks for Personalized Mealtime Insulin Dosing in People\n  with Type 1 Diabetes", "abstract": "  Calculating mealtime insulin doses poses a significant challenge for\nindividuals with Type 1 Diabetes (T1D). Doses should perfectly compensate for\nexpected post-meal glucose excursions, requiring a profound understanding of\nthe individual's insulin sensitivity and the meal macronutrients'. Usually,\npeople rely on intuition and experience to develop this understanding. In this\nwork, we demonstrate how a reinforcement learning agent, employing a\nself-attention encoder network, can effectively mimic and enhance this\nintuitive process. Trained on 80 virtual subjects from the FDA-approved\nUVA/Padova T1D adult cohort and tested on twenty, self-attention demonstrates\nsuperior performance compared to other network architectures. Results reveal a\nsignificant reduction in glycemic risk, from 16.5 to 9.6 in scenarios using\nsensor-augmented pump and from 9.1 to 6.7 in scenarios using automated insulin\ndelivery. This new paradigm bypasses conventional therapy parameters, offering\nthe potential to simplify treatment and promising improved quality of life and\nglycemic outcomes for people with T1D.\n", "date": "2024-06-18", "categories": []}
{"id": "2406.14757", "title": "A Large Language Model Outperforms Other Computational Approaches to the\n  High-Throughput Phenotyping of Physician Notes", "abstract": "  High-throughput phenotyping, the automated mapping of patient signs and\nsymptoms to standardized ontology concepts, is essential to gaining value from\nelectronic health records (EHR) in the support of precision medicine. Despite\ntechnological advances, high-throughput phenotyping remains a challenge. This\nstudy compares three computational approaches to high-throughput phenotyping: a\nLarge Language Model (LLM) incorporating generative AI, a Natural Language\nProcessing (NLP) approach utilizing deep learning for span categorization, and\na hybrid approach combining word vectors with machine learning. The approach\nthat implemented GPT-4 (a Large Language Model) demonstrated superior\nperformance, suggesting that Large Language Models are poised to be the\npreferred method for high-throughput phenotyping of physician notes.\n", "date": "2024-06-20", "categories": []}
{"id": "2406.14866", "title": "AI-based Anomaly Detection for Clinical-Grade Histopathological\n  Diagnostics", "abstract": "  While previous studies have demonstrated the potential of AI to diagnose\ndiseases in imaging data, clinical implementation is still lagging behind. This\nis partly because AI models require training with large numbers of examples\nonly available for common diseases. In clinical reality, however, only few\ndiseases are common, whereas the majority of diseases are less frequent\n(long-tail distribution). Current AI models overlook or misclassify these\ndiseases. We propose a deep anomaly detection approach that only requires\ntraining data from common diseases to detect also all less frequent diseases.\nWe collected two large real-world datasets of gastrointestinal biopsies, which\nare prototypical of the problem. Herein, the ten most common findings account\nfor approximately 90% of cases, whereas the remaining 10% contained 56 disease\nentities, including many cancers. 17 million histological images from 5,423\ncases were used for training and evaluation. Without any specific training for\nthe diseases, our best-performing model reliably detected a broad spectrum of\ninfrequent (\"anomalous\") pathologies with 95.0% (stomach) and 91.0% (colon)\nAUROC and generalized across scanners and hospitals. By design, the proposed\nanomaly detection can be expected to detect any pathological alteration in the\ndiagnostic tail of gastrointestinal biopsies, including rare primary or\nmetastatic cancers. This study establishes the first effective clinical\napplication of AI-based anomaly detection in histopathology that can flag\nanomalous cases, facilitate case prioritization, reduce missed diagnoses and\nenhance the general safety of AI models, thereby driving AI adoption and\nautomation in routine diagnostics and beyond.\n", "date": "2024-06-21", "categories": []}
{"id": "2406.15346", "title": "Privacy Preserved Blood Glucose Level Cross-Prediction: An Asynchronous\n  Decentralized Federated Learning Approach", "abstract": "  Newly diagnosed Type 1 Diabetes (T1D) patients often struggle to obtain\neffective Blood Glucose (BG) prediction models due to the lack of sufficient BG\ndata from Continuous Glucose Monitoring (CGM), presenting a significant \"cold\nstart\" problem in patient care. Utilizing population models to address this\nchallenge is a potential solution, but collecting patient data for training\npopulation models in a privacy-conscious manner is challenging, especially\ngiven that such data is often stored on personal devices. Considering the\nprivacy protection and addressing the \"cold start\" problem in diabetes care, we\npropose \"GluADFL\", blood Glucose prediction by Asynchronous Decentralized\nFederated Learning. We compared GluADFL with eight baseline methods using four\ndistinct T1D datasets, comprising 298 participants, which demonstrated its\nsuperior performance in accurately predicting BG levels for cross-patient\nanalysis. Furthermore, patients' data might be stored and shared across various\ncommunication networks in GluADFL, ranging from highly interconnected (e.g.,\nrandom, performs the best among others) to more structured topologies (e.g.,\ncluster and ring), suitable for various social networks. The asynchronous\ntraining framework supports flexible participation. By adjusting the ratios of\ninactive participants, we found it remains stable if less than 70% are\ninactive. Our results confirm that GluADFL offers a practical,\nprivacy-preserving solution for BG prediction in T1D, significantly enhancing\nthe quality of diabetes management.\n", "date": "2024-06-21", "categories": []}
{"id": "2309.04505", "title": "COVID-19 Detection System: A Comparative Analysis of System Performance\n  Based on Acoustic Features of Cough Audio Signals", "abstract": "  A wide range of respiratory diseases, such as cold and flu, asthma, and\nCOVID-19, affect people's daily lives worldwide. In medical practice,\nrespiratory sounds are widely used in medical services to diagnose various\nrespiratory illnesses and lung disorders. The traditional diagnosis of such\nsounds requires specialized knowledge, which can be costly and reliant on human\nexpertise. Despite this, recent advancements, such as cough audio recordings,\nhave emerged as a means to automate the detection of respiratory conditions.\nTherefore, this research aims to explore various acoustic features that enhance\nthe performance of machine learning (ML) models in detecting COVID-19 from\ncough signals. It investigates the efficacy of three feature extraction\ntechniques, including Mel Frequency Cepstral Coefficients (MFCC), Chroma, and\nSpectral Contrast features, when applied to two machine learning algorithms,\nSupport Vector Machine (SVM) and Multilayer Perceptron (MLP), and therefore\nproposes an efficient CovCepNet detection system. The proposed system provides\na practical solution and demonstrates state-of-the-art classification\nperformance, with an AUC of 0.843 on the COUGHVID dataset and 0.953 on the\nVirufy dataset for COVID-19 detection from cough audio signals.\n", "date": "2023-09-08", "categories": []}
{"id": "2406.14015", "title": "CohortNet: Empowering Cohort Discovery for Interpretable Healthcare\n  Analytics", "abstract": "  Cohort studies are of significant importance in the field of healthcare\nanalysis. However, existing methods typically involve manual, labor-intensive,\nand expert-driven pattern definitions or rely on simplistic clustering\ntechniques that lack medical relevance. Automating cohort studies with\ninterpretable patterns has great potential to facilitate healthcare analysis\nbut remains an unmet need in prior research efforts. In this paper, we propose\na cohort auto-discovery model, CohortNet, for interpretable healthcare\nanalysis, focusing on the effective identification, representation, and\nexploitation of cohorts characterized by medically meaningful patterns.\nCohortNet initially learns fine-grained patient representations by separately\nprocessing each feature, considering both individual feature trends and feature\ninteractions at each time step. Subsequently, it classifies each feature into\ndistinct states and employs a heuristic cohort exploration strategy to\neffectively discover substantial cohorts with concrete patterns. For each\nidentified cohort, it learns comprehensive cohort representations with credible\nevidence through associated patient retrieval. Ultimately, given a new patient,\nCohortNet can leverage relevant cohorts with distinguished importance, which\ncan provide a more holistic understanding of the patient's conditions.\nExtensive experiments on three real-world datasets demonstrate that it\nconsistently outperforms state-of-the-art approaches and offers interpretable\ninsights from diverse perspectives in a top-down fashion.\n", "date": "2024-06-20", "categories": []}
{"id": "2406.10807", "title": "Bayesian Networks and Machine Learning for COVID-19 Severity Explanation\n  and Demographic Symptom Classification", "abstract": "  With the prevailing efforts to combat the coronavirus disease 2019 (COVID-19)\npandemic, there are still uncertainties that are yet to be discovered about its\nspread, future impact, and resurgence. In this paper, we present a three-stage\ndata-driven approach to distill the hidden information about COVID-19. The\nfirst stage employs a Bayesian network structure learning method to identify\nthe causal relationships among COVID-19 symptoms and their intrinsic\ndemographic variables. As a second stage, the output from the Bayesian network\nstructure learning, serves as a useful guide to train an unsupervised machine\nlearning (ML) algorithm that uncovers the similarities in patients' symptoms\nthrough clustering. The final stage then leverages the labels obtained from\nclustering to train a demographic symptom identification (DSID) model which\npredicts a patient's symptom class and the corresponding demographic\nprobability distribution. We applied our method on the COVID-19 dataset\nobtained from the Centers for Disease Control and Prevention (CDC) in the\nUnited States. Results from the experiments show a testing accuracy of 99.99%,\nas against the 41.15% accuracy of a heuristic ML method. This strongly reveals\nthe viability of our Bayesian network and ML approach in understanding the\nrelationship between the virus symptoms, and providing insights on patients'\nstratification towards reducing the severity of the virus.\n", "date": "2024-06-16", "categories": []}
{"id": "2406.11928", "title": "FlexCare: Leveraging Cross-Task Synergy for Flexible Multimodal\n  Healthcare Prediction", "abstract": "  Multimodal electronic health record (EHR) data can offer a holistic\nassessment of a patient's health status, supporting various predictive\nhealthcare tasks. Recently, several studies have embraced the multitask\nlearning approach in the healthcare domain, exploiting the inherent\ncorrelations among clinical tasks to predict multiple outcomes simultaneously.\nHowever, existing methods necessitate samples to possess complete labels for\nall tasks, which places heavy demands on the data and restricts the flexibility\nof the model. Meanwhile, within a multitask framework with multimodal inputs,\nhow to comprehensively consider the information disparity among modalities and\namong tasks still remains a challenging problem. To tackle these issues, a\nunified healthcare prediction model, also named by \\textbf{FlexCare}, is\nproposed to flexibly accommodate incomplete multimodal inputs, promoting the\nadaption to multiple healthcare tasks. The proposed model breaks the\nconventional paradigm of parallel multitask prediction by decomposing it into a\nseries of asynchronous single-task prediction. Specifically, a task-agnostic\nmultimodal information extraction module is presented to capture decorrelated\nrepresentations of diverse intra- and inter-modality patterns. Taking full\naccount of the information disparities between different modalities and\ndifferent tasks, we present a task-guided hierarchical multimodal fusion module\nthat integrates the refined modality-level representations into an individual\npatient-level representation. Experimental results on multiple tasks from\nMIMIC-IV/MIMIC-CXR/MIMIC-NOTE datasets demonstrate the effectiveness of the\nproposed method. Additionally, further analysis underscores the feasibility and\npotential of employing such a multitask strategy in the healthcare domain. The\nsource code is available at https://github.com/mhxu1998/FlexCare.\n", "date": "2024-06-17", "categories": []}
{"id": "2406.12738", "title": "Large Language Model as a Universal Clinical Multi-task Decoder", "abstract": "  The development of effective machine learning methodologies for enhancing the\nefficiency and accuracy of clinical systems is crucial. Despite significant\nresearch efforts, managing a plethora of diversified clinical tasks and\nadapting to emerging new tasks remain significant challenges. This paper\npresents a novel paradigm that employs a pre-trained large language model as a\nuniversal clinical multi-task decoder. This approach leverages the flexibility\nand diversity of language expressions to handle task topic variations and\nassociated arguments. The introduction of a new task simply requires the\naddition of a new instruction template. We validate this framework across\nhundreds of tasks, demonstrating its robustness in facilitating multi-task\npredictions, performing on par with traditional multi-task learning and\nsingle-task learning approaches. Moreover, it shows exceptional adaptability to\nnew tasks, with impressive zero-shot performance in some instances and superior\ndata efficiency in few-shot scenarios. This novel approach offers a unified\nsolution to manage a wide array of new and emerging tasks in clinical\napplications.\n", "date": "2024-06-18", "categories": []}
{"id": "2406.12807", "title": "Probabilistic Temporal Prediction of Continuous Disease Trajectories and\n  Treatment Effects Using Neural SDEs", "abstract": "  Personalized medicine based on medical images, including predicting future\nindividualized clinical disease progression and treatment response, would have\nan enormous impact on healthcare and drug development, particularly for\ndiseases (e.g. multiple sclerosis (MS)) with long term, complex, heterogeneous\nevolutions and no cure. In this work, we present the first stochastic causal\ntemporal framework to model the continuous temporal evolution of disease\nprogression via Neural Stochastic Differential Equations (NSDE). The proposed\ncausal inference model takes as input the patient's high dimensional images\n(MRI) and tabular data, and predicts both factual and counterfactual\nprogression trajectories on different treatments in latent space. The NSDE\npermits the estimation of high-confidence personalized trajectories and\ntreatment effects. Extensive experiments were performed on a large,\nmulti-centre, proprietary dataset of patient 3D MRI and clinical data acquired\nduring several randomized clinical trials for MS treatments. Our results\npresent the first successful uncertainty-based causal Deep Learning (DL) model\nto: (a) accurately predict future patient MS disability evolution (e.g. EDSS)\nand treatment effects leveraging baseline MRI, and (b) permit the discovery of\nsubgroups of patients for which the model has high confidence in their response\nto treatment even in clinical trials which did not reach their clinical\nendpoints.\n", "date": "2024-06-18", "categories": []}
{"id": "2303.09780", "title": "Mpox-AISM: AI-Mediated Super Monitoring for Mpox and Like-Mpox", "abstract": "  Swift and accurate diagnosis for earlier-stage monkeypox (mpox) patients is\ncrucial to avoiding its spread. However, the similarities between common skin\ndisorders and mpox and the need for professional diagnosis unavoidably impaired\nthe diagnosis of earlier-stage mpox patients and contributed to mpox outbreak.\nTo address the challenge, we proposed \"Super Monitoring\", a real-time\nvisualization technique employing artificial intelligence (AI) and Internet\ntechnology to diagnose earlier-stage mpox cheaply, conveniently, and quickly.\nConcretely, AI-mediated \"Super Monitoring\" (mpox-AISM) integrates deep learning\nmodels, data augmentation, self-supervised learning, and cloud services.\nAccording to publicly accessible datasets, mpox-AISM's Precision, Recall,\nSpecificity, and F1-score in diagnosing mpox reach 99.3%, 94.1%, 99.9%, and\n96.6%, respectively, and it achieves 94.51% accuracy in diagnosing mpox, six\nlike-mpox skin disorders, and normal skin. With the Internet and communication\nterminal, mpox-AISM has the potential to perform real-time and accurate\ndiagnosis for earlier-stage mpox in real-world scenarios, thereby preventing\nmpox outbreak.\n", "date": "2023-03-17", "categories": []}
{"id": "2406.10893", "title": "Development and Validation of Fully Automatic Deep Learning-Based\n  Algorithms for Immunohistochemistry Reporting of Invasive Breast Ductal\n  Carcinoma", "abstract": "  Immunohistochemistry (IHC) analysis is a well-accepted and widely used method\nfor molecular subtyping, a procedure for prognosis and targeted therapy of\nbreast carcinoma, the most common type of tumor affecting women. There are four\nmolecular biomarkers namely progesterone receptor (PR), estrogen receptor (ER),\nantigen Ki67, and human epidermal growth factor receptor 2 (HER2) whose\nassessment is needed under IHC procedure to decide prognosis as well as\npredictors of response to therapy. However, IHC scoring is based on subjective\nmicroscopic examination of tumor morphology and suffers from poor\nreproducibility, high subjectivity, and often incorrect scoring in low-score\ncases. In this paper, we present, a deep learning-based semi-supervised\ntrained, fully automatic, decision support system (DSS) for IHC scoring of\ninvasive ductal carcinoma. Our system automatically detects the tumor region\nremoving artifacts and scores based on Allred standard. The system is developed\nusing 3 million pathologist-annotated image patches from 300 slides, fifty\nthousand in-house cell annotations, and forty thousand pixels marking of HER2\nmembrane. We have conducted multicentric trials at four centers with three\ndifferent types of digital scanners in terms of percentage agreement with\ndoctors. And achieved agreements of 95, 92, 88 and 82 percent for Ki67, HER2,\nER, and PR stain categories, respectively. In addition to overall accuracy, we\nfound that there is 5 percent of cases where pathologist have changed their\nscore in favor of algorithm score while reviewing with detailed algorithmic\nanalysis. Our approach could improve the accuracy of IHC scoring and subsequent\ntherapy decisions, particularly where specialist expertise is unavailable. Our\nsystem is highly modular. The proposed algorithm modules can be used to develop\nDSS for other cancer types.\n", "date": "2024-06-16", "categories": []}
{"id": "2402.06367", "title": "TEE4EHR: Transformer Event Encoder for Better Representation Learning in\n  Electronic Health Records", "abstract": "  Irregular sampling of time series in electronic health records (EHRs) is one\nof the main challenges for developing machine learning models. Additionally,\nthe pattern of missing data in certain clinical variables is not at random but\ndepends on the decisions of clinicians and the state of the patient. Point\nprocess is a mathematical framework for analyzing event sequence data that is\nconsistent with irregular sampling patterns. Our model, TEE4EHR, is a\ntransformer event encoder (TEE) with point process loss that encodes the\npattern of laboratory tests in EHRs. The utility of our TEE has been\ninvestigated in a variety of benchmark event sequence datasets. Additionally,\nwe conduct experiments on two real-world EHR databases to provide a more\ncomprehensive evaluation of our model. Firstly, in a self-supervised learning\napproach, the TEE is jointly learned with an existing attention-based deep\nneural network which gives superior performance in negative log-likelihood and\nfuture event prediction. Besides, we propose an algorithm for aggregating\nattention weights that can reveal the interaction between the events. Secondly,\nwe transfer and freeze the learned TEE to the downstream task for the outcome\nprediction, where it outperforms state-of-the-art models for handling\nirregularly sampled time series. Furthermore, our results demonstrate that our\napproach can improve representation learning in EHRs and can be useful for\nclinical prediction tasks.\n", "date": "2024-02-09", "categories": []}
{"id": "2406.09980", "title": "Deep Learning Models to Automate the Scoring of Hand Radiographs for\n  Rheumatoid Arthritis", "abstract": "  The van der Heijde modification of the Sharp (SvdH) score is a widely used\nradiographic scoring method to quantify damage in Rheumatoid Arthritis (RA) in\nclinical trials. However, its complexity with a necessity to score each\nindividual joint, and the expertise required limit its application in clinical\npractice, especially in disease progression measurement. In this work, we\naddressed this limitation by developing a bespoke, automated pipeline that is\ncapable of predicting the SvdH score and RA severity from hand radiographs\nwithout the need to localise the joints first. Using hand radiographs from RA\nand suspected RA patients, we first investigated the performance of the\nstate-of-the-art architectures in predicting the total SvdH score for hands and\nwrists and its corresponding severity class. Secondly, we leveraged publicly\navailable data sets to perform transfer learning with different finetuning\nschemes and ensemble learning, which resulted in substantial improvement in\nmodel performance being on par with an experienced human reader. The best model\nfor RA scoring achieved a Pearson's correlation coefficient (PCC) of 0.925 and\nroot mean squared error (RMSE) of 18.02, while the best model for RA severity\nclassification achieved an accuracy of 0.358 and PCC of 0.859. Our score\nprediction model attained almost comparable accuracy with experienced\nradiologists (PCC = 0.97, RMSE = 18.75). Finally, using Grad-CAM, we showed\nthat our models could focus on the anatomical structures in hands and wrists\nwhich clinicians deemed as relevant to RA progression in the majority of cases.\n", "date": "2024-06-14", "categories": []}
{"id": "2406.10061", "title": "TACCO: Task-guided Co-clustering of Clinical Concepts and Patient Visits\n  for Disease Subtyping based on EHR Data", "abstract": "  The growing availability of well-organized Electronic Health Records (EHR)\ndata has enabled the development of various machine learning models towards\ndisease risk prediction. However, existing risk prediction methods overlook the\nheterogeneity of complex diseases, failing to model the potential disease\nsubtypes regarding their corresponding patient visits and clinical concept\nsubgroups. In this work, we introduce TACCO, a novel framework that jointly\ndiscovers clusters of clinical concepts and patient visits based on a\nhypergraph modeling of EHR data. Specifically, we develop a novel\nself-supervised co-clustering framework that can be guided by the risk\nprediction task of specific diseases. Furthermore, we enhance the hypergraph\nmodel of EHR data with textual embeddings and enforce the alignment between the\nclusters of clinical concepts and patient visits through a contrastive\nobjective. Comprehensive experiments conducted on the public MIMIC-III dataset\nand Emory internal CRADLE dataset over the downstream clinical tasks of\nphenotype classification and cardiovascular risk prediction demonstrate an\naverage 31.25% performance improvement compared to traditional ML baselines and\na 5.26% improvement on top of the vanilla hypergraph model without our\nco-clustering mechanism. In-depth model analysis, clustering results analysis,\nand clinical case studies further validate the improved utilities and\ninsightful interpretations delivered by TACCO. Code is available at\nhttps://github.com/PericlesHat/TACCO.\n", "date": "2024-06-14", "categories": []}
{"id": "2406.10098", "title": "ECGMamba: Towards Efficient ECG Classification with BiSSM", "abstract": "  Electrocardiogram (ECG) signal analysis represents a pivotal technique in the\ndiagnosis of cardiovascular diseases. Although transformer-based models have\nmade significant progress in ECG classification, they exhibit inefficiencies in\nthe inference phase. The issue is primarily attributable to the secondary\ncomputational complexity of Transformer's self-attention mechanism.\nparticularly when processing lengthy sequences. To address this issue, we\npropose a novel model, ECGMamba, which employs a bidirectional state-space\nmodel (BiSSM) to enhance classification efficiency. ECGMamba is based on the\ninnovative Mamba-based block, which incorporates a range of time series\nmodeling techniques to enhance performance while maintaining the efficiency of\ninference. The experimental results on two publicly available ECG datasets\ndemonstrate that ECGMamba effectively balances the effectiveness and efficiency\nof classification, achieving competitive performance. This study not only\ncontributes to the body of knowledge in the field of ECG classification but\nalso provides a new research path for efficient and accurate ECG signal\nanalysis. This is of guiding significance for the development of diagnostic\nmodels for cardiovascular diseases.\n", "date": "2024-06-14", "categories": []}
{"id": "2406.10213", "title": "Selecting Interpretability Techniques for Healthcare Machine Learning\n  models", "abstract": "  In healthcare there is a pursuit for employing interpretable algorithms to\nassist healthcare professionals in several decision scenarios. Following the\nPredictive, Descriptive and Relevant (PDR) framework, the definition of\ninterpretable machine learning as a machine-learning model that explicitly and\nin a simple frame determines relationships either contained in data or learned\nby the model that are relevant for its functioning and the categorization of\nmodels by post-hoc, acquiring interpretability after training, or model-based,\nbeing intrinsically embedded in the algorithm design. We overview a selection\nof eight algorithms, both post-hoc and model-based, that can be used for such\npurposes.\n", "date": "2024-06-14", "categories": []}
{"id": "2406.08851", "title": "Inverse Probability of Treatment Weighting with Deep Sequence Models\n  Enables Accurate treatment effect Estimation from Electronic Health Records", "abstract": "  Observational data have been actively used to estimate treatment effect,\ndriven by the growing availability of electronic health records (EHRs).\nHowever, EHRs typically consist of longitudinal records, often introducing\ntime-dependent confoundings that hinder the unbiased estimation of treatment\neffect. Inverse probability of treatment weighting (IPTW) is a widely used\npropensity score method since it provides unbiased treatment effect estimation\nand its derivation is straightforward. In this study, we aim to utilize IPTW to\nestimate treatment effect in the presence of time-dependent confounding using\nclaims records. Previous studies have utilized propensity score methods with\nfeatures derived from claims records through feature processing, which\ngenerally requires domain knowledge and additional resources to extract\ninformation to accurately estimate propensity scores. Deep sequence models,\nparticularly recurrent neural networks and self-attention-based architectures,\nhave demonstrated good performance in modeling EHRs for various downstream\ntasks. We propose that these deep sequence models can provide accurate IPTW\nestimation of treatment effect by directly estimating the propensity scores\nfrom claims records without the need for feature processing. We empirically\ndemonstrate this by conducting comprehensive evaluations using synthetic and\nsemi-synthetic datasets.\n", "date": "2024-06-13", "categories": []}
{"id": "2303.03660", "title": "ECG Classification System for Arrhythmia Detection Using Convolutional\n  Neural Networks", "abstract": "  Arrhythmia is just one of the many cardiovascular illnesses that have been\nextensively studied throughout the years. Using multi-lead ECG data, this\nresearch describes a deep learning (DL) pipeline technique based on\nconvolutional neural network (CNN) algorithms to detect cardiovascular lar\narrhythmia in patients. The suggested model architecture has hidden layers with\na residual block in addition to the input and output layers. In this study, the\nclassification of the ECG signals into five main groups, namely: Left Bundle\nBranch Block (LBBB), Right Bundle Branch Block (RBBB), Atrial Premature\nContraction (APC), Premature Ventricular Contraction (PVC), and Normal Beat\n(N), are performed. Using the MIT-BIH arrhythmia dataset, we assessed the\nsuggested technique. The findings show that our suggested strategy classified\n15,000 cases with a high accuracy of 98.2%\n", "date": "2023-03-07", "categories": []}
{"id": "2311.04224", "title": "MELEP: A Novel Predictive Measure of Transferability in Multi-Label ECG\n  Diagnosis", "abstract": "  In practical electrocardiography (ECG) interpretation, the scarcity of\nwell-annotated data is a common challenge. Transfer learning techniques are\nvaluable in such situations, yet the assessment of transferability has received\nlimited attention. To tackle this issue, we introduce MELEP, which stands for\nMuti-label Expected Log of Empirical Predictions, a measure designed to\nestimate the effectiveness of knowledge transfer from a pre-trained model to a\ndownstream multi-label ECG diagnosis task. MELEP is generic, working with new\ntarget data with different label sets, and computationally efficient, requiring\nonly a single forward pass through the pre-trained model. To the best of our\nknowledge, MELEP is the first transferability metric specifically designed for\nmulti-label ECG classification problems. Our experiments show that MELEP can\npredict the performance of pre-trained convolutional and recurrent deep neural\nnetworks, on small and imbalanced ECG data. Specifically, we observed strong\ncorrelation coefficients (with absolute values exceeding 0.6 in most cases)\nbetween MELEP and the actual average F1 scores of the fine-tuned models. Our\nwork highlights the potential of MELEP to expedite the selection of suitable\npre-trained models for ECG diagnosis tasks, saving time and effort that would\notherwise be spent on fine-tuning these models.\n", "date": "2023-10-27", "categories": []}
{"id": "2405.19277", "title": "Deep Latent Variable Modeling of Physiological Signals", "abstract": "  A deep latent variable model is a powerful method for capturing complex\ndistributions. These models assume that underlying structures, but unobserved,\nare present within the data. In this dissertation, we explore high-dimensional\nproblems related to physiological monitoring using latent variable models.\nFirst, we present a novel deep state-space model to generate electrical\nwaveforms of the heart using optically obtained signals as inputs. This can\nbring about clinical diagnoses of heart disease via simple assessment through\nwearable devices. Second, we present a brain signal modeling scheme that\ncombines the strengths of probabilistic graphical models and deep adversarial\nlearning. The structured representations can provide interpretability and\nencode inductive biases to reduce the data complexity of neural oscillations.\nThe efficacy of the learned representations is further studied in epilepsy\nseizure detection formulated as an unsupervised learning problem. Third, we\npropose a framework for the joint modeling of physiological measures and\nbehavior. Existing methods to combine multiple sources of brain data provided\nare limited. Direct analysis of the relationship between different types of\nphysiological measures usually does not involve behavioral data. Our method can\nidentify the unique and shared contributions of brain regions to behavior and\ncan be used to discover new functions of brain regions. The success of these\ninnovative computational methods would allow the translation of biomarker\nfindings across species and provide insight into neurocognitive analysis in\nnumerous biological studies and clinical diagnoses, as well as emerging\nconsumer applications.\n", "date": "2024-05-29", "categories": []}
{"id": "2406.03510", "title": "Speech-based Clinical Depression Screening: An Empirical Study", "abstract": "  This study investigates the utility of speech signals for AI-based depression\nscreening across varied interaction scenarios, including psychiatric\ninterviews, chatbot conversations, and text readings. Participants include\ndepressed patients recruited from the outpatient clinics of Peking University\nSixth Hospital and control group members from the community, all diagnosed by\npsychiatrists following standardized diagnostic protocols. We extracted\nacoustic and deep speech features from each participant's segmented recordings.\nClassifications were made using neural networks or SVMs, with aggregated clip\noutcomes determining final assessments. Our analysis across interaction\nscenarios, speech processing techniques, and feature types confirms speech as a\ncrucial marker for depression screening. Specifically, human-computer\ninteraction matches clinical interview efficacy, surpassing reading tasks.\nSegment duration and quantity significantly affect model performance, with deep\nspeech features substantially outperforming traditional acoustic features.\n", "date": "2024-06-05", "categories": []}
{"id": "2406.08137", "title": "The impact of deep learning aid on the workload and interpretation\n  accuracy of radiologists on chest computed tomography: a cross-over reader\n  study", "abstract": "  Interpretation of chest computed tomography (CT) is time-consuming. Previous\nstudies have measured the time-saving effect of using a deep-learning-based aid\n(DLA) for CT interpretation. We evaluated the joint impact of a multi-pathology\nDLA on the time and accuracy of radiologists' reading.\n  40 radiologists were randomly split into three experimental arms: control\n(10), who interpret studies without assistance; informed group (10), who were\nbriefed about DLA pathologies, but performed readings without it; and the\nexperimental group (20), who interpreted half studies with DLA, and half\nwithout. Every arm used the same 200 CT studies retrospectively collected from\nBIMCV-COVID19 dataset; each radiologist provided readings for 20 CT studies. We\ncompared interpretation time, and accuracy of participants diagnostic report\nwith respect to 12 pathological findings.\n  Mean reading time per study was 15.6 minutes [SD 8.5] in the control arm,\n13.2 minutes [SD 8.7] in the informed arm, 14.4 [SD 10.3] in the experimental\narm without DLA, and 11.4 minutes [SD 7.8] in the experimental arm with DLA.\nMean sensitivity and specificity were 41.5 [SD 30.4], 86.8 [SD 28.3] in the\ncontrol arm; 53.5 [SD 22.7], 92.3 [SD 9.4] in the informed non-assisted arm;\n63.2 [SD 16.4], 92.3 [SD 8.2] in the experimental arm without DLA; and 91.6 [SD\n7.2], 89.9 [SD 6.0] in the experimental arm with DLA. DLA speed up\ninterpretation time per study by 2.9 minutes (CI95 [1.7, 4.3], p<0.0005),\nincreased sensitivity by 28.4 (CI95 [23.4, 33.4], p<0.0005), and decreased\nspecificity by 2.4 (CI95 [0.6, 4.3], p=0.13).\n  Of 20 radiologists in the experimental arm, 16 have improved reading time and\nsensitivity, two improved their time with a marginal drop in sensitivity, and\ntwo participants improved sensitivity with increased time. Overall, DLA\nintroduction decreased reading time by 20.6%.\n", "date": "2024-06-12", "categories": []}
{"id": "2306.04748", "title": "Analysis, Identification and Prediction of Parkinson Disease Sub-Types\n  and Progression through Machine Learning", "abstract": "  This paper represents a groundbreaking advancement in Parkinson disease (PD)\nresearch by employing a novel machine learning framework to categorize PD into\ndistinct subtypes and predict its progression. Utilizing a comprehensive\ndataset encompassing both clinical and neurological parameters, the research\napplies advanced supervised and unsupervised learning techniques. This\ninnovative approach enables the identification of subtle, yet critical,\npatterns in PD manifestation, which traditional methodologies often miss.\nSignificantly, this research offers a path toward personalized treatment\nstrategies, marking a major stride in the precision medicine domain and\nshowcasing the transformative potential of integrating machine learning into\nmedical research.\n", "date": "2023-06-07", "categories": []}
{"id": "2406.04364", "title": "Use of a Multiscale Vision Transformer to predict Nursing Activities\n  Score from Low Resolution Thermal Videos in an Intensive Care Unit", "abstract": "  Excessive caregiver workload in hospital nurses has been implicated in poorer\npatient care and increased worker burnout. Measurement of this workload in the\nIntensive Care Unit (ICU) is often done using the Nursing Activities Score\n(NAS), but this is usually recorded manually and sporadically. Previous work\nhas made use of Ambient Intelligence (AmI) by using computer vision to\npassively derive caregiver-patient interaction times to monitor staff workload.\nIn this letter, we propose using a Multiscale Vision Transformer (MViT) to\npassively predict the NAS from low-resolution thermal videos recorded in an\nICU. 458 videos were obtained from an ICU in Melbourne, Australia and used to\ntrain a MViTv2 model using an indirect prediction and a direct prediction\nmethod. The indirect method predicted 1 of 8 potentially identifiable NAS\nactivities from the video before inferring the NAS. The direct method predicted\nthe NAS score immediately from the video. The indirect method yielded an\naverage 5-fold accuracy of 57.21%, an area under the receiver operating\ncharacteristic curve (ROC AUC) of 0.865, a F1 score of 0.570 and a mean squared\nerror (MSE) of 28.16. The direct method yielded a MSE of 18.16. We also showed\nthat the MViTv2 outperforms similar models such as R(2+1)D and ResNet50-LSTM\nunder identical settings.\n  This study shows the feasibility of using a MViTv2 to passively predict the\nNAS in an ICU and monitor staff workload automatically. Our results above also\nshow an increased accuracy in predicting NAS directly versus predicting NAS\nindirectly. We hope that our study can provide a direction for future work and\nfurther improve the accuracy of passive NAS monitoring.\n", "date": "2024-05-30", "categories": []}
{"id": "2406.06636", "title": "LLM Questionnaire Completion for Automatic Psychiatric Assessment", "abstract": "  We employ a Large Language Model (LLM) to convert unstructured psychological\ninterviews into structured questionnaires spanning various psychiatric and\npersonality domains. The LLM is prompted to answer these questionnaires by\nimpersonating the interviewee. The obtained answers are coded as features,\nwhich are used to predict standardized psychiatric measures of depression\n(PHQ-8) and PTSD (PCL-C), using a Random Forest regressor. Our approach is\nshown to enhance diagnostic accuracy compared to multiple baselines. It thus\nestablishes a novel framework for interpreting unstructured psychological\ninterviews, bridging the gap between narrative-driven and data-driven\napproaches for mental health assessment.\n", "date": "2024-06-09", "categories": []}
{"id": "2307.03042", "title": "Parameter-Efficient Fine-Tuning of LLaMA for the Clinical Domain", "abstract": "  Adapting pretrained language models to novel domains, such as clinical\napplications, traditionally involves retraining their entire set of parameters.\nParameter-Efficient Fine-Tuning (PEFT) techniques for fine-tuning language\nmodels significantly reduce computational requirements by selectively\nfine-tuning small subsets of parameters. In this study, we propose a two-step\nPEFT framework and evaluate it in the clinical domain. Our approach combines a\nspecialised PEFT adapter layer designed for clinical domain adaptation with\nanother adapter specialised for downstream tasks. We evaluate the framework on\nmultiple clinical outcome prediction datasets, comparing it to clinically\ntrained language models. Our framework achieves a better AUROC score averaged\nacross all clinical downstream tasks compared to clinical language models. In\nparticular, we observe large improvements of 4-5% AUROC in large-scale\nmultilabel classification tasks, such as diagnoses and procedures\nclassification. To our knowledge, this study is the first to provide an\nextensive empirical analysis of the interplay between PEFT techniques and\ndomain adaptation in an important real-world domain of clinical applications.\n", "date": "2023-07-06", "categories": []}
{"id": "2404.18975", "title": "M3H: Multimodal Multitask Machine Learning for Healthcare", "abstract": "  Developing an integrated many-to-many framework leveraging multimodal data\nfor multiple tasks is crucial to unifying healthcare applications ranging from\ndiagnoses to operations. In resource-constrained hospital environments, a\nscalable and unified machine learning framework that improves previous forecast\nperformances could improve hospital operations and save costs. We introduce\nM3H, an explainable Multimodal Multitask Machine Learning for Healthcare\nframework that consolidates learning from tabular, time-series, language, and\nvision data for supervised binary/multiclass classification, regression, and\nunsupervised clustering. It features a novel attention mechanism balancing\nself-exploitation (learning source-task), and cross-exploration (learning\ncross-tasks), and offers explainability through a proposed TIM score, shedding\nlight on the dynamics of task learning interdependencies. M3H encompasses an\nunprecedented range of medical tasks and machine learning problem classes and\nconsistently outperforms traditional single-task models by on average 11.6%\nacross 40 disease diagnoses from 16 medical departments, three hospital\noperation forecasts, and one patient phenotyping task. The modular design of\nthe framework ensures its generalizability in data processing, task definition,\nand rapid model prototyping, making it production ready for both clinical and\noperational healthcare settings, especially those in constrained environments.\n", "date": "2024-04-29", "categories": []}
{"id": "2406.05682", "title": "From Basic to Extra Features: Hypergraph Transformer\n  Pretrain-then-Finetuning for Balanced Clinical Predictions on EHR", "abstract": "  Electronic Health Records (EHRs) contain rich patient information and are\ncrucial for clinical research and practice. In recent years, deep learning\nmodels have been applied to EHRs, but they often rely on massive features,\nwhich may not be readily available for all patients. We propose HTP-Star, which\nleverages hypergraph structures with a pretrain-then-finetune framework for\nmodeling EHR data, enabling seamless integration of additional features.\nAdditionally, we design two techniques, namely (1) Smoothness-inducing\nRegularization and (2) Group-balanced Reweighting, to enhance the model's\nrobustness during fine-tuning. Through experiments conducted on two real EHR\ndatasets, we demonstrate that HTP-Star consistently outperforms various\nbaselines while striking a balance between patients with basic and extra\nfeatures.\n", "date": "2024-06-09", "categories": []}
{"id": "2406.00085", "title": "Augmentation-based Unsupervised Cross-Domain Functional MRI Adaptation\n  for Major Depressive Disorder Identification", "abstract": "  Major depressive disorder (MDD) is a common mental disorder that typically\naffects a person's mood, cognition, behavior, and physical health.\nResting-state functional magnetic resonance imaging (rs-fMRI) data are widely\nused for computer-aided diagnosis of MDD. While multi-site fMRI data can\nprovide more data for training reliable diagnostic models, significant\ncross-site data heterogeneity would result in poor model generalizability. Many\ndomain adaptation methods are designed to reduce the distributional differences\nbetween sites to some extent, but usually ignore overfitting problem of the\nmodel on the source domain. Intuitively, target data augmentation can alleviate\nthe overfitting problem by forcing the model to learn more generalized features\nand reduce the dependence on source domain data. In this work, we propose a new\naugmentation-based unsupervised cross-domain fMRI adaptation (AUFA) framework\nfor automatic diagnosis of MDD. The AUFA consists of 1) a graph representation\nlearning module for extracting rs-fMRI features with spatial attention, 2) a\ndomain adaptation module for feature alignment between source and target data,\n3) an augmentation-based self-optimization module for alleviating model\noverfitting on the source domain, and 4) a classification module. Experimental\nresults on 1,089 subjects suggest that AUFA outperforms several\nstate-of-the-art methods in MDD identification. Our approach not only reduces\ndata heterogeneity between different sites, but also localizes disease-related\nfunctional connectivity abnormalities and provides interpretability for the\nmodel.\n", "date": "2024-05-31", "categories": []}
{"id": "2406.03161", "title": "Ethical considerations of use of hold-out sets in clinical prediction\n  model management", "abstract": "  Clinical prediction models are statistical or machine learning models used to\nquantify the risk of a certain health outcome using patient data. These can\nthen inform potential interventions on patients, causing an effect called\nperformative prediction: predictions inform interventions which influence the\noutcome they were trying to predict, leading to a potential underestimation of\nrisk in some patients if a model is updated on this data. One suggested\nresolution to this is the use of hold-out sets, in which a set of patients do\nnot receive model derived risk scores, such that a model can be safely\nretrained. We present an overview of clinical and research ethics regarding\npotential implementation of hold-out sets for clinical prediction models in\nhealth settings. We focus on the ethical principles of beneficence,\nnon-maleficence, autonomy and justice. We also discuss informed consent,\nclinical equipoise, and truth-telling. We present illustrative cases of\npotential hold-out set implementations and discuss statistical issues arising\nfrom different hold-out set sampling methods. We also discuss differences\nbetween hold-out sets and randomised control trials, in terms of ethics and\nstatistical issues. Finally, we give practical recommendations for researchers\ninterested in the use hold-out sets for clinical prediction models.\n", "date": "2024-06-05", "categories": []}
{"id": "2306.05109", "title": "Yet Another ICU Benchmark: A Flexible Multi-Center Framework for\n  Clinical ML", "abstract": "  Medical applications of machine learning (ML) have experienced a surge in\npopularity in recent years. The intensive care unit (ICU) is a natural habitat\nfor ML given the abundance of available data from electronic health records.\nModels have been proposed to address numerous ICU prediction tasks like the\nearly detection of complications. While authors frequently report\nstate-of-the-art performance, it is challenging to verify claims of\nsuperiority. Datasets and code are not always published, and cohort\ndefinitions, preprocessing pipelines, and training setups are difficult to\nreproduce. This work introduces Yet Another ICU Benchmark (YAIB), a modular\nframework that allows researchers to define reproducible and comparable\nclinical ML experiments; we offer an end-to-end solution from cohort definition\nto model evaluation. The framework natively supports most open-access ICU\ndatasets (MIMIC III/IV, eICU, HiRID, AUMCdb) and is easily adaptable to future\nICU datasets. Combined with a transparent preprocessing pipeline and extensible\ntraining code for multiple ML and deep learning models, YAIB enables unified\nmodel development. Our benchmark comes with five predefined established\nprediction tasks (mortality, acute kidney injury, sepsis, kidney function, and\nlength of stay) developed in collaboration with clinicians. Adding further\ntasks is straightforward by design. Using YAIB, we demonstrate that the choice\nof dataset, cohort definition, and preprocessing have a major impact on the\nprediction performance - often more so than model class - indicating an urgent\nneed for YAIB as a holistic benchmarking tool. We provide our work to the\nclinical ML community to accelerate method development and enable real-world\nclinical implementations. Software Repository:\nhttps://github.com/rvandewater/YAIB.\n", "date": "2023-06-08", "categories": []}
{"id": "2405.20142", "title": "MSSC-BiMamba: Multimodal Sleep Stage Classification and Early Diagnosis\n  of Sleep Disorders with Bidirectional Mamba", "abstract": "  Monitoring sleep states is essential for evaluating sleep quality and\ndiagnosing sleep disorders. Traditional manual staging is time-consuming and\nprone to subjective bias, often resulting in inconsistent outcomes. Here, we\ndeveloped an automated model for sleep staging and disorder classification to\nenhance diagnostic accuracy and efficiency. Considering the characteristics of\npolysomnography (PSG) multi-lead sleep monitoring, we designed a multimodal\nsleep state classification model, MSSC-BiMamba, that combines an Efficient\nChannel Attention (ECA) mechanism with a Bidirectional State Space Model\n(BSSM). The ECA module allows for weighting data from different sensor\nchannels, thereby amplifying the influence of diverse sensor inputs.\nAdditionally, the implementation of bidirectional Mamba (BiMamba) enables the\nmodel to effectively capture the multidimensional features and long-range\ndependencies of PSG data. The developed model demonstrated impressive\nperformance on sleep stage classification tasks on both the ISRUC-S3 and\nISRUC-S1 datasets, respectively containing data with healthy and unhealthy\nsleep patterns. Also, the model exhibited a high accuracy for sleep health\nprediction when evaluated on a combined dataset consisting of ISRUC and\nSleep-EDF. Our model, which can effectively handle diverse sleep conditions, is\nthe first to apply BiMamba to sleep staging with multimodal PSG data, showing\nsubstantial gains in computational and memory efficiency over traditional\nTransformer-style models. This method enhances sleep health management by\nmaking monitoring more accessible and extending advanced healthcare through\ninnovative technology.\n", "date": "2024-05-30", "categories": []}
{"id": "2405.20414", "title": "The Impact of Ontology on the Prediction of Cardiovascular Disease\n  Compared to Machine Learning Algorithms", "abstract": "  Cardiovascular disease is one of the chronic diseases that is on the rise.\nThe complications occur when cardiovascular disease is not discovered early and\ncorrectly diagnosed at the right time. Various machine learning approaches,\nincluding ontology-based Machine Learning techniques, have lately played an\nessential role in medical science by building an automated system that can\nidentify heart illness. This paper compares and reviews the most prominent\nmachine learning algorithms, as well as ontology-based Machine Learning\nclassification. Random Forest, Logistic regression, Decision Tree, Naive Bayes,\nk-Nearest Neighbours, Artificial Neural Network, and Support Vector Machine\nwere among the classification methods explored. The dataset used consists of\n70000 instances and can be downloaded from the Kaggle website. The findings are\nassessed using performance measures generated from the confusion matrix, such\nas F-Measure, Accuracy, Recall, and Precision. The results showed that the\nontology outperformed all the machine learning algorithms.\n", "date": "2024-05-30", "categories": []}
{"id": "2405.20419", "title": "Enhancing Antibiotic Stewardship using a Natural Language Approach for\n  Better Feature Representation", "abstract": "  The rapid emergence of antibiotic-resistant bacteria is recognized as a\nglobal healthcare crisis, undermining the efficacy of life-saving antibiotics.\nThis crisis is driven by the improper and overuse of antibiotics, which\nescalates bacterial resistance. In response, this study explores the use of\nclinical decision support systems, enhanced through the integration of\nelectronic health records (EHRs), to improve antibiotic stewardship. However,\nEHR systems present numerous data-level challenges, complicating the effective\nsynthesis and utilization of data. In this work, we transform EHR data into a\nserialized textual representation and employ pretrained foundation models to\ndemonstrate how this enhanced feature representation can aid in antibiotic\nsusceptibility predictions. Our results suggest that this text representation,\ncombined with foundation models, provides a valuable tool to increase\ninterpretability and support antibiotic stewardship efforts.\n", "date": "2024-05-30", "categories": []}
{"id": "2405.20562", "title": "Can Machine Learning Assist in Diagnosis of Primary Immune\n  Thrombocytopenia? A feasibility study", "abstract": "  Primary Immune thrombocytopenia (ITP) is a rare autoimmune disease\ncharacterised by immune-mediated destruction of peripheral blood platelets in\npatients leading to low platelet counts and bleeding. The diagnosis and\neffective management of ITP is challenging because there is no established test\nto confirm the disease and no biomarker with which one can predict the response\nto treatment and outcome. In this work we conduct a feasibility study to check\nif machine learning can be applied effectively for diagnosis of ITP using\nroutine blood tests and demographic data in a non-acute outpatient setting.\nVarious ML models, including Logistic Regression, Support Vector Machine,\nk-Nearest Neighbor, Decision Tree and Random Forest, were applied to data from\nthe UK Adult ITP Registry and a general hematology clinic. Two different\napproaches were investigated: a demographic-unaware and a demographic-aware\none. We conduct extensive experiments to evaluate the predictive performance of\nthese models and approaches, as well as their bias. The results revealed that\nDecision Tree and Random Forest models were both superior and fair, achieving\nnearly perfect predictive and fairness scores, with platelet count identified\nas the most significant variable. Models not provided with demographic\ninformation performed better in terms of predictive accuracy but showed lower\nfairness score, illustrating a trade-off between predictive performance and\nfairness.\n", "date": "2024-05-31", "categories": []}
{"id": "2405.17817", "title": "Benchmarking Skeleton-based Motion Encoder Models for Clinical\n  Applications: Estimating Parkinson's Disease Severity in Walking Sequences", "abstract": "  This study investigates the application of general human motion encoders\ntrained on large-scale human motion datasets for analyzing gait patterns in PD\npatients. Although these models have learned a wealth of human biomechanical\nknowledge, their effectiveness in analyzing pathological movements, such as\nparkinsonian gait, has yet to be fully validated. We propose a comparative\nframework and evaluate six pre-trained state-of-the-art human motion encoder\nmodels on their ability to predict the Movement Disorder Society - Unified\nParkinson's Disease Rating Scale (MDS-UPDRS-III) gait scores from motion\ncapture data. We compare these against a traditional gait feature-based\npredictive model in a recently released large public PD dataset, including PD\npatients on and off medication. The feature-based model currently shows higher\nweighted average accuracy, precision, recall, and F1-score. Motion encoder\nmodels with closely comparable results demonstrate promise for scalability and\nefficiency in clinical settings. This potential is underscored by the enhanced\nperformance of the encoder model upon fine-tuning on PD training set. Four of\nthe six human motion models examined provided prediction scores that were\nsignificantly different between on- and off-medication states. This finding\nreveals the sensitivity of motion encoder models to nuanced clinical changes.\nIt also underscores the necessity for continued customization of these models\nto better capture disease-specific features, thereby reducing the reliance on\nlabor-intensive feature engineering. Lastly, we establish a benchmark for the\nanalysis of skeleton-based motion encoder models in clinical settings. To the\nbest of our knowledge, this is the first study to provide a benchmark that\nenables state-of-the-art models to be tested and compete in a clinical context.\nCodes and benchmark leaderboard are available at code.\n", "date": "2024-05-28", "categories": []}
{"id": "2405.20071", "title": "A Staged Approach using Machine Learning and Uncertainty Quantification\n  to Predict the Risk of Hip Fracture", "abstract": "  Despite advancements in medical care, hip fractures impose a significant\nburden on individuals and healthcare systems. This paper focuses on the\nprediction of hip fracture risk in older and middle-aged adults, where falls\nand compromised bone quality are predominant factors. We propose a novel staged\nmodel that combines advanced imaging and clinical data to improve predictive\nperformance. By using CNNs to extract features from hip DXA images, along with\nclinical variables, shape measurements, and texture features, our method\nprovides a comprehensive framework for assessing fracture risk. A staged\nmachine learning-based model was developed using two ensemble models: Ensemble\n1 (clinical variables only) and Ensemble 2 (clinical variables and DXA imaging\nfeatures). This staged approach used uncertainty quantification from Ensemble 1\nto decide if DXA features are necessary for further prediction. Ensemble 2\nexhibited the highest performance, achieving an AUC of 0.9541, an accuracy of\n0.9195, a sensitivity of 0.8078, and a specificity of 0.9427. The staged model\nalso performed well, with an AUC of 0.8486, an accuracy of 0.8611, a\nsensitivity of 0.5578, and a specificity of 0.9249, outperforming Ensemble 1,\nwhich had an AUC of 0.5549, an accuracy of 0.7239, a sensitivity of 0.1956, and\na specificity of 0.8343. Furthermore, the staged model suggested that 54.49% of\npatients did not require DXA scanning. It effectively balanced accuracy and\nspecificity, offering a robust solution when DXA data acquisition is not always\nfeasible. Statistical tests confirmed significant differences between the\nmodels, highlighting the advantages of the advanced modeling strategies. Our\nstaged approach could identify individuals at risk with a high accuracy but\nreduce the unnecessary DXA scanning. It has great promise to guide\ninterventions to prevent hip fractures with reduced cost and radiation.\n", "date": "2024-05-30", "categories": []}
{"id": "2405.18536", "title": "Data-Driven Simulator for Mechanical Circulatory Support with Domain\n  Adversarial Neural Process", "abstract": "  Mechanical Circulatory Support (MCS) devices, implemented as a probabilistic\ndeep sequence model. Existing mechanical simulators for MCS rely on\noversimplifying assumptions and are insensitive to patient-specific behavior,\nlimiting their applicability to real-world treatment scenarios. To address\nthese shortcomings, our model Domain Adversarial Neural Process (DANP) employs\na neural process architecture, allowing it to capture the probabilistic\nrelationship between MCS pump levels and aortic pressure measurements with\nuncertainty. We use domain adversarial training to combine simulation data with\nreal-world observations, resulting in a more realistic and diverse\nrepresentation of potential outcomes. Empirical results with an improvement of\n19% in non-stationary trend prediction establish DANP as an effective tool for\nclinicians to understand and make informed decisions regarding MCS patient\ntreatment.\n", "date": "2024-05-28", "categories": []}
{"id": "2306.12462", "title": "Precision psychiatry: predicting predictability", "abstract": "  Precision psychiatry is an ermerging field that aims to provide\nindividualized approaches to mental health care. Multivariate analysis and\nmachine learning are used to create outcome prediction models based on clinical\ndata such as demographics, symptom assessments, genetic information, and brain\nimaging. While much emphasis has been placed on technical innovation, the\ncomplex and varied nature of mental health presents significant challenges to\nthe successful implementation of these models. From this perspective, I review\nten challenges in the field of precision psychiatry, including the need for\nstudies on real-world populations and realistic clinical outcome definitions,\nconsideration of treatment-related factors such as placebo effects and\nnon-adherence to prescriptions. Fairness, prospective validation in comparison\nto current practice and implementation studies of prediction models are other\nkey issues that are currently understudied. A shift is proposed from\nretrospective studies based on linear and static concepts of disease towards\nprospective research that considers the importance of contextual factors and\nthe dynamic and complex nature of mental health.\n", "date": "2023-06-21", "categories": []}
{"id": "2310.00457", "title": "Enhancing Mortality Prediction in Heart Failure Patients: Exploring\n  Preprocessing Methods for Imbalanced Clinical Datasets", "abstract": "  Heart failure (HF) is a critical condition in which the accurate prediction\nof mortality plays a vital role in guiding patient management decisions.\nHowever, clinical datasets used for mortality prediction in HF often suffer\nfrom an imbalanced distribution of classes, posing significant challenges. In\nthis paper, we explore preprocessing methods for enhancing one-month mortality\nprediction in HF patients. We present a comprehensive preprocessing framework\nincluding scaling, outliers processing and resampling as key techniques. We\nalso employed an aware encoding approach to effectively handle missing values\nin clinical datasets. Our study utilizes a comprehensive dataset from the\nPersian Registry Of cardio Vascular disease (PROVE) with a significant class\nimbalance. By leveraging appropriate preprocessing techniques and Machine\nLearning (ML) algorithms, we aim to improve mortality prediction performance\nfor HF patients. The results reveal an average enhancement of approximately\n3.6% in F1 score and 2.7% in MCC for tree-based models, specifically Random\nForest (RF) and XGBoost (XGB). This demonstrates the efficiency of our\npreprocessing approach in effectively handling Imbalanced Clinical Datasets\n(ICD). Our findings hold promise in guiding healthcare professionals to make\ninformed decisions and improve patient outcomes in HF management.\n", "date": "2023-09-30", "categories": []}
{"id": "2402.13448", "title": "ED-Copilot: Reduce Emergency Department Wait Time with Language Model\n  Diagnostic Assistance", "abstract": "  In the emergency department (ED), patients undergo triage and multiple\nlaboratory tests before diagnosis. This time-consuming process causes ED\ncrowding which impacts patient mortality, medical errors, staff burnout, etc.\nThis work proposes (time) cost-effective diagnostic assistance that leverages\nartificial intelligence systems to help ED clinicians make efficient and\naccurate diagnoses. In collaboration with ED clinicians, we use public patient\ndata to curate MIMIC-ED-Assist, a benchmark for AI systems to suggest\nlaboratory tests that minimize wait time while accurately predicting critical\noutcomes such as death. With MIMIC-ED-Assist, we develop ED-Copilot which\nsequentially suggests patient-specific laboratory tests and makes diagnostic\npredictions. ED-Copilot employs a pre-trained bio-medical language model to\nencode patient information and uses reinforcement learning to minimize ED wait\ntime and maximize prediction accuracy. On MIMIC-ED-Assist, ED-Copilot improves\nprediction accuracy over baselines while halving average wait time from four\nhours to two hours. ED-Copilot can also effectively personalize treatment\nrecommendations based on patient severity, further highlighting its potential\nas a diagnostic assistant. Since MIMIC-ED-Assist is a retrospective benchmark,\nED-Copilot is restricted to recommend only observed tests. We show ED-Copilot\nachieves competitive performance without this restriction as the maximum\nallowed time increases. Our code is available at\nhttps://github.com/cxcscmu/ED-Copilot.\n", "date": "2024-02-21", "categories": []}
{"id": "2405.15808", "title": "Ensuring Ground Truth Accuracy in Healthcare with the EVINCE framework", "abstract": "  Misdiagnosis is a significant issue in healthcare, leading to harmful\nconsequences for patients. The propagation of mislabeled data through machine\nlearning models into clinical practice is unacceptable. This paper proposes\nEVINCE, a system designed to 1) improve diagnosis accuracy and 2) rectify\nmisdiagnoses and minimize training data errors. EVINCE stands for Entropy\nVariation through Information Duality with Equal Competence, leveraging this\nnovel theory to optimize the diagnostic process using multiple Large Language\nModels (LLMs) in a structured debate framework. Our empirical study verifies\nEVINCE to be effective in achieving its design goals.\n", "date": "2024-05-20", "categories": []}
{"id": "2405.16413", "title": "Augmented Risk Prediction for the Onset of Alzheimer's Disease from\n  Electronic Health Records with Large Language Models", "abstract": "  Alzheimer's disease (AD) is the fifth-leading cause of death among Americans\naged 65 and older. Screening and early detection of AD and related dementias\n(ADRD) are critical for timely intervention and for identifying clinical trial\nparticipants. The widespread adoption of electronic health records (EHRs)\noffers an important resource for developing ADRD screening tools such as\nmachine learning based predictive models. Recent advancements in large language\nmodels (LLMs) demonstrate their unprecedented capability of encoding knowledge\nand performing reasoning, which offers them strong potential for enhancing risk\nprediction. This paper proposes a novel pipeline that augments risk prediction\nby leveraging the few-shot inference power of LLMs to make predictions on cases\nwhere traditional supervised learning methods (SLs) may not excel.\nSpecifically, we develop a collaborative pipeline that combines SLs and LLMs\nvia a confidence-driven decision-making mechanism, leveraging the strengths of\nSLs in clear-cut cases and LLMs in more complex scenarios. We evaluate this\npipeline using a real-world EHR data warehouse from Oregon Health \\& Science\nUniversity (OHSU) Hospital, encompassing EHRs from over 2.5 million patients\nand more than 20 million patient encounters. Our results show that our proposed\napproach effectively combines the power of SLs and LLMs, offering significant\nimprovements in predictive performance. This advancement holds promise for\nrevolutionizing ADRD screening and early detection practices, with potential\nimplications for better strategies of patient management and thus improving\nhealthcare.\n", "date": "2024-05-26", "categories": []}
{"id": "2405.17502", "title": "Exploring Nutritional Impact on Alzheimer's Mortality: An Explainable AI\n  Approach", "abstract": "  This article uses machine learning (ML) and explainable artificial\nintelligence (XAI) techniques to investigate the relationship between\nnutritional status and mortality rates associated with Alzheimers disease (AD).\nThe Third National Health and Nutrition Examination Survey (NHANES III)\ndatabase is employed for analysis. The random forest model is selected as the\nbase model for XAI analysis, and the Shapley Additive Explanations (SHAP)\nmethod is used to assess feature importance. The results highlight significant\nnutritional factors such as serum vitamin B12 and glycated hemoglobin. The\nstudy demonstrates the effectiveness of random forests in predicting AD\nmortality compared to other diseases. This research provides insights into the\nimpact of nutrition on AD and contributes to a deeper understanding of disease\nprogression.\n", "date": "2024-05-26", "categories": []}
{"id": "2405.17921", "title": "Towards Clinical AI Fairness: Filling Gaps in the Puzzle", "abstract": "  The ethical integration of Artificial Intelligence (AI) in healthcare\nnecessitates addressing fairness-a concept that is highly context-specific\nacross medical fields. Extensive studies have been conducted to expand the\ntechnical components of AI fairness, while tremendous calls for AI fairness\nhave been raised from healthcare. Despite this, a significant disconnect\npersists between technical advancements and their practical clinical\napplications, resulting in a lack of contextualized discussion of AI fairness\nin clinical settings. Through a detailed evidence gap analysis, our review\nsystematically pinpoints several deficiencies concerning both healthcare data\nand the provided AI fairness solutions. We highlight the scarcity of research\non AI fairness in many medical domains where AI technology is increasingly\nutilized. Additionally, our analysis highlights a substantial reliance on group\nfairness, aiming to ensure equality among demographic groups from a macro\nhealthcare system perspective; in contrast, individual fairness, focusing on\nequity at a more granular level, is frequently overlooked. To bridge these\ngaps, our review advances actionable strategies for both the healthcare and AI\nresearch communities. Beyond applying existing AI fairness methods in\nhealthcare, we further emphasize the importance of involving healthcare\nprofessionals to refine AI fairness concepts and methods to ensure contextually\nrelevant and ethically sound AI applications in healthcare.\n", "date": "2024-05-28", "categories": []}
{"id": "2405.18327", "title": "Histopathology Based AI Model Predicts Anti-Angiogenic Therapy Response\n  in Renal Cancer Clinical Trial", "abstract": "  Predictive biomarkers of treatment response are lacking for metastatic clear\ncell renal cell carcinoma (ccRCC), a tumor type that is treated with\nangiogenesis inhibitors, immune checkpoint inhibitors, mTOR inhibitors and a\nHIF2 inhibitor. The Angioscore, an RNA-based quantification of angiogenesis, is\narguably the best candidate to predict anti-angiogenic (AA) response. However,\nthe clinical adoption of transcriptomic assays faces several challenges\nincluding standardization, time delay, and high cost. Further, ccRCC tumors are\nhighly heterogenous, and sampling multiple areas for sequencing is impractical.\nHere we present a novel deep learning (DL) approach to predict the Angioscore\nfrom ubiquitous histopathology slides. To overcome the lack of\ninterpretability, one of the biggest limitations of typical DL models, our\nmodel produces a visual vascular network which is the basis of the model's\nprediction. To test its reliability, we applied this model to multiple cohorts\nincluding a clinical trial dataset. Our model accurately predicts the RNA-based\nAngioscore on multiple independent cohorts (spearman correlations of 0.77 and\n0.73). Further, the predictions help unravel meaningful biology such as\nassociation of angiogenesis with grade, stage, and driver mutation status.\nFinally, we find our model can predict response to AA therapy, in both a\nreal-world cohort and the IMmotion150 clinical trial. The predictive power of\nour model vastly exceeds that of CD31, a marker of vasculature, and nearly\nrivals the performance (c-index 0.66 vs 0.67) of the ground truth RNA-based\nAngioscore at a fraction of the cost. By providing a robust yet interpretable\nprediction of the Angioscore from histopathology slides alone, our approach\noffers insights into angiogenesis biology and AA treatment response.\n", "date": "2024-05-28", "categories": []}
{"id": "2312.07580", "title": "COVID-19 Detection Using Slices Processing Techniques and a Modified\n  Xception Classifier from Computed Tomography Images", "abstract": "  This paper extends our previous method for COVID-19 diagnosis, proposing an\nenhanced solution for detecting COVID-19 from computed tomography (CT) images.\nTo decrease model misclassifications, two key steps of image processing were\nemployed. Firstly, the uppermost and lowermost slices were removed, preserving\nsixty percent of each patient's slices. Secondly, all slices underwent manual\ncropping to emphasize the lung areas. Subsequently, resized CT scans (224 by\n224) were input into an Xception transfer learning model. Leveraging Xception's\narchitecture and pre-trained weights, the modified model achieved binary\nclassification. Promising results on the COV19-CT database showcased higher\nvalidation accuracy and macro F1 score at both the slice and patient levels\ncompared to our previous solution and alternatives on the same dataset.\n", "date": "2023-12-10", "categories": []}
{"id": "2402.17501", "title": "Intensive Care as One Big Sequence Modeling Problem", "abstract": "  Reinforcement Learning in Healthcare is typically concerned with narrow\nself-contained tasks such as sepsis prediction or anesthesia control. However,\nprevious research has demonstrated the potential of generalist models (the\nprime example being Large Language Models) to outperform task-specific\napproaches due to their capability for implicit transfer learning. To enable\ntraining of foundation models for Healthcare as well as leverage the\ncapabilities of state of the art Transformer architectures, we propose the\nparadigm of Healthcare as Sequence Modeling, in which interaction between the\npatient and the healthcare provider is represented as an event stream and tasks\nlike diagnosis and treatment selection are modeled as prediction of future\nevents in the stream. To explore this paradigm experimentally we develop\nMIMIC-SEQ, a sequence modeling benchmark derived by translating heterogenous\nclinical records from MIMIC-IV dataset into a uniform event stream format,\ntrain a baseline model and explore its capabilities.\n", "date": "2024-02-27", "categories": []}
{"id": "2405.15882", "title": "Risk Factor Identification In Osteoporosis Using Unsupervised Machine\n  Learning Techniques", "abstract": "  In this study, the reliability of identified risk factors associated with\nosteoporosis is investigated using a new clustering-based method on electronic\nmedical records. This study proposes utilizing a new CLustering Iterations\nFramework (CLIF) that includes an iterative clustering framework that can adapt\nany of the following three components: clustering, feature selection, and\nprincipal feature identification. The study proposes using Wasserstein distance\nto identify principal features, borrowing concepts from the optimal transport\ntheory. The study also suggests using a combination of ANOVA and ablation tests\nto select influential features from a data set. Some risk factors presented in\nexisting works are endorsed by our identified significant clusters, while the\nreliability of some other risk factors is weakened.\n", "date": "2024-05-24", "categories": []}
{"id": "2405.17059", "title": "Comparative Study of Machine Learning Algorithms in Detecting\n  Cardiovascular Diseases", "abstract": "  The detection of cardiovascular diseases (CVD) using machine learning\ntechniques represents a significant advancement in medical diagnostics, aiming\nto enhance early detection, accuracy, and efficiency. This study explores a\ncomparative analysis of various machine learning algorithms, including Logistic\nRegression, Decision Tree, Random Forest, Gradient Boosting, Support Vector\nMachine (SVM), K-Nearest Neighbors (KNN), and XGBoost. By utilising a\nstructured workflow encompassing data collection, preprocessing, model\nselection and hyperparameter tuning, training, evaluation, and choice of the\noptimal model, this research addresses the critical need for improved\ndiagnostic tools. The findings highlight the efficacy of ensemble methods and\nadvanced algorithms in providing reliable predictions, thereby offering a\ncomprehensive framework for CVD detection that can be readily implemented and\nadapted in clinical settings.\n", "date": "2024-05-27", "categories": []}
{"id": "2405.17352", "title": "Assessing the significance of longitudinal data in Alzheimer's Disease\n  forecasting", "abstract": "  In this study, we employ a transformer encoder model to characterize the\nsignificance of longitudinal patient data for forecasting the progression of\nAlzheimer's Disease (AD). Our model, Longitudinal Forecasting Model for\nAlzheimer's Disease (LongForMAD), harnesses the comprehensive temporal\ninformation embedded in sequences of patient visits that incorporate multimodal\ndata, providing a deeper understanding of disease progression than can be drawn\nfrom single-visit data alone. We present an empirical analysis across two\npatient groups-Cognitively Normal (CN) and Mild Cognitive Impairment (MCI)-over\na span of five follow-up years. Our findings reveal that models incorporating\nmore extended patient histories can outperform those relying solely on present\ninformation, suggesting a deeper historical context is critical in enhancing\npredictive accuracy for future AD progression. Our results support the\nincorporation of longitudinal data in clinical settings to enhance the early\ndetection and monitoring of AD. Our code is available at\n\\url{https://github.com/batuhankmkaraman/LongForMAD}.\n", "date": "2024-05-27", "categories": []}
{"id": "2404.16127", "title": "Comparison of static and dynamic random forests models for EHR data in\n  the presence of competing risks: predicting central line-associated\n  bloodstream infection", "abstract": "  Prognostic outcomes related to hospital admissions typically do not suffer\nfrom censoring, and can be modeled either categorically or as time-to-event.\nCompeting events are common but often ignored. We compared the performance of\nrandom forest (RF) models to predict the risk of central line-associated\nbloodstream infections (CLABSI) using different outcome operationalizations. We\nincluded data from 27478 admissions to the University Hospitals Leuven,\ncovering 30862 catheter episodes (970 CLABSI, 1466 deaths and 28426 discharges)\nto build static and dynamic RF models for binary (CLABSI vs no CLABSI),\nmultinomial (CLABSI, discharge, death or no event), survival (time to CLABSI)\nand competing risks (time to CLABSI, discharge or death) outcomes to predict\nthe 7-day CLABSI risk. We evaluated model performance across 100 train/test\nsplits. Performance of binary, multinomial and competing risks models was\nsimilar: AUROC was 0.74 for baseline predictions, rose to 0.78 for predictions\nat day 5 in the catheter episode, and decreased thereafter. Survival models\noverestimated the risk of CLABSI (E:O ratios between 1.2 and 1.6), and had\nAUROCs about 0.01 lower than other models. Binary and multinomial models had\nlowest computation times. Models including multiple outcome events (multinomial\nand competing risks) display a different internal structure compared to binary\nand survival models. In the absence of censoring, complex modelling choices do\nnot considerably improve the predictive performance compared to a binary model\nfor CLABSI prediction in our studied settings. Survival models censoring the\ncompeting events at their time of occurrence should be avoided.\n", "date": "2024-04-24", "categories": []}
{"id": "2405.01714", "title": "Interpretable Vital Sign Forecasting with Model Agnostic Attention Maps", "abstract": "  Sepsis is a leading cause of mortality in intensive care units (ICUs),\nrepresenting a substantial medical challenge. The complexity of analyzing\ndiverse vital signs to predict sepsis further aggravates this issue. While deep\nlearning techniques have been advanced for early sepsis prediction, their\n'black-box' nature obscures the internal logic, impairing interpretability in\ncritical settings like ICUs. This paper introduces a framework that combines a\ndeep learning model with an attention mechanism that highlights the critical\ntime steps in the forecasting process, thus improving model interpretability\nand supporting clinical decision-making. We show that the attention mechanism\ncould be adapted to various black box time series forecasting models such as\nN-HiTS and N-BEATS. Our method preserves the accuracy of conventional deep\nlearning models while enhancing interpretability through\nattention-weight-generated heatmaps. We evaluated our model on the eICU-CRD\ndataset, focusing on forecasting vital signs for sepsis patients. We assessed\nits performance using mean squared error (MSE) and dynamic time warping (DTW)\nmetrics. We explored the attention maps of N-HiTS and N-BEATS, examining the\ndifferences in their performance and identifying crucial factors influencing\nvital sign forecasting.\n", "date": "2024-05-02", "categories": []}
{"id": "2405.13187", "title": "A machine learning framework for interpretable predictions in patient\n  pathways: The case of predicting ICU admission for patients with symptoms of\n  sepsis", "abstract": "  Proactive analysis of patient pathways helps healthcare providers anticipate\ntreatment-related risks, identify outcomes, and allocate resources. Machine\nlearning (ML) can leverage a patient's complete health history to make informed\ndecisions about future events. However, previous work has mostly relied on\nso-called black-box models, which are unintelligible to humans, making it\ndifficult for clinicians to apply such models. Our work introduces PatWay-Net,\nan ML framework designed for interpretable predictions of admission to the\nintensive care unit (ICU) for patients with symptoms of sepsis. We propose a\nnovel type of recurrent neural network and combine it with multi-layer\nperceptrons to process the patient pathways and produce predictive yet\ninterpretable results. We demonstrate its utility through a comprehensive\ndashboard that visualizes patient health trajectories, predictive outcomes, and\nassociated risks. Our evaluation includes both predictive performance - where\nPatWay-Net outperforms standard models such as decision trees, random forests,\nand gradient-boosted decision trees - and clinical utility, validated through\nstructured interviews with clinicians. By providing improved predictive\naccuracy along with interpretable and actionable insights, PatWay-Net serves as\na valuable tool for healthcare decision support in the critical case of\npatients with symptoms of sepsis.\n", "date": "2024-05-21", "categories": []}
{"id": "2405.14113", "title": "Multi-modality Regional Alignment Network for Covid X-Ray Survival\n  Prediction and Report Generation", "abstract": "  In response to the worldwide COVID-19 pandemic, advanced automated\ntechnologies have emerged as valuable tools to aid healthcare professionals in\nmanaging an increased workload by improving radiology report generation and\nprognostic analysis. This study proposes Multi-modality Regional Alignment\nNetwork (MRANet), an explainable model for radiology report generation and\nsurvival prediction that focuses on high-risk regions. By learning spatial\ncorrelation in the detector, MRANet visually grounds region-specific\ndescriptions, providing robust anatomical regions with a completion strategy.\nThe visual features of each region are embedded using a novel survival\nattention mechanism, offering spatially and risk-aware features for sentence\nencoding while maintaining global coherence across tasks. A cross LLMs\nalignment is employed to enhance the image-to-text transfer process, resulting\nin sentences rich with clinical detail and improved explainability for\nradiologist. Multi-center experiments validate both MRANet's overall\nperformance and each module's composition within the model, encouraging further\nadvancements in radiology report generation research emphasizing clinical\ninterpretation and trustworthiness in AI models applied to medical studies. The\ncode is available at https://github.com/zzs95/MRANet.\n", "date": "2024-05-23", "categories": []}
{"id": "2405.06065", "title": "Driving down Poisson error can offset classification error in clinical\n  tasks", "abstract": "  Medical machine learning algorithms are typically evaluated based on accuracy\nvs. a clinician-defined ground truth, a reasonable initial choice since trained\nclinicians are usually better classifiers than ML models. However, this metric\ndoes not fully capture the actual clinical task: it neglects the fact that\nhumans, even with perfect accuracy, are subject to non-trivial error from the\nPoisson statistics of rare events, because clinical protocols often specify a\nrelatively small sample size. For example, to quantitate malaria on a thin\nblood film a clinician examines only 2000 red blood cells (0.0004 uL), which\ncan yield large Poisson variation in the actual number of parasites present, so\nthat a perfect human's count can differ substantially from the true average\nload. In contrast, an ML system may be less accurate on an object level, but it\nmay also have the option to examine more blood (e.g. 0.1 uL, or 250x). Then\nwhile its parasite identification error is higher, the Poisson variability of\nits estimate is lower due to larger sample size.\n  To qualify for clinical deployment, an ML system's performance must match\ncurrent standard of care, typically a very demanding target. To achieve this,\nit may be possible to offset the ML system's lower accuracy by increasing its\nsample size to reduce Poisson error, and thus attain the same net clinical\nperformance as a perfectly accurate human limited by smaller sample size. In\nthis paper, we analyse the mathematics of the relationship between Poisson\nerror, classification error, and total error. This mathematical toolkit enables\nteams optimizing ML systems to leverage a relative strength (larger sample\nsizes) to offset a relative weakness (classification accuracy). We illustrate\nthe methods with two concrete examples: diagnosis and quantitation of malaria\non blood films.\n", "date": "2024-05-09", "categories": []}
{"id": "2405.11275", "title": "Predicting and Explaining Hearing Aid Usage Using Encoder-Decoder with\n  Attention Mechanism and SHAP", "abstract": "  It is essential to understand the personal, behavioral, environmental, and\nother factors that correlate with optimal hearing aid fitting and hearing aid\nusers' experiences in order to improve hearing loss patient satisfaction and\nquality of life, as well as reduce societal and financial burdens. This work\nproposes a novel framework that uses Encoder-decoder with attention mechanism\n(attn-ED) for predicting future hearing aid usage and SHAP to explain the\nfactors contributing to this prediction. It has been demonstrated in\nexperiments that attn-ED performs well at predicting future hearing aid usage,\nand that SHAP can be utilized to calculate the contribution of different\nfactors affecting hearing aid usage. This framework aims to establish\nconfidence that AI models can be utilized in the medical domain with the use of\nXAI methods. Moreover, the proposed framework can also assist clinicians in\ndetermining the nature of interventions.\n", "date": "2024-05-18", "categories": []}
{"id": "2405.12126", "title": "Alzheimer's Magnetic Resonance Imaging Classification Using Deep and\n  Meta-Learning Models", "abstract": "  Deep learning, a cutting-edge machine learning approach, outperforms\ntraditional machine learning in identifying intricate structures in complex\nhigh-dimensional data, particularly in the domain of healthcare. This study\nfocuses on classifying Magnetic Resonance Imaging (MRI) data for Alzheimer's\ndisease (AD) by leveraging deep learning techniques characterized by\nstate-of-the-art CNNs. Brain imaging techniques such as MRI have enabled the\nmeasurement of pathophysiological brain changes related to Alzheimer's disease.\nAlzheimer's disease is the leading cause of dementia in the elderly, and it is\nan irreversible brain illness that causes gradual cognitive function disorder.\nIn this paper, we train some benchmark deep models individually for the\napproach of the solution and later use an ensembling approach to combine the\neffect of multiple CNNs towards the observation of higher recall and accuracy.\nHere, the model's effectiveness is evaluated using various methods, including\nstacking, majority voting, and the combination of models with high recall\nvalues. The majority voting performs better than the alternative modelling\napproach as the majority voting approach typically reduces the variance in the\npredictions. We report a test accuracy of 90% with a precision score of 0.90\nand a recall score of 0.89 in our proposed approach. In future, this study can\nbe extended to incorporate other types of medical data, including signals,\nimages, and other data. The same or alternative datasets can be used with\nadditional classifiers, neural networks, and AI techniques to enhance\nAlzheimer's detection.\n", "date": "2024-05-20", "categories": []}
{"id": "2405.10345", "title": "Machine Learning Driven Biomarker Selection for Medical Diagnosis", "abstract": "  Recent advances in experimental methods have enabled researchers to collect\ndata on thousands of analytes simultaneously. This has led to correlational\nstudies that associated molecular measurements with diseases such as\nAlzheimer's, Liver, and Gastric Cancer. However, the use of thousands of\nbiomarkers selected from the analytes is not practical for real-world medical\ndiagnosis and is likely undesirable due to potentially formed spurious\ncorrelations. In this study, we evaluate 4 different methods for biomarker\nselection and 4 different machine learning (ML) classifiers for identifying\ncorrelations, evaluating 16 approaches in all. We found that contemporary\nmethods outperform previously reported logistic regression in cases where 3 and\n10 biomarkers are permitted. When specificity is fixed at 0.9, ML approaches\nproduced a sensitivity of 0.240 (3 biomarkers) and 0.520 (10 biomarkers), while\nstandard logistic regression provided a sensitivity of 0.000 (3 biomarkers) and\n0.040 (10 biomarkers). We also noted that causal-based methods for biomarker\nselection proved to be the most performant when fewer biomarkers were\npermitted, while univariate feature selection was the most performant when a\ngreater number of biomarkers were permitted.\n", "date": "2024-05-16", "categories": []}
{"id": "2405.02770", "title": "PhilHumans: Benchmarking Machine Learning for Personal Health", "abstract": "  The use of machine learning in Healthcare has the potential to improve\npatient outcomes as well as broaden the reach and affordability of Healthcare.\nThe history of other application areas indicates that strong benchmarks are\nessential for the development of intelligent systems. We present Personal\nHealth Interfaces Leveraging HUman-MAchine Natural interactions (PhilHumans), a\nholistic suite of benchmarks for machine learning across different Healthcare\nsettings - talk therapy, diet coaching, emergency care, intensive care,\nobstetric sonography - as well as different learning settings, such as action\nanticipation, timeseries modeling, insight mining, language modeling, computer\nvision, reinforcement learning and program synthesis\n", "date": "2024-05-04", "categories": []}
{"id": "2301.09362", "title": "A Comprehensive Survey on Heart Sound Analysis in the Deep Learning Era", "abstract": "  Heart sound auscultation has been applied in clinical usage for early\nscreening of cardiovascular diseases. Due to the high demand for auscultation\nexpertise, automatic auscultation can help with auxiliary diagnosis and reduce\nthe burden of training professional clinicians. Nevertheless, there is a limit\nto classic machine learning's performance improvement in the era of big data.\nDeep learning has outperformed classic machine learning in many research\nfields, as it employs more complex model architectures with a stronger\ncapability of extracting effective representations. Moreover, it has been\nsuccessfully applied to heart sound analysis in the past years. As most review\nworks about heart sound analysis were carried out before 2017, the present\nsurvey is the first to work on a comprehensive overview to summarise papers on\nheart sound analysis with deep learning published in 2017--2022. This work\nintroduces both classic machine learning and deep learning for comparison, and\nfurther offer insights about the advances and future research directions in\ndeep learning for heart sound analysis. Our repository is publicly available at\n\\url{https://github.com/zhaoren91/awesome-heart-sound-analysis}.\n", "date": "2023-01-23", "categories": []}
{"id": "2404.11171", "title": "Personalized Heart Disease Detection via ECG Digital Twin Generation", "abstract": "  Heart diseases rank among the leading causes of global mortality,\ndemonstrating a crucial need for early diagnosis and intervention. Most\ntraditional electrocardiogram (ECG) based automated diagnosis methods are\ntrained at population level, neglecting the customization of personalized ECGs\nto enhance individual healthcare management. A potential solution to address\nthis limitation is to employ digital twins to simulate symptoms of diseases in\nreal patients. In this paper, we present an innovative prospective learning\napproach for personalized heart disease detection, which generates digital\ntwins of healthy individuals' anomalous ECGs and enhances the model sensitivity\nto the personalized symptoms. In our approach, a vector quantized feature\nseparator is proposed to locate and isolate the disease symptom and normal\nsegments in ECG signals with ECG report guidance. Thus, the ECG digital twins\ncan simulate specific heart diseases used to train a personalized heart disease\ndetection model. Experiments demonstrate that our approach not only excels in\ngenerating high-fidelity ECG signals but also improves personalized heart\ndisease detection. Moreover, our approach ensures robust privacy protection,\nsafeguarding patient data in model development.\n", "date": "2024-04-17", "categories": []}
{"id": "2405.06662", "title": "Language Interaction Network for Clinical Trial Approval Estimation", "abstract": "  Clinical trial outcome prediction seeks to estimate the likelihood that a\nclinical trial will successfully reach its intended endpoint. This process\npredominantly involves the development of machine learning models that utilize\na variety of data sources such as descriptions of the clinical trials,\ncharacteristics of the drug molecules, and specific disease conditions being\ntargeted. Accurate predictions of trial outcomes are crucial for optimizing\ntrial planning and prioritizing investments in a drug portfolio. While previous\nresearch has largely concentrated on small-molecule drugs, there is a growing\nneed to focus on biologics-a rapidly expanding category of therapeutic agents\nthat often lack the well-defined molecular properties associated with\ntraditional drugs. Additionally, applying conventional methods like graph\nneural networks to biologics data proves challenging due to their complex\nnature. To address these challenges, we introduce the Language Interaction\nNetwork (LINT), a novel approach that predicts trial outcomes using only the\nfree-text descriptions of the trials. We have rigorously tested the\neffectiveness of LINT across three phases of clinical trials, where it achieved\nROC-AUC scores of 0.770, 0.740, and 0.748 for phases I, II, and III,\nrespectively, specifically concerning trials involving biologic interventions.\n", "date": "2024-04-26", "categories": []}
{"id": "2405.07674", "title": "CoVScreen: Pitfalls and recommendations for screening COVID-19 using\n  Chest X-rays", "abstract": "  The novel coronavirus (COVID-19), a highly infectious respiratory disease\ncaused by the SARS-CoV-2 has emerged as an unprecedented healthcare crisis. The\npandemic had a devastating impact on the health, well-being, and economy of the\nglobal population. Early screening and diagnosis of symptomatic patients plays\ncrucial role in isolation of patient to help stop community transmission as\nwell as providing early treatment helping in reducing the mortality rate.\nAlthough, the RT-PCR test is the gold standard for COVID-19 testing, it is a\nmanual, laborious, time consuming, uncomfortable, and invasive process. Due to\nits accessibility, availability, lower-cost, ease of sanitisation, and portable\nsetup, chest X-Ray imaging can serve as an effective screening and diagnostic\ntool. In this study, we first highlight limitations of existing datasets and\nstudies in terms of data quality, data imbalance, and evaluation strategy.\nSecond, we curated a large-scale COVID-19 chest X-ray dataset from many\npublicly available COVID-19 imaging databases and proposed a pre-processing\npipeline to improve quality of the dataset. We proposed CoVScreen, an CNN\narchitecture to train and test the curated dataset. The experimental results\napplying different classification scenarios on the curated dataset in terms of\nvarious evaluation metrics demonstrate the effectiveness of proposed\nmethodology in the screening of COVID-19 infection.\n", "date": "2024-05-13", "categories": []}
{"id": "2405.07869", "title": "Enhancing Clinically Significant Prostate Cancer Prediction in\n  T2-weighted Images through Transfer Learning from Breast Cancer", "abstract": "  In 2020, prostate cancer saw a staggering 1.4 million new cases, resulting in\nover 375,000 deaths. The accurate identification of clinically significant\nprostate cancer is crucial for delivering effective treatment to patients.\nConsequently, there has been a surge in research exploring the application of\ndeep neural networks to predict clinical significance based on magnetic\nresonance images. However, these networks demand extensive datasets to attain\noptimal performance. Recently, transfer learning emerged as a technique that\nleverages acquired features from a domain with richer data to enhance the\nperformance of a domain with limited data. In this paper, we investigate the\nimprovement of clinically significant prostate cancer prediction in T2-weighted\nimages through transfer learning from breast cancer. The results demonstrate a\nremarkable improvement of over 30% in leave-one-out cross-validation accuracy.\n", "date": "2024-05-13", "categories": []}
{"id": "2312.07399", "title": "Large Language Models are Clinical Reasoners: Reasoning-Aware Diagnosis\n  Framework with Prompt-Generated Rationales", "abstract": "  Machine reasoning has made great progress in recent years owing to large\nlanguage models (LLMs). In the clinical domain, however, most NLP-driven\nprojects mainly focus on clinical classification or reading comprehension, and\nunder-explore clinical reasoning for disease diagnosis due to the expensive\nrationale annotation with clinicians. In this work, we present a\n\"reasoning-aware\" diagnosis framework that rationalizes the diagnostic process\nvia prompt-based learning in a time- and labor-efficient manner, and learns to\nreason over the prompt-generated rationales. Specifically, we address the\nclinical reasoning for disease diagnosis, where the LLM generates diagnostic\nrationales providing its insight on presented patient data and the reasoning\npath towards the diagnosis, namely Clinical Chain-of-Thought (Clinical CoT). We\nempirically demonstrate LLMs/LMs' ability of clinical reasoning via extensive\nexperiments and analyses on both rationale generation and disease diagnosis in\nvarious settings. We further propose a novel set of criteria for evaluating\nmachine-generated rationales' potential for real-world clinical settings,\nfacilitating and benefiting future research in this area.\n", "date": "2023-12-12", "categories": []}
{"id": "2405.05993", "title": "Precision Rehabilitation for Patients Post-Stroke based on Electronic\n  Health Records and Machine Learning", "abstract": "  In this study, we utilized statistical analysis and machine learning methods\nto examine whether rehabilitation exercises can improve patients post-stroke\nfunctional abilities, as well as forecast the improvement in functional\nabilities. Our dataset is patients' rehabilitation exercises and demographic\ninformation recorded in the unstructured electronic health records (EHRs) data\nand free-text rehabilitation procedure notes. We collected data for 265 stroke\npatients from the University of Pittsburgh Medical Center. We employed a\npre-existing natural language processing (NLP) algorithm to extract data on\nrehabilitation exercises and developed a rule-based NLP algorithm to extract\nActivity Measure for Post-Acute Care (AM-PAC) scores, covering basic mobility\n(BM) and applied cognitive (AC) domains, from procedure notes. Changes in\nAM-PAC scores were classified based on the minimal clinically important\ndifference (MCID), and significance was assessed using Friedman and Wilcoxon\ntests. To identify impactful exercises, we used Chi-square tests, Fisher's\nexact tests, and logistic regression for odds ratios. Additionally, we\ndeveloped five machine learning models-logistic regression (LR), Adaboost\n(ADB), support vector machine (SVM), gradient boosting (GB), and random forest\n(RF)-to predict outcomes in functional ability. Statistical analyses revealed\nsignificant associations between functional improvements and specific\nexercises. The RF model achieved the best performance in predicting functional\noutcomes. In this study, we identified three rehabilitation exercises that\nsignificantly contributed to patient post-stroke functional ability improvement\nin the first two months. Additionally, the successful application of a machine\nlearning model to predict patient-specific functional outcomes underscores the\npotential for precision rehabilitation.\n", "date": "2024-05-09", "categories": []}
{"id": "2305.19604", "title": "DKINet: Medication Recommendation via Domain Knowledge Informed Deep\n  Learning", "abstract": "  Medication recommendation is a fundamental yet crucial branch of healthcare\nthat presents opportunities to assist physicians in making more accurate\nmedication prescriptions for patients with complex health conditions. Previous\nstudies have primarily focused on learning patient representation from\nelectronic health records (EHR). While considering the clinical manifestations\nof the patient is important, incorporating domain-specific prior knowledge is\nequally significant in diagnosing the patient's health conditions. However,\neffectively integrating domain knowledge with the patient's clinical\nmanifestations can be challenging, particularly when dealing with complex\nclinical manifestations. Therefore, in this paper, we first identify\ncomprehensive domain-specific prior knowledge, namely the Unified Medical\nLanguage System (UMLS), which is a comprehensive repository of biomedical\nvocabularies and standards, for knowledge extraction. Subsequently, we propose\na knowledge injection module that addresses the effective integration of domain\nknowledge with complex clinical manifestations, enabling an effective\ncharacterization of the health conditions of the patient. Furthermore,\nconsidering the significant impact of a patient's medication history on their\ncurrent medication, we introduce a historical medication-aware patient\nrepresentation module to capture the longitudinal influence of historical\nmedication information on the representation of current patients. Extensive\nexperiments on three publicly benchmark datasets verify the superiority of our\nproposed method, which outperformed other methods by a significant margin. The\ncode is available at: https://github.com/sherry6247/DKINet.\n", "date": "2023-05-31", "categories": []}
{"id": "2310.07918", "title": "Contextualized Policy Recovery: Modeling and Interpreting Medical\n  Decisions with Adaptive Imitation Learning", "abstract": "  Interpretable policy learning seeks to estimate intelligible decision\npolicies from observed actions; however, existing models force a tradeoff\nbetween accuracy and interpretability, limiting data-driven interpretations of\nhuman decision-making processes. Fundamentally, existing approaches are\nburdened by this tradeoff because they represent the underlying decision\nprocess as a universal policy, when in fact human decisions are dynamic and can\nchange drastically under different contexts. Thus, we develop Contextualized\nPolicy Recovery (CPR), which re-frames the problem of modeling complex decision\nprocesses as a multi-task learning problem, where each context poses a unique\ntask and complex decision policies can be constructed piece-wise from many\nsimple context-specific policies. CPR models each context-specific policy as a\nlinear map, and generates new policy models $\\textit{on-demand}$ as contexts\nare updated with new observations. We provide two flavors of the CPR framework:\none focusing on exact local interpretability, and one retaining full global\ninterpretability. We assess CPR through studies on simulated and real data,\nachieving state-of-the-art performance on predicting antibiotic prescription in\nintensive care units ($+22\\%$ AUROC vs. previous SOTA) and predicting MRI\nprescription for Alzheimer's patients ($+7.7\\%$ AUROC vs. previous SOTA). With\nthis improvement, CPR closes the accuracy gap between interpretable and\nblack-box methods, allowing high-resolution exploration and analysis of\ncontext-specific decision models.\n", "date": "2023-10-11", "categories": []}
{"id": "2405.03943", "title": "Predictive Modeling with Temporal Graphical Representation on Electronic\n  Health Records", "abstract": "  Deep learning-based predictive models, leveraging Electronic Health Records\n(EHR), are receiving increasing attention in healthcare. An effective\nrepresentation of a patient's EHR should hierarchically encompass both the\ntemporal relationships between historical visits and medical events, and the\ninherent structural information within these elements. Existing patient\nrepresentation methods can be roughly categorized into sequential\nrepresentation and graphical representation. The sequential representation\nmethods focus only on the temporal relationships among longitudinal visits. On\nthe other hand, the graphical representation approaches, while adept at\nextracting the graph-structured relationships between various medical events,\nfall short in effectively integrate temporal information. To capture both types\nof information, we model a patient's EHR as a novel temporal heterogeneous\ngraph. This graph includes historical visits nodes and medical events nodes. It\npropagates structured information from medical event nodes to visit nodes and\nutilizes time-aware visit nodes to capture changes in the patient's health\nstatus. Furthermore, we introduce a novel temporal graph transformer (TRANS)\nthat integrates temporal edge features, global positional encoding, and local\nstructural encoding into heterogeneous graph convolution, capturing both\ntemporal and structural information. We validate the effectiveness of TRANS\nthrough extensive experiments on three real-world datasets. The results show\nthat our proposed approach achieves state-of-the-art performance.\n", "date": "2024-05-07", "categories": []}
{"id": "2405.02563", "title": "Deep Representation Learning-Based Dynamic Trajectory Phenotyping for\n  Acute Respiratory Failure in Medical Intensive Care Units", "abstract": "  Sepsis-induced acute respiratory failure (ARF) is a serious complication with\na poor prognosis. This paper presents a deep representation learningbased\nphenotyping method to identify distinct groups of clinical trajectories of\nseptic patients with ARF. For this retrospective study, we created a dataset\nfrom electronic medical records (EMR) consisting of data from sepsis patients\nadmitted to medical intensive care units who required at least 24 hours of\ninvasive mechanical ventilation at a quarternary care academic hospital in\nsoutheast USA for the years 2016-2021. A total of N=3349 patient encounters\nwere included in this study. Clustering Representation Learning on Incomplete\nTime Series Data (CRLI) algorithm was applied to a parsimonious set of EMR\nvariables in this data set. To validate the optimal number of clusters, the\nK-means algorithm was used in conjunction with dynamic time warping. Our model\nyielded four distinct patient phenotypes that were characterized as liver\ndysfunction/heterogeneous, hypercapnia, hypoxemia, and multiple organ\ndysfunction syndrome by a critical care expert. A Kaplan-Meier analysis to\ncompare the 28-day mortality trends exhibited significant differences (p <\n0.005) between the four phenotypes. The study demonstrates the utility of our\ndeep representation learning-based approach in unraveling phenotypes that\nreflect the heterogeneity in sepsis-induced ARF in terms of different mortality\noutcomes and severity. These phenotypes might reveal important clinical\ninsights into an effective prognosis and tailored treatment strategies.\n", "date": "2024-05-04", "categories": []}
{"id": "2405.02935", "title": "Enabling Patient-side Disease Prediction via the Integration of Patient\n  Narratives", "abstract": "  Disease prediction holds considerable significance in modern healthcare,\nbecause of its crucial role in facilitating early intervention and implementing\neffective prevention measures. However, most recent disease prediction\napproaches heavily rely on laboratory test outcomes (e.g., blood tests and\nmedical imaging from X-rays). Gaining access to such data for precise disease\nprediction is often a complex task from the standpoint of a patient and is\nalways only available post-patient consultation. To make disease prediction\navailable from patient-side, we propose Personalized Medical Disease Prediction\n(PoMP), which predicts diseases using patient health narratives including\ntextual descriptions and demographic information. By applying PoMP, patients\ncan gain a clearer comprehension of their conditions, empowering them to\ndirectly seek appropriate medical specialists and thereby reducing the time\nspent navigating healthcare communication to locate suitable doctors. We\nconducted extensive experiments using real-world data from Haodf to showcase\nthe effectiveness of PoMP.\n", "date": "2024-05-05", "categories": []}
{"id": "2405.03327", "title": "Clustering of Disease Trajectories with Explainable Machine Learning: A\n  Case Study on Postoperative Delirium Phenotypes", "abstract": "  The identification of phenotypes within complex diseases or syndromes is a\nfundamental component of precision medicine, which aims to adapt healthcare to\nindividual patient characteristics. Postoperative delirium (POD) is a complex\nneuropsychiatric condition with significant heterogeneity in its clinical\nmanifestations and underlying pathophysiology. We hypothesize that POD\ncomprises several distinct phenotypes, which cannot be directly observed in\nclinical practice. Identifying these phenotypes could enhance our understanding\nof POD pathogenesis and facilitate the development of targeted prevention and\ntreatment strategies. In this paper, we propose an approach that combines\nsupervised machine learning for personalized POD risk prediction with\nunsupervised clustering techniques to uncover potential POD phenotypes. We\nfirst demonstrate our approach using synthetic data, where we simulate patient\ncohorts with predefined phenotypes based on distinct sets of informative\nfeatures. We aim to mimic any clinical disease with our synthetic data\ngeneration method. By training a predictive model and applying SHAP, we show\nthat clustering patients in the SHAP feature importance space successfully\nrecovers the true underlying phenotypes, outperforming clustering in the raw\nfeature space. We then present a case study using real-world data from a cohort\nof elderly surgical patients. The results showcase the utility of our approach\nin uncovering clinically relevant subtypes of complex disorders like POD,\npaving the way for more precise and personalized treatment strategies.\n", "date": "2024-05-06", "categories": []}
{"id": "2405.01554", "title": "Early-stage detection of cognitive impairment by hybrid\n  quantum-classical algorithm using resting-state functional MRI time-series", "abstract": "  Following the recent development of quantum machine learning techniques, the\nliterature has reported several quantum machine learning algorithms for disease\ndetection. This study explores the application of a hybrid quantum-classical\nalgorithm for classifying region-of-interest time-series data obtained from\nresting-state functional magnetic resonance imaging in patients with\nearly-stage cognitive impairment based on the importance of cognitive decline\nfor dementia or aging. Classical one-dimensional convolutional layers are used\ntogether with quantum convolutional neural networks in our hybrid algorithm. In\nthe classical simulation, the proposed hybrid algorithms showed higher balanced\naccuracies than classical convolutional neural networks under the similar\ntraining conditions. Moreover, a total of nine brain regions (left precentral\ngyrus, right superior temporal gyrus, left rolandic operculum, right rolandic\noperculum, left parahippocampus, right hippocampus, left medial frontal gyrus,\nright cerebellum crus, and cerebellar vermis) among 116 brain regions were\nfound to be relatively effective brain regions for the classification based on\nthe model performances. The associations of the selected nine regions with\ncognitive decline, as found in previous studies, were additionally validated\nthrough seed-based functional connectivity analysis. We confirmed both the\nimprovement of model performance with the quantum convolutional neural network\nand neuroscientific validities of brain regions from our hybrid\nquantum-classical model.\n", "date": "2024-03-16", "categories": []}
{"id": "2405.01658", "title": "MMIST-ccRCC: A Real World Medical Dataset for the Development of\n  Multi-Modal Systems", "abstract": "  The acquisition of different data modalities can enhance our knowledge and\nunderstanding of various diseases, paving the way for a more personalized\nhealthcare. Thus, medicine is progressively moving towards the generation of\nmassive amounts of multi-modal data (\\emph{e.g,} molecular, radiology, and\nhistopathology). While this may seem like an ideal environment to capitalize\ndata-centric machine learning approaches, most methods still focus on exploring\na single or a pair of modalities due to a variety of reasons: i) lack of ready\nto use curated datasets; ii) difficulty in identifying the best multi-modal\nfusion strategy; and iii) missing modalities across patients. In this paper we\nintroduce a real world multi-modal dataset called MMIST-CCRCC that comprises 2\nradiology modalities (CT and MRI), histopathology, genomics, and clinical data\nfrom 618 patients with clear cell renal cell carcinoma (ccRCC). We provide\nsingle and multi-modal (early and late fusion) benchmarks in the task of\n12-month survival prediction in the challenging scenario of one or more missing\nmodalities for each patient, with missing rates that range from 26$\\%$ for\ngenomics data to more than 90$\\%$ for MRI. We show that even with such severe\nmissing rates the fusion of modalities leads to improvements in the survival\nforecasting. Additionally, incorporating a strategy to generate the latent\nrepresentations of the missing modalities given the available ones further\nimproves the performance, highlighting a potential complementarity across\nmodalities. Our dataset and code are available here:\nhttps://multi-modal-ist.github.io/datasets/ccRCC\n", "date": "2024-05-02", "categories": []}
{"id": "2306.05779", "title": "Transformer-based Time-to-Event Prediction for Chronic Kidney Disease\n  Deterioration", "abstract": "  Deep-learning techniques, particularly the transformer model, have shown\ngreat potential in enhancing the prediction performance of longitudinal health\nrecords. While previous methods have mainly focused on fixed-time risk\nprediction, time-to-event prediction (also known as survival analysis) is often\nmore appropriate for clinical scenarios. Here, we present a novel deep-learning\narchitecture we named STRAFE, a generalizable survival analysis\ntransformer-based architecture for electronic health records. The performance\nof STRAFE was evaluated using a real-world claim dataset of over 130,000\nindividuals with stage 3 chronic kidney disease (CKD) and was found to\noutperform other time-to-event prediction algorithms in predicting the exact\ntime of deterioration to stage 5. Additionally, STRAFE was found to outperform\nbinary outcome algorithms in predicting fixed-time risk, possibly due to its\nability to train on censored data. We show that STRAFE predictions can improve\nthe positive predictive value of high-risk patients by 3-fold, demonstrating\npossible usage to improve targeting for intervention programs. Finally, we\nsuggest a novel visualization approach to predictions on a per-patient basis.\nIn conclusion, STRAFE is a cutting-edge time-to-event prediction algorithm that\nhas the potential to enhance risk predictions in large claims datasets.\n", "date": "2023-06-09", "categories": []}
{"id": "2309.11295", "title": "CPLLM: Clinical Prediction with Large Language Models", "abstract": "  We present Clinical Prediction with Large Language Models (CPLLM), a method\nthat involves fine-tuning a pre-trained Large Language Model (LLM) for clinical\ndisease and readmission prediction. We utilized quantization and fine-tuned the\nLLM using prompts. For diagnosis prediction, we predict whether patients will\nbe diagnosed with a target disease during their next visit or in the subsequent\ndiagnosis, leveraging their historical diagnosis records. We compared our\nresults to various baselines, including RETAIN, and Med-BERT, the current\nstate-of-the-art model for disease prediction using temporal structured EHR\ndata. In addition, We also evaluated CPLLM for patient hospital readmission\nprediction and compared our method's performance with benchmark baselines. Our\nexperiments have shown that our proposed method, CPLLM, surpasses all the\ntested models in terms of PR-AUC and ROC-AUC metrics, showing state-of-the-art\nresults for diagnosis prediction and patient hospital readmission prediction.\nSuch a method can be easily implemented and integrated into the clinical\nprocess to help care providers estimate the next steps of patients\n", "date": "2023-09-20", "categories": []}
{"id": "2405.00741", "title": "Diagnosis of Parkinson's Disease Using EEG Signals and Machine Learning\n  Techniques: A Comprehensive Study", "abstract": "  Parkinson's disease is a widespread neurodegenerative condition necessitating\nearly diagnosis for effective intervention. This paper introduces an innovative\nmethod for diagnosing Parkinson's disease through the analysis of human EEG\nsignals, employing a Support Vector Machine (SVM) classification model. this\nresearch presents novel contributions to enhance diagnostic accuracy and\nreliability. Our approach incorporates a comprehensive review of EEG signal\nanalysis techniques and machine learning methods. Drawing from recent studies,\nwe have engineered an advanced SVM-based model optimized for Parkinson's\ndisease diagnosis. Utilizing cutting-edge feature engineering, extensive\nhyperparameter tuning, and kernel selection, our method achieves not only\nheightened diagnostic accuracy but also emphasizes model interpretability,\ncatering to both clinicians and researchers. Moreover, ethical concerns in\nhealthcare machine learning, such as data privacy and biases, are\nconscientiously addressed. We assess our method's performance through\nexperiments on a diverse dataset comprising EEG recordings from Parkinson's\ndisease patients and healthy controls, demonstrating significantly improved\ndiagnostic accuracy compared to conventional techniques. In conclusion, this\npaper introduces an innovative SVM-based approach for diagnosing Parkinson's\ndisease from human EEG signals. Building upon the IEEE framework and previous\nresearch, its novelty lies in the capacity to enhance diagnostic accuracy while\nupholding interpretability and ethical considerations for practical healthcare\napplications. These advances promise to revolutionize early Parkinson's disease\ndetection and management, ultimately contributing to enhanced patient outcomes\nand quality of life.\n", "date": "2024-04-30", "categories": []}
{"id": "2405.00819", "title": "ICU Bloodstream Infection Prediction: A Transformer-Based Approach for\n  EHR Analysis", "abstract": "  We introduce RatchetEHR, a novel transformer-based framework designed for the\npredictive analysis of electronic health records (EHR) data in intensive care\nunit (ICU) settings, with a specific focus on bloodstream infection (BSI)\nprediction. Leveraging the MIMIC-IV dataset, RatchetEHR demonstrates superior\npredictive performance compared to other methods, including RNN, LSTM, and\nXGBoost, particularly due to its advanced handling of sequential and temporal\nEHR data. A key innovation in RatchetEHR is the integration of the Graph\nConvolutional Transformer (GCT) component, which significantly enhances the\nability to identify hidden structural relationships within EHR data, resulting\nin more accurate clinical predictions. Through SHAP value analysis, we provide\ninsights into influential features for BSI prediction. RatchetEHR integrates\nmultiple advancements in deep learning which together provide accurate\npredictions even with a relatively small sample size and highly imbalanced\ndataset. This study contributes to medical informatics by showcasing the\napplication of advanced AI techniques in healthcare and sets a foundation for\nfurther research to optimize these capabilities in EHR data analysis.\n", "date": "2024-05-01", "categories": []}
{"id": "2405.00908", "title": "Transformer-Based Self-Supervised Learning for Histopathological\n  Classification of Ischemic Stroke Clot Origin", "abstract": "  Background and Purpose: Identifying the thromboembolism source in ischemic\nstroke is crucial for treatment and secondary prevention yet is often\nundetermined. This study describes a self-supervised deep learning approach in\ndigital pathology of emboli for classifying ischemic stroke clot origin from\nhistopathological images. Methods: The dataset included whole slide images\n(WSI) from the STRIP AI Kaggle challenge, consisting of retrieved clots from\nischemic stroke patients following mechanical thrombectomy. Transformer-based\ndeep learning models were developed using transfer learning and self-supervised\npretraining for classifying WSI. Customizations included an attention pooling\nlayer, weighted loss function, and threshold optimization. Various model\narchitectures were tested and compared, and model performances were primarily\nevaluated using weighted logarithmic loss. Results: The model achieved a\nlogloss score of 0.662 in cross-validation and 0.659 on the test set. Different\nmodel backbones were compared, with the swin_large_patch4_window12_384 showed\nhigher performance. Thresholding techniques for clot origin classification were\nemployed to balance false positives and negatives. Conclusion: The study\ndemonstrates the extent of efficacy of transformer-based deep learning models\nin identifying ischemic stroke clot origins from histopathological images and\nemphasizes the need for refined modeling techniques specifically adapted to\nthrombi WSI. Further research is needed to improve model performance,\ninterpretability, validate its effectiveness. Future enhancement could include\nintegrating larger patient cohorts, advanced preprocessing strategies, and\nexploring ensemble multimodal methods for enhanced diagnostic accuracy.\n", "date": "2024-05-01", "categories": []}
{"id": "2405.01126", "title": "Detecting and clustering swallow events in esophageal long-term\n  high-resolution manometry", "abstract": "  High-resolution manometry (HRM) is the gold standard in diagnosing esophageal\nmotility disorders. As HRM is typically conducted under short-term laboratory\nsettings, intermittently occurring disorders are likely to be missed.\nTherefore, long-term (up to 24h) HRM (LTHRM) is used to gain detailed insights\ninto the swallowing behavior. However, analyzing the extensive data from LTHRM\nis challenging and time consuming as medical experts have to analyze the data\nmanually, which is slow and prone to errors. To address this challenge, we\npropose a Deep Learning based swallowing detection method to accurately\nidentify swallowing events and secondary non-deglutitive-induced esophageal\nmotility disorders in LTHRM data. We then proceed with clustering the\nidentified swallows into distinct classes, which are analyzed by highly\nexperienced clinicians to validate the different swallowing patterns. We\nevaluate our computational pipeline on a total of 25 LTHRMs, which were\nmeticulously annotated by medical experts. By detecting more than 94% of all\nrelevant swallow events and providing all relevant clusters for a more reliable\ndiagnostic process among experienced clinicians, we are able to demonstrate the\neffectiveness as well as positive clinical impact of our approach to make LTHRM\nfeasible in clinical care.\n", "date": "2024-05-02", "categories": []}
{"id": "2405.01488", "title": "Digital Twin Generators for Disease Modeling", "abstract": "  A patient's digital twin is a computational model that describes the\nevolution of their health over time. Digital twins have the potential to\nrevolutionize medicine by enabling individual-level computer simulations of\nhuman health, which can be used to conduct more efficient clinical trials or to\nrecommend personalized treatment options. Due to the overwhelming complexity of\nhuman biology, machine learning approaches that leverage large datasets of\nhistorical patients' longitudinal health records to generate patients' digital\ntwins are more tractable than potential mechanistic models. In this manuscript,\nwe describe a neural network architecture that can learn conditional generative\nmodels of clinical trajectories, which we call Digital Twin Generators (DTGs),\nthat can create digital twins of individual patients. We show that the same\nneural network architecture can be trained to generate accurate digital twins\nfor patients across 13 different indications simply by changing the training\nset and tuning hyperparameters. By introducing a general purpose architecture,\nwe aim to unlock the ability to scale machine learning approaches to larger\ndatasets and across more indications so that a digital twin could be created\nfor any patient in the world.\n", "date": "2024-05-02", "categories": []}
{"id": "2305.12081", "title": "MediTab: Scaling Medical Tabular Data Predictors via Data Consolidation,\n  Enrichment, and Refinement", "abstract": "  Tabular data prediction has been employed in medical applications such as\npatient health risk prediction. However, existing methods usually revolve\naround the algorithm design while overlooking the significance of data\nengineering. Medical tabular datasets frequently exhibit significant\nheterogeneity across different sources, with limited sample sizes per source.\nAs such, previous predictors are often trained on manually curated small\ndatasets that struggle to generalize across different tabular datasets during\ninference. This paper proposes to scale medical tabular data predictors\n(MediTab) to various tabular inputs with varying features. The method uses a\ndata engine that leverages large language models (LLMs) to consolidate tabular\nsamples to overcome the barrier across tables with distinct schema. It also\naligns out-domain data with the target task using a \"learn, annotate, and\nrefinement\" pipeline. The expanded training data then enables the pre-trained\nMediTab to infer for arbitrary tabular input in the domain without fine-tuning,\nresulting in significant improvements over supervised baselines: it reaches an\naverage ranking of 1.57 and 1.00 on 7 patient outcome prediction datasets and 3\ntrial outcome prediction datasets, respectively. In addition, MediTab exhibits\nimpressive zero-shot performances: it outperforms supervised XGBoost models by\n8.9% and 17.2% on average in two prediction tasks, respectively.\n", "date": "2023-05-20", "categories": []}
{"id": "2401.11648", "title": "Next Visit Diagnosis Prediction via Medical Code-Centric Multimodal\n  Contrastive EHR Modelling with Hierarchical Regularisation", "abstract": "  Predicting next visit diagnosis using Electronic Health Records (EHR) is an\nessential task in healthcare, critical for devising proactive future plans for\nboth healthcare providers and patients. Nonetheless, many preceding studies\nhave not sufficiently addressed the heterogeneous and hierarchical\ncharacteristics inherent in EHR data, inevitably leading to sub-optimal\nperformance. To this end, we propose NECHO, a novel medical code-centric\nmultimodal contrastive EHR learning framework with hierarchical regularisation.\nFirst, we integrate multifaceted information encompassing medical codes,\ndemographics, and clinical notes using a tailored network design and a pair of\nbimodal contrastive losses, all of which pivot around a medical codes\nrepresentation. We also regularise modality-specific encoders using a parental\nlevel information in medical ontology to learn hierarchical structure of EHR\ndata. A series of experiments on MIMIC-III data demonstrates effectiveness of\nour approach.\n", "date": "2024-01-22", "categories": []}
{"id": "2404.19083", "title": "Longitudinal Mammogram Risk Prediction", "abstract": "  Breast cancer is one of the leading causes of mortality among women\nworldwide. Early detection and risk assessment play a crucial role in improving\nsurvival rates. Therefore, annual or biennial mammograms are often recommended\nfor screening in high-risk groups. Mammograms are typically interpreted by\nexpert radiologists based on the Breast Imaging Reporting and Data System\n(BI-RADS), which provides a uniform way to describe findings and categorizes\nthem to indicate the level of concern for breast cancer. Recently, machine\nlearning (ML) and computational approaches have been developed to automate and\nimprove the interpretation of mammograms. However, both BI-RADS and the\nML-based methods focus on the analysis of data from the present and sometimes\nthe most recent prior visit. While it is clear that temporal changes in image\nfeatures of the longitudinal scans should carry value for quantifying breast\ncancer risk, no prior work has conducted a systematic study of this. In this\npaper, we extend a state-of-the-art ML model to ingest an arbitrary number of\nlongitudinal mammograms and predict future breast cancer risk. On a large-scale\ndataset, we demonstrate that our model, LoMaR, achieves state-of-the-art\nperformance when presented with only the present mammogram. Furthermore, we use\nLoMaR to characterize the predictive value of prior visits. Our results show\nthat longer histories (e.g., up to four prior annual mammograms) can\nsignificantly boost the accuracy of predicting future breast cancer risk,\nparticularly beyond the short-term. Our code and model weights are available at\nhttps://github.com/batuhankmkaraman/LoMaR.\n", "date": "2024-04-29", "categories": []}
{"id": "2404.19359", "title": "Evaluating Lexicon Incorporation for Depression Symptom Estimation", "abstract": "  This paper explores the impact of incorporating sentiment, emotion, and\ndomain-specific lexicons into a transformer-based model for depression symptom\nestimation. Lexicon information is added by marking the words in the input\ntranscripts of patient-therapist conversations as well as in social media\nposts. Overall results show that the introduction of external knowledge within\npre-trained language models can be beneficial for prediction performance, while\ndifferent lexicons show distinct behaviours depending on the targeted task.\nAdditionally, new state-of-the-art results are obtained for the estimation of\ndepression level over patient-therapist interviews.\n", "date": "2024-04-30", "categories": []}
{"id": "2404.19579", "title": "Automatic Cardiac Pathology Recognition in Echocardiography Images Using\n  Higher Order Dynamic Mode Decomposition and a Vision Transformer for Small\n  Datasets", "abstract": "  Heart diseases are the main international cause of human defunction.\nAccording to the WHO, nearly 18 million people decease each year because of\nheart diseases. Also considering the increase of medical data, much pressure is\nput on the health industry to develop systems for early and accurate heart\ndisease recognition. In this work, an automatic cardiac pathology recognition\nsystem based on a novel deep learning framework is proposed, which analyses in\nreal-time echocardiography video sequences. The system works in two stages. The\nfirst one transforms the data included in a database of echocardiography\nsequences into a machine-learning-compatible collection of annotated images\nwhich can be used in the training stage of any kind of machine learning-based\nframework, and more specifically with deep learning. This includes the use of\nthe Higher Order Dynamic Mode Decomposition (HODMD) algorithm, for the first\ntime to the authors' knowledge, for both data augmentation and feature\nextraction in the medical field. The second stage is focused on building and\ntraining a Vision Transformer (ViT), barely explored in the related literature.\nThe ViT is adapted for an effective training from scratch, even with small\ndatasets. The designed neural network analyses images from an echocardiography\nsequence to predict the heart state. The results obtained show the superiority\nof the proposed system and the efficacy of the HODMD algorithm, even\noutperforming pretrained Convolutional Neural Networks (CNNs), which are so far\nthe method of choice in the literature.\n", "date": "2024-04-30", "categories": []}
{"id": "2404.19598", "title": "Artificial Intelligence in Bone Metastasis Analysis: Current\n  Advancements, Opportunities and Challenges", "abstract": "  In recent years, Artificial Intelligence (AI) has been widely used in\nmedicine, particularly in the analysis of medical imaging, which has been\ndriven by advances in computer vision and deep learning methods. This is\nparticularly important in overcoming the challenges posed by diseases such as\nBone Metastases (BM), a common and complex malignancy of the bones. Indeed,\nthere have been an increasing interest in developing Machine Learning (ML)\ntechniques into oncologic imaging for BM analysis. In order to provide a\ncomprehensive overview of the current state-of-the-art and advancements for BM\nanalysis using artificial intelligence, this review is conducted with the\naccordance with PRISMA guidelines. Firstly, this review highlights the clinical\nand oncologic perspectives of BM and the used medical imaging modalities, with\ndiscussing their advantages and limitations. Then the review focuses on modern\napproaches with considering the main BM analysis tasks, which includes:\nclassification, detection and segmentation. The results analysis show that ML\ntechnologies can achieve promising performance for BM analysis and have\nsignificant potential to improve clinician efficiency and cope with time and\ncost limitations. Furthermore, there are requirements for further research to\nvalidate the clinical performance of ML tools and facilitate their integration\ninto routine clinical practice.\n", "date": "2024-04-30", "categories": []}
{"id": "2309.08415", "title": "A new method of modeling the multi-stage decision-making process of CRT\n  using machine learning with uncertainty quantification", "abstract": "  Aims. The purpose of this study is to create a multi-stage machine learning\nmodel to predict cardiac resynchronization therapy (CRT) response for heart\nfailure (HF) patients. This model exploits uncertainty quantification to\nrecommend additional collection of single-photon emission computed tomography\nmyocardial perfusion imaging (SPECT MPI) variables if baseline clinical\nvariables and features from electrocardiogram (ECG) are not sufficient.\nMethods. 218 patients who underwent rest-gated SPECT MPI were enrolled in this\nstudy. CRT response was defined as an increase in left ventricular ejection\nfraction (LVEF) > 5% at a 6+-1 month follow-up. A multi-stage ML model was\ncreated by combining two ensemble models: Ensemble 1 was trained with clinical\nvariables and ECG; Ensemble 2 included Ensemble 1 plus SPECT MPI features.\nUncertainty quantification from Ensemble 1 allowed for multi-stage\ndecision-making to determine if the acquisition of SPECT data for a patient is\nnecessary. The performance of the multi-stage model was compared with that of\nEnsemble models 1 and 2. Results. The response rate for CRT was 55.5% (n = 121)\nwith overall male gender 61.0% (n = 133), an average age of 62.0+-11.8, and\nLVEF of 27.7+-11.0. The multi-stage model performed similarly to Ensemble 2\n(which utilized the additional SPECT data) with AUC of 0.75 vs. 0.77, accuracy\nof 0.71 vs. 0.69, sensitivity of 0.70 vs. 0.72, and specificity 0.72 vs. 0.65,\nrespectively. However, the multi-stage model only required SPECT MPI data for\n52.7% of the patients across all folds. Conclusions. By using rule-based logic\nstemming from uncertainty quantification, the multi-stage model was able to\nreduce the need for additional SPECT MPI data acquisition without sacrificing\nperformance.\n", "date": "2023-09-15", "categories": []}
{"id": "2404.18631", "title": "Feature importance to explain multimodal prediction models. A clinical\n  use case", "abstract": "  Surgery to treat elderly hip fracture patients may cause complications that\ncan lead to early mortality. An early warning system for complications could\nprovoke clinicians to monitor high-risk patients more carefully and address\npotential complications early, or inform the patient. In this work, we develop\na multimodal deep-learning model for post-operative mortality prediction using\npre-operative and per-operative data from elderly hip fracture patients.\nSpecifically, we include static patient data, hip and chest images before\nsurgery in pre-operative data, vital signals, and medications administered\nduring surgery in per-operative data. We extract features from image modalities\nusing ResNet and from vital signals using LSTM. Explainable model outcomes are\nessential for clinical applicability, therefore we compute Shapley values to\nexplain the predictions of our multimodal black box model. We find that i)\nShapley values can be used to estimate the relative contribution of each\nmodality both locally and globally, and ii) a modified version of the chain\nrule can be used to propagate Shapley values through a sequence of models\nsupporting interpretable local explanations. Our findings imply that a\nmultimodal combination of black box models can be explained by propagating\nShapley values through the model sequence.\n", "date": "2024-04-29", "categories": []}
{"id": "2209.07397", "title": "From algorithms to action: improving patient care requires causality", "abstract": "  In cancer research there is much interest in building and validating outcome\npredicting outcomes to support treatment decisions. However, because most\noutcome prediction models are developed and validated without regard to the\ncausal aspects of treatment decision making, many published outcome prediction\nmodels may cause harm when used for decision making, despite being found\naccurate in validation studies. Guidelines on prediction model validation and\nthe checklist for risk model endorsement by the American Joint Committee on\nCancer do not protect against prediction models that are accurate during\ndevelopment and validation but harmful when used for decision making. We\nexplain why this is the case and how to build and validate models that are\nuseful for decision making.\n", "date": "2022-09-15", "categories": []}
{"id": "2404.14057", "title": "Bored to Death: Artificial Intelligence Research Reveals the Role of\n  Boredom in Suicide Behavior", "abstract": "  Background: Recent advancements in Artificial Intelligence (AI) contributed\nsignificantly to suicide assessment, however, our theoretical understanding of\nthis complex behavior is still limited. Objective: This study aimed to harness\nAI methodologies to uncover hidden risk factors that trigger or aggravate\nsuicide behaviors. Method: The primary dataset included 228,052 Facebook\npostings by 1,006 users who completed the gold-standard Columbia Suicide\nSeverity Rating Scale. This dataset was analyzed using a bottom-up research\npipeline without a-priory hypotheses and its findings were validated using a\ntop-down analysis of a new dataset. This secondary dataset included responses\nby 1,062 participants to the same suicide scale as well as to well-validated\nscales measuring depression and boredom. Results: An almost fully automated,\nAI-guided research pipeline resulted in four Facebook topics that predicted the\nrisk of suicide, of which the strongest predictor was boredom. A comprehensive\nliterature review using APA PsycInfo revealed that boredom is rarely perceived\nas a unique risk factor of suicide. A complementing top-down path analysis of\nthe secondary dataset uncovered an indirect relationship between boredom and\nsuicide, which was mediated by depression. An equivalent mediated relationship\nwas observed in the primary Facebook dataset as well. However, here, a direct\nrelationship between boredom and suicide risk was also observed. Conclusions:\nIntegrating AI methods allowed the discovery of an under-researched risk factor\nof suicide. The study signals boredom as a maladaptive 'ingredient' that might\ntrigger suicide behaviors, regardless of depression. Further studies are\nrecommended to direct clinicians' attention to this burdening, and sometimes\nexistential experience.\n", "date": "2024-04-22", "categories": []}
{"id": "2404.16885", "title": "Adapting an Artificial Intelligence Sexually Transmitted Diseases\n  Symptom Checker Tool for Mpox Detection: The HeHealth Experience", "abstract": "  Artificial Intelligence applications have shown promise in the management of\npandemics and have been widely used to assist the identification,\nclassification, and diagnosis of medical images. In response to the global\noutbreak of Monkeypox (Mpox), the HeHealth.ai team leveraged an existing tool\nto screen for sexually transmitted diseases to develop a digital screening test\nfor symptomatic Mpox through AI approaches. Prior to the global outbreak of\nMpox, the team developed a smartphone app, where app users can use their own\nsmartphone cameras to take pictures of their own penises to screen for\nsymptomatic STD. The AI model was initially developed using 5000 cases and use\na modified convolutional neural network to output prediction scores across\nvisually diagnosable penis pathologies including Syphilis, Herpes Simplex\nVirus, and Human Papilloma Virus. From June 2022 to October 2022, a total of\nabout 22,000 users downloaded the HeHealth app, and about 21,000 images have\nbeen analyzed using HeHealth AI technology. We then engaged in formative\nresearch, stakeholder engagement, rapid consolidation images, a validation\nstudy, and implementation of the tool from July 2022. From July 2022 to October\n2022, a total of 1000 Mpox related images had been used to train the Mpox\nsymptom checker tool. Our digital symptom checker tool showed accuracy of 87%\nto rule in Mpox and 90% to rule out symptomatic Mpox. Several hurdles\nidentified included issues of data privacy and security for app users, initial\nlack of data to train the AI tool, and the potential generalizability of input\ndata. We offer several suggestions to help others get started on similar\nprojects in emergency situations, including engaging a wide range of\nstakeholders, having a multidisciplinary team, prioritizing pragmatism, as well\nas the concept that big data in fact is made up of small data.\n", "date": "2024-04-23", "categories": []}
{"id": "2310.11385", "title": "A voxel-level approach to brain age prediction: A method to assess\n  regional brain aging", "abstract": "  Brain aging is a regional phenomenon, a facet that remains relatively\nunder-explored within the realm of brain age prediction research using machine\nlearning methods. Voxel-level predictions can provide localized brain age\nestimates that can provide granular insights into the regional aging processes.\nThis is essential to understand the differences in aging trajectories in\nhealthy versus diseased subjects. In this work, a deep learning-based multitask\nmodel is proposed for voxel-level brain age prediction from T1-weighted\nmagnetic resonance images. The proposed model outperforms the models existing\nin the literature and yields valuable clinical insights when applied to both\nhealthy and diseased populations. Regional analysis is performed on the\nvoxel-level brain age predictions to understand aging trajectories of known\nanatomical regions in the brain and show that there exist disparities in\nregional aging trajectories of healthy subjects compared to ones with\nunderlying neurological disorders such as Dementia and more specifically,\nAlzheimer's disease. Our code is available at\nhttps://github.com/nehagianchandani/Voxel-level-brain-age-prediction.\n", "date": "2023-10-17", "categories": []}
{"id": "2404.16522", "title": "A Deep Learning-Driven Pipeline for Differentiating Hypertrophic\n  Cardiomyopathy from Cardiac Amyloidosis Using 2D Multi-View Echocardiography", "abstract": "  Hypertrophic cardiomyopathy (HCM) and cardiac amyloidosis (CA) are both heart\nconditions that can progress to heart failure if untreated. They exhibit\nsimilar echocardiographic characteristics, often leading to diagnostic\nchallenges. This paper introduces a novel multi-view deep learning approach\nthat utilizes 2D echocardiography for differentiating between HCM and CA. The\nmethod begins by classifying 2D echocardiography data into five distinct\nechocardiographic views: apical 4-chamber, parasternal long axis of left\nventricle, parasternal short axis at levels of the mitral valve, papillary\nmuscle, and apex. It then extracts features of each view separately and\ncombines five features for disease classification. A total of 212 patients\ndiagnosed with HCM, and 30 patients diagnosed with CA, along with 200\nindividuals with normal cardiac function(Normal), were enrolled in this study\nfrom 2018 to 2022. This approach achieved a precision, recall of 0.905, and\nmicro-F1 score of 0.904, demonstrating its effectiveness in accurately\nidentifying HCM and CA using a multi-view analysis.\n", "date": "2024-04-25", "categories": []}
{"id": "2403.19011", "title": "Sequential Inference of Hospitalization Electronic Health Records Using\n  Probabilistic Models", "abstract": "  In the dynamic hospital setting, decision support can be a valuable tool for\nimproving patient outcomes. Data-driven inference of future outcomes is\nchallenging in this dynamic setting, where long sequences such as laboratory\ntests and medications are updated frequently. This is due in part to\nheterogeneity of data types and mixed-sequence types contained in variable\nlength sequences. In this work we design a probabilistic unsupervised model for\nmultiple arbitrary-length sequences contained in hospitalization Electronic\nHealth Record (EHR) data. The model uses a latent variable structure and\ncaptures complex relationships between medications, diagnoses, laboratory\ntests, neurological assessments, and medications. It can be trained on original\ndata, without requiring any lossy transformations or time binning. Inference\nalgorithms are derived that use partial data to infer properties of the\ncomplete sequences, including their length and presence of specific values. We\ntrain this model on data from subjects receiving medical care in the Kaiser\nPermanente Northern California integrated healthcare delivery system. The\nresults are evaluated against held-out data for predicting the length of\nsequences and presence of Intensive Care Unit (ICU) in hospitalization bed\nsequences. Our method outperforms a baseline approach, showing that in these\nexperiments the trained model captures information in the sequences that is\ninformative of their future values.\n", "date": "2024-03-27", "categories": []}
{"id": "2404.15418", "title": "Machine Learning Techniques with Fairness for Prediction of Completion\n  of Drug and Alcohol Rehabilitation", "abstract": "  The aim of this study is to look at predicting whether a person will complete\na drug and alcohol rehabilitation program and the number of times a person\nattends. The study is based on demographic data obtained from Substance Abuse\nand Mental Health Services Administration (SAMHSA) from both admissions and\ndischarge data from drug and alcohol rehabilitation centers in Oklahoma.\nDemographic data is highly categorical which led to binary encoding being used\nand various fairness measures being utilized to mitigate bias of nine\ndemographic variables. Kernel methods such as linear, polynomial, sigmoid, and\nradial basis functions were compared using support vector machines at various\nparameter ranges to find the optimal values. These were then compared to\nmethods such as decision trees, random forests, and neural networks. Synthetic\nMinority Oversampling Technique Nominal (SMOTEN) for categorical data was used\nto balance the data with imputation for missing data. The nine bias variables\nwere then intersectionalized to mitigate bias and the dual and triple\ninteractions were integrated to use the probabilities to look at worst case\nratio fairness mitigation. Disparate Impact, Statistical Parity difference,\nConditional Statistical Parity Ratio, Demographic Parity, Demographic Parity\nRatio, Equalized Odds, Equalized Odds Ratio, Equal Opportunity, and Equalized\nOpportunity Ratio were all explored at both the binary and multiclass\nscenarios.\n", "date": "2024-04-23", "categories": []}
{"id": "2404.15757", "title": "Exploring Machine Learning Algorithms for Infection Detection Using\n  GC-IMS Data: A Preliminary Study", "abstract": "  The developing field of enhanced diagnostic techniques in the diagnosis of\ninfectious diseases, constitutes a crucial domain in modern healthcare. By\nutilizing Gas Chromatography-Ion Mobility Spectrometry (GC-IMS) data and\nincorporating machine learning algorithms into one platform, our research aims\nto tackle the ongoing issue of precise infection identification. Inspired by\nthese difficulties, our goals consist of creating a strong data analytics\nprocess, enhancing machine learning (ML) models, and performing thorough\nvalidation for clinical applications. Our research contributes to the emerging\nfield of advanced diagnostic technologies by integrating Gas Chromatography-Ion\nMobility Spectrometry (GC-IMS) data and machine learning algorithms within a\nunified Laboratory Information Management System (LIMS) platform. Preliminary\ntrials demonstrate encouraging levels of accuracy when employing various ML\nalgorithms to differentiate between infected and non-infected samples.\nContinuing endeavors are currently concentrated on enhancing the effectiveness\nof the model, investigating techniques to clarify its functioning, and\nincorporating many types of data to further support the early detection of\ndiseases.\n", "date": "2024-04-24", "categories": []}
{"id": "2305.07877", "title": "Differentiating Viral and Bacterial Infections: A Machine Learning Model\n  Based on Routine Blood Test Values", "abstract": "  The growing threat of antibiotic resistance necessitates accurate\ndifferentiation between bacterial and viral infections for proper antibiotic\nadministration. In this study, a Virus vs. Bacteria machine learning model was\ndeveloped to distinguish between these infection types using 16 routine blood\ntest results, C-reactive protein concentration (CRP), biological sex, and age.\nWith a dataset of 44,120 cases from a single medical center, the model achieved\nan accuracy of 82.2 %, a sensitivity of 79.7 %, a specificity of 84.5 %, a\nBrier score of 0.129, and an area under the ROC curve (AUC) of 0.905,\noutperforming a CRP-based decision rule. Notably, the machine learning model\nenhanced accuracy within the CRP range of 10-40 mg/L, a range where CRP alone\nis less informative. These results highlight the advantage of integrating\nmultiple blood parameters in diagnostics. The \"Virus vs. Bacteria\" model paves\nthe way for advanced diagnostic tools, leveraging machine learning to optimize\ninfection management.\n", "date": "2023-05-13", "categories": []}
{"id": "2309.07136", "title": "Masked Transformer for Electrocardiogram Classification", "abstract": "  Electrocardiogram (ECG) is one of the most important diagnostic tools in\nclinical applications. With the advent of advanced algorithms, various deep\nlearning models have been adopted for ECG tasks. However, the potential of\nTransformer for ECG data has not been fully realized, despite their widespread\nsuccess in computer vision and natural language processing. In this work, we\npresent Masked Transformer for ECG classification (MTECG), a simple yet\neffective method which significantly outperforms recent state-of-the-art\nalgorithms in ECG classification. Our approach adapts the image-based masked\nautoencoders to self-supervised representation learning from ECG time series.\nWe utilize a lightweight Transformer for the encoder and a 1-layer Transformer\nfor the decoder. The ECG signal is split into a sequence of non-overlapping\nsegments along the time dimension, and learnable positional embeddings are\nadded to preserve the sequential information. We construct the Fuwai dataset\ncomprising 220,251 ECG recordings with a broad range of diagnoses, annotated by\nmedical experts, to explore the potential of Transformer. A strong pre-training\nand fine-tuning recipe is proposed from the empirical study. The experiments\ndemonstrate that the proposed method increases the macro F1 scores by\n3.4%-27.5% on the Fuwai dataset, 9.9%-32.0% on the PTB-XL dataset, and\n9.4%-39.1% on a multicenter dataset, compared to the alternative methods. We\nhope that this study could direct future research on the application of\nTransformer to more ECG tasks.\n", "date": "2023-08-31", "categories": []}
{"id": "2404.14689", "title": "Interpretable Prediction and Feature Selection for Survival Analysis", "abstract": "  Survival analysis is widely used as a technique to model time-to-event data\nwhen some data is censored, particularly in healthcare for predicting future\npatient risk. In such settings, survival models must be both accurate and\ninterpretable so that users (such as doctors) can trust the model and\nunderstand model predictions. While most literature focuses on discrimination,\ninterpretability is equally as important. A successful interpretable model\nshould be able to describe how changing each feature impacts the outcome, and\nshould only use a small number of features. In this paper, we present DyS\n(pronounced ``dice''), a new survival analysis model that achieves both strong\ndiscrimination and interpretability. DyS is a feature-sparse Generalized\nAdditive Model, combining feature selection and interpretable prediction into\none model. While DyS works well for all survival analysis problems, it is\nparticularly useful for large (in $n$ and $p$) survival datasets such as those\ncommonly found in observational healthcare studies. Empirical studies show that\nDyS competes with other state-of-the-art machine learning models for survival\nanalysis, while being highly interpretable.\n", "date": "2024-04-23", "categories": []}
{"id": "2404.15029", "title": "Explainable LightGBM Approach for Predicting Myocardial Infarction\n  Mortality", "abstract": "  Myocardial Infarction is a main cause of mortality globally, and accurate\nrisk prediction is crucial for improving patient outcomes. Machine Learning\ntechniques have shown promise in identifying high-risk patients and predicting\noutcomes. However, patient data often contain vast amounts of information and\nmissing values, posing challenges for feature selection and imputation methods.\nIn this article, we investigate the impact of the data preprocessing task and\ncompare three ensembles boosted tree methods to predict the risk of mortality\nin patients with myocardial infarction. Further, we use the Tree Shapley\nAdditive Explanations method to identify relationships among all the features\nfor the performed predictions, leveraging the entirety of the available data in\nthe analysis. Notably, our approach achieved a superior performance when\ncompared to other existing machine learning approaches, with an F1-score of\n91,2% and an accuracy of 91,8% for LightGBM without data preprocessing.\n", "date": "2024-04-23", "categories": []}
{"id": "2305.17261", "title": "Closing the Gap in High-Risk Pregnancy Care Using Machine Learning and\n  Human-AI Collaboration", "abstract": "  A high-risk pregnancy is a pregnancy complicated by factors that can\nadversely affect the outcomes of the mother or the infant. Health insurers use\nalgorithms to identify members who would benefit from additional clinical\nsupport. This work presents the implementation of a real-world ML-based system\nto assist care managers in identifying pregnant patients at risk of\ncomplications. In this retrospective evaluation study, we developed a novel\nhybrid-ML classifier to predict whether patients are pregnant and trained a\nstandard classifier using claims data from a health insurance company in the US\nto predict whether a patient will develop pregnancy complications. These models\nwere developed in cooperation with the care management team and integrated into\na user interface with explanations for the nurses. The proposed models\noutperformed commonly used claim codes for the identification of pregnant\npatients at the expense of a manageable false positive rate. Our risk\ncomplication classifier shows that we can accurately triage patients by risk of\ncomplication. Our approach and evaluation are guided by human-centric design.\nIn user studies with the nurses, they preferred the proposed models over\nexisting approaches.\n", "date": "2023-05-26", "categories": []}
{"id": "2310.07463", "title": "Using explainable AI to investigate electrocardiogram changes during\n  healthy aging -- from expert features to raw signals", "abstract": "  Cardiovascular diseases remain the leading global cause of mortality. Age is\nan important covariate whose effect is most easily investigated in a healthy\ncohort to properly distinguish the former from disease-related changes.\nTraditionally, most of such insights have been drawn from the analysis of\nelectrocardiogram (ECG) feature changes in individuals as they age. However,\nthese features, while informative, may potentially obscure underlying data\nrelationships. In this paper we present the following contributions: (1) We\nemploy a deep-learning model and a tree-based model to analyze ECG data from a\nrobust dataset of healthy individuals across varying ages in both raw signals\nand ECG feature format. (2) We use explainable AI methods to identify the most\ndiscriminative ECG features across age groups.(3) Our analysis with tree-based\nclassifiers reveals age-related declines in inferred breathing rates and\nidentifies notably high SDANN values as indicative of elderly individuals,\ndistinguishing them from younger adults. (4) Furthermore, the deep-learning\nmodel underscores the pivotal role of the P-wave in age predictions across all\nage groups, suggesting potential changes in the distribution of different\nP-wave types with age. These findings shed new light on age-related ECG\nchanges, offering insights that transcend traditional feature-based approaches.\n", "date": "2023-10-11", "categories": []}
{"id": "2404.13260", "title": "Predicting Diabetes with Machine Learning Analysis of Income and Health\n  Factors", "abstract": "  In this study, we delve into the intricate relationships between diabetes and\na range of health indicators, with a particular focus on the newly added\nvariable of income. Utilizing data from the 2015 Behavioral Risk Factor\nSurveillance System (BRFSS), we analyze the impact of various factors such as\nblood pressure, cholesterol, BMI, smoking habits, and more on the prevalence of\ndiabetes. Our comprehensive analysis not only investigates each factor in\nisolation but also explores their interdependencies and collective influence on\ndiabetes. A novel aspect of our research is the examination of income as a\ndeterminant of diabetes risk, which to the best of our knowledge has been\nrelatively underexplored in previous studies. We employ statistical and machine\nlearning techniques to unravel the complex interplay between socio-economic\nstatus and diabetes, providing new insights into how financial well-being\ninfluences health outcomes. Our research reveals a discernible trend where\nlower income brackets are associated with a higher incidence of diabetes. In\nanalyzing a blend of 33 variables, including health factors and lifestyle\nchoices, we identified that features such as high blood pressure, high\ncholesterol, cholesterol checks, income, and Body Mass Index (BMI) are of\nconsiderable significance. These elements stand out among the myriad of factors\nexamined, suggesting that they play a pivotal role in the prevalence and\nmanagement of diabetes.\n", "date": "2024-04-20", "categories": []}
{"id": "2404.12394", "title": "A Big Data Analytics System for Predicting Suicidal Ideation in\n  Real-Time Based on Social Media Streaming Data", "abstract": "  Online social media platforms have recently become integral to our society\nand daily routines. Every day, users worldwide spend a couple of hours on such\nplatforms, expressing their sentiments and emotional state and contacting each\nother. Analyzing such huge amounts of data from these platforms can provide a\nclear insight into public sentiments and help detect their mental status. The\nearly identification of these health condition risks may assist in preventing\nor reducing the number of suicide ideation and potentially saving people's\nlives. The traditional techniques have become ineffective in processing such\nstreams and large-scale datasets. Therefore, the paper proposed a new\nmethodology based on a big data architecture to predict suicidal ideation from\nsocial media content. The proposed approach provides a practical analysis of\nsocial media data in two phases: batch processing and real-time streaming\nprediction. The batch dataset was collected from the Reddit forum and used for\nmodel building and training, while streaming big data was extracted using\nTwitter streaming API and used for real-time prediction. After the raw data was\npreprocessed, the extracted features were fed to multiple Apache Spark ML\nclassifiers: NB, LR, LinearSVC, DT, RF, and MLP. We conducted various\nexperiments using various feature-extraction techniques with different testing\nscenarios. The experimental results of the batch processing phase showed that\nthe features extracted of (Unigram + Bigram) + CV-IDF with MLP classifier\nprovided high performance for classifying suicidal ideation, with an accuracy\nof 93.47%, and then applied for real-time streaming prediction phase.\n", "date": "2024-03-19", "categories": []}
{"id": "2404.12605", "title": "GluMarker: A Novel Predictive Modeling of Glycemic Control Through\n  Digital Biomarkers", "abstract": "  The escalating prevalence of diabetes globally underscores the need for\ndiabetes management. Recent research highlights the growing focus on digital\nbiomarkers in diabetes management, with innovations in computational frameworks\nand noninvasive monitoring techniques using personalized glucose metrics.\nHowever, they predominantly focus on insulin dosing and specific glucose\nvalues, or with limited attention given to overall glycemic control. This\nleaves a gap in expanding the scope of digital biomarkers for overall glycemic\ncontrol in diabetes management. To address such a research gap, we propose\nGluMarker -- an end-to-end framework for modeling digital biomarkers using\nbroader factors sources to predict glycemic control. Through the assessment and\nrefinement of various machine learning baselines, GluMarker achieves\nstate-of-the-art on Anderson's dataset in predicting next-day glycemic control.\nMoreover, our research identifies key digital biomarkers for the next day's\nglycemic control prediction. These identified biomarkers are instrumental in\nilluminating the daily factors that influence glycemic management, offering\nvital insights for diabetes care.\n", "date": "2024-04-19", "categories": []}
{"id": "2302.04143", "title": "Predicting Thrombectomy Recanalization from CT Imaging Using Deep\n  Learning Models", "abstract": "  For acute ischemic stroke (AIS) patients with large vessel occlusions,\nclinicians must decide if the benefit of mechanical thrombectomy (MTB)\noutweighs the risks and potential complications following an invasive\nprocedure. Pre-treatment computed tomography (CT) and angiography (CTA) are\nwidely used to characterize occlusions in the brain vasculature. If a patient\nis deemed eligible, a modified treatment in cerebral ischemia (mTICI) score\nwill be used to grade how well blood flow is reestablished throughout and\nfollowing the MTB procedure. An estimation of the likelihood of successful\nrecanalization can support treatment decision-making. In this study, we\nproposed a fully automated prediction of a patient's recanalization score using\npre-treatment CT and CTA imaging. We designed a spatial cross attention network\n(SCANet) that utilizes vision transformers to localize to pertinent slices and\nbrain regions. Our top model achieved an average cross-validated ROC-AUC of\n77.33 $\\pm$ 3.9\\%. This is a promising result that supports future applications\nof deep learning on CT and CTA for the identification of eligible AIS patients\nfor MTB.\n", "date": "2023-02-08", "categories": []}
{"id": "2404.11924", "title": "Toward Short-Term Glucose Prediction Solely Based on CGM Time Series", "abstract": "  The global diabetes epidemic highlights the importance of maintaining good\nglycemic control. Glucose prediction is a fundamental aspect of diabetes\nmanagement, facilitating real-time decision-making. Recent research has\nintroduced models focusing on long-term glucose trend prediction, which are\nunsuitable for real-time decision-making and result in delayed responses.\nConversely, models designed to respond to immediate glucose level changes\ncannot analyze glucose variability comprehensively. Moreover, contemporary\nresearch generally integrates various physiological parameters (e.g. insulin\ndoses, food intake, etc.), which inevitably raises data privacy concerns. To\nbridge such a research gap, we propose TimeGlu -- an end-to-end pipeline for\nshort-term glucose prediction solely based on CGM time series data. We\nimplement four baseline methods to conduct a comprehensive comparative analysis\nof the model's performance. Through extensive experiments on two contrasting\ndatasets (CGM Glucose and Colas dataset), TimeGlu achieves state-of-the-art\nperformance without the need for additional personal data from patients,\nproviding effective guidance for real-world diabetic glucose management.\n", "date": "2024-04-18", "categories": []}
{"id": "2312.13454", "title": "MixEHR-SurG: a joint proportional hazard and guided topic model for\n  inferring mortality-associated topics from electronic health records", "abstract": "  Survival models can help medical practitioners to evaluate the prognostic\nimportance of clinical variables to patient outcomes such as mortality or\nhospital readmission and subsequently design personalized treatment regimes.\nElectronic Health Records (EHRs) hold the promise for large-scale survival\nanalysis based on systematically recorded clinical features for each patient.\nHowever, existing survival models either do not scale to high dimensional and\nmulti-modal EHR data or are difficult to interpret. In this study, we present a\nsupervised topic model called MixEHR-SurG to simultaneously integrate\nheterogeneous EHR data and model survival hazard. Our contributions are\nthree-folds: (1) integrating EHR topic inference with Cox proportional hazards\nlikelihood; (2) integrating patient-specific topic hyperparameters using the\nPheCode concepts such that each topic can be identified with exactly one\nPheCode-associated phenotype; (3) multi-modal survival topic inference. This\nleads to a highly interpretable survival topic model that can infer\nPheCode-specific phenotype topics associated with patient mortality. We\nevaluated MixEHR-SurG using a simulated dataset and two real-world EHR\ndatasets: the Quebec Congenital Heart Disease (CHD) data consisting of 8,211\nsubjects with 75,187 outpatient claim records of 1,767 unique ICD codes; the\nMIMIC-III consisting of 1,458 subjects with multi-modal EHR records. Compared\nto the baselines, MixEHR-SurG achieved a superior dynamic AUROC for mortality\nprediction, with a mean AUROC score of 0.89 in the simulation dataset and a\nmean AUROC of 0.645 on the CHD dataset. Qualitatively, MixEHR-SurG associates\nsevere cardiac conditions with high mortality risk among the CHD patients after\nthe first heart failure hospitalization and critical brain injuries with\nincreased mortality among the MIMIC-III patients after their ICU discharge.\n", "date": "2023-12-20", "categories": []}
{"id": "2404.10901", "title": "CrossGP: Cross-Day Glucose Prediction Excluding Physiological\n  Information", "abstract": "  The increasing number of diabetic patients is a serious issue in society\ntoday, which has significant negative impacts on people's health and the\ncountry's financial expenditures. Because diabetes may develop into potential\nserious complications, early glucose prediction for diabetic patients is\nnecessary for timely medical treatment. Existing glucose prediction methods\ntypically utilize patients' private data (e.g. age, gender, ethnicity) and\nphysiological parameters (e.g. blood pressure, heart rate) as reference\nfeatures for glucose prediction, which inevitably leads to privacy protection\nconcerns. Moreover, these models generally focus on either long-term\n(monthly-based) or short-term (minute-based) predictions. Long-term prediction\nmethods are generally inaccurate because of the external uncertainties that can\ngreatly affect the glucose values, while short-term ones fail to provide timely\nmedical guidance. Based on the above issues, we propose CrossGP, a novel\nmachine-learning framework for cross-day glucose prediction solely based on the\npatient's external activities without involving any physiological parameters.\nMeanwhile, we implement three baseline models for comparison. Extensive\nexperiments on Anderson's dataset strongly demonstrate the superior performance\nof CrossGP and prove its potential for future real-life applications.\n", "date": "2024-04-16", "categories": []}
{"id": "2404.11428", "title": "Explainable Lung Disease Classification from Chest X-Ray Images\n  Utilizing Deep Learning and XAI", "abstract": "  Lung diseases remain a critical global health concern, and it's crucial to\nhave accurate and quick ways to diagnose them. This work focuses on classifying\ndifferent lung diseases into five groups: viral pneumonia, bacterial pneumonia,\nCOVID, tuberculosis, and normal lungs. Employing advanced deep learning\ntechniques, we explore a diverse range of models including CNN, hybrid models,\nensembles, transformers, and Big Transfer. The research encompasses\ncomprehensive methodologies such as hyperparameter tuning, stratified k-fold\ncross-validation, and transfer learning with fine-tuning.Remarkably, our\nfindings reveal that the Xception model, fine-tuned through 5-fold\ncross-validation, achieves the highest accuracy of 96.21\\%. This success shows\nthat our methods work well in accurately identifying different lung diseases.\nThe exploration of explainable artificial intelligence (XAI) methodologies\nfurther enhances our understanding of the decision-making processes employed by\nthese models, contributing to increased trust in their clinical applications.\n", "date": "2024-04-17", "categories": []}
{"id": "2404.10290", "title": "NeuroMorphix: A Novel Brain MRI Asymmetry-specific Feature Construction\n  Approach For Seizure Recurrence Prediction", "abstract": "  Seizure recurrence is an important concern after an initial unprovoked\nseizure; without drug treatment, it occurs within 2 years in 40-50% of cases.\nThe decision to treat currently relies on predictors of seizure recurrence risk\nthat are inaccurate, resulting in unnecessary, possibly harmful, treatment in\nsome patients and potentially preventable seizures in others. Because of the\nlink between brain lesions and seizure recurrence, we developed a recurrence\nprediction tool using machine learning and clinical 3T brain MRI. We developed\nNeuroMorphix, a feature construction approach based on MRI brain anatomy. Each\nof seven NeuroMorphix features measures the absolute or relative difference\nbetween corresponding regions in each cerebral hemisphere. FreeSurfer was used\nto segment brain regions and to generate values for morphometric parameters (8\nfor each cortical region and 5 for each subcortical region). The parameters\nwere then mapped to whole brain NeuroMorphix features, yielding a total of 91\nfeatures per subject. Features were generated for a first seizure patient\ncohort (n = 169) categorised into seizure recurrence and non-recurrence\nsubgroups. State-of-the-art classification algorithms were trained and tested\nusing NeuroMorphix features to predict seizure recurrence. Classification\nmodels using the top 5 features, ranked by sequential forward selection,\ndemonstrated excellent performance in predicting seizure recurrence, with area\nunder the ROC curve of 88-93%, accuracy of 83-89%, and F1 score of 83-90%.\nHighly ranked features aligned with structural alterations known to be\nassociated with epilepsy. This study highlights the potential for targeted,\ndata-driven approaches to aid clinical decision-making in brain disorders.\n", "date": "2024-04-16", "categories": []}
{"id": "2404.10580", "title": "Data-driven subgrouping of patient trajectories with chronic diseases:\n  Evidence from low back pain", "abstract": "  Clinical data informs the personalization of health care with a potential for\nmore effective disease management. In practice, this is achieved by\nsubgrouping, whereby clusters with similar patient characteristics are\nidentified and then receive customized treatment plans with the goal of\ntargeting subgroup-specific disease dynamics. In this paper, we propose a novel\nmixture hidden Markov model for subgrouping patient trajectories from chronic\ndiseases. Our model is probabilistic and carefully designed to capture\ndifferent trajectory phases of chronic diseases (i.e., \"severe\", \"moderate\",\nand \"mild\") through tailored latent states. We demonstrate our subgrouping\nframework based on a longitudinal study across 847 patients with non-specific\nlow back pain. Here, our subgrouping framework identifies 8 subgroups. Further,\nwe show that our subgrouping framework outperforms common baselines in terms of\ncluster validity indices. Finally, we discuss the applicability of the model to\nother chronic and long-lasting diseases.\n", "date": "2024-04-16", "categories": []}
{"id": "2312.05933", "title": "Temporal Supervised Contrastive Learning for Modeling Patient Risk\n  Progression", "abstract": "  We consider the problem of predicting how the likelihood of an outcome of\ninterest for a patient changes over time as we observe more of the patient\ndata. To solve this problem, we propose a supervised contrastive learning\nframework that learns an embedding representation for each time step of a\npatient time series. Our framework learns the embedding space to have the\nfollowing properties: (1) nearby points in the embedding space have similar\npredicted class probabilities, (2) adjacent time steps of the same time series\nmap to nearby points in the embedding space, and (3) time steps with very\ndifferent raw feature vectors map to far apart regions of the embedding space.\nTo achieve property (3), we employ a nearest neighbor pairing mechanism in the\nraw feature space. This mechanism also serves as an alternative to data\naugmentation, a key ingredient of contrastive learning, which lacks a standard\nprocedure that is adequately realistic for clinical tabular data, to our\nknowledge. We demonstrate that our approach outperforms state-of-the-art\nbaselines in predicting mortality of septic patients (MIMIC-III dataset) and\ntracking progression of cognitive impairment (ADNI dataset). Our method also\nconsistently recovers the correct synthetic dataset embedding structure across\nexperiments, a feat not achieved by baselines. Our ablation experiments show\nthe pivotal role of our nearest neighbor pairing.\n", "date": "2023-12-10", "categories": []}
{"id": "2309.07430", "title": "Adapted Large Language Models Can Outperform Medical Experts in Clinical\n  Text Summarization", "abstract": "  Analyzing vast textual data and summarizing key information from electronic\nhealth records imposes a substantial burden on how clinicians allocate their\ntime. Although large language models (LLMs) have shown promise in natural\nlanguage processing (NLP), their effectiveness on a diverse range of clinical\nsummarization tasks remains unproven. In this study, we apply adaptation\nmethods to eight LLMs, spanning four distinct clinical summarization tasks:\nradiology reports, patient questions, progress notes, and doctor-patient\ndialogue. Quantitative assessments with syntactic, semantic, and conceptual NLP\nmetrics reveal trade-offs between models and adaptation methods. A clinical\nreader study with ten physicians evaluates summary completeness, correctness,\nand conciseness; in a majority of cases, summaries from our best adapted LLMs\nare either equivalent (45%) or superior (36%) compared to summaries from\nmedical experts. The ensuing safety analysis highlights challenges faced by\nboth LLMs and medical experts, as we connect errors to potential medical harm\nand categorize types of fabricated information. Our research provides evidence\nof LLMs outperforming medical experts in clinical text summarization across\nmultiple tasks. This suggests that integrating LLMs into clinical workflows\ncould alleviate documentation burden, allowing clinicians to focus more on\npatient care.\n", "date": "2023-09-14", "categories": []}
{"id": "2404.08007", "title": "Interpretable Neural Temporal Point Processes for Modelling Electronic\n  Health Records", "abstract": "  Electronic Health Records (EHR) can be represented as temporal sequences that\nrecord the events (medical visits) from patients. Neural temporal point process\n(NTPP) has achieved great success in modeling event sequences that occur in\ncontinuous time space. However, due to the black-box nature of neural networks,\nexisting NTPP models fall short in explaining the dependencies between\ndifferent event types. In this paper, inspired by word2vec and Hawkes process,\nwe propose an interpretable framework inf2vec for event sequence modelling,\nwhere the event influences are directly parameterized and can be learned\nend-to-end. In the experiment, we demonstrate the superiority of our model on\nevent prediction as well as type-type influences learning.\n", "date": "2024-04-09", "categories": []}
{"id": "2404.07239", "title": "Advancements in Radiomics and Artificial Intelligence for Thyroid Cancer\n  Diagnosis", "abstract": "  Thyroid cancer is an increasing global health concern that requires advanced\ndiagnostic methods. The application of AI and radiomics to thyroid cancer\ndiagnosis is examined in this review. A review of multiple databases was\nconducted in compliance with PRISMA guidelines until October 2023. A\ncombination of keywords led to the discovery of an English academic publication\non thyroid cancer and related subjects. 267 papers were returned from the\noriginal search after 109 duplicates were removed. Relevant studies were\nselected according to predetermined criteria after 124 articles were eliminated\nbased on an examination of their abstract and title. After the comprehensive\nanalysis, an additional six studies were excluded. Among the 28 included\nstudies, radiomics analysis, which incorporates ultrasound (US) images,\ndemonstrated its effectiveness in diagnosing thyroid cancer. Various results\nwere noted, some of the studies presenting new strategies that outperformed the\nstatus quo. The literature has emphasized various challenges faced by AI\nmodels, including interpretability issues, dataset constraints, and operator\ndependence. The synthesized findings of the 28 included studies mentioned the\nneed for standardization efforts and prospective multicenter studies to address\nthese concerns. Furthermore, approaches to overcome these obstacles were\nidentified, such as advances in explainable AI technology and personalized\nmedicine techniques. The review focuses on how AI and radiomics could transform\nthe diagnosis and treatment of thyroid cancer. Despite challenges, future\nresearch on multidisciplinary cooperation, clinical applicability validation,\nand algorithm improvement holds the potential to improve patient outcomes and\ndiagnostic precision in the treatment of thyroid cancer.\n", "date": "2024-04-09", "categories": []}
{"id": "2402.05125", "title": "Zero-Shot Clinical Trial Patient Matching with LLMs", "abstract": "  Matching patients to clinical trials is a key unsolved challenge in bringing\nnew drugs to market. Today, identifying patients who meet a trial's eligibility\ncriteria is highly manual, taking up to 1 hour per patient. Automated screening\nis challenging, however, as it requires understanding unstructured clinical\ntext. Large language models (LLMs) offer a promising solution. In this work, we\nexplore their application to trial matching. First, we design an LLM-based\nsystem which, given a patient's medical history as unstructured clinical text,\nevaluates whether that patient meets a set of inclusion criteria (also\nspecified as free text). Our zero-shot system achieves state-of-the-art scores\non the n2c2 2018 cohort selection benchmark. Second, we improve the data and\ncost efficiency of our method by identifying a prompting strategy which matches\npatients an order of magnitude faster and more cheaply than the status quo, and\ndevelop a two-stage retrieval pipeline that reduces the number of tokens\nprocessed by up to a third while retaining high performance. Third, we evaluate\nthe interpretability of our system by having clinicians evaluate the natural\nlanguage justifications generated by the LLM for each eligibility decision, and\nshow that it can output coherent explanations for 97% of its correct decisions\nand 75% of its incorrect ones. Our results establish the feasibility of using\nLLMs to accelerate clinical trial operations.\n", "date": "2024-02-05", "categories": []}
{"id": "2404.06723", "title": "Global Contrastive Training for Multimodal Electronic Health Records\n  with Language Supervision", "abstract": "  Modern electronic health records (EHRs) hold immense promise in tracking\npersonalized patient health trajectories through sequential deep learning,\nowing to their extensive breadth, scale, and temporal granularity. Nonetheless,\nhow to effectively leverage multiple modalities from EHRs poses significant\nchallenges, given its complex characteristics such as high dimensionality,\nmultimodality, sparsity, varied recording frequencies, and temporal\nirregularities. To this end, this paper introduces a novel multimodal\ncontrastive learning framework, specifically focusing on medical time series\nand clinical notes. To tackle the challenge of sparsity and irregular time\nintervals in medical time series, the framework integrates temporal\ncross-attention transformers with a dynamic embedding and tokenization scheme\nfor learning multimodal feature representations. To harness the interconnected\nrelationships between medical time series and clinical notes, the framework\nequips a global contrastive loss, aligning a patient's multimodal feature\nrepresentations with the corresponding discharge summaries. Since discharge\nsummaries uniquely pertain to individual patients and represent a holistic view\nof the patient's hospital stay, machine learning models are led to learn\ndiscriminative multimodal features via global contrasting. Extensive\nexperiments with a real-world EHR dataset demonstrated that our framework\noutperformed state-of-the-art approaches on the exemplar task of predicting the\noccurrence of nine postoperative complications for more than 120,000 major\ninpatient surgeries using multimodal data from UF health system split among\nthree hospitals (UF Health Gainesville, UF Health Jacksonville, and UF Health\nJacksonville-North).\n", "date": "2024-04-10", "categories": []}
{"id": "2404.07091", "title": "LaTiM: Longitudinal representation learning in continuous-time models to\n  predict disease progression", "abstract": "  This work proposes a novel framework for analyzing disease progression using\ntime-aware neural ordinary differential equations (NODE). We introduce a\n\"time-aware head\" in a framework trained through self-supervised learning (SSL)\nto leverage temporal information in latent space for data augmentation. This\napproach effectively integrates NODEs with SSL, offering significant\nperformance improvements compared to traditional methods that lack explicit\ntemporal integration. We demonstrate the effectiveness of our strategy for\ndiabetic retinopathy progression prediction using the OPHDIAT database.\nCompared to the baseline, all NODE architectures achieve statistically\nsignificant improvements in area under the ROC curve (AUC) and Kappa metrics,\nhighlighting the efficacy of pre-training with SSL-inspired approaches.\nAdditionally, our framework promotes stable training for NODEs, a commonly\nencountered challenge in time-aware modeling.\n", "date": "2024-04-10", "categories": []}
{"id": "2303.07540", "title": "Tensor-based Multimodal Learning for Prediction of Pulmonary Arterial\n  Wedge Pressure from Cardiac MRI", "abstract": "  Heart failure is a serious and life-threatening condition that can lead to\nelevated pressure in the left ventricle. Pulmonary Arterial Wedge Pressure\n(PAWP) is an important surrogate marker indicating high pressure in the left\nventricle. PAWP is determined by Right Heart Catheterization (RHC) but it is an\ninvasive procedure. A non-invasive method is useful in quickly identifying\nhigh-risk patients from a large population. In this work, we develop a tensor\nlearning-based pipeline for identifying PAWP from multimodal cardiac Magnetic\nResonance Imaging (MRI). This pipeline extracts spatial and temporal features\nfrom high-dimensional scans. For quality control, we incorporate an epistemic\nuncertainty-based binning strategy to identify poor-quality training samples.\nTo improve the performance, we learn complementary information by integrating\nfeatures from multimodal data: cardiac MRI with short-axis and four-chamber\nviews, and Electronic Health Records. The experimental analysis on a large\ncohort of $1346$ subjects who underwent the RHC procedure for PAWP estimation\nindicates that the proposed pipeline has a diagnostic value and can produce\npromising performance with significant improvement over the baseline in\nclinical practice (i.e., $\\Delta$AUC $=0.10$, $\\Delta$Accuracy $=0.06$, and\n$\\Delta$MCC $=0.39$). The decision curve analysis further confirms the clinical\nutility of our method.\n", "date": "2023-03-14", "categories": []}
{"id": "2402.15755", "title": "Dental Severity Assessment through Few-shot Learning and SBERT\n  Fine-tuning", "abstract": "  Dental diseases have a significant impact on a considerable portion of the\npopulation, leading to various health issues that can detrimentally affect\nindividuals' overall well-being. The integration of automated systems in oral\nhealthcare has become increasingly crucial. Machine learning approaches offer a\nviable solution to address challenges such as diagnostic difficulties,\ninefficiencies, and errors in oral disease diagnosis. These methods prove\nparticularly useful when physicians struggle to predict or diagnose diseases at\ntheir early stages. In this study, thirteen different machine learning, deep\nlearning, and large language models were employed to determine the severity\nlevel of oral health issues based on radiologists' reports. The results\nrevealed that the Few-shot learning with SBERT and Multi-Layer Perceptron model\noutperformed all other models across various experiments, achieving an\nimpressive accuracy of 94.1% as the best result. Consequently, this model\nexhibits promise as a reliable tool for evaluating the severity of oral\ndiseases, enabling patients to receive more effective treatment and aiding\nhealthcare professionals in making informed decisions regarding resource\nallocation and the management of high-risk patients.\n", "date": "2024-02-24", "categories": []}
{"id": "2403.09722", "title": "Enhancing Readmission Prediction with Deep Learning: Extracting\n  Biomedical Concepts from Clinical Texts", "abstract": "  Hospital readmission, defined as patients being re-hospitalized shortly after\ndischarge, is a critical concern as it impacts patient outcomes and healthcare\ncosts. Identifying patients at risk of readmission allows for timely\ninterventions, reducing re-hospitalization rates and overall treatment costs.\nThis study focuses on predicting patient readmission within less than 30 days\nusing text mining techniques applied to discharge report texts from electronic\nhealth records (EHR). Various machine learning and deep learning methods were\nemployed to develop a classification model for this purpose. A novel aspect of\nthis research involves leveraging the Bio-Discharge Summary Bert (BDSS) model\nalong with principal component analysis (PCA) feature extraction to preprocess\ndata for deep learning model input. Our analysis of the MIMIC-III dataset\nindicates that our approach, which combines the BDSS model with a multilayer\nperceptron (MLP), outperforms state-of-the-art methods. This model achieved a\nrecall of 94% and an area under the curve (AUC) of 75%, showcasing its\neffectiveness in predicting patient readmissions. This study contributes to the\nadvancement of predictive modeling in healthcare by integrating text mining\ntechniques with deep learning algorithms to improve patient outcomes and\noptimize resource allocation.\n", "date": "2024-03-12", "categories": []}
{"id": "2404.04284", "title": "Assessing ML Classification Algorithms and NLP Techniques for Depression\n  Detection: An Experimental Case Study", "abstract": "  Depression has affected millions of people worldwide and has become one of\nthe most common mental disorders. Early mental disorder detection can reduce\ncosts for public health agencies and prevent other major comorbidities.\nAdditionally, the shortage of specialized personnel is very concerning since\nDepression diagnosis is highly dependent on expert professionals and is\ntime-consuming. Recent research has evidenced that machine learning (ML) and\nNatural Language Processing (NLP) tools and techniques have significantly bene\nted the diagnosis of depression. However, there are still several challenges in\nthe assessment of depression detection approaches in which other conditions\nsuch as post-traumatic stress disorder (PTSD) are present. These challenges\ninclude assessing alternatives in terms of data cleaning and pre-processing\ntechniques, feature selection, and appropriate ML classification algorithms.\nThis paper tackels such an assessment based on a case study that compares\ndifferent ML classifiers, specifically in terms of data cleaning and\npre-processing, feature selection, parameter setting, and model choices. The\ncase study is based on the Distress Analysis Interview Corpus - Wizard-of-Oz\n(DAIC-WOZ) dataset, which is designed to support the diagnosis of mental\ndisorders such as depression, anxiety, and PTSD. Besides the assessment of\nalternative techniques, we were able to build models with accuracy levels\naround 84% with Random Forest and XGBoost models, which is significantly higher\nthan the results from the comparable literature which presented the level of\naccuracy of 72% from the SVM model.\n", "date": "2024-04-03", "categories": []}
{"id": "2404.04686", "title": "Predictive Modeling for Breast Cancer Classification in the Context of\n  Bangladeshi Patients: A Supervised Machine Learning Approach with Explainable\n  AI", "abstract": "  Breast cancer has rapidly increased in prevalence in recent years, making it\none of the leading causes of mortality worldwide. Among all cancers, it is by\nfar the most common. Diagnosing this illness manually requires significant time\nand expertise. Since detecting breast cancer is a time-consuming process,\npreventing its further spread can be aided by creating machine-based forecasts.\nMachine learning and Explainable AI are crucial in classification as they not\nonly provide accurate predictions but also offer insights into how the model\narrives at its decisions, aiding in the understanding and trustworthiness of\nthe classification results. In this study, we evaluate and compare the\nclassification accuracy, precision, recall, and F-1 scores of five different\nmachine learning methods using a primary dataset (500 patients from Dhaka\nMedical College Hospital). Five different supervised machine learning\ntechniques, including decision tree, random forest, logistic regression, naive\nbayes, and XGBoost, have been used to achieve optimal results on our dataset.\nAdditionally, this study applied SHAP analysis to the XGBoost model to\ninterpret the model's predictions and understand the impact of each feature on\nthe model's output. We compared the accuracy with which several algorithms\nclassified the data, as well as contrasted with other literature in this field.\nAfter final evaluation, this study found that XGBoost achieved the best model\naccuracy, which is 97%.\n", "date": "2024-04-06", "categories": []}
{"id": "2404.04718", "title": "Interpretable Multimodal Learning for Cardiovascular Hemodynamics\n  Assessment", "abstract": "  Pulmonary Arterial Wedge Pressure (PAWP) is an essential cardiovascular\nhemodynamics marker to detect heart failure. In clinical practice, Right Heart\nCatheterization is considered a gold standard for assessing cardiac\nhemodynamics while non-invasive methods are often needed to screen high-risk\npatients from a large population. In this paper, we propose a multimodal\nlearning pipeline to predict PAWP marker. We utilize complementary information\nfrom Cardiac Magnetic Resonance Imaging (CMR) scans (short-axis and\nfour-chamber) and Electronic Health Records (EHRs). We extract spatio-temporal\nfeatures from CMR scans using tensor-based learning. We propose a graph\nattention network to select important EHR features for prediction, where we\nmodel subjects as graph nodes and feature relationships as graph edges using\nthe attention mechanism. We design four feature fusion strategies: early,\nintermediate, late, and hybrid fusion. With a linear classifier and linear\nfusion strategies, our pipeline is interpretable. We validate our pipeline on a\nlarge dataset of $2,641$ subjects from our ASPIRE registry. The comparative\nstudy against state-of-the-art methods confirms the superiority of our\npipeline. The decision curve analysis further validates that our pipeline can\nbe applied to screen a large population. The code is available at\nhttps://github.com/prasunc/hemodynamics.\n", "date": "2024-04-06", "categories": []}
{"id": "2404.05613", "title": "Deep Representation Learning for Multi-functional Degradation Modeling\n  of Community-dwelling Aging Population", "abstract": "  As the aging population grows, particularly for the baby boomer generation,\nthe United States is witnessing a significant increase in the elderly\npopulation experiencing multifunctional disabilities. These disabilities,\nstemming from a variety of chronic diseases, injuries, and impairments, present\na complex challenge due to their multidimensional nature, encompassing both\nphysical and cognitive aspects. Traditional methods often use univariate\nregression-based methods to model and predict single degradation conditions and\nassume population homogeneity, which is inadequate to address the complexity\nand diversity of aging-related degradation. This study introduces a novel\nframework for multi-functional degradation modeling that captures the\nmultidimensional (e.g., physical and cognitive) and heterogeneous nature of\nelderly disabilities. Utilizing deep learning, our approach predicts health\ndegradation scores and uncovers latent heterogeneity from elderly health\nhistories, offering both efficient estimation and explainable insights into the\ndiverse effects and causes of aging-related degradation. A real-case study\ndemonstrates the effectiveness and marks a pivotal contribution to accurately\nmodeling the intricate dynamics of elderly degradation, and addresses the\nhealthcare challenges in the aging population.\n", "date": "2024-04-08", "categories": []}
{"id": "2306.00003", "title": "Detecting Heart Disease from Multi-View Ultrasound Images via Supervised\n  Attention Multiple Instance Learning", "abstract": "  Aortic stenosis (AS) is a degenerative valve condition that causes\nsubstantial morbidity and mortality. This condition is under-diagnosed and\nunder-treated. In clinical practice, AS is diagnosed with expert review of\ntransthoracic echocardiography, which produces dozens of ultrasound images of\nthe heart. Only some of these views show the aortic valve. To automate\nscreening for AS, deep networks must learn to mimic a human expert's ability to\nidentify views of the aortic valve then aggregate across these relevant images\nto produce a study-level diagnosis. We find previous approaches to AS detection\nyield insufficient accuracy due to relying on inflexible averages across\nimages. We further find that off-the-shelf attention-based multiple instance\nlearning (MIL) performs poorly. We contribute a new end-to-end MIL approach\nwith two key methodological innovations. First, a supervised attention\ntechnique guides the learned attention mechanism to favor relevant views.\nSecond, a novel self-supervised pretraining strategy applies contrastive\nlearning on the representation of the whole study instead of individual images\nas commonly done in prior literature. Experiments on an open-access dataset and\nan external validation set show that our approach yields higher accuracy while\nreducing model size.\n", "date": "2023-05-25", "categories": []}
{"id": "2301.06625", "title": "A Transformer-based Diffusion Probabilistic Model for Heart Rate and\n  Blood Pressure Forecasting in Intensive Care Unit", "abstract": "  Background and Objective: Vital sign monitoring in the Intensive Care Unit\n(ICU) is crucial for enabling prompt interventions for patients. This\nunderscores the need for an accurate predictive system. Therefore, this study\nproposes a novel deep learning approach for forecasting Heart Rate (HR),\nSystolic Blood Pressure (SBP), and Diastolic Blood Pressure (DBP) in the ICU.\n  Methods: We extracted $24,886$ ICU stays from the MIMIC-III database which\ncontains data from over $46$ thousand patients, to train and test the model.\nThe model proposed in this study, Transformer-based Diffusion Probabilistic\nModel for Sparse Time Series Forecasting (TDSTF), merges Transformer and\ndiffusion models to forecast vital signs. The TDSTF model showed\nstate-of-the-art performance in predicting vital signs in the ICU,\noutperforming other models' ability to predict distributions of vital signs and\nbeing more computationally efficient. The code is available at\nhttps://github.com/PingChang818/TDSTF.\n  Results: The results of the study showed that TDSTF achieved a Standardized\nAverage Continuous Ranked Probability Score (SACRPS) of $0.4438$ and a Mean\nSquared Error (MSE) of $0.4168$, an improvement of $18.9\\%$ and $34.3\\%$ over\nthe best baseline model, respectively. The inference speed of TDSTF is more\nthan $17$ times faster than the best baseline model.\n  Conclusion: TDSTF is an effective and efficient solution for forecasting\nvital signs in the ICU, and it shows a significant improvement compared to\nother models in the field.\n", "date": "2023-01-16", "categories": []}
{"id": "2404.02181", "title": "Leveraging Machine Learning for Early Autism Detection via INDT-ASD\n  Indian Database", "abstract": "  Machine learning (ML) has advanced quickly, particularly throughout the area\nof health care. The diagnosis of neurodevelopment problems using ML is a very\nimportant area of healthcare. Autism spectrum disorder (ASD) is one of the\ndevelopmental disorders that is growing the fastest globally. The clinical\nscreening tests used to identify autistic symptoms are expensive and\ntime-consuming. But now that ML has been advanced, it's feasible to identify\nautism early on. Previously, many different techniques have been used in\ninvestigations. Still, none of them have produced the anticipated outcomes when\nit comes to the capacity to predict autistic features utilizing a clinically\nvalidated Indian ASD database. Therefore, this study aimed to develop a simple,\nquick, and inexpensive technique for identifying ASD by using ML. Various\nmachine learning classifiers, including Adaboost (AB), Gradient Boost (GB),\nDecision Tree (DT), Logistic Regression (LR), Random Forest (RF), Gaussian\nNaive Bayes (GNB), Linear Discriminant Analysis (LDA), Quadratic Discriminant\nAnalysis (QDA), K-Nearest Neighbors (KNN), and Support Vector Machine (SVM),\nwere used to develop the autism prediction model. The proposed method was\ntested with records from the AIIMS Modified INDT-ASD (AMI) database, which were\ncollected through an application developed by AIIMS in Delhi, India. Feature\nengineering has been applied to make the proposed solution easier than already\navailable solutions. Using the proposed model, we succeeded in predicting ASD\nusing a minimized set of 20 questions rather than the 28 questions presented in\nAMI with promising accuracy. In a comparative evaluation, SVM emerged as the\nsuperior model among others, with 100 $\\pm$ 0.05\\% accuracy, higher recall by\n5.34\\%, and improved accuracy by 2.22\\%-6.67\\% over RF. We have also introduced\na web-based solution supporting both Hindi and English.\n", "date": "2024-04-02", "categories": []}
{"id": "2310.15333", "title": "Safe and Interpretable Estimation of Optimal Treatment Regimes", "abstract": "  Recent statistical and reinforcement learning methods have significantly\nadvanced patient care strategies. However, these approaches face substantial\nchallenges in high-stakes contexts, including missing data, inherent\nstochasticity, and the critical requirements for interpretability and patient\nsafety. Our work operationalizes a safe and interpretable framework to identify\noptimal treatment regimes. This approach involves matching patients with\nsimilar medical and pharmacological characteristics, allowing us to construct\nan optimal policy via interpolation. We perform a comprehensive simulation\nstudy to demonstrate the framework's ability to identify optimal policies even\nin complex settings. Ultimately, we operationalize our approach to study\nregimes for treating seizures in critically ill patients. Our findings strongly\nsupport personalized treatment strategies based on a patient's medical history\nand pharmacological features. Notably, we identify that reducing medication\ndoses for patients with mild and brief seizure episodes while adopting\naggressive treatment for patients in intensive care unit experiencing intense\nseizures leads to more favorable outcomes.\n", "date": "2023-10-23", "categories": []}
{"id": "2404.00464", "title": "Leveraging Pre-trained and Transformer-derived Embeddings from EHRs to\n  Characterize Heterogeneity Across Alzheimer's Disease and Related Dementias", "abstract": "  Alzheimer's disease is a progressive, debilitating neurodegenerative disease\nthat affects 50 million people globally. Despite this substantial health\nburden, available treatments for the disease are limited and its fundamental\ncauses remain poorly understood. Previous work has suggested the existence of\nclinically-meaningful sub-types, which it is suggested may correspond to\ndistinct etiologies, disease courses, and ultimately appropriate treatments.\nHere, we use unsupervised learning techniques on electronic health records\n(EHRs) from a cohort of memory disorder patients to characterise heterogeneity\nin this disease population. Pre-trained embeddings for medical codes as well as\ntransformer-derived Clinical BERT embeddings of free text are used to encode\npatient EHRs. We identify the existence of sub-populations on the basis of\ncomorbidities and shared textual features, and discuss their clinical\nsignificance.\n", "date": "2024-03-30", "categories": []}
{"id": "2404.01192", "title": "iMD4GC: Incomplete Multimodal Data Integration to Advance Precise\n  Treatment Response Prediction and Survival Analysis for Gastric Cancer", "abstract": "  Gastric cancer (GC) is a prevalent malignancy worldwide, ranking as the fifth\nmost common cancer with over 1 million new cases and 700 thousand deaths in\n2020. Locally advanced gastric cancer (LAGC) accounts for approximately\ntwo-thirds of GC diagnoses, and neoadjuvant chemotherapy (NACT) has emerged as\nthe standard treatment for LAGC. However, the effectiveness of NACT varies\nsignificantly among patients, with a considerable subset displaying treatment\nresistance. Ineffective NACT not only leads to adverse effects but also misses\nthe optimal therapeutic window, resulting in lower survival rate. However,\nexisting multimodal learning methods assume the availability of all modalities\nfor each patient, which does not align with the reality of clinical practice.\nThe limited availability of modalities for each patient would cause information\nloss, adversely affecting predictive accuracy. In this study, we propose an\nincomplete multimodal data integration framework for GC (iMD4GC) to address the\nchallenges posed by incomplete multimodal data, enabling precise response\nprediction and survival analysis. Specifically, iMD4GC incorporates unimodal\nattention layers for each modality to capture intra-modal information.\nSubsequently, the cross-modal interaction layers explore potential inter-modal\ninteractions and capture complementary information across modalities, thereby\nenabling information compensation for missing modalities. To evaluate iMD4GC,\nwe collected three multimodal datasets for GC study: GastricRes (698 cases) for\nresponse prediction, GastricSur (801 cases) for survival analysis, and\nTCGA-STAD (400 cases) for survival analysis. The scale of our datasets is\nsignificantly larger than previous studies. The iMD4GC achieved impressive\nperformance with an 80.2% AUC on GastricRes, 71.4% C-index on GastricSur, and\n66.1% C-index on TCGA-STAD, significantly surpassing other compared methods.\n", "date": "2024-04-01", "categories": []}
{"id": "2403.19790", "title": "Bespoke Large Language Models for Digital Triage Assistance in Mental\n  Health Care", "abstract": "  Contemporary large language models (LLMs) may have utility for processing\nunstructured, narrative free-text clinical data contained in electronic health\nrecords (EHRs) -- a particularly important use-case for mental health where a\nmajority of routinely-collected patient data lacks structured, machine-readable\ncontent.\n  A significant problem for the the United Kingdom's National Health Service\n(NHS) are the long waiting lists for specialist mental healthcare. According to\nNHS data, in each month of 2023, there were between 370,000 and 470,000\nindividual new referrals into secondary mental healthcare services. Referrals\nmust be triaged by clinicians, using clinical information contained in the\npatient's EHR to arrive at a decision about the most appropriate mental\nhealthcare team to assess and potentially treat these patients.\n  The ability to efficiently recommend a relevant team by ingesting potentially\nvoluminous clinical notes could help services both reduce referral waiting\ntimes and with the right technology, improve the evidence available to justify\ntriage decisions.\n  We present and evaluate three different approaches for LLM-based, end-to-end\ningestion of variable-length clinical EHR data to assist clinicians when\ntriaging referrals. Our model is able to deliver triage recommendations\nconsistent with existing clinical practices and it's architecture was\nimplemented on a single GPU, making it practical for implementation in\nresource-limited NHS environments where private implementations of LLM\ntechnology will be necessary to ensure confidential clinical data is\nappropriately controlled and governed.\n", "date": "2024-03-28", "categories": []}
{"id": "2403.20124", "title": "Application of Machine Learning Algorithms in Classifying Postoperative\n  Success in Metabolic Bariatric Surgery: A Comprehensive Study", "abstract": "  Objectives: Metabolic Bariatric Surgery (MBS) is a critical intervention for\npatients living with obesity and related health issues. Accurate classification\nand prediction of patient outcomes are vital for optimizing treatment\nstrategies. This study presents a novel machine learning approach to classify\npatients in the context of metabolic bariatric surgery, providing insights into\nthe efficacy of different models and variable types. Methods: Various machine\nlearning models, including GaussianNB, ComplementNB, KNN, Decision Tree, KNN\nwith RandomOverSampler, and KNN with SMOTE, were applied to a dataset of 73\npatients. The dataset, comprising psychometric, socioeconomic, and analytical\nvariables, was analyzed to determine the most efficient predictive model. The\nstudy also explored the impact of different variable groupings and oversampling\ntechniques. Results: Experimental results indicate average accuracy values as\nhigh as 66.7% for the best model. Enhanced versions of KNN and Decision Tree,\nalong with variations of KNN such as RandomOverSampler and SMOTE, yielded the\nbest results. Conclusions: The study unveils a promising avenue for classifying\npatients in the realm of metabolic bariatric surgery. The results underscore\nthe importance of selecting appropriate variables and employing diverse\napproaches to achieve optimal performance. The developed system holds potential\nas a tool to assist healthcare professionals in decision-making, thereby\nenhancing metabolic bariatric surgery outcomes. These findings lay the\ngroundwork for future collaboration between hospitals and healthcare entities\nto improve patient care through the utilization of machine learning algorithms.\nMoreover, the findings suggest room for improvement, potentially achievable\nwith a larger dataset and careful parameter tuning.\n", "date": "2024-03-29", "categories": []}
{"id": "2403.19355", "title": "Artificial Intelligence (AI) Based Prediction of Mortality, for COVID-19\n  Patients", "abstract": "  For severely affected COVID-19 patients, it is crucial to identify high-risk\npatients and predict survival and need for intensive care (ICU). Most of the\nproposed models are not well reported making them less reproducible and prone\nto high risk of bias particularly in presence of imbalance data/class. In this\nstudy, the performances of nine machine and deep learning algorithms in\ncombination with two widely used feature selection methods were investigated to\npredict last status representing mortality, ICU requirement, and ventilation\ndays. Fivefold cross-validation was used for training and validation purposes.\nTo minimize bias, the training and testing sets were split maintaining similar\ndistributions. Only 10 out of 122 features were found to be useful in\nprediction modelling with Acute kidney injury during hospitalization feature\nbeing the most important one. The algorithms performances depend on feature\nnumbers and data pre-processing techniques. LSTM performs the best in\npredicting last status and ICU requirement with 90%, 92%, 86% and 95% accuracy,\nsensitivity, specificity, and AUC respectively. DNN performs the best in\npredicting Ventilation days with 88% accuracy. Considering all the factors and\nlimitations including absence of exact time point of clinical onset, LSTM with\ncarefully selected features can accurately predict last status and ICU\nrequirement. DNN performs the best in predicting Ventilation days. Appropriate\nmachine learning algorithm with carefully selected features and balance data\ncan accurately predict mortality, ICU requirement and ventilation support. Such\nmodel can be very useful in emergency and pandemic where prompt and precise\n", "date": "2024-03-28", "categories": []}
{"id": "2403.19441", "title": "A Novel Stochastic Transformer-based Approach for Post-Traumatic Stress\n  Disorder Detection using Audio Recording of Clinical Interviews", "abstract": "  Post-traumatic stress disorder (PTSD) is a mental disorder that can be\ndeveloped after witnessing or experiencing extremely traumatic events. PTSD can\naffect anyone, regardless of ethnicity, or culture. An estimated one in every\neleven people will experience PTSD during their lifetime. The\nClinician-Administered PTSD Scale (CAPS) and the PTSD Check List for Civilians\n(PCL-C) interviews are gold standards in the diagnosis of PTSD. These\nquestionnaires can be fooled by the subject's responses. This work proposes a\ndeep learning-based approach that achieves state-of-the-art performances for\nPTSD detection using audio recordings during clinical interviews. Our approach\nis based on MFCC low-level features extracted from audio recordings of clinical\ninterviews, followed by deep high-level learning using a Stochastic\nTransformer. Our proposed approach achieves state-of-the-art performances with\nan RMSE of 2.92 on the eDAIC dataset thanks to the stochastic depth, stochastic\ndeep learning layers, and stochastic activation function.\n", "date": "2024-03-28", "categories": []}
{"id": "2403.18128", "title": "HealthGAT: Node Classifications in Electronic Health Records using Graph\n  Attention Networks", "abstract": "  While electronic health records (EHRs) are widely used across various\napplications in healthcare, most applications use the EHRs in their raw\n(tabular) format. Relying on raw or simple data pre-processing can greatly\nlimit the performance or even applicability of downstream tasks using EHRs. To\naddress this challenge, we present HealthGAT, a novel graph attention network\nframework that utilizes a hierarchical approach to generate embeddings from\nEHR, surpassing traditional graph-based methods. Our model iteratively refines\nthe embeddings for medical codes, resulting in improved EHR data analysis. We\nalso introduce customized EHR-centric auxiliary pre-training tasks to leverage\nthe rich medical knowledge embedded within the data. This approach provides a\ncomprehensive analysis of complex medical relationships and offers significant\nadvancement over standard data representation techniques. HealthGAT has\ndemonstrated its effectiveness in various healthcare scenarios through\ncomprehensive evaluations against established methodologies. Specifically, our\nmodel shows outstanding performance in node classification and downstream tasks\nsuch as predicting readmissions and diagnosis classifications.\n  Our code is available at https://github.com/healthylaife/HealthGAT\n", "date": "2024-03-26", "categories": []}
{"id": "2403.18316", "title": "Multi-Modal Contrastive Learning for Online Clinical Time-Series\n  Applications", "abstract": "  Electronic Health Record (EHR) datasets from Intensive Care Units (ICU)\ncontain a diverse set of data modalities. While prior works have successfully\nleveraged multiple modalities in supervised settings, we apply advanced\nself-supervised multi-modal contrastive learning techniques to ICU data,\nspecifically focusing on clinical notes and time-series for clinically relevant\nonline prediction tasks. We introduce a loss function Multi-Modal Neighborhood\nContrastive Loss (MM-NCL), a soft neighborhood function, and showcase the\nexcellent linear probe and zero-shot performance of our approach.\n", "date": "2024-03-27", "categories": []}
{"id": "2403.18668", "title": "Aiming for Relevance", "abstract": "  Vital signs are crucial in intensive care units (ICUs). They are used to\ntrack the patient's state and to identify clinically significant changes.\nPredicting vital sign trajectories is valuable for early detection of adverse\nevents. However, conventional machine learning metrics like RMSE often fail to\ncapture the true clinical relevance of such predictions. We introduce novel\nvital sign prediction performance metrics that align with clinical contexts,\nfocusing on deviations from clinical norms, overall trends, and trend\ndeviations. These metrics are derived from empirical utility curves obtained in\na previous study through interviews with ICU clinicians. We validate the\nmetrics' usefulness using simulated and real clinical datasets (MIMIC and\neICU). Furthermore, we employ these metrics as loss functions for neural\nnetworks, resulting in models that excel in predicting clinically significant\nevents. This research paves the way for clinically relevant machine learning\nmodel evaluation and optimization, promising to improve ICU patient care. 10\npages, 9 figures.\n", "date": "2024-03-27", "categories": []}
{"id": "2403.18756", "title": "Detection of subclinical atherosclerosis by image-based deep learning on\n  chest x-ray", "abstract": "  Aims. To develop a deep-learning based system for recognition of subclinical\natherosclerosis on a plain frontal chest x-ray. Methods and Results. A\ndeep-learning algorithm to predict coronary artery calcium (CAC) score (the\nAI-CAC model) was developed on 460 chest x-ray (80% training cohort, 20%\ninternal validation cohort) of primary prevention patients (58.4% male, median\nage 63 [51-74] years) with available paired chest x-ray and chest computed\ntomography (CT) indicated for any clinical reason and performed within 3\nmonths. The CAC score calculated on chest CT was used as ground truth. The\nmodel was validated on an temporally-independent cohort of 90 patients from the\nsame institution (external validation). The diagnostic accuracy of the AI-CAC\nmodel assessed by the area under the curve (AUC) was the primary outcome.\nOverall, median AI-CAC score was 35 (0-388) and 28.9% patients had no AI-CAC.\nAUC of the AI-CAC model to identify a CAC>0 was 0.90 in the internal validation\ncohort and 0.77 in the external validation cohort. Sensitivity was consistently\nabove 92% in both cohorts. In the overall cohort (n=540), among patients with\nAI-CAC=0, a single ASCVD event occurred, after 4.3 years. Patients with\nAI-CAC>0 had significantly higher Kaplan Meier estimates for ASCVD events\n(13.5% vs. 3.4%, log-rank=0.013). Conclusion. The AI-CAC model seems to\naccurately detect subclinical atherosclerosis on chest x-ray with elevated\nsensitivity, and to predict ASCVD events with elevated negative predictive\nvalue. Adoption of the AI-CAC model to refine CV risk stratification or as an\nopportunistic screening tool requires prospective evaluation.\n", "date": "2024-03-27", "categories": []}
{"id": "2309.10837", "title": "Improving Opioid Use Disorder Risk Modelling through Behavioral and\n  Genetic Feature Integration", "abstract": "  Opioids are an effective analgesic for acute and chronic pain, but also carry\na considerable risk of addiction leading to millions of opioid use disorder\n(OUD) cases and tens of thousands of premature deaths in the United States\nyearly. Estimating OUD risk prior to prescription could improve the efficacy of\ntreatment regimens, monitoring programs, and intervention strategies, but risk\nestimation is typically based on self-reported data or questionnaires. We\ndevelop an experimental design and computational methods that combine genetic\nvariants associated with OUD with behavioral features extracted from GPS and\nWi-Fi spatiotemporal coordinates to assess OUD risk. Since both OUD mobility\nand genetic data do not exist for the same cohort, we develop algorithms to (1)\ngenerate mobility features from empirical distributions and (2) synthesize\nmobility and genetic samples assuming an expected level of disease\nco-occurrence. We show that integrating genetic and mobility modalities\nimproves risk modelling using classification accuracy, area under the\nprecision-recall and receiver operator characteristic curves, and $F_1$ score.\nInterpreting the fitted models suggests that mobility features have more\ninfluence on OUD risk, although the genetic contribution was significant,\nparticularly in linear models. While there exist concerns with respect to\nprivacy, security, bias, and generalizability that must be evaluated in\nclinical trials before being implemented in practice, our framework provides\npreliminary evidence that behavioral and genetic features may improve OUD risk\nestimation to assist with personalized clinical decision-making.\n", "date": "2023-09-19", "categories": []}
{"id": "2403.17011", "title": "SUDO: a framework for evaluating clinical artificial intelligence\n  systems without ground-truth annotations", "abstract": "  A clinical artificial intelligence (AI) system is often validated on a\nheld-out set of data which it has not been exposed to before (e.g., data from a\ndifferent hospital with a distinct electronic health record system). This\nevaluation process is meant to mimic the deployment of an AI system on data in\nthe wild; those which are currently unseen by the system yet are expected to be\nencountered in a clinical setting. However, when data in the wild differ from\nthe held-out set of data, a phenomenon referred to as distribution shift, and\nlack ground-truth annotations, it becomes unclear the extent to which AI-based\nfindings can be trusted on data in the wild. Here, we introduce SUDO, a\nframework for evaluating AI systems without ground-truth annotations. SUDO\nassigns temporary labels to data points in the wild and directly uses them to\ntrain distinct models, with the highest performing model indicative of the most\nlikely label. Through experiments with AI systems developed for dermatology\nimages, histopathology patches, and clinical reports, we show that SUDO can be\na reliable proxy for model performance and thus identify unreliable\npredictions. We also demonstrate that SUDO informs the selection of models and\nallows for the previously out-of-reach assessment of algorithmic bias for data\nin the wild without ground-truth annotations. The ability to triage unreliable\npredictions for further inspection and assess the algorithmic bias of AI\nsystems can improve the integrity of research findings and contribute to the\ndeployment of ethical AI systems in medicine.\n", "date": "2024-01-02", "categories": []}
{"id": "2307.02591", "title": "ODD: A Benchmark Dataset for the Natural Language Processing based\n  Opioid Related Aberrant Behavior Detection", "abstract": "  Opioid related aberrant behaviors (ORABs) present novel risk factors for\nopioid overdose. This paper introduces a novel biomedical natural language\nprocessing benchmark dataset named ODD, for ORAB Detection Dataset. ODD is an\nexpert-annotated dataset designed to identify ORABs from patients' EHR notes\nand classify them into nine categories; 1) Confirmed Aberrant Behavior, 2)\nSuggested Aberrant Behavior, 3) Opioids, 4) Indication, 5) Diagnosed opioid\ndependency, 6) Benzodiazepines, 7) Medication Changes, 8) Central Nervous\nSystem-related, and 9) Social Determinants of Health. We explored two\nstate-of-the-art natural language processing models (fine-tuning and\nprompt-tuning approaches) to identify ORAB. Experimental results show that the\nprompt-tuning models outperformed the fine-tuning models in most categories and\nthe gains were especially higher among uncommon categories (Suggested Aberrant\nBehavior, Confirmed Aberrant Behaviors, Diagnosed Opioid Dependence, and\nMedication Change). Although the best model achieved the highest 88.17% on\nmacro average area under precision recall curve, uncommon classes still have a\nlarge room for performance improvement. ODD is publicly available.\n", "date": "2023-07-05", "categories": []}
{"id": "2403.13786", "title": "Chain-of-Interaction: Enhancing Large Language Models for Psychiatric\n  Behavior Understanding by Dyadic Contexts", "abstract": "  Automatic coding patient behaviors is essential to support decision making\nfor psychotherapists during the motivational interviewing (MI), a collaborative\ncommunication intervention approach to address psychiatric issues, such as\nalcohol and drug addiction. While the behavior coding task has rapidly adapted\nmachine learning to predict patient states during the MI sessions, lacking of\ndomain-specific knowledge and overlooking patient-therapist interactions are\nmajor challenges in developing and deploying those models in real practice. To\nencounter those challenges, we introduce the Chain-of-Interaction (CoI)\nprompting method aiming to contextualize large language models (LLMs) for\npsychiatric decision support by the dyadic interactions. The CoI prompting\napproach systematically breaks down the coding task into three key reasoning\nsteps, extract patient engagement, learn therapist question strategies, and\nintegrates dyadic interactions between patients and therapists. This approach\nenables large language models to leverage the coding scheme, patient state, and\ndomain knowledge for patient behavioral coding. Experiments on real-world\ndatasets can prove the effectiveness and flexibility of our prompting method\nwith multiple state-of-the-art LLMs over existing prompting baselines. We have\nconducted extensive ablation analysis and demonstrate the critical role of\ndyadic interactions in applying LLMs for psychotherapy behavior understanding.\n", "date": "2024-03-20", "categories": []}
{"id": "2403.15408", "title": "Multi-modal Heart Failure Risk Estimation based on Short ECG and Sampled\n  Long-Term HRV", "abstract": "  Cardiovascular diseases, including Heart Failure (HF), remain a leading\nglobal cause of mortality, often evading early detection. In this context,\naccessible and effective risk assessment is indispensable. Traditional\napproaches rely on resource-intensive diagnostic tests, typically administered\nafter the onset of symptoms. The widespread availability of electrocardiogram\n(ECG) technology and the power of Machine Learning are emerging as viable\nalternatives within smart healthcare. In this paper, we propose several\nmulti-modal approaches that combine 30-second ECG recordings and approximate\nlong-term Heart Rate Variability (HRV) data to estimate the risk of HF\nhospitalization. We introduce two survival models: an XGBoost model with\nAccelerated Failure Time (AFT) incorporating comprehensive ECG features and a\nResNet model that learns from the raw ECG. We extend these with our novel\nlong-term HRVs extracted from the combination of ultra-short-term beat-to-beat\nmeasurements taken over the day. To capture their temporal dynamics, we propose\na survival model comprising ResNet and Transformer architectures (TFM-ResNet).\nOur experiments demonstrate high model performance for HF risk assessment with\na concordance index of 0.8537 compared to 14 survival models and competitive\ndiscrimination power on various external ECG datasets. After transferability\ntests with Apple Watch data, our approach implemented in the myHeartScore App\noffers cost-effective and highly accessible HF risk assessment, contributing to\nits prevention and management.\n", "date": "2024-03-01", "categories": []}
{"id": "2403.15464", "title": "LLMs-based Few-Shot Disease Predictions using EHR: A Novel Approach\n  Combining Predictive Agent Reasoning and Critical Agent Instruction", "abstract": "  Electronic health records (EHRs) contain valuable patient data for\nhealth-related prediction tasks, such as disease prediction. Traditional\napproaches rely on supervised learning methods that require large labeled\ndatasets, which can be expensive and challenging to obtain. In this study, we\ninvestigate the feasibility of applying Large Language Models (LLMs) to convert\nstructured patient visit data (e.g., diagnoses, labs, prescriptions) into\nnatural language narratives. We evaluate the zero-shot and few-shot performance\nof LLMs using various EHR-prediction-oriented prompting strategies.\nFurthermore, we propose a novel approach that utilizes LLM agents with\ndifferent roles: a predictor agent that makes predictions and generates\nreasoning processes and a critic agent that analyzes incorrect predictions and\nprovides guidance for improving the reasoning of the predictor agent. Our\nresults demonstrate that with the proposed approach, LLMs can achieve decent\nfew-shot performance compared to traditional supervised learning methods in\nEHR-based disease predictions, suggesting its potential for health-oriented\napplications.\n", "date": "2024-03-19", "categories": []}
{"id": "2403.12211", "title": "A Unified Model for Longitudinal Multi-Modal Multi-View Prediction with\n  Missingness", "abstract": "  Medical records often consist of different modalities, such as images, text,\nand tabular information. Integrating all modalities offers a holistic view of a\npatient's condition, while analyzing them longitudinally provides a better\nunderstanding of disease progression. However, real-world longitudinal medical\nrecords present challenges: 1) patients may lack some or all of the data for a\nspecific timepoint, and 2) certain modalities or views might be absent for all\npatients during a particular period. In this work, we introduce a unified model\nfor longitudinal multi-modal multi-view prediction with missingness. Our method\nallows as many timepoints as desired for input, and aims to leverage all\navailable data, regardless of their availability. We conduct extensive\nexperiments on the knee osteoarthritis dataset from the Osteoarthritis\nInitiative for pain and Kellgren-Lawrence grade prediction at a future\ntimepoint. We demonstrate the effectiveness of our method by comparing results\nfrom our unified model to specific models that use the same modality and view\ncombinations during training and evaluation. We also show the benefit of having\nextended temporal data and provide post-hoc analysis for a deeper understanding\nof each modality/view's importance for different tasks.\n", "date": "2024-03-18", "categories": []}
{"id": "2403.13842", "title": "Analyzing the Variations in Emergency Department Boarding and Testing\n  the Transferability of Forecasting Models across COVID-19 Pandemic Waves in\n  Hong Kong: Hybrid CNN-LSTM approach to quantifying building-level\n  socioecological risk", "abstract": "  Emergency department's (ED) boarding (defined as ED waiting time greater than\nfour hours) has been linked to poor patient outcomes and health system\nperformance. Yet, effective forecasting models is rare before COVID-19, lacking\nduring the peri-COVID era. Here, a hybrid convolutional neural network\n(CNN)-Long short-term memory (LSTM) model was applied to public-domain data\nsourced from Hong Kong's Hospital Authority, Department of Health, and Housing\nAuthority. In addition, we sought to identify the phase of the COVID-19\npandemic that most significantly perturbed our complex adaptive healthcare\nsystem, thereby revealing a stable pattern of interconnectedness among its\ncomponents, using deep transfer learning methodology.\n  Our result shows that 1) the greatest proportion of days with ED boarding was\nfound between waves four and five; 2) the best-performing model for forecasting\nED boarding was observed between waves four and five, which was based on\nfeatures representing time-invariant residential buildings' built environment\nand sociodemographic profiles and the historical time series of ED boarding and\ncase counts, compared to during the waves when best-performing forecasting is\nbased on time-series features alone; and 3) when the model built from the\nperiod between waves four and five was applied to data from other waves via\ndeep transfer learning, the transferred model enhanced the performance of\nindigenous models.\n", "date": "2024-03-17", "categories": []}
{"id": "2403.13111", "title": "Deep learning with noisy labels in medical prediction problems: a\n  scoping review", "abstract": "  Objectives: Medical research faces substantial challenges from noisy labels\nattributed to factors like inter-expert variability and machine-extracted\nlabels. Despite this, the adoption of label noise management remains limited,\nand label noise is largely ignored. To this end, there is a critical need to\nconduct a scoping review focusing on the problem space. This scoping review\naims to comprehensively review label noise management in deep learning-based\nmedical prediction problems, which includes label noise detection, label noise\nhandling, and evaluation. Research involving label uncertainty is also\nincluded.\n  Methods: Our scoping review follows the Preferred Reporting Items for\nSystematic Reviews and Meta-Analyses (PRISMA) guidelines. We searched 4\ndatabases, including PubMed, IEEE Xplore, Google Scholar, and Semantic Scholar.\nOur search terms include \"noisy label AND medical / healthcare / clinical\",\n\"un-certainty AND medical / healthcare / clinical\", and \"noise AND medical /\nhealthcare / clinical\".\n  Results: A total of 60 papers met inclusion criteria between 2016 and 2023. A\nseries of practical questions in medical research are investigated. These\ninclude the sources of label noise, the impact of label noise, the detection of\nlabel noise, label noise handling techniques, and their evaluation.\nCategorization of both label noise detection methods and handling techniques\nare provided.\n  Discussion: From a methodological perspective, we observe that the medical\ncommunity has been up to date with the broader deep-learning community, given\nthat most techniques have been evaluated on medical data. We recommend\nconsidering label noise as a standard element in medical research, even if it\nis not dedicated to handling noisy labels. Initial experiments can start with\neasy-to-implement methods, such as noise-robust loss functions, weighting, and\ncurriculum learning.\n", "date": "2024-03-19", "categories": []}
{"id": "2402.10109", "title": "Towards Reducing Diagnostic Errors with Interpretable Risk Prediction", "abstract": "  Many diagnostic errors occur because clinicians cannot easily access relevant\ninformation in patient Electronic Health Records (EHRs). In this work we\npropose a method to use LLMs to identify pieces of evidence in patient EHR data\nthat indicate increased or decreased risk of specific diagnoses; our ultimate\naim is to increase access to evidence and reduce diagnostic errors. In\nparticular, we propose a Neural Additive Model to make predictions backed by\nevidence with individualized risk estimates at time-points where clinicians are\nstill uncertain, aiming to specifically mitigate delays in diagnosis and errors\nstemming from an incomplete differential. To train such a model, it is\nnecessary to infer temporally fine-grained retrospective labels of eventual\n\"true\" diagnoses. We do so with LLMs, to ensure that the input text is from\nbefore a confident diagnosis can be made. We use an LLM to retrieve an initial\npool of evidence, but then refine this set of evidence according to\ncorrelations learned by the model. We conduct an in-depth evaluation of the\nusefulness of our approach by simulating how it might be used by a clinician to\ndecide between a pre-defined list of differential diagnoses.\n", "date": "2024-02-15", "categories": []}
{"id": "2403.12297", "title": "Leveraging Large Language Models to Extract Information on Substance Use\n  Disorder Severity from Clinical Notes: A Zero-shot Learning Approach", "abstract": "  Substance use disorder (SUD) poses a major concern due to its detrimental\neffects on health and society. SUD identification and treatment depend on a\nvariety of factors such as severity, co-determinants (e.g., withdrawal\nsymptoms), and social determinants of health. Existing diagnostic coding\nsystems used by American insurance providers, like the International\nClassification of Diseases (ICD-10), lack granularity for certain diagnoses,\nbut clinicians will add this granularity (as that found within the Diagnostic\nand Statistical Manual of Mental Disorders classification or DSM-5) as\nsupplemental unstructured text in clinical notes. Traditional natural language\nprocessing (NLP) methods face limitations in accurately parsing such diverse\nclinical language. Large Language Models (LLMs) offer promise in overcoming\nthese challenges by adapting to diverse language patterns. This study\ninvestigates the application of LLMs for extracting severity-related\ninformation for various SUD diagnoses from clinical notes. We propose a\nworkflow employing zero-shot learning of LLMs with carefully crafted prompts\nand post-processing techniques. Through experimentation with Flan-T5, an\nopen-source LLM, we demonstrate its superior recall compared to the rule-based\napproach. Focusing on 11 categories of SUD diagnoses, we show the effectiveness\nof LLMs in extracting severity information, contributing to improved risk\nassessment and treatment planning for SUD patients.\n", "date": "2024-03-18", "categories": []}
{"id": "2403.12818", "title": "Dynamic Survival Analysis for Early Event Prediction", "abstract": "  This study advances Early Event Prediction (EEP) in healthcare through\nDynamic Survival Analysis (DSA), offering a novel approach by integrating risk\nlocalization into alarm policies to enhance clinical event metrics. By adapting\nand evaluating DSA models against traditional EEP benchmarks, our research\ndemonstrates their ability to match EEP models on a time-step level and\nsignificantly improve event-level metrics through a new alarm prioritization\nscheme (up to 11% AuPRC difference). This approach represents a significant\nstep forward in predictive healthcare, providing a more nuanced and actionable\nframework for early event prediction and management.\n", "date": "2024-03-19", "categories": []}
{"id": "2302.01622", "title": "Private, fair and accurate: Training large-scale, privacy-preserving AI\n  models in medical imaging", "abstract": "  Artificial intelligence (AI) models are increasingly used in the medical\ndomain. However, as medical data is highly sensitive, special precautions to\nensure its protection are required. The gold standard for privacy preservation\nis the introduction of differential privacy (DP) to model training. Prior work\nindicates that DP has negative implications on model accuracy and fairness,\nwhich are unacceptable in medicine and represent a main barrier to the\nwidespread use of privacy-preserving techniques. In this work, we evaluated the\neffect of privacy-preserving training of AI models regarding accuracy and\nfairness compared to non-private training. For this, we used two datasets: (1)\nA large dataset (N=193,311) of high quality clinical chest radiographs, and (2)\na dataset (N=1,625) of 3D abdominal computed tomography (CT) images, with the\ntask of classifying the presence of pancreatic ductal adenocarcinoma (PDAC).\nBoth were retrospectively collected and manually labeled by experienced\nradiologists. We then compared non-private deep convolutional neural networks\n(CNNs) and privacy-preserving (DP) models with respect to privacy-utility\ntrade-offs measured as area under the receiver-operator-characteristic curve\n(AUROC), and privacy-fairness trade-offs, measured as Pearson's r or\nStatistical Parity Difference. We found that, while the privacy-preserving\ntrainings yielded lower accuracy, they did largely not amplify discrimination\nagainst age, sex or co-morbidity. Our study shows that -- under the challenging\nrealistic circumstances of a real-life clinical dataset -- the\nprivacy-preserving training of diagnostic deep learning models is possible with\nexcellent diagnostic accuracy and fairness.\n", "date": "2023-02-03", "categories": []}
{"id": "2306.06693", "title": "Open Brain AI. Automatic Language Assessment", "abstract": "  Language assessment plays a crucial role in diagnosing and treating\nindividuals with speech, language, and communication disorders caused by\nneurogenic conditions, whether developmental or acquired. However, current\nassessment methods are manual, laborious, and time-consuming to administer and\nscore, causing additional patient stress. To address these challenges, we\ndeveloped Open Brain AI (https://openbrainai.com). This computational platform\nharnesses innovative AI techniques, namely machine learning, natural language\nprocessing, large language models, and automatic speech-to-text transcription,\nto automatically analyze multilingual spoken and written speech productions.\nThis paper discusses the development of Open Brain AI, the AI language\nprocessing modules, and the linguistic measurements of discourse\nmacro-structure and micro-structure. The fast and automatic analysis of\nlanguage alleviates the burden on clinicians, enabling them to streamline their\nworkflow and allocate more time and resources to direct patient care. Open\nBrain AI is freely accessible, empowering clinicians to conduct critical data\nanalyses and give more attention and resources to other critical aspects of\ntherapy and treatment.\n", "date": "2023-06-11", "categories": []}
{"id": "2309.07183", "title": "Respiratory Disease Classification and Biometric Analysis Using\n  Biosignals from Digital Stethoscopes", "abstract": "  Respiratory diseases remain a leading cause of mortality worldwide,\nhighlighting the need for faster and more accurate diagnostic tools. This work\npresents a novel approach leveraging digital stethoscope technology for\nautomatic respiratory disease classification and biometric analysis. Our\napproach has the potential to significantly enhance traditional auscultation\npractices. By leveraging one of the largest publicly available medical database\nof respiratory sounds, we train machine learning models to classify various\nrespiratory health conditions. Our method differs from conventional methods by\nusing Empirical Mode Decomposition (EMD) and spectral analysis techniques to\nisolate clinically relevant biosignals embedded within acoustic data captured\nby digital stethoscopes. This approach focuses on information closely tied to\ncardiovascular and respiratory patterns within the acoustic data. Spectral\nanalysis and filtering techniques isolate Intrinsic Mode Functions (IMFs)\nstrongly correlated with these physiological phenomena. These biosignals\nundergo a comprehensive feature extraction process for predictive modeling.\nThese features then serve as input to train several machine learning models for\nboth classification and regression tasks. Our approach achieves high accuracy\nin both binary classification (89% balanced accuracy for healthy vs. diseased)\nand multi-class classification (72% balanced accuracy for specific diseases\nlike pneumonia and COPD). For the first time, this work introduces regression\nmodels capable of estimating age and body mass index (BMI) based solely on\nacoustic data, as well as a model for sex classification. Our findings\nunderscore the potential of intelligent digital stethoscopes to significantly\nenhance assistive and remote diagnostic capabilities, contributing to\nadvancements in digital health, telehealth, and remote patient monitoring.\n", "date": "2023-09-12", "categories": []}
{"id": "2403.10603", "title": "SurvRNC: Learning Ordered Representations for Survival Prediction using\n  Rank-N-Contrast", "abstract": "  Predicting the likelihood of survival is of paramount importance for\nindividuals diagnosed with cancer as it provides invaluable information\nregarding prognosis at an early stage. This knowledge enables the formulation\nof effective treatment plans that lead to improved patient outcomes. In the\npast few years, deep learning models have provided a feasible solution for\nassessing medical images, electronic health records, and genomic data to\nestimate cancer risk scores. However, these models often fall short of their\npotential because they struggle to learn regression-aware feature\nrepresentations. In this study, we propose Survival Rank-N Contrast (SurvRNC)\nmethod, which introduces a loss function as a regularizer to obtain an ordered\nrepresentation based on the survival times. This function can handle censored\ndata and can be incorporated into any survival model to ensure that the learned\nrepresentation is ordinal. The model was extensively evaluated on a HEad \\&\nNeCK TumOR (HECKTOR) segmentation and the outcome-prediction task dataset. We\ndemonstrate that using the SurvRNC method for training can achieve higher\nperformance on different deep survival models. Additionally, it outperforms\nstate-of-the-art methods by 3.6% on the concordance index. The code is publicly\navailable on https://github.com/numanai/SurvRNC\n", "date": "2024-03-15", "categories": []}
{"id": "2403.11032", "title": "FH-TabNet: Multi-Class Familial Hypercholesterolemia Detection via a\n  Multi-Stage Tabular Deep Learning", "abstract": "  Familial Hypercholesterolemia (FH) is a genetic disorder characterized by\nelevated levels of Low-Density Lipoprotein (LDL) cholesterol or its associated\ngenes. Early-stage and accurate categorization of FH is of significance\nallowing for timely interventions to mitigate the risk of life-threatening\nconditions. Conventional diagnosis approach, however, is complex, costly, and a\nchallenging interpretation task even for experienced clinicians resulting in\nhigh underdiagnosis rates. Although there has been a recent surge of interest\nin using Machine Learning (ML) models for early FH detection, existing\nsolutions only consider a binary classification task solely using classical ML\nmodels. Despite its significance, application of Deep Learning (DL) for FH\ndetection is in its infancy, possibly, due to categorical nature of the\nunderlying clinical data. The paper addresses this gap by introducing the\nFH-TabNet, which is a multi-stage tabular DL network for multi-class (Definite,\nProbable, Possible, and Unlikely) FH detection. The FH-TabNet initially\ninvolves applying a deep tabular data learning architecture (TabNet) for\nprimary categorization into healthy (Possible/Unlikely) and patient\n(Probable/Definite) classes. Subsequently, independent TabNet classifiers are\napplied to each subgroup, enabling refined classification. The model's\nperformance is evaluated through 5-fold cross-validation illustrating superior\nperformance in categorizing FH patients, particularly in the challenging\nlow-prevalence subcategories.\n", "date": "2024-03-16", "categories": []}
{"id": "2403.11953", "title": "Advancing COVID-19 Detection in 3D CT Scans", "abstract": "  To make a more accurate diagnosis of COVID-19, we propose a straightforward\nyet effective model. Firstly, we analyse the characteristics of 3D CT scans and\nremove the non-lung parts, facilitating the model to focus on lesion-related\nareas and reducing computational cost. We use ResNeSt50 as the strong feature\nextractor, initializing it with pretrained weights which have COVID-19-specific\nprior knowledge. Our model achieves a Macro F1 Score of 0.94 on the validation\nset of the 4th COV19D Competition Challenge $\\mathrm{I}$, surpassing the\nbaseline by 16%. This indicates its effectiveness in distinguishing between\nCOVID-19 and non-COVID-19 cases, making it a robust method for COVID-19\ndetection.\n", "date": "2024-03-18", "categories": []}
{"id": "2403.09708", "title": "Institutional-Level Monitoring of Immune Checkpoint Inhibitor IrAEs\n  Using a Novel Natural Language Processing Algorithmic Pipeline", "abstract": "  Background: Immune checkpoint inhibitors (ICIs) have revolutionized cancer\ntreatment but can result in severe immune-related adverse events (IrAEs).\nMonitoring IrAEs on a large scale is essential for personalized risk profiling\nand assisting in treatment decisions.\n  Methods: In this study, we conducted an analysis of clinical notes from\npatients who received ICIs at the Tel Aviv Sourasky Medical Center. By\nemploying a Natural Language Processing algorithmic pipeline, we systematically\nidentified seven common or severe IrAEs. We examined the utilization of\ncorticosteroids, treatment discontinuation rates following IrAEs, and\nconstructed survival curves to visualize the occurrence of adverse events\nduring treatment.\n  Results: Our analysis encompassed 108,280 clinical notes associated with\n1,635 patients who had undergone ICI therapy. The detected incidence of IrAEs\nwas consistent with previous reports, exhibiting substantial variation across\ndifferent ICIs. Treatment with corticosteroids varied depending on the specific\nIrAE, ranging from 17.3% for thyroiditis to 57.4% for myocarditis. Our\nalgorithm demonstrated high accuracy in identifying IrAEs, as indicated by an\narea under the curve (AUC) of 0.89 for each suspected note and F1 scores of\n0.87 or higher for five out of the seven IrAEs examined at the patient level.\n  Conclusions: This study presents a novel, large-scale monitoring approach\nutilizing deep neural networks for IrAEs. Our method provides accurate results,\nenhancing understanding of detrimental consequences experienced by ICI-treated\npatients. Moreover, it holds potential for monitoring other medications,\nenabling comprehensive post-marketing surveillance to identify susceptible\npopulations and establish personalized drug safety profiles.\n", "date": "2024-03-09", "categories": []}
{"id": "2403.10156", "title": "Cardiac valve event timing in echocardiography using deep learning and\n  triplane recordings", "abstract": "  Cardiac valve event timing plays a crucial role when conducting clinical\nmeasurements using echocardiography. However, established automated approaches\nare limited by the need of external electrocardiogram sensors, and manual\nmeasurements often rely on timing from different cardiac cycles. Recent methods\nhave applied deep learning to cardiac timing, but they have mainly been\nrestricted to only detecting two key time points, namely end-diastole (ED) and\nend-systole (ES). In this work, we propose a deep learning approach that\nleverages triplane recordings to enhance detection of valve events in\nechocardiography. Our method demonstrates improved performance detecting six\ndifferent events, including valve events conventionally associated with ED and\nES. Of all events, we achieve an average absolute frame difference (aFD) of\nmaximum 1.4 frames (29 ms) for start of diastasis, down to 0.6 frames (12 ms)\nfor mitral valve opening when performing a ten-fold cross-validation with test\nsplits on triplane data from 240 patients. On an external independent test\nconsisting of apical long-axis data from 180 other patients, the worst\nperforming event detection had an aFD of 1.8 (30 ms). The proposed approach has\nthe potential to significantly impact clinical practice by enabling more\naccurate, rapid and comprehensive event detection, leading to improved clinical\nmeasurements.\n", "date": "2024-03-15", "categories": []}
{"id": "2403.08818", "title": "Multimodal Fusion of EHR in Structures and Semantics: Integrating\n  Clinical Records and Notes with Hypergraph and LLM", "abstract": "  Electronic Health Records (EHRs) have become increasingly popular to support\nclinical decision-making and healthcare in recent decades. EHRs usually contain\nheterogeneous information, such as structural data in tabular form and\nunstructured data in textual notes. Different types of information in EHRs can\ncomplement each other and provide a more complete picture of the health status\nof a patient. While there has been a lot of research on representation learning\nof structured EHR data, the fusion of different types of EHR data (multimodal\nfusion) is not well studied. This is mostly because of the complex medical\ncoding systems used and the noise and redundancy present in the written notes.\nIn this work, we propose a new framework called MINGLE, which integrates both\nstructures and semantics in EHR effectively. Our framework uses a two-level\ninfusion strategy to combine medical concept semantics and clinical note\nsemantics into hypergraph neural networks, which learn the complex interactions\nbetween different types of data to generate visit representations for\ndownstream prediction. Experiment results on two EHR datasets, the public\nMIMIC-III and private CRADLE, show that MINGLE can effectively improve\npredictive performance by 11.83% relatively, enhancing semantic integration as\nwell as multimodal fusion for structural and textual EHR data.\n", "date": "2024-02-19", "categories": []}
{"id": "2403.08834", "title": "Predictive Analysis of Tuberculosis Treatment Outcomes Using Machine\n  Learning: A Karnataka TB Data Study at a Scale", "abstract": "  Tuberculosis (TB) remains a global health threat, ranking among the leading\ncauses of mortality worldwide. In this context, machine learning (ML) has\nemerged as a transformative force, providing innovative solutions to the\ncomplexities associated with TB treatment.This study explores how machine\nlearning, especially with tabular data, can be used to predict Tuberculosis\n(TB) treatment outcomes more accurately. It transforms this prediction task\ninto a binary classification problem, generating risk scores from patient data\nsourced from NIKSHAY, India's national TB control program, which includes over\n500,000 patient records.\n  Data preprocessing is a critical component of the study, and the model\nachieved an recall of 98% and an AUC-ROC score of 0.95 on the validation set,\nwhich includes 20,000 patient records.We also explore the use of Natural\nLanguage Processing (NLP) for improved model learning. Our results,\ncorroborated by various metrics and ablation studies, validate the\neffectiveness of our approach. The study concludes by discussing the potential\nramifications of our research on TB eradication efforts and proposing potential\navenues for future work. This study marks a significant stride in the battle\nagainst TB, showcasing the potential of machine learning in healthcare.\n", "date": "2024-03-13", "categories": []}
{"id": "2403.03229", "title": "Embracing Uncertainty Flexibility: Harnessing a Supervised Tree Kernel\n  to Empower Ensemble Modelling for 2D Echocardiography-Based Prediction of\n  Right Ventricular Volume", "abstract": "  The right ventricular (RV) function deterioration strongly predicts clinical\noutcomes in numerous circumstances. To boost the clinical deployment of\nensemble regression methods that quantify RV volumes using tabular data from\nthe widely available two-dimensional echocardiography (2DE), we propose to\ncomplement the volume predictions with uncertainty scores. To this end, we\nemploy an instance-based method which uses the learned tree structure to\nidentify the nearest training samples to a target instance and then uses a\nnumber of distribution types to more flexibly model the output. The\nprobabilistic and point-prediction performances of the proposed framework are\nevaluated on a relatively small-scale dataset, comprising 100 end-diastolic and\nend-systolic RV volumes. The reference values for point performance were\nobtained from MRI. The results demonstrate that our flexible approach yields\nimproved probabilistic and point performances over other state-of-the-art\nmethods. The appropriateness of the proposed framework is showcased by\nproviding exemplar cases. The estimated uncertainty embodies both aleatoric and\nepistemic types. This work aligns with trustworthy artificial intelligence\nsince it can be used to enhance the decision-making process and reduce risks.\nThe feature importance scores of our framework can be exploited to reduce the\nnumber of required 2DE views which could enhance the proposed pipeline's\nclinical application.\n", "date": "2024-03-04", "categories": []}
{"id": "2403.05818", "title": "PR-NET: Leveraging Pathway Refined Network Structures for Prostate\n  Cancer Patient Condition Prediction", "abstract": "  The diagnosis and monitoring of Castrate Resistant Prostate Cancer (CRPC) are\ncrucial for cancer patients, but the current models (such as P-NET) have\nlimitations in terms of parameter count, generalization, and cost. To address\nthe issue, we develop a more accurate and efficient Prostate Cancer patient\ncondition prediction model, named PR-NET. By compressing and optimizing the\nnetwork structure of P-NET, the model complexity is reduced while maintaining\nhigh accuracy and interpretability. The PR-NET demonstrated superior\nperformance in predicting prostate cancer patient outcomes, outshining P-NET\nand six other traditional models with a significant margin. In our rigorous\nevaluation, PR-NET not only achieved impressive average AUC and Recall scores\nof 0.94 and 0.83, respectively, on known data but also maintained robust\ngeneralizability on five unknown datasets with a higher average AUC of 0.73 and\nRecall of 0.72, compared to P-NET's 0.68 and 0.5. PR-NET's efficiency was\nevidenced by its shorter average training and inference times, and its\ngene-level analysis revealed 46 key genes, demonstrating its enhanced\npredictive power and efficiency in identifying critical biomarkers for prostate\ncancer. Future research can further expand its application domains and optimize\nthe model's performance and reliability.\n", "date": "2024-03-09", "categories": []}
{"id": "2403.06242", "title": "COVID-19 Computer-aided Diagnosis through AI-assisted CT Imaging\n  Analysis: Deploying a Medical AI System", "abstract": "  Computer-aided diagnosis (CAD) systems stand out as potent aids for\nphysicians in identifying the novel Coronavirus Disease 2019 (COVID-19) through\nmedical imaging modalities. In this paper, we showcase the integration and\nreliable and fast deployment of a state-of-the-art AI system designed to\nautomatically analyze CT images, offering infection probability for the swift\ndetection of COVID-19. The suggested system, comprising both classification and\nsegmentation components, is anticipated to reduce physicians' detection time\nand enhance the overall efficiency of COVID-19 detection. We successfully\nsurmounted various challenges, such as data discrepancy and anonymisation,\ntesting the time-effectiveness of the model, and data security, enabling\nreliable and scalable deployment of the system on both cloud and edge\nenvironments. Additionally, our AI system assigns a probability of infection to\neach 3D CT scan and enhances explainability through anchor set similarity,\nfacilitating timely confirmation and segregation of infected patients by\nphysicians.\n", "date": "2024-03-10", "categories": []}
{"id": "2403.07038", "title": "Leveraging graph neural networks for supporting Automatic Triage of\n  Patients", "abstract": "  Patient triage plays a crucial role in emergency departments, ensuring timely\nand appropriate care based on correctly evaluating the emergency grade of\npatient conditions.\n  Triage methods are generally performed by human operator based on her own\nexperience and information that are gathered from the patient management\nprocess.\n  Thus, it is a process that can generate errors in emergency level\nassociations. Recently, Traditional triage methods heavily rely on human\ndecisions, which can be subjective and prone to errors.\n  Recently, a growing interest has been focused on leveraging artificial\nintelligence (AI) to develop algorithms able to maximize information gathering\nand minimize errors in patient triage processing.\n  We define and implement an AI based module to manage patients emergency code\nassignments in emergency departments. It uses emergency department historical\ndata to train the medical decision process. Data containing relevant patient\ninformation, such as vital signs, symptoms, and medical history, are used to\naccurately classify patients into triage categories. Experimental results\ndemonstrate that the proposed algorithm achieved high accuracy outperforming\ntraditional triage methods. By using the proposed method we claim that\nhealthcare professionals can predict severity index to guide patient management\nprocessing and resource allocation.\n", "date": "2024-03-11", "categories": []}
{"id": "2403.07201", "title": "A multi-cohort study on prediction of acute brain dysfunction states\n  using selective state space models", "abstract": "  Assessing acute brain dysfunction (ABD), including delirium and coma in the\nintensive care unit (ICU), is a critical challenge due to its prevalence and\nsevere implications for patient outcomes. Current diagnostic methods rely on\ninfrequent clinical observations, which can only determine a patient's ABD\nstatus after onset. Our research attempts to solve these problems by harnessing\nElectronic Health Records (EHR) data to develop automated methods for ABD\nprediction for patients in the ICU. Existing models solely predict a single\nstate (e.g., either delirium or coma), require at least 24 hours of observation\ndata to make predictions, do not dynamically predict fluctuating ABD conditions\nduring ICU stay (typically a one-time prediction), and use small sample size,\nproprietary single-hospital datasets. Our research fills these gaps in the\nexisting literature by dynamically predicting delirium, coma, and mortality for\n12-hour intervals throughout an ICU stay and validating on two public datasets.\nOur research also introduces the concept of dynamically predicting critical\ntransitions from non-ABD to ABD and between different ABD states in real time,\nwhich could be clinically more informative for the hospital staff. We compared\nthe predictive performance of two state-of-the-art neural network models, the\nMAMBA selective state space model and the Longformer Transformer model. Using\nthe MAMBA model, we achieved a mean area under the receiving operator\ncharacteristic curve (AUROC) of 0.95 on outcome prediction of ABD for 12-hour\nintervals. The model achieves a mean AUROC of 0.79 when predicting transitions\nbetween ABD states. Our study uses a curated dataset from the University of\nFlorida Health Shands Hospital for internal validation and two publicly\navailable datasets, MIMIC-IV and eICU, for external validation, demonstrating\nrobustness across ICU stays from 203 hospitals and 140,945 patients.\n", "date": "2024-03-11", "categories": []}
{"id": "2403.07309", "title": "Reinforced Sequential Decision-Making for Sepsis Treatment: The POSNEGDM\n  Framework with Mortality Classifier and Transformer", "abstract": "  Sepsis, a life-threatening condition triggered by the body's exaggerated\nresponse to infection, demands urgent intervention to prevent severe\ncomplications. Existing machine learning methods for managing sepsis struggle\nin offline scenarios, exhibiting suboptimal performance with survival rates\nbelow 50%. This paper introduces the POSNEGDM -- ``Reinforcement Learning with\nPositive and Negative Demonstrations for Sequential Decision-Making\" framework\nutilizing an innovative transformer-based model and a feedback reinforcer to\nreplicate expert actions while considering individual patient characteristics.\nA mortality classifier with 96.7\\% accuracy guides treatment decisions towards\npositive outcomes. The POSNEGDM framework significantly improves patient\nsurvival, saving 97.39% of patients, outperforming established machine learning\nalgorithms (Decision Transformer and Behavioral Cloning) with survival rates of\n33.4% and 43.5%, respectively. Additionally, ablation studies underscore the\ncritical role of the transformer-based decision maker and the integration of a\nmortality classifier in enhancing overall survival rates. In summary, our\nproposed approach presents a promising avenue for enhancing sepsis treatment\noutcomes, contributing to improved patient care and reduced healthcare costs.\n", "date": "2024-03-12", "categories": []}
{"id": "2305.16363", "title": "Subpopulation-Specific Synthetic EHR for Better Mortality Prediction", "abstract": "  Electronic health records (EHR) often contain different rates of\nrepresentation of certain subpopulations (SP). Factors like patient\ndemographics, clinical condition prevalence, and medical center type contribute\nto this underrepresentation. Consequently, when training machine learning\nmodels on such datasets, the models struggle to generalize well and perform\npoorly on underrepresented SPs. To address this issue, we propose a novel\nensemble framework that utilizes generative models. Specifically, we train a\nGAN-based synthetic data generator for each SP and incorporate synthetic\nsamples into each SP training set. Ultimately, we train SP-specific prediction\nmodels. To properly evaluate this method, we design an evaluation pipeline with\n2 real-world use case datasets, queried from the MIMIC database. Our approach\nshows increased model performance over underrepresented SPs. Our code and\nmodels are given as supplementary and will be made available on a public\nrepository.\n", "date": "2023-05-25", "categories": []}
{"id": "2310.09203", "title": "SiamAF: Learning Shared Information from ECG and PPG Signals for Robust\n  Atrial Fibrillation Detection", "abstract": "  Atrial fibrillation (AF) is the most common type of cardiac arrhythmia. It is\nassociated with an increased risk of stroke, heart failure, and other\ncardiovascular complications, but can be clinically silent. Passive AF\nmonitoring with wearables may help reduce adverse clinical outcomes related to\nAF. Detecting AF in noisy wearable data poses a significant challenge, leading\nto the emergence of various deep learning techniques. Previous deep learning\nmodels learn from a single modality, either electrocardiogram (ECG) or\nphotoplethysmography (PPG) signals. However, deep learning models often\nstruggle to learn generalizable features and rely on features that are more\nsusceptible to corruption from noise, leading to sub-optimal performances in\ncertain scenarios, especially with low-quality signals. Given the increasing\navailability of ECG and PPG signal pairs from wearables and bedside monitors,\nwe propose a new approach, SiamAF, leveraging a novel Siamese network\narchitecture and joint learning loss function to learn shared information from\nboth ECG and PPG signals. At inference time, the proposed model is able to\npredict AF from either PPG or ECG and outperforms baseline methods on three\nexternal test sets. It learns medically relevant features as a result of our\nnovel architecture design. The proposed model also achieves comparable\nperformance to traditional learning regimes while requiring much fewer training\nlabels, providing a potential approach to reduce future reliance on manual\nlabeling.\n", "date": "2023-10-13", "categories": []}
{"id": "2403.05795", "title": "ClinicalMamba: A Generative Clinical Language Model on Longitudinal\n  Clinical Notes", "abstract": "  The advancement of natural language processing (NLP) systems in healthcare\nhinges on language model ability to interpret the intricate information\ncontained within clinical notes. This process often requires integrating\ninformation from various time points in a patient's medical history. However,\nmost earlier clinical language models were pretrained with a context length\nlimited to roughly one clinical document. In this study, We introduce\nClinicalMamba, a specialized version of the Mamba language model, pretrained on\na vast corpus of longitudinal clinical notes to address the unique linguistic\ncharacteristics and information processing needs of the medical domain.\nClinicalMamba, with 130 million and 2.8 billion parameters, demonstrates a\nsuperior performance in modeling clinical language across extended text lengths\ncompared to Mamba and clinical Llama. With few-shot learning, ClinicalMamba\nachieves notable benchmarks in speed and accuracy, outperforming existing\nclinical language models and general domain large models like GPT-4 in\nlongitudinal clinical notes information extraction tasks.\n", "date": "2024-03-09", "categories": []}
{"id": "2403.06024", "title": "Semi-Supervised Multimodal Multi-Instance Learning for Aortic Stenosis\n  Diagnosis", "abstract": "  Automated interpretation of ultrasound imaging of the heart (echocardiograms)\ncould improve the detection and treatment of aortic stenosis (AS), a deadly\nheart disease. However, existing deep learning pipelines for assessing AS from\nechocardiograms have two key limitations. First, most methods rely on limited\n2D cineloops, thereby ignoring widely available Doppler imaging that contains\nimportant complementary information about pressure gradients and blood flow\nabnormalities associated with AS. Second, obtaining labeled data is difficult.\nThere are often far more unlabeled echocardiogram recordings available, but\nthese remain underutilized by existing methods. To overcome these limitations,\nwe introduce Semi-supervised Multimodal Multiple-Instance Learning (SMMIL), a\nnew deep learning framework for automatic interpretation for structural heart\ndiseases like AS. When deployed, SMMIL can combine information from two input\nmodalities, spectral Dopplers and 2D cineloops, to produce a study-level AS\ndiagnosis. During training, SMMIL can combine a smaller labeled set and an\nabundant unlabeled set of both modalities to improve its classifier.\nExperiments demonstrate that SMMIL outperforms recent alternatives at 3-level\nAS severity classification as well as several clinically relevant AS detection\ntasks.\n", "date": "2024-03-09", "categories": []}
{"id": "2403.06033", "title": "Predicting Depression and Anxiety: A Multi-Layer Perceptron for\n  Analyzing the Mental Health Impact of COVID-19", "abstract": "  We introduce a multi-layer perceptron (MLP) called the COVID-19 Depression\nand Anxiety Predictor (CoDAP) to predict mental health trends, particularly\nanxiety and depression, during the COVID-19 pandemic. Our method utilizes a\ncomprehensive dataset, which tracked mental health symptoms weekly over ten\nweeks during the initial COVID-19 wave (April to June 2020) in a diverse cohort\nof U.S. adults. This period, characterized by a surge in mental health symptoms\nand conditions, offers a critical context for our analysis. Our focus was to\nextract and analyze patterns of anxiety and depression through a unique lens of\nqualitative individual attributes using CoDAP. This model not only predicts\npatterns of anxiety and depression during the pandemic but also unveils key\ninsights into the interplay of demographic factors, behavioral changes, and\nsocial determinants of mental health. These findings contribute to a more\nnuanced understanding of the complexity of mental health issues in times of\nglobal health crises, potentially guiding future early interventions.\n", "date": "2024-03-09", "categories": []}
{"id": "2403.06843", "title": "Towards an educational tool for supporting neonatologists in the\n  delivery room", "abstract": "  Nowadays, there is evidence that several factors may increase the risk, for\nan infant, to require stabilisation or resuscitation manoeuvres at birth.\nHowever, this risk factors are not completely known, and a universally\napplicable model for predicting high-risk situations is not available yet.\nConsidering both these limitations and the fact that the need for resuscitation\nat birth is a rare event, periodic training of the healthcare personnel\nresponsible for newborn caring in the delivery room is mandatory.\n  In this paper, we propose a machine learning approach for identifying risk\nfactors and their impact on the birth event from real data, which can be used\nby personnel to progressively increase and update their knowledge. Our final\ngoal will be the one of designing a user-friendly mobile application, able to\nimprove the recognition rate and the planning of the appropriate interventions\non high-risk patients.\n", "date": "2024-03-11", "categories": []}
{"id": "2311.02026", "title": "APRICOT-Mamba: Acuity Prediction in Intensive Care Unit (ICU):\n  Development and Validation of a Stability, Transitions, and Life-Sustaining\n  Therapies Prediction Model", "abstract": "  The acuity state of patients in the intensive care unit (ICU) can quickly\nchange from stable to unstable. Early detection of deteriorating conditions can\nresult in providing timely interventions and improved survival rates. In this\nstudy, we propose APRICOT-M (Acuity Prediction in Intensive Care Unit-Mamba), a\n150k-parameter state space-based neural network to predict acuity state,\ntransitions, and the need for life-sustaining therapies in real-time in ICU\npatients. The model uses data obtained in the prior four hours in the ICU and\npatient information obtained at admission to predict the acuity outcomes in the\nnext four hours. We validated APRICOT-M externally on data from hospitals not\nused in development (75,668 patients from 147 hospitals), temporally on data\nfrom a period not used in development (12,927 patients from one hospital from\n2018-2019), and prospectively on data collected in real-time (215 patients from\none hospital from 2021-2023) using three large datasets: the University of\nFlorida Health (UFH) dataset, the electronic ICU Collaborative Research\nDatabase (eICU), and the Medical Information Mart for Intensive Care\n(MIMIC)-IV. The area under the receiver operating characteristic curve (AUROC)\nof APRICOT-M for mortality (external 0.94-0.95, temporal 0.97-0.98, prospective\n0.96-1.00) and acuity (external 0.95-0.95, temporal 0.97-0.97, prospective\n0.96-0.96) shows comparable results to state-of-the-art models. Furthermore,\nAPRICOT-M can predict transitions to instability (external 0.81-0.82, temporal\n0.77-0.78, prospective 0.68-0.75) and need for life-sustaining therapies,\nincluding mechanical ventilation (external 0.82-0.83, temporal 0.87-0.88,\nprospective 0.67-0.76), and vasopressors (external 0.81-0.82, temporal\n0.73-0.75, prospective 0.66-0.74). This tool allows for real-time acuity\nmonitoring in critically ill patients and can help clinicians make timely\ninterventions.\n", "date": "2023-11-03", "categories": []}
{"id": "2311.15545", "title": "Out-of-Distribution Generalized Dynamic Graph Neural Network for Human\n  Albumin Prediction", "abstract": "  Human albumin is essential for indicating the body's overall health.\nAccurately predicting plasma albumin levels and determining appropriate doses\nare urgent clinical challenges, particularly in critically ill patients, to\nmaintain optimal blood levels. However, human albumin prediction is non-trivial\nthat has to leverage the dynamics of biochemical markers as well as the\nexperience of treating patients. Moreover, the problem of distribution shift is\noften encountered in real clinical data, which may lead to a decline in the\nmodel prediction performance and reduce the reliability of the model's\napplication. In this paper, we propose a framework named Out-of-Distribution\nGeneralized Dynamic Graph Neural Network for Human Albumin Prediction\n(DyG-HAP), which is able to provide accurate albumin predictions for Intensity\nCare Unit (ICU) patients during hospitalization. We first model human albumin\nprediction as a dynamic graph regression problem to model the dynamics and\npatient relationship. Then, we propose a disentangled dynamic graph attention\nmechanism to capture and disentangle the patterns whose relationship to labels\nunder distribution shifts is invariant and variant respectively. Last, we\npropose an invariant dynamic graph regression method to encourage the model to\nrely on invariant patterns to make predictions. Moreover, we propose a dataset\nnamed Albumin level testing and nutritional dosing data for Intensive Care\n(ANIC) for evaluation. Extensive experiments demonstrate the superiority of our\nmethod compared to several baseline methods in human albumin prediction.\n", "date": "2023-11-27", "categories": []}
{"id": "2402.06295", "title": "Multimodal Interpretable Data-Driven Models for Early Prediction of\n  Antimicrobial Multidrug Resistance Using Multivariate Time-Series", "abstract": "  Electronic health records (EHR) is an inherently multimodal register of the\npatient's health status characterized by static data and multivariate time\nseries (MTS). While MTS are a valuable tool for clinical prediction, their\nfusion with other data modalities can possibly result in more thorough insights\nand more accurate results. Deep neural networks (DNNs) have emerged as\nfundamental tools for identifying and defining underlying patterns in the\nhealthcare domain. However, fundamental improvements in interpretability are\nneeded for DNN models to be widely used in the clinical setting. In this study,\nwe present an approach built on a collection of interpretable multimodal\ndata-driven models that may anticipate and understand the emergence of\nantimicrobial multidrug resistance (AMR) germs in the intensive care unit (ICU)\nof the University Hospital of Fuenlabrada (Madrid, Spain). The profile and\ninitial health status of the patient are modeled using static variables, while\nthe evolution of the patient's health status during the ICU stay is modeled\nusing several MTS, including mechanical ventilation and antibiotics intake. The\nmultimodal DNNs models proposed in this paper include interpretable principles\nin addition to being effective at predicting AMR and providing an explainable\nprediction support system for AMR in the ICU. Furthermore, our proposed\nmethodology based on multimodal models and interpretability schemes can be\nleveraged in additional clinical problems dealing with EHR data, broadening the\nimpact and applicability of our results.\n", "date": "2024-02-09", "categories": []}
{"id": "2403.03368", "title": "Leveraging Federated Learning for Automatic Detection of Clopidogrel\n  Treatment Failures", "abstract": "  The effectiveness of clopidogrel, a widely used antiplatelet medication,\nvaries significantly among individuals, necessitating the development of\nprecise predictive models to optimize patient care. In this study, we leverage\nfederated learning strategies to address clopidogrel treatment failure\ndetection. Our research harnesses the collaborative power of multiple\nhealthcare institutions, allowing them to jointly train machine learning models\nwhile safeguarding sensitive patient data. Utilizing the UK Biobank dataset,\nwhich encompasses a vast and diverse population, we partitioned the data based\non geographic centers and evaluated the performance of federated learning. Our\nresults show that while centralized training achieves higher Area Under the\nCurve (AUC) values and faster convergence, federated learning approaches can\nsubstantially narrow this performance gap. Our findings underscore the\npotential of federated learning in addressing clopidogrel treatment failure\ndetection, offering a promising avenue for enhancing patient care through\npersonalized treatment strategies while respecting data privacy. This study\ncontributes to the growing body of research on federated learning in healthcare\nand lays the groundwork for secure and privacy-preserving predictive models for\nvarious medical conditions.\n", "date": "2024-03-05", "categories": []}
{"id": "2308.06354", "title": "Large Language Models to Identify Social Determinants of Health in\n  Electronic Health Records", "abstract": "  Social determinants of health (SDoH) have an important impact on patient\noutcomes but are incompletely collected from the electronic health records\n(EHR). This study researched the ability of large language models to extract\nSDoH from free text in EHRs, where they are most commonly documented, and\nexplored the role of synthetic clinical text for improving the extraction of\nthese scarcely documented, yet extremely valuable, clinical data. 800 patient\nnotes were annotated for SDoH categories, and several transformer-based models\nwere evaluated. The study also experimented with synthetic data generation and\nassessed for algorithmic bias. Our best-performing models were fine-tuned\nFlan-T5 XL (macro-F1 0.71) for any SDoH, and Flan-T5 XXL (macro-F1 0.70). The\nbenefit of augmenting fine-tuning with synthetic data varied across model\narchitecture and size, with smaller Flan-T5 models (base and large) showing the\ngreatest improvements in performance (delta F1 +0.12 to +0.23). Model\nperformance was similar on the in-hospital system dataset but worse on the\nMIMIC-III dataset. Our best-performing fine-tuned models outperformed zero- and\nfew-shot performance of ChatGPT-family models for both tasks. These fine-tuned\nmodels were less likely than ChatGPT to change their prediction when\nrace/ethnicity and gender descriptors were added to the text, suggesting less\nalgorithmic bias (p<0.05). At the patient-level, our models identified 93.8% of\npatients with adverse SDoH, while ICD-10 codes captured 2.0%. Our method can\neffectively extracted SDoH information from clinic notes, performing better\ncompare to GPT zero- and few-shot settings. These models could enhance\nreal-world evidence on SDoH and aid in identifying patients needing social\nsupport.\n", "date": "2023-08-11", "categories": []}
{"id": "2403.02945", "title": "Unsupervised Learning Approaches for Identifying ICU Patient Subgroups:\n  Do Results Generalise?", "abstract": "  The use of unsupervised learning to identify patient subgroups has emerged as\na potentially promising direction to improve the efficiency of Intensive Care\nUnits (ICUs). By identifying subgroups of patients with similar levels of\nmedical resource need, ICUs could be restructured into a collection of smaller\nsubunits, each catering to a specific group. However, it is unclear whether\ncommon patient subgroups exist across different ICUs, which would determine\nwhether ICU restructuring could be operationalised in a standardised manner. In\nthis paper, we tested the hypothesis that common ICU patient subgroups exist by\nexamining whether the results from one existing study generalise to a different\ndataset. We extracted 16 features representing medical resource need and used\nconsensus clustering to derive patient subgroups, replicating the previous\nstudy. We found limited similarities between our results and those of the\nprevious study, providing evidence against the hypothesis. Our findings imply\nthat there is significant variation between ICUs; thus, a standardised\nrestructuring approach is unlikely to be appropriate. Instead, potential\nefficiency gains might be greater when the number and nature of the subunits\nare tailored to each ICU individually.\n", "date": "2024-03-05", "categories": []}
{"id": "2403.01533", "title": "Machine learning predicts long-term mortality after acute myocardial\n  infarction using systolic time intervals and routinely collected clinical\n  data", "abstract": "  Precise estimation of cardiac patients' current and future comorbidities is\nan important factor in prioritizing continuous physiological monitoring and new\ntherapies. ML models have shown satisfactory performance in short-term\nmortality prediction of patients with heart disease, while their utility in\nlong-term predictions is limited. This study aims to investigate the\nperformance of tree-based ML models on long-term mortality prediction and the\neffect of two recently introduced biomarkers on long-term mortality. This study\nutilized publicly available data from CCHIA at the Ministry of Health and\nWelfare, Taiwan, China. Medical records were used to gather demographic and\nclinical data, including age, gender, BMI, percutaneous coronary intervention\n(PCI) status, and comorbidities such as hypertension, dyslipidemia, ST-segment\nelevation myocardial infarction (STEMI), and non-STEMI. Using medical and\ndemographic records as well as two recently introduced biomarkers, brachial\npre-ejection period (bPEP) and brachial ejection time (bET), collected from 139\npatients with acute myocardial infarction, we investigated the performance of\nadvanced ensemble tree-based ML algorithms (random forest, AdaBoost, and\nXGBoost) to predict all-cause mortality within 14 years. The developed ML\nmodels achieved significantly better performance compared to the baseline LR\n(C-Statistic, 0.80 for random forest, 0.79 for AdaBoost, and 0.78 for XGBoost,\nvs 0.77 for LR) (P-RF<0.001, PAdaBoost<0.001, PXGBoost<0.05). Adding bPEP and\nbET to our feature set significantly improved the algorithms' performance,\nleading to an absolute increase in C-Statistic of up to 0.03 (C-Statistic, 0.83\nfor random forest, 0.82 for AdaBoost, and 0.80 for XGBoost, vs 0.74 for LR)\n(P-RF<0.001, PAdaBoost<0.001, PXGBoost<0.05). This advancement may enable\nbetter treatment prioritization for high-risk individuals.\n", "date": "2024-03-03", "categories": []}
{"id": "2304.02780", "title": "A Transformer-Based Deep Learning Approach for Fairly Predicting\n  Post-Liver Transplant Risk Factors", "abstract": "  Liver transplantation is a life-saving procedure for patients with end-stage\nliver disease. There are two main challenges in liver transplant: finding the\nbest matching patient for a donor and ensuring transplant equity among\ndifferent subpopulations. The current MELD scoring system evaluates a patient's\nmortality risk if not receiving an organ within 90 days. However, the\ndonor-patient matching should also consider post-transplant risk factors, such\nas cardiovascular disease, chronic rejection, etc., which are all common\ncomplications after transplant. Accurate prediction of these risk scores\nremains a significant challenge. In this study, we used predictive models to\nsolve the above challenges. Specifically, we proposed a deep-learning model to\npredict multiple risk factors after a liver transplant. By formulating it as a\nmulti-task learning problem, the proposed deep neural network was trained to\nsimultaneously predict the five post-transplant risks and achieve equal good\nperformance by exploiting task-balancing techniques. We also proposed a novel\nfairness-achieving algorithm to ensure prediction fairness across different\nsubpopulations. We used electronic health records of 160,360 liver transplant\npatients, including demographic information, clinical variables, and laboratory\nvalues, collected from the liver transplant records of the United States from\n1987 to 2018. The model's performance was evaluated using various performance\nmetrics such as AUROC and AUPRC. Our experiment results highlighted the success\nof our multitask model in achieving task balance while maintaining accuracy.\nThe model significantly reduced the task discrepancy by 39%. Further\napplication of the fairness-achieving algorithm substantially reduced fairness\ndisparity among all sensitive attributes (gender, age group, and\nrace/ethnicity) in each risk factor.\n", "date": "2023-04-05", "categories": []}
{"id": "2402.18851", "title": "Applications of 0-1 Neural Networks in Prescription and Prediction", "abstract": "  A key challenge in medical decision making is learning treatment policies for\npatients with limited observational data. This challenge is particularly\nevident in personalized healthcare decision-making, where models need to take\ninto account the intricate relationships between patient characteristics,\ntreatment options, and health outcomes. To address this, we introduce\nprescriptive networks (PNNs), shallow 0-1 neural networks trained with mixed\ninteger programming that can be used with counterfactual estimation to optimize\npolicies in medium data settings. These models offer greater interpretability\nthan deep neural networks and can encode more complex policies than common\nmodels such as decision trees. We show that PNNs can outperform existing\nmethods in both synthetic data experiments and in a case study of assigning\ntreatments for postpartum hypertension. In particular, PNNs are shown to\nproduce policies that could reduce peak blood pressure by 5.47 mm Hg (p=0.02)\nover existing clinical practice, and by 2 mm Hg (p=0.01) over the next best\nprescriptive modeling technique. Moreover PNNs were more likely than all other\nmodels to correctly identify clinically significant features while existing\nmodels relied on potentially dangerous features such as patient insurance\ninformation and race that could lead to bias in treatment.\n", "date": "2024-02-29", "categories": []}
{"id": "2402.19296", "title": "An AI based Digital Score of Tumour-Immune Microenvironment Predicts\n  Benefit to Maintenance Immunotherapy in Advanced Oesophagogastric\n  Adenocarcinoma", "abstract": "  Gastric and oesophageal (OG) cancers are the leading causes of cancer\nmortality worldwide. In OG cancers, recent studies have showed that PDL1 immune\ncheckpoint inhibitors (ICI) in combination with chemotherapy improves patient\nsurvival. However, our understanding of the tumour immune microenvironment in\nOG cancers remains limited. In this study, we interrogate multiplex\nimmunofluorescence (mIF) images taken from patients with advanced\nOesophagogastric Adenocarcinoma (OGA) who received first-line fluoropyrimidine\nand platinum-based chemotherapy in the PLATFORM trial (NCT02678182) to predict\nthe efficacy of the treatment and to explore the biological basis of patients\nresponding to maintenance durvalumab (PDL1 inhibitor). Our proposed Artificial\nIntelligence (AI) based marker successfully identified responder from\nnon-responder (p < 0.05) as well as those who could potentially benefit from\nICI with statistical significance (p < 0.05) for both progression free and\noverall survival. Our findings suggest that T cells that express FOXP3 seem to\nheavily influence the patient treatment response and survival outcome. We also\nobserved that higher levels of CD8+PD1+ cells are consistently linked to poor\nprognosis for both OS and PFS, regardless of ICI.\n", "date": "2024-02-29", "categories": []}
{"id": "2402.18046", "title": "Data augmentation method for modeling health records with applications\n  to clopidogrel treatment failure detection", "abstract": "  We present a novel data augmentation method to address the challenge of data\nscarcity in modeling longitudinal patterns in Electronic Health Records (EHR)\nof patients using natural language processing (NLP) algorithms. The proposed\nmethod generates augmented data by rearranging the orders of medical records\nwithin a visit where the order of elements are not obvious, if any. Applying\nthe proposed method to the clopidogrel treatment failure detection task enabled\nup to 5.3% absolute improvement in terms of ROC-AUC (from 0.908 without\naugmentation to 0.961 with augmentation) when it was used during the\npre-training procedure. It was also shown that the augmentation helped to\nimprove performance during fine-tuning procedures, especially when the amount\nof labeled training data is limited.\n", "date": "2024-02-28", "categories": []}
{"id": "2402.18417", "title": "Prediction of recurrence free survival of head and neck cancer using\n  PET/CT radiomics and clinical information", "abstract": "  The 5-year survival rate of Head and Neck Cancer (HNC) has not improved over\nthe past decade and one common cause of treatment failure is recurrence. In\nthis paper, we built Cox proportional hazard (CoxPH) models that predict the\nrecurrence free survival (RFS) of oropharyngeal HNC patients. Our models\nutilise both clinical information and multimodal radiomics features extracted\nfrom tumour regions in Computed Tomography (CT) and Positron Emission\nTomography (PET). Furthermore, we were one of the first studies to explore the\nimpact of segmentation accuracy on the predictive power of the extracted\nradiomics features, through under- and over-segmentation study. Our models were\ntrained using the HEad and neCK TumOR (HECKTOR) challenge data, and the best\nperforming model achieved a concordance index (C-index) of 0.74 for the model\nutilising clinical information and multimodal CT and PET radiomics features,\nwhich compares favourably with the model that only used clinical information\n(C-index of 0.67). Our under- and over-segmentation study confirms that\nsegmentation accuracy affects radiomics extraction, however, it affects PET and\nCT differently.\n", "date": "2024-02-28", "categories": []}
{"id": "2206.09107", "title": "Tree-Guided Rare Feature Selection and Logic Aggregation with Electronic\n  Health Records Data", "abstract": "  Statistical learning with a large number of rare binary features is commonly\nencountered in analyzing electronic health records (EHR) data, especially in\nthe modeling of disease onset with prior medical diagnoses and procedures.\nDealing with the resulting highly sparse and large-scale binary feature matrix\nis notoriously challenging as conventional methods may suffer from a lack of\npower in testing and inconsistency in model fitting while machine learning\nmethods may suffer from the inability of producing interpretable results or\nclinically-meaningful risk factors. To improve EHR-based modeling and utilize\nthe natural hierarchical structure of disease classification, we propose a\ntree-guided feature selection and logic aggregation approach for large-scale\nregression with rare binary features, in which dimension reduction is achieved\nthrough not only a sparsity pursuit but also an aggregation promoter with the\nlogic operator of ``or''. We convert the combinatorial problem into a convex\nlinearly-constrained regularized estimation, which enables scalable computation\nwith theoretical guarantees. In a suicide risk study with EHR data, our\napproach is able to select and aggregate prior mental health diagnoses as\nguided by the diagnosis hierarchy of the International Classification of\nDiseases. By balancing the rarity and specificity of the EHR diagnosis records,\nour strategy improves both prediction and model interpretation. We identify\nimportant higher-level categories and subcategories of mental health conditions\nand simultaneously determine the level of specificity needed for each of them\nin predicting suicide risk.\n", "date": "2022-06-18", "categories": []}
{"id": "2401.01646", "title": "Prototypical Information Bottlenecking and Disentangling for Multimodal\n  Cancer Survival Prediction", "abstract": "  Multimodal learning significantly benefits cancer survival prediction,\nespecially the integration of pathological images and genomic data. Despite\nadvantages of multimodal learning for cancer survival prediction, massive\nredundancy in multimodal data prevents it from extracting discriminative and\ncompact information: (1) An extensive amount of intra-modal task-unrelated\ninformation blurs discriminability, especially for gigapixel whole slide images\n(WSIs) with many patches in pathology and thousands of pathways in genomic\ndata, leading to an ``intra-modal redundancy\" issue. (2) Duplicated information\namong modalities dominates the representation of multimodal data, which makes\nmodality-specific information prone to being ignored, resulting in an\n``inter-modal redundancy\" issue. To address these, we propose a new framework,\nPrototypical Information Bottlenecking and Disentangling (PIBD), consisting of\nPrototypical Information Bottleneck (PIB) module for intra-modal redundancy and\nPrototypical Information Disentanglement (PID) module for inter-modal\nredundancy. Specifically, a variant of information bottleneck, PIB, is proposed\nto model prototypes approximating a bunch of instances for different risk\nlevels, which can be used for selection of discriminative instances within\nmodality. PID module decouples entangled multimodal data into compact distinct\ncomponents: modality-common and modality-specific knowledge, under the guidance\nof the joint prototypical distribution. Extensive experiments on five cancer\nbenchmark datasets demonstrated our superiority over other methods.\n", "date": "2024-01-03", "categories": []}
{"id": "2402.15700", "title": "CoRelation: Boosting Automatic ICD Coding Through Contextualized Code\n  Relation Learning", "abstract": "  Automatic International Classification of Diseases (ICD) coding plays a\ncrucial role in the extraction of relevant information from clinical notes for\nproper recording and billing. One of the most important directions for boosting\nthe performance of automatic ICD coding is modeling ICD code relations.\nHowever, current methods insufficiently model the intricate relationships among\nICD codes and often overlook the importance of context in clinical notes. In\nthis paper, we propose a novel approach, a contextualized and flexible\nframework, to enhance the learning of ICD code representations. Our approach,\nunlike existing methods, employs a dependent learning paradigm that considers\nthe context of clinical notes in modeling all possible code relations. We\nevaluate our approach on six public ICD coding datasets and the experimental\nresults demonstrate the effectiveness of our approach compared to\nstate-of-the-art baselines.\n", "date": "2024-02-24", "categories": []}
{"id": "2402.15730", "title": "Understanding Missingness in Time-series Electronic Health Records for\n  Individualized Representation", "abstract": "  With the widespread of machine learning models for healthcare applications,\nthere is increased interest in building applications for personalized medicine.\nDespite the plethora of proposed research for personalized medicine, very few\nfocus on representing missingness and learning from the missingness patterns in\ntime-series Electronic Health Records (EHR) data. The lack of focus on\nmissingness representation in an individualized way limits the full utilization\nof machine learning applications towards true personalization. In this brief\ncommunication, we highlight new insights into patterns of missingness with\nreal-world examples and implications of missingness in EHRs. The insights in\nthis work aim to bridge the gap between theoretical assumptions and practical\nobservations in real-world EHRs. We hope this work will open new doors for\nexploring directions for better representation in predictive modelling for true\npersonalization.\n", "date": "2024-02-24", "categories": []}
{"id": "2402.15005", "title": "Comparison of Machine Learning Classification Algorithms and Application\n  to the Framingham Heart Study", "abstract": "  The use of machine learning algorithms in healthcare can amplify social\ninjustices and health inequities. While the exacerbation of biases can occur\nand compound during the problem selection, data collection, and outcome\ndefinition, this research pertains to some generalizability impediments that\noccur during the development and the post-deployment of machine learning\nclassification algorithms. Using the Framingham coronary heart disease data as\na case study, we show how to effectively select a probability cutoff to convert\na regression model for a dichotomous variable into a classifier. We then\ncompare the sampling distribution of the predictive performance of eight\nmachine learning classification algorithms under four training/testing\nscenarios to test their generalizability and their potential to perpetuate\nbiases. We show that both the Extreme Gradient Boosting, and Support Vector\nMachine are flawed when trained on an unbalanced dataset. We introduced and\nshow that the double discriminant scoring of type I is the most generalizable\nas it consistently outperforms the other classification algorithms regardless\nof the training/testing scenario. Finally, we introduce a methodology to\nextract an optimal variable hierarchy for a classification algorithm, and\nillustrate it on the overall, male and female Framingham coronary heart disease\ndata.\n", "date": "2024-02-22", "categories": []}
{"id": "2308.14120", "title": "Large Language Models Streamline Automated Machine Learning for Clinical\n  Studies", "abstract": "  A knowledge gap persists between machine learning (ML) developers (e.g., data\nscientists) and practitioners (e.g., clinicians), hampering the full\nutilization of ML for clinical data analysis. We investigated the potential of\nthe ChatGPT Advanced Data Analysis (ADA), an extension of GPT-4, to bridge this\ngap and perform ML analyses efficiently. Real-world clinical datasets and study\ndetails from large trials across various medical specialties were presented to\nChatGPT ADA without specific guidance. ChatGPT ADA autonomously developed\nstate-of-the-art ML models based on the original study's training data to\npredict clinical outcomes such as cancer development, cancer progression,\ndisease complications, or biomarkers such as pathogenic gene sequences.\nFollowing the re-implementation and optimization of the published models, the\nhead-to-head comparison of the ChatGPT ADA-crafted ML models and their\nrespective manually crafted counterparts revealed no significant differences in\ntraditional performance metrics (P>0.071). Strikingly, the ChatGPT ADA-crafted\nML models often outperformed their counterparts. In conclusion, ChatGPT ADA\noffers a promising avenue to democratize ML in medicine by simplifying complex\ndata analyses, yet should enhance, not replace, specialized training and\nresources, to promote broader applications in medical research and practice.\n", "date": "2023-08-27", "categories": []}
{"id": "2402.13635", "title": "The METRIC-framework for assessing data quality for trustworthy AI in\n  medicine: a systematic review", "abstract": "  The adoption of machine learning (ML) and, more specifically, deep learning\n(DL) applications into all major areas of our lives is underway. The\ndevelopment of trustworthy AI is especially important in medicine due to the\nlarge implications for patients' lives. While trustworthiness concerns various\naspects including ethical, technical and privacy requirements, we focus on the\nimportance of data quality (training/test) in DL. Since data quality dictates\nthe behaviour of ML products, evaluating data quality will play a key part in\nthe regulatory approval of medical AI products. We perform a systematic review\nfollowing PRISMA guidelines using the databases PubMed and ACM Digital Library.\nWe identify 2362 studies, out of which 62 records fulfil our eligibility\ncriteria. From this literature, we synthesise the existing knowledge on data\nquality frameworks and combine it with the perspective of ML applications in\nmedicine. As a result, we propose the METRIC-framework, a specialised data\nquality framework for medical training data comprising 15 awareness dimensions,\nalong which developers of medical ML applications should investigate a dataset.\nThis knowledge helps to reduce biases as a major source of unfairness, increase\nrobustness, facilitate interpretability and thus lays the foundation for\ntrustworthy AI in medicine. Incorporating such systematic assessment of medical\ndatasets into regulatory approval processes has the potential to accelerate the\napproval of ML products and builds the basis for new standards.\n", "date": "2024-02-21", "categories": []}
{"id": "2302.06727", "title": "Deep Learning Predicts Prevalent and Incident Parkinson's Disease From\n  UK Biobank Fundus Imaging", "abstract": "  Parkinson's disease is the world's fastest-growing neurological disorder.\nResearch to elucidate the mechanisms of Parkinson's disease and automate\ndiagnostics would greatly improve the treatment of patients with Parkinson's\ndisease. Current diagnostic methods are expensive and have limited\navailability. Considering the insidious and preclinical onset and progression\nof the disease, a desirable screening should be diagnostically accurate even\nbefore the onset of symptoms to allow medical interventions. We highlight\nretinal fundus imaging, often termed a window to the brain, as a diagnostic\nscreening modality for Parkinson's disease. We conducted a systematic\nevaluation of conventional machine learning and deep learning techniques to\nclassify Parkinson's disease from UK Biobank fundus imaging. Our results show\nthat Parkinson's disease individuals can be differentiated from age and\ngender-matched healthy subjects with an Area Under the Curve (AUC) of 0.77.\nThis accuracy is maintained when predicting either prevalent or incident\nParkinson's disease. Explainability and trustworthiness are enhanced by visual\nattribution maps of localized biomarkers and quantified metrics of model\nrobustness to data perturbations.\n", "date": "2023-02-13", "categories": []}
{"id": "2402.10999", "title": "Analysis and Mortality Prediction using Multiclass Classification for\n  Older Adults with Type 2 Diabetes", "abstract": "  Designing proper treatment plans to manage diabetes requires health\npractitioners to pay heed to the individuals remaining life along with the\ncomorbidities affecting them. Older adults with Type 2 Diabetes Mellitus (T2DM)\nare prone to experience premature death or even hypoglycaemia. The structured\ndataset utilized has 68 potential mortality predictors for 275,190 diabetic\nU.S. military Veterans aged 65 years or older. A new target variable is\ninvented by combining the two original target variables. Outliers are handled\nby discretizing the continuous variables. Categorical variables have been dummy\nencoded. Class balancing is achieved by random under-sampling. A benchmark\nregression model is built using Multinomial Logistic Regression with LASSO.\nChi-Squared and Information Gain are the filter-based feature selection\ntechniques utilized. Classifiers such as Multinomial Logistic Regression,\nRandom Forest, Extreme Gradient Boosting (XGBoost), and One-vs-Rest classifier\nare employed to build various models. Contrary to expectations, all the models\nhave constantly underperformed. XGBoost has given the highest accuracy of 53.03\npercent with Chi-Squared feature selection. All the models have consistently\nshown an acceptable performance for Class 3 (remaining life is more than 10\nyears), significantly low for Class 1 (remaining life is up to 5 years), and\nthe worst for Class 2 (remaining life is more than 5 but up to 10 years).\nFeatures analysis has deduced that almost all input variables are associated\nwith multiple target classes. The high dimensionality of the input data after\ndummy encoding seems to have confused the models, leading to\nmisclassifications. The approach taken in this study is ineffective in\nproducing a high-performing predictive model but lays a foundation as this\nproblem has never been viewed from a multiclass classification perspective.\n", "date": "2024-02-16", "categories": []}
{"id": "2402.11307", "title": "ICHPro: Intracerebral Hemorrhage Prognosis Classification Via\n  Joint-attention Fusion-based 3d Cross-modal Network", "abstract": "  Intracerebral Hemorrhage (ICH) is the deadliest subtype of stroke,\nnecessitating timely and accurate prognostic evaluation to reduce mortality and\ndisability. However, the multi-factorial nature and complexity of ICH make\nmethods based solely on computed tomography (CT) image features inadequate.\nDespite the capacity of cross-modal networks to fuse additional information,\nthe effective combination of different modal features remains a significant\nchallenge. In this study, we propose a joint-attention fusion-based 3D\ncross-modal network termed ICHPro that simulates the ICH prognosis\ninterpretation process utilized by neurosurgeons. ICHPro includes a\njoint-attention fusion module to fuse features from CT images with demographic\nand clinical textual data. To enhance the representation of cross-modal\nfeatures, we introduce a joint loss function. ICHPro facilitates the extraction\nof richer cross-modal features, thereby improving classification performance.\nUpon testing our method using a five-fold cross-validation, we achieved an\naccuracy of 89.11%, an F1 score of 0.8767, and an AUC value of 0.9429. These\nresults outperform those obtained from other advanced methods based on the test\ndataset, thereby demonstrating the superior efficacy of ICHPro. The code is\navailable at our Github: https://github.com/YU-deep/ICH.\n", "date": "2024-02-17", "categories": []}
{"id": "2211.11965", "title": "Predicting adverse outcomes following catheter ablation treatment for\n  atrial fibrillation", "abstract": "  Objective: To develop prognostic survival models for predicting adverse\noutcomes after catheter ablation treatment for non-valvular atrial fibrillation\n(AF).\n  Methods: We used a linked dataset including hospital administrative data,\nprescription medicine claims, emergency department presentations, and death\nregistrations of patients in New South Wales, Australia. The cohort included\npatients who received catheter ablation for AF. Traditional and deep survival\nmodels were trained to predict major bleeding events and a composite of heart\nfailure, stroke, cardiac arrest, and death.\n  Results: Out of a total of 3285 patients in the cohort, 177 (5.3%)\nexperienced the composite outcome (heart failure, stroke, cardiac arrest,\ndeath) and 167 (5.1%) experienced major bleeding events after catheter ablation\ntreatment. Models predicting the composite outcome had high risk discrimination\naccuracy, with the best model having a concordance index > 0.79 at the\nevaluated time horizons. Models for predicting major bleeding events had poor\nrisk discrimination performance, with all models having a concordance index <\n0.66. The most impactful features for the models predicting higher risk were\ncomorbidities indicative of poor health, older age, and therapies commonly used\nin sicker patients to treat heart failure and AF.\n  Conclusions: Diagnosis and medication history did not contain sufficient\ninformation for precise risk prediction of experiencing major bleeding events.\nThe models for predicting the composite outcome have the potential to enable\nclinicians to identify and manage high-risk patients following catheter\nablation proactively. Future research is needed to validate the usefulness of\nthese models in clinical practice.\n", "date": "2022-11-22", "categories": []}
{"id": "2401.15814", "title": "OntoMedRec: Logically-Pretrained Model-Agnostic Ontology Encoders for\n  Medication Recommendation", "abstract": "  Most existing medication recommendation models learn representations for\nmedical concepts based on electronic health records (EHRs) and make\nrecommendations with learnt representations. However, most medications appear\nin the dataset for limited times, resulting in insufficient learning of their\nrepresentations. Medical ontologies are the hierarchical classification systems\nfor medical terms where similar terms are in the same class on a certain level.\nIn this paper, we propose OntoMedRec, the logically-pretrained and\nmodel-agnostic medical Ontology Encoders for Medication Recommendation that\naddresses data sparsity problem with medical ontologies. We conduct\ncomprehensive experiments on benchmark datasets to evaluate the effectiveness\nof OntoMedRec, and the result shows the integration of OntoMedRec improves the\nperformance of various models in both the entire EHR datasets and the\nadmissions with few-shot medications. We provide the GitHub repository for the\nsource code on https://anonymous.4open.science/r/OntoMedRec-D123\n", "date": "2024-01-29", "categories": []}
{"id": "2402.06808", "title": "Explain Variance of Prediction in Variational Time Series Models for\n  Clinical Deterioration Prediction", "abstract": "  Missingness and measurement frequency are two sides of the same coin. How\nfrequent should we measure clinical variables and conduct laboratory tests? It\ndepends on many factors such as the stability of patient conditions, diagnostic\nprocess, treatment plan and measurement costs. The utility of measurements\nvaries disease by disease, patient by patient. In this study we propose a novel\nview of clinical variable measurement frequency from a predictive modeling\nperspective, namely the measurements of clinical variables reduce uncertainty\nin model predictions. To achieve this goal, we propose variance SHAP with\nvariational time series models, an application of Shapley Additive\nExpanation(SHAP) algorithm to attribute epistemic prediction uncertainty. The\nprediction variance is estimated by sampling the conditional hidden space in\nvariational models and can be approximated deterministically by delta's method.\nThis approach works with variational time series models such as variational\nrecurrent neural networks and variational transformers. Since SHAP values are\nadditive, the variance SHAP of binary data imputation masks can be directly\ninterpreted as the contribution to prediction variance by measurements. We\ntested our ideas on a public ICU dataset with deterioration prediction task and\nstudy the relation between variance SHAP and measurement time intervals.\n", "date": "2024-02-09", "categories": []}
{"id": "2402.09476", "title": "AI-Enabled Lung Cancer Prognosis", "abstract": "  Lung cancer is the primary cause of cancer-related mortality, claiming\napproximately 1.79 million lives globally in 2020, with an estimated 2.21\nmillion new cases diagnosed within the same period. Among these, Non-Small Cell\nLung Cancer (NSCLC) is the predominant subtype, characterized by a notably\nbleak prognosis and low overall survival rate of approximately 25% over five\nyears across all disease stages. However, survival outcomes vary considerably\nbased on the stage at diagnosis and the therapeutic interventions administered.\nRecent advancements in artificial intelligence (AI) have revolutionized the\nlandscape of lung cancer prognosis. AI-driven methodologies, including machine\nlearning and deep learning algorithms, have shown promise in enhancing survival\nprediction accuracy by efficiently analyzing complex multi-omics data and\nintegrating diverse clinical variables. By leveraging AI techniques, clinicians\ncan harness comprehensive prognostic insights to tailor personalized treatment\nstrategies, ultimately improving patient outcomes in NSCLC. Overviewing\nAI-driven data processing can significantly help bolster the understanding and\nprovide better directions for using such systems.\n", "date": "2024-02-12", "categories": []}
{"id": "2402.09498", "title": "Detection of the most influential variables for preventing postpartum\n  urinary incontinence using machine learning techniques", "abstract": "  Background: Postpartum urinary incontinence (PUI) is a common issue among\npostnatal women. Previous studies identified potential related variables, but\nlacked analysis on certain intrinsic and extrinsic patient variables during\npregnancy.\n  Objective: The study aims to evaluate the most influential variables in PUI\nusing machine learning, focusing on intrinsic, extrinsic, and combined variable\ngroups.\n  Methods: Data from 93 pregnant women were analyzed using machine learning and\noversampling techniques. Four key variables were predicted: occurrence,\nfrequency, intensity of urinary incontinence, and stress urinary incontinence.\n  Results: Models using extrinsic variables were most accurate, with 70%\naccuracy for urinary incontinence, 77% for frequency, 71% for intensity, and\n93% for stress urinary incontinence.\n  Conclusions: The study highlights extrinsic variables as significant\npredictors of PUI issues. This suggests that PUI prevention might be achievable\nthrough healthy habits during pregnancy, although further research is needed\nfor confirmation.\n", "date": "2024-02-14", "categories": []}
{"id": "2402.09596", "title": "Pulmonologists-Level lung cancer detection based on standard blood test\n  results and smoking status using an explainable machine learning approach", "abstract": "  Lung cancer (LC) remains the primary cause of cancer-related mortality,\nlargely due to late-stage diagnoses. Effective strategies for early detection\nare therefore of paramount importance. In recent years, machine learning (ML)\nhas demonstrated considerable potential in healthcare by facilitating the\ndetection of various diseases. In this retrospective development and validation\nstudy, we developed an ML model based on dynamic ensemble selection (DES) for\nLC detection. The model leverages standard blood sample analysis and smoking\nhistory data from a large population at risk in Denmark. The study includes all\npatients examined on suspicion of LC in the Region of Southern Denmark from\n2009 to 2018. We validated and compared the predictions by the DES model with\ndiagnoses provided by five pulmonologists. Among the 38,944 patients, 9,940 had\ncomplete data of which 2,505 (25\\%) had LC. The DES model achieved an area\nunder the roc curve of 0.77$\\pm$0.01, sensitivity of 76.2\\%$\\pm$2.4\\%,\nspecificity of 63.8\\%$\\pm$2.3\\%, positive predictive value of 41.6\\%$\\pm$1.2\\%,\nand F\\textsubscript{1}-score of 53.8\\%$\\pm$1.1\\%. The DES model outperformed\nall five pulmonologists, achieving a sensitivity 9\\% higher than their average.\nThe model identified smoking status, age, total calcium levels, neutrophil\ncount, and lactate dehydrogenase as the most important factors for the\ndetection of LC. The results highlight the successful application of the ML\napproach in detecting LC, surpassing pulmonologists' performance. Incorporating\nclinical and laboratory data in future risk assessment models can improve\ndecision-making and facilitate timely referrals.\n", "date": "2024-02-14", "categories": []}
{"id": "2402.08789", "title": "Leveraging cough sounds to optimize chest x-ray usage in low-resource\n  settings", "abstract": "  Chest X-ray is a commonly used tool during triage, diagnosis and management\nof respiratory diseases. In resource-constricted settings, optimizing this\nresource can lead to valuable cost savings for the health care system and the\npatients as well as to and improvement in consult time. We used\nprospectively-collected data from 137 patients referred for chest X-ray at the\nChristian Medical Center and Hospital (CMCH) in Purnia, Bihar, India. Each\npatient provided at least five coughs while awaiting radiography. Collected\ncough sounds were analyzed using acoustic AI methods. Cross-validation was done\non temporal and spectral features on the cough sounds of each patient. Features\nwere summarized using standard statistical approaches. Three models were\ndeveloped, tested and compared in their capacity to predict an abnormal result\nin the chest X-ray. All three methods yielded models that could discriminate to\nsome extent between normal and abnormal with the logistic regression performing\nbest with an area under the receiver operating characteristic curves ranging\nfrom 0.7 to 0.78. Despite limitations and its relatively small sample size,\nthis study shows that AI-enabled algorithms can use cough sounds to predict\nwhich individuals presenting for chest radiographic examination will have a\nnormal or abnormal results. These results call for expanding this research\ngiven the potential optimization of limited health care resources in low- and\nmiddle-income countries.\n", "date": "2024-02-13", "categories": []}
{"id": "2401.10386", "title": "Noninvasive Acute Compartment Syndrome Diagnosis Using Random Forest\n  Machine Learning", "abstract": "  Acute compartment syndrome (ACS) is an orthopedic emergency, caused by\nelevated pressure within a muscle compartment, that leads to permanent tissue\ndamage and eventually death. Diagnosis of ACS relies heavily on\npatient-reported symptoms, a method that is clinically unreliable and often\nsupplemented with invasive intracompartmental pressure measurements that can\nmalfunction in motion settings. This study proposes an objective and\nnoninvasive diagnostic for ACS. The device detects ACS through a random forest\nmachine learning model that uses surrogate pressure readings from\nforce-sensitive resistors (FSRs) placed on the skin. To validate the\ndiagnostic, a data set containing FSR measurements and the corresponding\nsimulated intracompartmental pressure was created for motion and motionless\nscenarios. The diagnostic achieved up to 98% accuracy. The device excelled in\nkey performance metrics, including sensitivity and specificity, with a\nstatistically insignificant performance difference in motion present cases.\nManufactured for 73 USD, our device may be a cost-effective solution. These\nresults demonstrate the potential of noninvasive ACS diagnostics to meet\nclinical accuracy standards in real world settings.\n", "date": "2024-01-18", "categories": []}
{"id": "2402.08539", "title": "Intelligent Diagnosis of Alzheimer's Disease Based on Machine Learning", "abstract": "  This study is based on the Alzheimer's Disease Neuroimaging Initiative (ADNI)\ndataset and aims to explore early detection and disease progression in\nAlzheimer's disease (AD). We employ innovative data preprocessing strategies,\nincluding the use of the random forest algorithm to fill missing data and the\nhandling of outliers and invalid data, thereby fully mining and utilizing these\nlimited data resources. Through Spearman correlation coefficient analysis, we\nidentify some features strongly correlated with AD diagnosis. We build and test\nthree machine learning models using these features: random forest, XGBoost, and\nsupport vector machine (SVM). Among them, the XGBoost model performs the best\nin terms of diagnostic performance, achieving an accuracy of 91%. Overall, this\nstudy successfully overcomes the challenge of missing data and provides\nvaluable insights into early detection of Alzheimer's disease, demonstrating\nits unique research value and practical significance.\n", "date": "2024-02-13", "categories": []}
{"id": "2311.13912", "title": "Expanding the deep-learning model to diagnosis LVNC: Limitations and\n  trade-offs", "abstract": "  Hyper-trabeculation or non-compaction in the left ventricle of the myocardium\n(LVNC) is a recently classified form of cardiomyopathy. Several methods have\nbeen proposed to quantify the trabeculae accurately in the left ventricle, but\nthere is no general agreement in the medical community to use a particular\napproach. In previous work, we proposed DL-LVTQ, a deep learning approach for\nleft ventricular trabecular quantification based on a U-Net CNN architecture.\nDL-LVTQ was an automatic diagnosis tool developed from a dataset of patients\nwith the same cardiomyopathy (hypertrophic cardiomyopathy).\n  In this work, we have extended and adapted DL-LVTQ to cope with patients with\ndifferent cardiomyopathies. The dataset consists of up 379 patients in three\ngroups with different particularities and cardiomyopathies. Patient images were\ntaken from different scanners and hospitals. We have modified and adapted the\nU-Net convolutional neural network to account for the different particularities\nof a heterogeneous group of patients with various unclassifiable or mixed and\ninherited cardiomyopathies.\n  The inclusion of new groups of patients has increased the accuracy,\nspecificity and kappa values while maintaining the sensitivity of the automatic\ndeep learning method proposed. Therefore, a better-prepared diagnosis tool is\nready for various cardiomyopathies with different characteristics.\nCardiologists have considered that 98.9% of the evaluated outputs are verified\nclinically for diagnosis. Therefore, the high precision to segment the\ndifferent cardiac structures allows us to make a robust diagnostic system\nobjective and faster, decreasing human error and time spent.\n", "date": "2023-11-23", "categories": []}
{"id": "2402.01713", "title": "Prompting Large Language Models for Zero-Shot Clinical Prediction with\n  Structured Longitudinal Electronic Health Record Data", "abstract": "  The inherent complexity of structured longitudinal Electronic Health Records\n(EHR) data poses a significant challenge when integrated with Large Language\nModels (LLMs), which are traditionally tailored for natural language\nprocessing. Motivated by the urgent need for swift decision-making during new\ndisease outbreaks, where traditional predictive models often fail due to a lack\nof historical data, this research investigates the adaptability of LLMs, like\nGPT-4, to EHR data. We particularly focus on their zero-shot capabilities,\nwhich enable them to make predictions in scenarios in which they haven't been\nexplicitly trained. In response to the longitudinal, sparse, and\nknowledge-infused nature of EHR data, our prompting approach involves taking\ninto account specific EHR characteristics such as units and reference ranges,\nand employing an in-context learning strategy that aligns with clinical\ncontexts. Our comprehensive experiments on the MIMIC-IV and TJH datasets\ndemonstrate that with our elaborately designed prompting framework, LLMs can\nimprove prediction performance in key tasks such as mortality, length-of-stay,\nand 30-day readmission by about 35\\%, surpassing ML models in few-shot\nsettings. Our research underscores the potential of LLMs in enhancing clinical\ndecision-making, especially in urgent healthcare situations like the outbreak\nof emerging diseases with no labeled data. The code is publicly available at\nhttps://github.com/yhzhu99/llm4healthcare for reproducibility.\n", "date": "2024-01-25", "categories": []}
{"id": "2402.02460", "title": "Review of multimodal machine learning approaches in healthcare", "abstract": "  Machine learning methods in healthcare have traditionally focused on using\ndata from a single modality, limiting their ability to effectively replicate\nthe clinical practice of integrating multiple sources of information for\nimproved decision making. Clinicians typically rely on a variety of data\nsources including patients' demographic information, laboratory data, vital\nsigns and various imaging data modalities to make informed decisions and\ncontextualise their findings. Recent advances in machine learning have\nfacilitated the more efficient incorporation of multimodal data, resulting in\napplications that better represent the clinician's approach. Here, we provide a\nreview of multimodal machine learning approaches in healthcare, offering a\ncomprehensive overview of recent literature. We discuss the various data\nmodalities used in clinical diagnosis, with a particular emphasis on imaging\ndata. We evaluate fusion techniques, explore existing multimodal datasets and\nexamine common training strategies.\n", "date": "2024-02-04", "categories": []}
{"id": "2402.06675", "title": "A Masked language model for multi-source EHR trajectories contextual\n  representation learning", "abstract": "  Using electronic health records data and machine learning to guide future\ndecisions needs to address challenges, including 1) long/short-term\ndependencies and 2) interactions between diseases and interventions.\nBidirectional transformers have effectively addressed the first challenge. Here\nwe tackled the latter challenge by masking one source (e.g., ICD10 codes) and\ntraining the transformer to predict it using other sources (e.g., ATC codes).\n", "date": "2024-02-07", "categories": []}
{"id": "2402.06812", "title": "A Kalman Filter Based Framework for Monitoring the Performance of\n  In-Hospital Mortality Prediction Models Over Time", "abstract": "  Unlike in a clinical trial, where researchers get to determine the least\nnumber of positive and negative samples required, or in a machine learning\nstudy where the size and the class distribution of the validation set is static\nand known, in a real-world scenario, there is little control over the size and\ndistribution of incoming patients. As a result, when measured during different\ntime periods, evaluation metrics like Area under the Receiver Operating Curve\n(AUCROC) and Area Under the Precision-Recall Curve(AUCPR) may not be directly\ncomparable. Therefore, in this study, for binary classifiers running in a long\ntime period, we proposed to adjust these performance metrics for sample size\nand class distribution, so that a fair comparison can be made between two time\nperiods. Note that the number of samples and the class distribution, namely the\nratio of positive samples, are two robustness factors which affect the variance\nof AUCROC. To better estimate the mean of performance metrics and understand\nthe change of performance over time, we propose a Kalman filter based framework\nwith extrapolated variance adjusted for the total number of samples and the\nnumber of positive samples during different time periods. The efficacy of this\nmethod is demonstrated first on a synthetic dataset and then retrospectively\napplied to a 2-days ahead in-hospital mortality prediction model for COVID-19\npatients during 2021 and 2022. Further, we conclude that our prediction model\nis not significantly affected by the evolution of the disease, improved\ntreatments and changes in hospital operational plans.\n", "date": "2024-02-09", "categories": []}
{"id": "2402.06982", "title": "Treatment-wise Glioblastoma Survival Inference with Multi-parametric\n  Preoperative MRI", "abstract": "  In this work, we aim to predict the survival time (ST) of glioblastoma (GBM)\npatients undergoing different treatments based on preoperative magnetic\nresonance (MR) scans. The personalized and precise treatment planning can be\nachieved by comparing the ST of different treatments. It is well established\nthat both the current status of the patient (as represented by the MR scans)\nand the choice of treatment are the cause of ST. While previous related\nMR-based glioblastoma ST studies have focused only on the direct mapping of MR\nscans to ST, they have not included the underlying causal relationship between\ntreatments and ST. To address this limitation, we propose a\ntreatment-conditioned regression model for glioblastoma ST that incorporates\ntreatment information in addition to MR scans. Our approach allows us to\neffectively utilize the data from all of the treatments in a unified manner,\nrather than having to train separate models for each of the treatments.\nFurthermore, treatment can be effectively injected into each convolutional\nlayer through the adaptive instance normalization we employ. We evaluate our\nframework on the BraTS20 ST prediction task. Three treatment options are\nconsidered: Gross Total Resection (GTR), Subtotal Resection (STR), and no\nresection. The evaluation results demonstrate the effectiveness of injecting\nthe treatment for estimating GBM survival.\n", "date": "2024-02-10", "categories": []}
{"id": "2402.07079", "title": "The Relevance Feature and Vector Machine for health applications", "abstract": "  This paper presents the Relevance Feature and Vector Machine (RFVM), a novel\nmodel that addresses the challenges of the fat-data problem when dealing with\nclinical prospective studies. The fat-data problem refers to the limitations of\nMachine Learning (ML) algorithms when working with databases in which the\nnumber of features is much larger than the number of samples (a common scenario\nin certain medical fields). To overcome such limitations, the RFVM incorporates\ndifferent characteristics: (1) A Bayesian formulation which enables the model\nto infer its parameters without overfitting thanks to the Bayesian model\naveraging. (2) A joint optimisation that overcomes the limitations arising from\nthe fat-data characteristic by simultaneously including the variables that\ndefine the primal space (features) and those that define the dual space\n(observations). (3) An integrated prunning that removes the irrelevant features\nand samples during the training iterative optimization. Also, this last point\nturns out crucial when performing medical prospective studies, enabling\nresearchers to exclude unnecessary medical tests, reducing costs and\ninconvenience for patients, and identifying the critical patients/subjects that\ncharacterize the disorder and, subsequently, optimize the patient recruitment\nprocess that leads to a balanced cohort. The model capabilities are tested\nagainst state-of-the-art models in several medical datasets with fat-data\nproblems. These experimental works show that RFVM is capable of achieving\ncompetitive classification accuracies while providing the most compact subset\nof data (in both terms of features and samples). Moreover, the selected\nfeatures (medical tests) seem to be aligned with the existing medical\nliterature.\n", "date": "2024-02-11", "categories": []}
{"id": "2402.07344", "title": "Measurement Scheduling for ICU Patients with Offline Reinforcement\n  Learning", "abstract": "  Scheduling laboratory tests for ICU patients presents a significant\nchallenge. Studies show that 20-40% of lab tests ordered in the ICU are\nredundant and could be eliminated without compromising patient safety. Prior\nwork has leveraged offline reinforcement learning (Offline-RL) to find optimal\npolicies for ordering lab tests based on patient information. However, new ICU\npatient datasets have since been released, and various advancements have been\nmade in Offline-RL methods. In this study, we first introduce a preprocessing\npipeline for the newly-released MIMIC-IV dataset geared toward time-series\ntasks. We then explore the efficacy of state-of-the-art Offline-RL methods in\nidentifying better policies for ICU patient lab test scheduling. Besides\nassessing methodological performance, we also discuss the overall suitability\nand practicality of using Offline-RL frameworks for scheduling laboratory tests\nin ICU settings.\n", "date": "2024-02-12", "categories": []}
{"id": "2402.07645", "title": "Detecting the Clinical Features of Difficult-to-Treat Depression using\n  Synthetic Data from Large Language Models", "abstract": "  Difficult-to-treat depression (DTD) has been proposed as a broader and more\nclinically comprehensive perspective on a person's depressive disorder where\ndespite treatment, they continue to experience significant burden. We sought to\ndevelop a Large Language Model (LLM)-based tool capable of interrogating\nroutinely-collected, narrative (free-text) electronic health record (EHR) data\nto locate published prognostic factors that capture the clinical syndrome of\nDTD. In this work, we use LLM-generated synthetic data (GPT3.5) and a\nNon-Maximum Suppression (NMS) algorithm to train a BERT-based span extraction\nmodel. The resulting model is then able to extract and label spans related to a\nvariety of relevant positive and negative factors in real clinical data (i.e.\nspans of text that increase or decrease the likelihood of a patient matching\nthe DTD syndrome). We show it is possible to obtain good overall performance\n(0.70 F1 across polarity) on real clinical data on a set of as many as 20\ndifferent factors, and high performance (0.85 F1 with 0.95 precision) on a\nsubset of important DTD factors such as history of abuse, family history of\naffective disorder, illness severity and suicidality by training the model\nexclusively on synthetic data. Our results show promise for future healthcare\napplications especially in applications where traditionally, highly\nconfidential medical data and human-expert annotation would normally be\nrequired.\n", "date": "2024-02-12", "categories": []}
{"id": "2402.07858", "title": "Multiscale Neuroimaging Features for the Identification of Medication\n  Class and Non-Responders in Mood Disorder Treatment", "abstract": "  In the clinical treatment of mood disorders, the complex behavioral symptoms\npresented by patients and variability of patient response to particular\nmedication classes can create difficulties in providing fast and reliable\ntreatment when standard diagnostic and prescription methods are used.\nIncreasingly, the incorporation of physiological information such as\nneuroimaging scans and derivatives into the clinical process promises to\nalleviate some of the uncertainty surrounding this process. Particularly, if\nneural features can help to identify patients who may not respond to standard\ncourses of anti-depressants or mood stabilizers, clinicians may elect to avoid\nlengthy and side-effect-laden treatments and seek out a different, more\neffective course that might otherwise not have been under consideration.\nPreviously, approaches for the derivation of relevant neuroimaging features\nwork at only one scale in the data, potentially limiting the depth of\ninformation available for clinical decision support. In this work, we show that\nthe utilization of multi spatial scale neuroimaging features - particularly\nresting state functional networks and functional network connectivity measures\n- provide a rich and robust basis for the identification of relevant medication\nclass and non-responders in the treatment of mood disorders. We demonstrate\nthat the generated features, along with a novel approach for fast and automated\nfeature selection, can support high accuracy rates in the identification of\nmedication class and non-responders as well as the identification of novel,\nmulti-scale biomarkers.\n", "date": "2024-02-12", "categories": []}
{"id": "2402.06563", "title": "What is Hiding in Medicine's Dark Matter? Learning with Missing Data in\n  Medical Practices", "abstract": "  Electronic patient records (EPRs) produce a wealth of data but contain\nsignificant missing information. Understanding and handling this missing data\nis an important part of clinical data analysis and if left unaddressed could\nresult in bias in analysis and distortion in critical conclusions. Missing data\nmay be linked to health care professional practice patterns and imputation of\nmissing data can increase the validity of clinical decisions. This study\nfocuses on statistical approaches for understanding and interpreting the\nmissing data and machine learning based clinical data imputation using a single\ncentre's paediatric emergency data and the data from UK's largest clinical\naudit for traumatic injury database (TARN). In the study of 56,961 data points\nrelated to initial vital signs and observations taken on children presenting to\nan Emergency Department, we have shown that missing data are likely to be\nnon-random and how these are linked to health care professional practice\npatterns. We have then examined 79 TARN fields with missing values for 5,791\ntrauma cases. Singular Value Decomposition (SVD) and k-Nearest Neighbour (kNN)\nbased missing data imputation methods are used and imputation results against\nthe original dataset are compared and statistically tested. We have concluded\nthat the 1NN imputer is the best imputation which indicates a usual pattern of\nclinical decision making: find the most similar patients and take their\nattributes as imputation.\n", "date": "2024-02-09", "categories": []}
{"id": "2402.05293", "title": "A comparative study on feature selection for a risk prediction model for\n  colorectal cancer", "abstract": "  Background and objective\n  Risk prediction models aim at identifying people at higher risk of developing\na target disease. Feature selection is particularly important to improve the\nprediction model performance avoiding overfitting and to identify the leading\ncancer risk (and protective) factors. Assessing the stability of feature\nselection/ranking algorithms becomes an important issue when the aim is to\nanalyze the features with more prediction power. Methods\n  This work is focused on colorectal cancer, assessing several feature ranking\nalgorithms in terms of performance for a set of risk prediction models (Neural\nNetworks, Support Vector Machines (SVM), Logistic Regression, k-Nearest\nNeighbors and Boosted Trees). Additionally, their robustness is evaluated\nfollowing a conventional approach with scalar stability metrics and a visual\napproach proposed in this work to study both similarity among feature ranking\ntechniques as well as their individual stability. A comparative analysis is\ncarried out between the most relevant features found out in this study and\nfeatures provided by the experts according to the state-of-the-art knowledge.\nResults\n  The two best performance results in terms of Area Under the ROC Curve (AUC)\nare achieved with a SVM classifier using the top-41 features selected by the\nSVM wrapper approach (AUC=0.693) and Logistic Regression with the top-40\nfeatures selected by the Pearson (AUC=0.689). Experiments showed that\nperforming feature selection contributes to classification performance with a\n3.9% and 1.9% improvement in AUC for the SVM and Logistic Regression\nclassifier, respectively, with respect to the results using the full feature\nset. The visual approach proposed in this work allows to see that the Neural\nNetwork-based wrapper ranking is the most unstable while the Random Forest is\nthe most stable.\n", "date": "2024-02-07", "categories": []}
{"id": "2402.05491", "title": "Determining the severity of Parkinson's disease in patients using a\n  multi task neural network", "abstract": "  Parkinson's disease is easy to diagnose when it is advanced, but it is very\ndifficult to diagnose in its early stages. Early diagnosis is essential to be\nable to treat the symptoms. It impacts on daily activities and reduces the\nquality of life of both the patients and their families and it is also the\nsecond most prevalent neurodegenerative disorder after Alzheimer in people over\nthe age of 60. Most current studies on the prediction of Parkinson's severity\nare carried out in advanced stages of the disease. In this work, the study\nanalyzes a set of variables that can be easily extracted from voice analysis,\nmaking it a very non-intrusive technique. In this paper, a method based on\ndifferent deep learning techniques is proposed with two purposes. On the one\nhand, to find out if a person has severe or non-severe Parkinson's disease, and\non the other hand, to determine by means of regression techniques the degree of\nevolution of the disease in a given patient. The UPDRS (Unified Parkinson's\nDisease Rating Scale) has been used by taking into account both the motor and\ntotal labels, and the best results have been obtained using a mixed multi-layer\nperceptron (MLP) that classifies and regresses at the same time and the most\nimportant features of the data obtained are taken as input, using an\nautoencoder. A success rate of 99.15% has been achieved in the problem of\npredicting whether a person suffers from severe Parkinson's disease or\nnon-severe Parkinson's disease. In the degree of disease involvement prediction\nproblem case, a MSE (Mean Squared Error) of 0.15 has been obtained. Using a\nfull deep learning pipeline for data preprocessing and classification has\nproven to be very promising in the field Parkinson's outperforming the\nstate-of-the-art proposals.\n", "date": "2024-02-08", "categories": []}
{"id": "2402.05495", "title": "Heart disease risk prediction using deep learning techniques with\n  feature augmentation", "abstract": "  Cardiovascular diseases state as one of the greatest risks of death for the\ngeneral population. Late detection in heart diseases highly conditions the\nchances of survival for patients. Age, sex, cholesterol level, sugar level,\nheart rate, among other factors, are known to have an influence on\nlife-threatening heart problems, but, due to the high amount of variables, it\nis often difficult for an expert to evaluate each patient taking this\ninformation into account. In this manuscript, the authors propose using deep\nlearning methods, combined with feature augmentation techniques for evaluating\nwhether patients are at risk of suffering cardiovascular disease. The results\nof the proposed methods outperform other state of the art methods by 4.4%,\nleading to a precision of a 90%, which presents a significant improvement, even\nmore so when it comes to an affliction that affects a large population.\n", "date": "2024-02-08", "categories": []}
{"id": "2402.05802", "title": "Unsupervised Discovery of Clinical Disease Signatures Using\n  Probabilistic Independence", "abstract": "  Insufficiently precise diagnosis of clinical disease is likely responsible\nfor many treatment failures, even for common conditions and treatments. With a\nlarge enough dataset, it may be possible to use unsupervised machine learning\nto define clinical disease patterns more precisely. We present an approach to\nlearning these patterns by using probabilistic independence to disentangle the\nimprint on the medical record of causal latent sources of disease. We inferred\na broad set of 2000 clinical signatures of latent sources from 9195 variables\nin 269,099 Electronic Health Records. The learned signatures produced better\ndiscrimination than the original variables in a lung cancer prediction task\nunknown to the inference algorithm, predicting 3-year malignancy in patients\nwith no history of cancer before a solitary lung nodule was discovered. More\nimportantly, the signatures' greater explanatory power identified pre-nodule\nsignatures of apparently undiagnosed cancer in many of those patients.\n", "date": "2024-02-08", "categories": []}
{"id": "2402.01824", "title": "Identification of Cognitive Decline from Spoken Language through Feature\n  Selection and the Bag of Acoustic Words Model", "abstract": "  Memory disorders are a central factor in the decline of functioning and daily\nactivities in elderly individuals. The confirmation of the illness, initiation\nof medication to slow its progression, and the commencement of occupational\ntherapy aimed at maintaining and rehabilitating cognitive abilities require a\nmedical diagnosis. The early identification of symptoms of memory disorders,\nespecially the decline in cognitive abilities, plays a significant role in\nensuring the well-being of populations. Features related to speech production\nare known to connect with the speaker's cognitive ability and changes. The lack\nof standardized speech tests in clinical settings has led to a growing emphasis\non developing automatic machine learning techniques for analyzing naturally\nspoken language. Non-lexical but acoustic properties of spoken language have\nproven useful when fast, cost-effective, and scalable solutions are needed for\nthe rapid diagnosis of a disease. The work presents an approach related to\nfeature selection, allowing for the automatic selection of the essential\nfeatures required for diagnosis from the Geneva minimalistic acoustic parameter\nset and relative speech pauses, intended for automatic paralinguistic and\nclinical speech analysis. These features are refined into word histogram\nfeatures, in which machine learning classifiers are trained to classify control\nsubjects and dementia patients from the Dementia Bank's Pitt audio database.\nThe results show that achieving a 75% average classification accuracy with only\ntwenty-five features with the separate ADReSS 2020 competition test data and\nthe Leave-One-Subject-Out cross-validation of the entire competition data is\npossible. The results rank at the top compared to international research, where\nthe same dataset and only acoustic features have been used to diagnose\npatients.\n", "date": "2024-02-02", "categories": []}
{"id": "2402.04267", "title": "Application analysis of ai technology combined with spiral CT scanning\n  in early lung cancer screening", "abstract": "  At present, the incidence and fatality rate of lung cancer in China rank\nfirst among all malignant tumors. Despite the continuous development and\nimprovement of China's medical level, the overall 5-year survival rate of lung\ncancer patients is still lower than 20% and is staged. A number of studies have\nconfirmed that early diagnosis and treatment of early stage lung cancer is of\ngreat significance to improve the prognosis of patients. In recent years,\nartificial intelligence technology has gradually begun to be applied in\noncology. ai is used in cancer screening, clinical diagnosis, radiation therapy\n(image acquisition, at-risk organ segmentation, image calibration and delivery)\nand other aspects of rapid development. However, whether medical ai can be\nsocialized depends on the public's attitude and acceptance to a certain extent.\nHowever, at present, there are few studies on the diagnosis of early lung\ncancer by AI technology combined with SCT scanning. In view of this, this study\napplied the combined method in early lung cancer screening, aiming to find a\nsafe and efficient screening mode and provide a reference for clinical\ndiagnosis and treatment.\n", "date": "2024-01-26", "categories": []}
{"id": "2402.04668", "title": "A Perspective on Individualized Treatment Effects Estimation from\n  Time-series Health Data", "abstract": "  The burden of diseases is rising worldwide, with unequal treatment efficacy\nfor patient populations that are underrepresented in clinical trials.\nHealthcare, however, is driven by the average population effect of medical\ntreatments and, therefore, operates in a \"one-size-fits-all\" approach, not\nnecessarily what best fits each patient. These facts suggest a pressing need\nfor methodologies to study individualized treatment effects (ITE) to drive\npersonalized treatment. Despite the increased interest in\nmachine-learning-driven ITE estimation models, the vast majority focus on\ntabular data with limited review and understanding of methodologies proposed\nfor time-series electronic health records (EHRs). To this end, this work\nprovides an overview of ITE works for time-series data and insights into future\nresearch. The work summarizes the latest work in the literature and reviews it\nin light of theoretical assumptions, types of treatment settings, and\ncomputational frameworks. Furthermore, this work discusses challenges and\nfuture research directions for ITEs in a time-series setting. We hope this work\nopens new directions and serves as a resource for understanding one of the\nexciting yet under-studied research areas.\n", "date": "2024-02-07", "categories": []}
{"id": "2311.00768", "title": "Language Model Training Paradigms for Clinical Feature Embeddings", "abstract": "  In research areas with scarce data, representation learning plays a\nsignificant role. This work aims to enhance representation learning for\nclinical time series by deriving universal embeddings for clinical features,\nsuch as heart rate and blood pressure. We use self-supervised training\nparadigms for language models to learn high-quality clinical feature\nembeddings, achieving a finer granularity than existing time-step and\npatient-level representation learning. We visualize the learnt embeddings via\nunsupervised dimension reduction techniques and observe a high degree of\nconsistency with prior clinical knowledge. We also evaluate the model\nperformance on the MIMIC-III benchmark and demonstrate the effectiveness of\nusing clinical feature embeddings. We publish our code online for replication.\n", "date": "2023-11-01", "categories": []}
{"id": "2311.08902", "title": "On the Importance of Step-wise Embeddings for Heterogeneous Clinical\n  Time-Series", "abstract": "  Recent advances in deep learning architectures for sequence modeling have not\nfully transferred to tasks handling time-series from electronic health records.\nIn particular, in problems related to the Intensive Care Unit (ICU), the\nstate-of-the-art remains to tackle sequence classification in a tabular manner\nwith tree-based methods. Recent findings in deep learning for tabular data are\nnow surpassing these classical methods by better handling the severe\nheterogeneity of data input features. Given the similar level of feature\nheterogeneity exhibited by ICU time-series and motivated by these findings, we\nexplore these novel methods' impact on clinical sequence modeling tasks. By\njointly using such advances in deep learning for tabular data, our primary\nobjective is to underscore the importance of step-wise embeddings in\ntime-series modeling, which remain unexplored in machine learning methods for\nclinical data. On a variety of clinically relevant tasks from two large-scale\nICU datasets, MIMIC-III and HiRID, our work provides an exhaustive analysis of\nstate-of-the-art methods for tabular time-series as time-step embedding models,\nshowing overall performance improvement. In particular, we evidence the\nimportance of feature grouping in clinical time-series, with significant\nperformance gains when considering features within predefined semantic groups\nin the step-wise embedding module.\n", "date": "2023-11-15", "categories": []}
{"id": "2402.02401", "title": "AI-Generated Content Enhanced Computer-Aided Diagnosis Model for Thyroid\n  Nodules: A ChatGPT-Style Assistant", "abstract": "  An artificial intelligence-generated content-enhanced computer-aided\ndiagnosis (AIGC-CAD) model, designated as ThyGPT, has been developed. This\nmodel, inspired by the architecture of ChatGPT, could assist radiologists in\nassessing the risk of thyroid nodules through semantic-level human-machine\ninteraction. A dataset comprising 19,165 thyroid nodule ultrasound cases from\nZhejiang Cancer Hospital was assembled to facilitate the training and\nvalidation of the model. After training, ThyGPT could automatically evaluate\nthyroid nodule and engage in effective communication with physicians through\nhuman-computer interaction. The performance of ThyGPT was rigorously quantified\nusing established metrics such as the receiver operating characteristic (ROC)\ncurve, area under the curve (AUC), sensitivity, and specificity. The empirical\nfindings revealed that radiologists, when supplemented with ThyGPT, markedly\nsurpassed the diagnostic acumen of their peers utilizing traditional methods as\nwell as the performance of the model in isolation. These findings suggest that\nAIGC-CAD systems, exemplified by ThyGPT, hold the promise to fundamentally\ntransform the diagnostic workflows of radiologists in forthcoming years.\n", "date": "2024-02-04", "categories": []}
{"id": "2402.03384", "title": "Survival and grade of the glioma prediction using transfer learning", "abstract": "  Glioblastoma is a highly malignant brain tumor with a life expectancy of only\n3 to 6 months without treatment. Detecting and predicting its survival and\ngrade accurately are crucial. This study introduces a novel approach using\ntransfer learning techniques. Various pre-trained networks, including\nEfficientNet, ResNet, VGG16, and Inception, were tested through exhaustive\noptimization to identify the most suitable architecture. Transfer learning was\napplied to fine-tune these models on a glioblastoma image dataset, aiming to\nachieve two objectives: survival and tumor grade prediction.The experimental\nresults show 65% accuracy in survival prediction, classifying patients into\nshort, medium, or long survival categories. Additionally, the prediction of\ntumor grade achieved an accuracy of 97%, accurately differentiating low-grade\ngliomas (LGG) and high-grade gliomas (HGG). The success of the approach is\nattributed to the effectiveness of transfer learning, surpassing the current\nstate-of-the-art methods. In conclusion, this study presents a promising method\nfor predicting the survival and grade of glioblastoma. Transfer learning\ndemonstrates its potential in enhancing prediction models, particularly in\nscenarios with limited large datasets. These findings hold promise for\nimproving diagnostic and treatment approaches for glioblastoma patients.\n", "date": "2024-02-04", "categories": []}
{"id": "2402.03486", "title": "Early prediction of onset of sepsis in Clinical Setting", "abstract": "  This study proposes the use of Machine Learning models to predict the early\nonset of sepsis using deidentified clinical data from Montefiore Medical Center\nin Bronx, NY, USA. A supervised learning approach was adopted, wherein an\nXGBoost model was trained utilizing 80\\% of the train dataset, encompassing 107\nfeatures (including the original and derived features). Subsequently, the model\nwas evaluated on the remaining 20\\% of the test data. The model was validated\non prospective data that was entirely unseen during the training phase. To\nassess the model's performance at the individual patient level and timeliness\nof the prediction, a normalized utility score was employed, a widely recognized\nscoring methodology for sepsis detection, as outlined in the PhysioNet Sepsis\nChallenge paper. Metrics such as F1 Score, Sensitivity, Specificity, and Flag\nRate were also devised. The model achieved a normalized utility score of 0.494\non test data and 0.378 on prospective data at threshold 0.3. The F1 scores were\n80.8\\% and 67.1\\% respectively for the test data and the prospective data for\nthe same threshold, highlighting its potential to be integrated into clinical\ndecision-making processes effectively. These results bear testament to the\nmodel's robust predictive capabilities and its potential to substantially\nimpact clinical decision-making processes.\n", "date": "2024-02-05", "categories": []}
{"id": "2402.04209", "title": "Acute kidney injury prediction for non-critical care patients: a\n  retrospective external and internal validation study", "abstract": "  Background: Acute kidney injury (AKI), the decline of kidney excretory\nfunction, occurs in up to 18% of hospitalized admissions. Progression of AKI\nmay lead to irreversible kidney damage. Methods: This retrospective cohort\nstudy includes adult patients admitted to a non-intensive care unit at the\nUniversity of Pittsburgh Medical Center (UPMC) (n = 46,815) and University of\nFlorida Health (UFH) (n = 127,202). We developed and compared deep learning and\nconventional machine learning models to predict progression to Stage 2 or\nhigher AKI within the next 48 hours. We trained local models for each site (UFH\nModel trained on UFH, UPMC Model trained on UPMC) and a separate model with a\ndevelopment cohort of patients from both sites (UFH-UPMC Model). We internally\nand externally validated the models on each site and performed subgroup\nanalyses across sex and race. Results: Stage 2 or higher AKI occurred in 3%\n(n=3,257) and 8% (n=2,296) of UFH and UPMC patients, respectively. Area under\nthe receiver operating curve values (AUROC) for the UFH test cohort ranged\nbetween 0.77 (UPMC Model) and 0.81 (UFH Model), while AUROC values ranged\nbetween 0.79 (UFH Model) and 0.83 (UPMC Model) for the UPMC test cohort.\nUFH-UPMC Model achieved an AUROC of 0.81 (95% confidence interval [CI] [0.80,\n0.83]) for UFH and 0.82 (95% CI [0.81,0.84]) for UPMC test cohorts; an area\nunder the precision recall curve values (AUPRC) of 0.6 (95% CI, [0.05, 0.06])\nfor UFH and 0.13 (95% CI, [0.11,0.15]) for UPMC test cohorts. Kinetic estimated\nglomerular filtration rate, nephrotoxic drug burden and blood urea nitrogen\nremained the top three features with the highest influence across the models\nand health centers. Conclusion: Locally developed models displayed marginally\nreduced discrimination when tested on another institution, while the top set of\ninfluencing features remained the same across the models and sites.\n", "date": "2024-02-06", "categories": []}
{"id": "2304.14922", "title": "Supervised and Unsupervised Deep Learning Approaches for EEG Seizure\n  Prediction", "abstract": "  Epilepsy affects more than 50 million people worldwide, making it one of the\nworld's most prevalent neurological diseases. The main symptom of epilepsy is\nseizures, which occur abruptly and can cause serious injury or death. The\nability to predict the occurrence of an epileptic seizure could alleviate many\nrisks and stresses people with epilepsy face. We formulate the problem of\ndetecting preictal (or pre-seizure) with reference to normal EEG as a precursor\nto incoming seizure. To this end, we developed several supervised deep learning\napproaches to identify preictal EEG from normal EEG. We further develop novel\nunsupervised deep learning approaches to train the models on only normal EEG,\nand detecting pre-seizure EEG as an anomalous event. These deep learning models\nwere trained and evaluated on two large EEG seizure datasets in a\nperson-specific manner. We found that both supervised and unsupervised\napproaches are feasible; however, their performance varies depending on the\npatient, approach and architecture. This new line of research has the potential\nto develop therapeutic interventions and save human lives.\n", "date": "2023-04-24", "categories": []}
{"id": "2402.01067", "title": "Assessing Patient Eligibility for Inspire Therapy through Machine\n  Learning and Deep Learning Models", "abstract": "  Inspire therapy is an FDA-approved internal neurostimulation treatment for\nobstructive sleep apnea. However, not all patients respond to this therapy,\nposing a challenge even for experienced otolaryngologists to determine\ncandidacy. This paper makes the first attempt to leverage both machine learning\nand deep learning techniques in discerning patient responsiveness to Inspire\ntherapy using medical data and videos captured through Drug-Induced Sleep\nEndoscopy (DISE), an essential procedure for Inspire therapy. To achieve this,\nwe gathered and annotated three datasets from 127 patients. Two of these\ndatasets comprise endoscopic videos focused on the Base of the Tongue and\nVelopharynx. The third dataset composes the patient's clinical information. By\nutilizing these datasets, we benchmarked and compared the performance of six\ndeep learning models and five classical machine learning algorithms. The\nresults demonstrate the potential of employing machine learning and deep\nlearning techniques to determine a patient's eligibility for Inspire therapy,\npaving the way for future advancements in this field.\n", "date": "2024-02-01", "categories": []}
{"id": "2402.00638", "title": "Random Forest-Based Prediction of Stroke Outcome", "abstract": "  We research into the clinical, biochemical and neuroimaging factors\nassociated with the outcome of stroke patients to generate a predictive model\nusing machine learning techniques for prediction of mortality and morbidity 3\nmonths after admission. The dataset consisted of patients with ischemic stroke\n(IS) and non-traumatic intracerebral hemorrhage (ICH) admitted to Stroke Unit\nof a European Tertiary Hospital prospectively registered. We identified the\nmain variables for machine learning Random Forest (RF), generating a predictive\nmodel that can estimate patient mortality/morbidity. In conclusion, machine\nlearning algorithms RF can be effectively used in stroke patients for long-term\noutcome prediction of mortality and morbidity.\n", "date": "2024-02-01", "categories": []}
{"id": "2401.09795", "title": "A Comparative Analysis on Metaheuristic Algorithms Based Vision\n  Transformer Model for Early Detection of Alzheimer's Disease", "abstract": "  A number of life threatening neuro-degenerative disorders had degraded the\nquality of life for the older generation in particular. Dementia is one such\nsymptom which may lead to a severe condition called Alzheimer's disease if not\ndetected at an early stage. It has been reported that the progression of such\ndisease from a normal stage is due to the change in several parameters inside\nthe human brain. In this paper, an innovative metaheuristic algorithms based\nViT model has been proposed for the identification of dementia at different\nstage. A sizeable number of test data have been utilized for the validation of\nthe proposed scheme. It has also been demonstrated that our model exhibits\nsuperior performance in terms of accuracy, precision, recall as well as\nF1-score.\n", "date": "2024-01-18", "categories": []}
{"id": "2401.17705", "title": "Predicting suicidal behavior among Indian adults using childhood trauma,\n  mental health questionnaires and machine learning cascade ensembles", "abstract": "  Among young adults, suicide is India's leading cause of death, accounting for\nan alarming national suicide rate of around 16%. In recent years, machine\nlearning algorithms have emerged to predict suicidal behavior using various\nbehavioral traits. But to date, the efficacy of machine learning algorithms in\npredicting suicidal behavior in the Indian context has not been explored in\nliterature. In this study, different machine learning algorithms and ensembles\nwere developed to predict suicide behavior based on childhood trauma, different\nmental health parameters, and other behavioral factors. The dataset was\nacquired from 391 individuals from a wellness center in India. Information\nregarding their childhood trauma, psychological wellness, and other mental\nhealth issues was acquired through standardized questionnaires. Results\nrevealed that cascade ensemble learning methods using a support vector machine,\ndecision trees, and random forest were able to classify suicidal behavior with\nan accuracy of 95.04% using data from childhood trauma and mental health\nquestionnaires. The study highlights the potential of using these machine\nlearning ensembles to identify individuals with suicidal tendencies so that\ntargeted interinterventions could be provided efficiently.\n", "date": "2024-01-31", "categories": []}
{"id": "2309.16742", "title": "Supervised Learning Models for Early Detection of Albuminuria Risk in\n  Type-2 Diabetes Mellitus Patients", "abstract": "  Diabetes, especially T2DM, continues to be a significant health problem. One\nof the major concerns associated with diabetes is the development of its\ncomplications. Diabetic nephropathy, one of the chronic complication of\ndiabetes, adversely affects the kidneys, leading to kidney damage. Diagnosing\ndiabetic nephropathy involves considering various criteria, one of which is the\npresence of a pathologically significant quantity of albumin in urine, known as\nalbuminuria. Thus, early prediction of albuminuria in diabetic patients holds\nthe potential for timely preventive measures. This study aimed to develop a\nsupervised learning model to predict the risk of developing albuminuria in T2DM\npatients. The selected supervised learning algorithms included Na\\\"ive Bayes,\nSupport Vector Machine (SVM), decision tree, random forest, AdaBoost, XGBoost,\nand Multi-Layer Perceptron (MLP). Our private dataset, comprising 184 entries\nof diabetes complications risk factors, was used to train the algorithms. It\nconsisted of 10 attributes as features and 1 attribute as the target\n(albuminuria). Upon conducting the experiments, the MLP demonstrated superior\nperformance compared to the other algorithms. It achieved accuracy and f1-score\nvalues as high as 0.74 and 0.75, respectively, making it suitable for screening\npurposes in predicting albuminuria in T2DM. Nonetheless, further studies are\nwarranted to enhance the model's performance.\n", "date": "2023-09-28", "categories": []}
{"id": "2401.16190", "title": "AI prediction of cardiovascular events using opportunistic epicardial\n  adipose tissue assessments from CT calcium score", "abstract": "  Background: Recent studies have used basic epicardial adipose tissue (EAT)\nassessments (e.g., volume and mean HU) to predict risk of\natherosclerosis-related, major adverse cardiovascular events (MACE).\nObjectives: Create novel, hand-crafted EAT features, 'fat-omics', to capture\nthe pathophysiology of EAT and improve MACE prediction. Methods: We segmented\nEAT using a previously-validated deep learning method with optional manual\ncorrection. We extracted 148 radiomic features (morphological, spatial, and\nintensity) and used Cox elastic-net for feature reduction and prediction of\nMACE. Results: Traditional fat features gave marginal prediction\n(EAT-volume/EAT-mean-HU/ BMI gave C-index 0.53/0.55/0.57, respectively).\nSignificant improvement was obtained with 15 fat-omics features (C-index=0.69,\ntest set). High-risk features included\nvolume-of-voxels-having-elevated-HU-[-50, -30-HU] and HU-negative-skewness,\nboth of which assess high HU, which as been implicated in fat inflammation.\nOther high-risk features include kurtosis-of-EAT-thickness, reflecting the\nheterogeneity of thicknesses, and EAT-volume-in-the-top-25%-of-the-heart,\nemphasizing adipose near the proximal coronary arteries. Kaplan-Meyer plots of\nCox-identified, high- and low-risk patients were well separated with the median\nof the fat-omics risk, while high-risk group having HR 2.4 times that of the\nlow-risk group (P<0.001). Conclusion: Preliminary findings indicate an\nopportunity to use more finely tuned, explainable assessments on EAT for\nimproved cardiovascular risk prediction.\n", "date": "2024-01-29", "categories": []}
{"id": "2401.17027", "title": "Heterogeneous treatment effect estimation with subpopulation\n  identification for personalized medicine in opioid use disorder", "abstract": "  Deep learning models have demonstrated promising results in estimating\ntreatment effects (TEE). However, most of them overlook the variations in\ntreatment outcomes among subgroups with distinct characteristics. This\nlimitation hinders their ability to provide accurate estimations and treatment\nrecommendations for specific subgroups. In this study, we introduce a novel\nneural network-based framework, named SubgroupTE, which incorporates subgroup\nidentification and treatment effect estimation. SubgroupTE identifies diverse\nsubgroups and simultaneously estimates treatment effects for each subgroup,\nimproving the treatment effect estimation by considering the heterogeneity of\ntreatment responses. Comparative experiments on synthetic data show that\nSubgroupTE outperforms existing models in treatment effect estimation.\nFurthermore, experiments on a real-world dataset related to opioid use disorder\n(OUD) demonstrate the potential of our approach to enhance personalized\ntreatment recommendations for OUD patients.\n", "date": "2024-01-30", "categories": []}
{"id": "2401.15290", "title": "Benchmarking with MIMIC-IV, an irregular, spare clinical time series\n  dataset", "abstract": "  Electronic health record (EHR) is more and more popular, and it comes with\napplying machine learning solutions to resolve various problems in the domain.\nThis growing research area also raises the need for EHRs accessibility. Medical\nInformation Mart for Intensive Care (MIMIC) dataset is a popular, public, and\nfree EHR dataset in a raw format that has been used in numerous studies.\nHowever, despite of its popularity, it is lacking benchmarking work, especially\nwith recent state of the art works in the field of deep learning with\ntime-series tabular data. The aim of this work is to fill this lack by\nproviding a benchmark for latest version of MIMIC dataset, MIMIC-IV. We also\ngive a detailed literature survey about studies that has been already done for\nMIIMIC-III.\n", "date": "2024-01-27", "categories": []}
{"id": "2401.15337", "title": "Deep Learning with Information Fusion and Model Interpretation for\n  Health Monitoring of Fetus based on Long-term Prenatal Electronic Fetal Heart\n  Rate Monitoring Data", "abstract": "  Long-term fetal heart rate (FHR) monitoring during the antepartum period,\nincreasingly popularized by electronic FHR monitoring, represents a growing\napproach in FHR monitoring. This kind of continuous monitoring, in contrast to\nthe short-term one, collects an extended period of fetal heart data. This\noffers a more comprehensive understanding of fetus's conditions. However, the\ninterpretation of long-term antenatal fetal heart monitoring is still in its\nearly stages, lacking corresponding clinical standards. Furthermore, the\nsubstantial amount of data generated by continuous monitoring imposes a\nsignificant burden on clinical work when analyzed manually. To address above\nchallenges, this study develops an automatic analysis system named LARA\n(Long-term Antepartum Risk Analysis system) for continuous FHR monitoring,\ncombining deep learning and information fusion methods. LARA's core is a\nwell-established convolutional neural network (CNN) model. It processes\nlong-term FHR data as input and generates a Risk Distribution Map (RDM) and\nRisk Index (RI) as the analysis results. We evaluate LARA on inner test\ndataset, the performance metrics are as follows: AUC 0.872, accuracy 0.816,\nspecificity 0.811, sensitivity 0.806, precision 0.271, and F1 score 0.415. In\nour study, we observe that long-term FHR monitoring data with higher RI is more\nlikely to result in adverse outcomes (p=0.0021). In conclusion, this study\nintroduces LARA, the first automated analysis system for long-term FHR\nmonitoring, initiating the further explorations into its clinical value in the\nfuture.\n", "date": "2024-01-27", "categories": []}
{"id": "2401.15672", "title": "Evaluating Echo State Network for Parkinson's Disease Prediction using\n  Voice Features", "abstract": "  Parkinson's disease (PD) is a debilitating neurological disorder that\nnecessitates precise and early diagnosis for effective patient care. This study\naims to develop a diagnostic model capable of achieving both high accuracy and\nminimizing false negatives, a critical factor in clinical practice. Given the\nlimited training data, a feature selection strategy utilizing ANOVA is employed\nto identify the most informative features. Subsequently, various machine\nlearning methods, including Echo State Networks (ESN), Random Forest, k-nearest\nNeighbors, Support Vector Classifier, Extreme Gradient Boosting, and Decision\nTree, are employed and thoroughly evaluated. The statistical analyses of the\nresults highlight ESN's exceptional performance, showcasing not only superior\naccuracy but also the lowest false negative rate among all methods.\nConsistently, statistical data indicates that the ESN method consistently\nmaintains a false negative rate of less than 8% in 83% of cases. ESN's capacity\nto strike a delicate balance between diagnostic precision and minimizing\nmisclassifications positions it as an exemplary choice for PD diagnosis,\nespecially in scenarios characterized by limited data. This research marks a\nsignificant step towards more efficient and reliable PD diagnosis, with\npotential implications for enhanced patient outcomes and healthcare dynamics.\n", "date": "2024-01-28", "categories": []}
{"id": "2401.15805", "title": "Prediction of Breast Cancer Recurrence Risk Using a Multi-Model Approach\n  Integrating Whole Slide Imaging and Clinicopathologic Features", "abstract": "  Breast cancer is the most common malignancy affecting women worldwide and is\nnotable for its morphologic and biologic diversity, with varying risks of\nrecurrence following treatment. The Oncotype DX Breast Recurrence Score test is\nan important predictive and prognostic genomic assay for estrogen\nreceptor-positive breast cancer that guides therapeutic strategies; however,\nsuch tests can be expensive, delay care, and are not widely available. The aim\nof this study was to develop a multi-model approach integrating the analysis of\nwhole slide images and clinicopathologic data to predict their associated\nbreast cancer recurrence risks and categorize these patients into two risk\ngroups according to the predicted score: low and high risk. The proposed novel\nmethodology uses convolutional neural networks for feature extraction and\nvision transformers for contextual aggregation, complemented by a logistic\nregression model that analyzes clinicopathologic data for classification into\ntwo risk categories. This method was trained and tested on 993 hematoxylin and\neosin-stained whole-slide images of breast cancers with corresponding\nclinicopathological features that had prior Oncotype DX testing. The model's\nperformance was evaluated using an internal test set of 198 patients from\nDartmouth Health and an external test set of 418 patients from the University\nof Chicago. The multi-model approach achieved an AUC of 0.92 (95 percent CI:\n0.88-0.96) on the internal set and an AUC of 0.85 (95 percent CI: 0.79-0.90) on\nthe external cohort. These results suggest that with further validation, the\nproposed methodology could provide an alternative to assist clinicians in\npersonalizing treatment for breast cancer patients and potentially improving\ntheir outcomes.\n", "date": "2024-01-28", "categories": []}
{"id": "2303.16416", "title": "Improving Large Language Models for Clinical Named Entity Recognition\n  via Prompt Engineering", "abstract": "  Objective: This study quantifies the capabilities of GPT-3.5 and GPT-4 for\nclinical named entity recognition (NER) tasks and proposes task-specific\nprompts to improve their performance. Materials and Methods: We evaluated these\nmodels on two clinical NER tasks: (1) to extract medical problems, treatments,\nand tests from clinical notes in the MTSamples corpus, following the 2010 i2b2\nconcept extraction shared task, and (2) identifying nervous system\ndisorder-related adverse events from safety reports in the vaccine adverse\nevent reporting system (VAERS). To improve the GPT models' performance, we\ndeveloped a clinical task-specific prompt framework that includes (1) baseline\nprompts with task description and format specification, (2) annotation\nguideline-based prompts, (3) error analysis-based instructions, and (4)\nannotated samples for few-shot learning. We assessed each prompt's\neffectiveness and compared the models to BioClinicalBERT. Results: Using\nbaseline prompts, GPT-3.5 and GPT-4 achieved relaxed F1 scores of 0.634, 0.804\nfor MTSamples, and 0.301, 0.593 for VAERS. Additional prompt components\nconsistently improved model performance. When all four components were used,\nGPT-3.5 and GPT-4 achieved relaxed F1 socres of 0.794, 0.861 for MTSamples and\n0.676, 0.736 for VAERS, demonstrating the effectiveness of our prompt\nframework. Although these results trail BioClinicalBERT (F1 of 0.901 for the\nMTSamples dataset and 0.802 for the VAERS), it is very promising considering\nfew training samples are needed. Conclusion: While direct application of GPT\nmodels to clinical NER tasks falls short of optimal performance, our\ntask-specific prompt framework, incorporating medical knowledge and training\nsamples, significantly enhances GPT models' feasibility for potential clinical\napplications.\n", "date": "2023-03-29", "categories": []}
{"id": "2401.13657", "title": "Inadequacy of common stochastic neural networks for reliable clinical\n  decision support", "abstract": "  Widespread adoption of AI for medical decision making is still hindered due\nto ethical and safety-related concerns. For AI-based decision support systems\nin healthcare settings it is paramount to be reliable and trustworthy. Common\ndeep learning approaches, however, have the tendency towards overconfidence\nunder data shift. Such inappropriate extrapolation beyond evidence-based\nscenarios may have dire consequences. This highlights the importance of\nreliable estimation of local uncertainty and its communication to the end user.\nWhile stochastic neural networks have been heralded as a potential solution to\nthese issues, this study investigates their actual reliability in clinical\napplications. We centered our analysis on the exemplary use case of mortality\nprediction for ICU hospitalizations using EHR from MIMIC3 study. For\npredictions on the EHR time series, Encoder-Only Transformer models were\nemployed. Stochasticity of model functions was achieved by incorporating common\nmethods such as Bayesian neural network layers and model ensembles. Our models\nachieve state of the art performance in terms of discrimination performance\n(AUC ROC: 0.868+-0.011, AUC PR: 0.554+-0.034) and calibration on the mortality\nprediction benchmark. However, epistemic uncertainty is critically\nunderestimated by the selected stochastic deep learning methods. A heuristic\nproof for the responsible collapse of the posterior distribution is provided.\nOur findings reveal the inadequacy of commonly used stochastic deep learning\napproaches to reliably recognize OoD samples. In both methods, unsubstantiated\nmodel confidence is not prevented due to strongly biased functional posteriors,\nrendering them inappropriate for reliable clinical decision support. This\nhighlights the need for approaches with more strictly enforced or inherent\ndistance-awareness to known data points, e.g., using kernel-based techniques.\n", "date": "2024-01-24", "categories": []}
{"id": "2401.13756", "title": "NLICE: Synthetic Medical Record Generation for Effective Primary\n  Healthcare Differential Diagnosis", "abstract": "  This paper offers a systematic method for creating medical knowledge-grounded\npatient records for use in activities involving differential diagnosis.\nAdditionally, an assessment of machine learning models that can differentiate\nbetween various conditions based on given symptoms is also provided. We use a\npublic disease-symptom data source called SymCat in combination with Synthea to\nconstruct the patients records. In order to increase the expressive nature of\nthe synthetic data, we use a medically-standardized symptom modeling method\ncalled NLICE to augment the synthetic data with additional contextual\ninformation for each condition. In addition, Naive Bayes and Random Forest\nmodels are evaluated and compared on the synthetic data. The paper shows how to\nsuccessfully construct SymCat-based and NLICE-based datasets. We also show\nresults for the effectiveness of using the datasets to train predictive disease\nmodels. The SymCat-based dataset is able to train a Naive Bayes and Random\nForest model yielding a 58.8% and 57.1% Top-1 accuracy score, respectively. In\ncontrast, the NLICE-based dataset improves the results, with a Top-1 accuracy\nof 82.0% and Top-5 accuracy values of more than 90% for both models. Our\nproposed data generation approach solves a major barrier to the application of\nartificial intelligence methods in the healthcare domain. Our novel NLICE\nsymptom modeling approach addresses the incomplete and insufficient information\nproblem in the current binary symptom representation approach. The NLICE code\nis open sourced at https://github.com/guozhuoran918/NLICE.\n", "date": "2024-01-24", "categories": []}
{"id": "2401.13887", "title": "A comparative study of zero-shot inference with large language models\n  and supervised modeling in breast cancer pathology classification", "abstract": "  Although supervised machine learning is popular for information extraction\nfrom clinical notes, creating large annotated datasets requires extensive\ndomain expertise and is time-consuming. Meanwhile, large language models (LLMs)\nhave demonstrated promising transfer learning capability. In this study, we\nexplored whether recent LLMs can reduce the need for large-scale data\nannotations. We curated a manually-labeled dataset of 769 breast cancer\npathology reports, labeled with 13 categories, to compare zero-shot\nclassification capability of the GPT-4 model and the GPT-3.5 model with\nsupervised classification performance of three model architectures: random\nforests classifier, long short-term memory networks with attention (LSTM-Att),\nand the UCSF-BERT model. Across all 13 tasks, the GPT-4 model performed either\nsignificantly better than or as well as the best supervised model, the LSTM-Att\nmodel (average macro F1 score of 0.83 vs. 0.75). On tasks with high imbalance\nbetween labels, the differences were more prominent. Frequent sources of GPT-4\nerrors included inferences from multiple samples and complex task design. On\ncomplex tasks where large annotated datasets cannot be easily collected, LLMs\ncan reduce the burden of large-scale data labeling. However, if the use of LLMs\nis prohibitive, the use of simpler supervised models with large annotated\ndatasets can provide comparable results. LLMs demonstrated the potential to\nspeed up the execution of clinical NLP studies by reducing the need for\ncurating large annotated datasets. This may result in an increase in the\nutilization of NLP-based variables and outcomes in observational clinical\nstudies.\n", "date": "2024-01-25", "categories": []}
{"id": "2312.09442", "title": "A Compact LSTM-SVM Fusion Model for Long-Duration Cardiovascular\n  Diseases Detection", "abstract": "  Globally, cardiovascular diseases (CVDs) are the leading cause of mortality,\naccounting for an estimated 17.9 million deaths annually. One critical clinical\nobjective is the early detection of CVDs using electrocardiogram (ECG) data, an\narea that has received significant attention from the research community.\nRecent advancements based on machine learning and deep learning have achieved\ngreat progress in this domain. However, existing methodologies exhibit inherent\nlimitations, including inappropriate model evaluations and instances of data\nleakage. In this study, we present a streamlined workflow paradigm for\npreprocessing ECG signals into consistent 10-second durations, eliminating the\nneed for manual feature extraction/beat detection. We also propose a hybrid\nmodel of Long Short-Term Memory (LSTM) with Support Vector Machine (SVM) for\nfraud detection. This architecture consists of two LSTM layers and an SVM\nclassifier, which achieves a SOTA results with an Average precision score of\n0.9402 on the MIT-BIH arrhythmia dataset and 0.9563 on the MIT-BIH atrial\nfibrillation dataset. Based on the results, we believe our method can\nsignificantly benefit the early detection and management of CVDs.\n", "date": "2023-11-20", "categories": []}
{"id": "2401.13197", "title": "Predicting Mitral Valve mTEER Surgery Outcomes Using Machine Learning\n  and Deep Learning Techniques", "abstract": "  Mitral Transcatheter Edge-to-Edge Repair (mTEER) is a medical procedure\nutilized for the treatment of mitral valve disorders. However, predicting the\noutcome of the procedure poses a significant challenge. This paper makes the\nfirst attempt to harness classical machine learning (ML) and deep learning (DL)\ntechniques for predicting mitral valve mTEER surgery outcomes. To achieve this,\nwe compiled a dataset from 467 patients, encompassing labeled echocardiogram\nvideos and patient reports containing Transesophageal Echocardiography (TEE)\nmeasurements detailing Mitral Valve Repair (MVR) treatment outcomes. Leveraging\nthis dataset, we conducted a benchmark evaluation of six ML algorithms and two\nDL models. The results underscore the potential of ML and DL in predicting\nmTEER surgery outcomes, providing insight for future investigation and\nadvancements in this domain.\n", "date": "2024-01-24", "categories": []}
{"id": "2209.07805", "title": "A Comprehensive Benchmark for COVID-19 Predictive Modeling Using\n  Electronic Health Records in Intensive Care", "abstract": "  The COVID-19 pandemic has posed a heavy burden to the healthcare system\nworldwide and caused huge social disruption and economic loss. Many deep\nlearning models have been proposed to conduct clinical predictive tasks such as\nmortality prediction for COVID-19 patients in intensive care units using\nElectronic Health Record (EHR) data. Despite their initial success in certain\nclinical applications, there is currently a lack of benchmarking results to\nachieve a fair comparison so that we can select the optimal model for clinical\nuse. Furthermore, there is a discrepancy between the formulation of traditional\nprediction tasks and real-world clinical practice in intensive care. To fill\nthese gaps, we propose two clinical prediction tasks, Outcome-specific\nlength-of-stay prediction and Early mortality prediction for COVID-19 patients\nin intensive care units. The two tasks are adapted from the naive\nlength-of-stay and mortality prediction tasks to accommodate the clinical\npractice for COVID-19 patients. We propose fair, detailed, open-source\ndata-preprocessing pipelines and evaluate 17 state-of-the-art predictive models\non two tasks, including 5 machine learning models, 6 basic deep learning models\nand 6 deep learning predictive models specifically designed for EHR data. We\nprovide benchmarking results using data from two real-world COVID-19 EHR\ndatasets. One dataset is publicly available without needing any inquiry and\nanother dataset can be accessed on request. We provide fair, reproducible\nbenchmarking results for two tasks. We deploy all experiment results and models\non an online platform. We also allow clinicians and researchers to upload their\ndata to the platform and get quick prediction results using our trained models.\nWe hope our efforts can further facilitate deep learning and machine learning\nresearch for COVID-19 predictive modeling.\n", "date": "2022-09-16", "categories": []}
{"id": "2401.12438", "title": "Secure Federated Learning Approaches to Diagnosing COVID-19", "abstract": "  The recent pandemic has underscored the importance of accurately diagnosing\nCOVID-19 in hospital settings. A major challenge in this regard is\ndifferentiating COVID-19 from other respiratory illnesses based on chest\nX-rays, compounded by the restrictions of HIPAA compliance which limit the\ncomparison of patient X-rays. This paper introduces a HIPAA-compliant model to\naid in the diagnosis of COVID-19, utilizing federated learning. Federated\nlearning is a distributed machine learning approach that allows for algorithm\ntraining across multiple decentralized devices using local data samples,\nwithout the need for data sharing. Our model advances previous efforts in chest\nX-ray diagnostic models. We examined leading models from established\ncompetitions in this domain and developed our own models tailored to be\neffective with specific hospital data. Considering the model's operation in a\nfederated learning context, we explored the potential impact of biased data\nupdates on the model's performance. To enhance hospital understanding of the\nmodel's decision-making process and to verify that the model is not focusing on\nirrelevant features, we employed a visualization technique that highlights key\nfeatures in chest X-rays indicative of a positive COVID-19 diagnosis.\n", "date": "2024-01-23", "categories": []}
{"id": "2311.02332", "title": "Multimodal Machine Learning in Image-Based and Clinical Biomedicine:\n  Survey and Prospects", "abstract": "  Machine learning (ML) applications in medical artificial intelligence (AI)\nsystems have shifted from traditional and statistical methods to increasing\napplication of deep learning models. This survey navigates the current\nlandscape of multimodal ML, focusing on its profound impact on medical image\nanalysis and clinical decision support systems. Emphasizing challenges and\ninnovations in addressing multimodal representation, fusion, translation,\nalignment, and co-learning, the paper explores the transformative potential of\nmultimodal models for clinical predictions. It also highlights the need for\nprincipled assessments and practical implementation of such models, bringing\nattention to the dynamics between decision support systems and healthcare\nproviders and personnel. Despite advancements, challenges such as data biases\nand the scarcity of \"big data\" in many biomedical domains persist. We conclude\nwith a discussion on principled innovation and collaborative efforts to further\nthe mission of seamless integration of multimodal ML models into biomedical\npractice.\n", "date": "2023-11-04", "categories": []}
{"id": "2401.11252", "title": "Automated Fusion of Multimodal Electronic Health Records for Better\n  Medical Predictions", "abstract": "  The widespread adoption of Electronic Health Record (EHR) systems in\nhealthcare institutes has generated vast amounts of medical data, offering\nsignificant opportunities for improving healthcare services through deep\nlearning techniques. However, the complex and diverse modalities and feature\nstructures in real-world EHR data pose great challenges for deep learning model\ndesign. To address the multi-modality challenge in EHR data, current approaches\nprimarily rely on hand-crafted model architectures based on intuition and\nempirical experiences, leading to sub-optimal model architectures and limited\nperformance. Therefore, to automate the process of model design for mining EHR\ndata, we propose a novel neural architecture search (NAS) framework named\nAutoFM, which can automatically search for the optimal model architectures for\nencoding diverse input modalities and fusion strategies. We conduct thorough\nexperiments on real-world multi-modal EHR data and prediction tasks, and the\nresults demonstrate that our framework not only achieves significant\nperformance improvement over existing state-of-the-art methods but also\ndiscovers meaningful network architectures effectively.\n", "date": "2024-01-20", "categories": []}
{"id": "2401.12002", "title": "HgbNet: predicting hemoglobin level/anemia degree from EHR data", "abstract": "  Anemia is a prevalent medical condition that typically requires invasive\nblood tests for diagnosis and monitoring. Electronic health records (EHRs) have\nemerged as valuable data sources for numerous medical studies. EHR-based\nhemoglobin level/anemia degree prediction is non-invasive and rapid but still\nfaces some challenges due to the fact that EHR data is typically an irregular\nmultivariate time series containing a significant number of missing values and\nirregular time intervals. To address these issues, we introduce HgbNet, a\nmachine learning-based prediction model that emulates clinicians'\ndecision-making processes for hemoglobin level/anemia degree prediction. The\nmodel incorporates a NanDense layer with a missing indicator to handle missing\nvalues and employs attention mechanisms to account for both local irregularity\nand global irregularity. We evaluate the proposed method using two real-world\ndatasets across two use cases. In our first use case, we predict hemoglobin\nlevel/anemia degree at moment T+1 by utilizing records from moments prior to\nT+1. In our second use case, we integrate all historical records with\nadditional selected test results at moment T+1 to predict hemoglobin\nlevel/anemia degree at the same moment, T+1. HgbNet outperforms the best\nbaseline results across all datasets and use cases. These findings demonstrate\nthe feasibility of estimating hemoglobin levels and anemia degree from EHR\ndata, positioning HgbNet as an effective non-invasive anemia diagnosis solution\nthat could potentially enhance the quality of life for millions of affected\nindividuals worldwide. To our knowledge, HgbNet is the first machine learning\nmodel leveraging EHR data for hemoglobin level/anemia degree prediction.\n", "date": "2024-01-22", "categories": []}
{"id": "2312.00067", "title": "Predicting breast cancer with AI for individual risk-adjusted MRI\n  screening and early detection", "abstract": "  Women with an increased life-time risk of breast cancer undergo supplemental\nannual screening MRI. We propose to predict the risk of developing breast\ncancer within one year based on the current MRI, with the objective of reducing\nscreening burden and facilitating early detection. An AI algorithm was\ndeveloped on 53,858 breasts from 12,694 patients who underwent screening or\ndiagnostic MRI and accrued over 12 years, with 2,331 confirmed cancers. A first\nU-Net was trained to segment lesions and identify regions of concern. A second\nconvolutional network was trained to detect malignant cancer using features\nextracted by the U-Net. This network was then fine-tuned to estimate the risk\nof developing cancer within a year in cases that radiologists considered normal\nor likely benign. Risk predictions from this AI were evaluated with a\nretrospective analysis of 9,183 breasts from a high-risk screening cohort,\nwhich were not used for training. Statistical analysis focused on the tradeoff\nbetween number of omitted exams versus negative predictive value, and number of\npotential early detections versus positive predictive value. The AI algorithm\nidentified regions of concern that coincided with future tumors in 52% of\nscreen-detected cancers. Upon directed review, a radiologist found that 71.3%\nof cancers had a visible correlate on the MRI prior to diagnosis, 65% of these\ncorrelates were identified by the AI model. Reevaluating these regions in 10%\nof all cases with higher AI-predicted risk could have resulted in up to 33%\nearly detections by a radiologist. Additionally, screening burden could have\nbeen reduced in 16% of lower-risk cases by recommending a later follow-up\nwithout compromising current interval cancer rate. With increasing datasets and\nimproving image quality we expect this new AI-aided, adaptive screening to\nmeaningfully reduce screening burden and improve early detection.\n", "date": "2023-11-29", "categories": []}
{"id": "2401.10283", "title": "Window Stacking Meta-Models for Clinical EEG Classification", "abstract": "  Windowing is a common technique in EEG machine learning classification and\nother time series tasks. However, a challenge arises when employing this\ntechnique: computational expense inhibits learning global relationships across\nan entire recording or set of recordings. Furthermore, the labels inherited by\nwindows from their parent recordings may not accurately reflect the content of\nthat window in isolation. To resolve these issues, we introduce a multi-stage\nmodel architecture, incorporating meta-learning principles tailored to\ntime-windowed data aggregation. We further tested two distinct strategies to\nalleviate these issues: lengthening the window and utilizing overlapping to\naugment data. Our methods, when tested on the Temple University Hospital\nAbnormal EEG Corpus (TUAB), dramatically boosted the benchmark accuracy from\n89.8 percent to 99.0 percent. This breakthrough performance surpasses prior\nperformance projections for this dataset and paves the way for clinical\napplications of machine learning solutions to EEG interpretation challenges. On\na broader and more varied dataset from the Temple University Hospital EEG\nCorpus (TUEG), we attained an accuracy of 86.7%, nearing the assumed\nperformance ceiling set by variable inter-rater agreement on such datasets.\n", "date": "2024-01-14", "categories": []}
{"id": "2401.10014", "title": "Optimizing Medication Decisions for Patients with Atrial Fibrillation\n  through Path Development Network", "abstract": "  Atrial fibrillation (AF) is a common cardiac arrhythmia characterized by\nrapid and irregular contractions of the atria. It significantly elevates the\nrisk of strokes due to slowed blood flow in the atria, especially in the left\natrial appendage, which is prone to blood clot formation. Such clots can\nmigrate into cerebral arteries, leading to ischemic stroke. To assess whether\nAF patients should be prescribed anticoagulants, doctors often use the\nCHA2DS2-VASc scoring system. However, anticoagulant use must be approached with\ncaution as it can impact clotting functions. This study introduces a machine\nlearning algorithm that predicts whether patients with AF should be recommended\nanticoagulant therapy using 12-lead ECG data. In this model, we use STOME to\nenhance time-series data and then process it through a Convolutional Neural\nNetwork (CNN). By incorporating a path development layer, the model achieves a\nspecificity of 30.6% under the condition of an NPV of 1. In contrast, LSTM\nalgorithms without path development yield a specificity of only 2.7% under the\nsame NPV condition.\n", "date": "2024-01-18", "categories": []}
{"id": "2401.08695", "title": "Enabling Collaborative Clinical Diagnosis of Infectious Keratitis by\n  Integrating Expert Knowledge and Interpretable Data-driven Intelligence", "abstract": "  Although data-driven artificial intelligence (AI) in medical image diagnosis\nhas shown impressive performance in silico, the lack of interpretability makes\nit difficult to incorporate the \"black box\" into clinicians' workflows. To make\nthe diagnostic patterns learned from data understandable by clinicians, we\ndevelop an interpretable model, knowledge-guided diagnosis model (KGDM), that\nprovides a visualized reasoning process containing AI-based biomarkers and\nretrieved cases that with the same diagnostic patterns. It embraces clinicians'\nprompts into the interpreted reasoning through human-AI interaction, leading to\npotentially enhanced safety and more accurate predictions. This study\ninvestigates the performance, interpretability, and clinical utility of KGDM in\nthe diagnosis of infectious keratitis (IK), which is the leading cause of\ncorneal blindness. The classification performance of KGDM is evaluated on a\nprospective validation dataset, an external testing dataset, and an publicly\navailable testing dataset. The diagnostic odds ratios (DOR) of the interpreted\nAI-based biomarkers are effective, ranging from 3.011 to 35.233 and exhibit\nconsistent diagnostic patterns with clinic experience. Moreover, a human-AI\ncollaborative diagnosis test is conducted and the participants with\ncollaboration achieved a performance exceeding that of both humans and AI. By\nsynergistically integrating interpretability and interaction, this study\nfacilitates the convergence of clinicians' expertise and data-driven\nintelligence. The promotion of inexperienced ophthalmologists with the aid of\nAI-based biomarkers, as well as increased AI prediction by intervention from\nexperienced ones, demonstrate a promising diagnostic paradigm for infectious\nkeratitis using KGDM, which holds the potential for extension to other diseases\nwhere experienced medical practitioners are limited and the safety of AI is\nconcerned.\n", "date": "2024-01-14", "categories": []}
{"id": "2312.14129", "title": "WellFactor: Patient Profiling using Integrative Embedding of Healthcare\n  Data", "abstract": "  In the rapidly evolving healthcare industry, platforms now have access to not\nonly traditional medical records, but also diverse data sets encompassing\nvarious patient interactions, such as those from healthcare web portals. To\naddress this rich diversity of data, we introduce WellFactor: a method that\nderives patient profiles by integrating information from these sources. Central\nto our approach is the utilization of constrained low-rank approximation.\nWellFactor is optimized to handle the sparsity that is often inherent in\nhealthcare data. Moreover, by incorporating task-specific label information,\nour method refines the embedding results, offering a more informed perspective\non patients. One important feature of WellFactor is its ability to compute\nembeddings for new, previously unobserved patient data instantaneously,\neliminating the need to revisit the entire data set or recomputing the\nembedding. Comprehensive evaluations on real-world healthcare data demonstrate\nWellFactor's effectiveness. It produces better results compared to other\nexisting methods in classification performance, yields meaningful clustering of\npatients, and delivers consistent results in patient similarity searches and\npredictions.\n", "date": "2023-12-21", "categories": []}
{"id": "2401.08058", "title": "Toward Clinically Trustworthy Deep Learning: Applying Conformal\n  Prediction to Intracranial Hemorrhage Detection", "abstract": "  As deep learning (DL) continues to demonstrate its ability in radiological\ntasks, it is critical that we optimize clinical DL solutions to include safety.\nOne of the principal concerns in the clinical adoption of DL tools is trust.\nThis study aims to apply conformal prediction as a step toward trustworthiness\nfor DL in radiology. This is a retrospective study of 491 non-contrast head CTs\nfrom the CQ500 dataset, in which three senior radiologists annotated slices\ncontaining intracranial hemorrhage (ICH). The dataset was split into definite\nand challenging subsets, where challenging images were defined to those in\nwhich there was disagreement among readers. A DL model was trained on 146\npatients (10,815 slices) from the definite data (training dataset) to perform\nICH localization and classification for five classes of ICH. To develop an\nuncertainty-aware DL model, 1,546 cases of the definite data (calibration\ndataset) was used for Mondrian conformal prediction (MCP). The\nuncertainty-aware DL model was tested on 8,401 definite and challenging cases\nto assess its ability to identify challenging cases. After the MCP procedure,\nthe model achieved an F1 score of 0.920 for ICH classification on the test\ndataset. Additionally, it correctly identified 6,837 of the 6,856 total\nchallenging cases as challenging (99.7% accuracy). It did not incorrectly label\nany definite cases as challenging. The uncertainty-aware ICH detector performs\non par with state-of-the-art models. MCP's performance in detecting challenging\ncases demonstrates that it is useful in automated ICH detection and promising\nfor trustworthiness in radiological DL.\n", "date": "2024-01-16", "categories": []}
{"id": "2401.06406", "title": "Knowledge-Informed Machine Learning for Cancer Diagnosis and Prognosis:\n  A review", "abstract": "  Cancer remains one of the most challenging diseases to treat in the medical\nfield. Machine learning has enabled in-depth analysis of rich multi-omics\nprofiles and medical imaging for cancer diagnosis and prognosis. Despite these\nadvancements, machine learning models face challenges stemming from limited\nlabeled sample sizes, the intricate interplay of high-dimensionality data\ntypes, the inherent heterogeneity observed among patients and within tumors,\nand concerns about interpretability and consistency with existing biomedical\nknowledge. One approach to surmount these challenges is to integrate biomedical\nknowledge into data-driven models, which has proven potential to improve the\naccuracy, robustness, and interpretability of model results. Here, we review\nthe state-of-the-art machine learning studies that adopted the fusion of\nbiomedical knowledge and data, termed knowledge-informed machine learning, for\ncancer diagnosis and prognosis. Emphasizing the properties inherent in four\nprimary data types including clinical, imaging, molecular, and treatment data,\nwe highlight modeling considerations relevant to these contexts. We provide an\noverview of diverse forms of knowledge representation and current strategies of\nknowledge integration into machine learning pipelines with concrete examples.\nWe conclude the review article by discussing future directions to advance\ncancer research through knowledge-informed machine learning.\n", "date": "2024-01-12", "categories": []}
{"id": "2310.06059", "title": "Early Warning Prediction with Automatic Labeling in Epilepsy Patients", "abstract": "  Early warning for epilepsy patients is crucial for their safety and\nwell-being, in particular to prevent or minimize the severity of seizures.\nThrough the patients' EEG data, we propose a meta learning framework to improve\nthe prediction of early ictal signals. The proposed bi-level optimization\nframework can help automatically label noisy data at the early ictal stage, as\nwell as optimize the training accuracy of the backbone model. To validate our\napproach, we conduct a series of experiments to predict seizure onset in\nvarious long-term windows, with LSTM and ResNet implemented as the baseline\nmodels. Our study demonstrates that not only the ictal prediction accuracy\nobtained by meta learning is significantly improved, but also the resulting\nmodel captures some intrinsic patterns of the noisy data that a single backbone\nmodel could not learn. As a result, the predicted probability generated by the\nmeta network serves as a highly effective early warning indicator.\n", "date": "2023-10-09", "categories": []}
{"id": "2311.09229", "title": "Developing a Novel Holistic, Personalized Dementia Risk Prediction Model\n  via Integration of Machine Learning and Network Systems Biology Approaches", "abstract": "  The prevalence of dementia has increased over time as global life expectancy\nimproves and populations age. An individual's risk of developing dementia is\ninfluenced by various genetic, lifestyle, and environmental factors, among\nothers. Predicting dementia risk may enable individuals to employ mitigation\nstrategies or lifestyle changes to delay dementia onset. Current computational\napproaches to dementia prediction only return risk upon narrow categories of\nvariables and do not account for interactions between different risk variables.\nThe proposed framework utilizes a novel holistic approach to dementia risk\nprediction and is the first to incorporate various sources of tabular\nenvironmental pollution and lifestyle factor data with network systems\nbiology-based genetic data. LightGBM gradient boosting was employed to ensure\nvalidity of included factors. This approach successfully models interactions\nbetween variables through an original weighted integration method coined\nSysable. Multiple machine learning models trained the algorithm to reduce\nreliance on a single model. The developed approach surpassed all existing\ndementia risk prediction approaches, with a sensitivity of 85%, specificity of\n99%, geometric accuracy of 92%, and AUROC of 91.7%. A transfer learning model\nwas implemented as well. De-biasing algorithms were run on the model via the AI\nFairness 360 Library. Effects of demographic disparities on dementia prevalence\nwere analyzed to potentially highlight areas in need and promote equitable and\naccessible care. The resulting model was additionally integrated into a\nuser-friendly app providing holistic predictions and personalized risk\nmitigation strategies. The developed model successfully employs holistic\ncomputational dementia risk prediction for clinical use.\n", "date": "2023-10-04", "categories": []}
{"id": "2312.08567", "title": "ConFormer: A Novel Collection of Deep Learning Models to Assist\n  Cardiologists in the Assessment of Cardiac Function", "abstract": "  Cardiovascular diseases, particularly heart failure, are a leading cause of\ndeath globally. The early detection of heart failure through routine\nechocardiogram screenings is often impeded by the high cost and labor-intensive\nnature of these procedures, a barrier that can mean the difference between life\nand death. This paper presents ConFormer, a novel deep learning model designed\nto automate the estimation of Ejection Fraction (EF) and Left Ventricular Wall\nThickness from echocardiograms. The implementation of ConFormer has the\npotential to enhance preventative cardiology by enabling cost-effective,\naccessible, and comprehensive heart health monitoring, thereby saving countless\nlives. The source code is available at https://github.com/Aether111/ConFormer.\n", "date": "2023-12-13", "categories": []}
{"id": "2401.03621", "title": "Machine Learning Applications in Traumatic Brain Injury: A Spotlight on\n  Mild TBI", "abstract": "  Traumatic Brain Injury (TBI) poses a significant global public health\nchallenge, contributing to high morbidity and mortality rates and placing a\nsubstantial economic burden on healthcare systems worldwide. The diagnosis of\nTBI relies on clinical information along with Computed Tomography (CT) scans.\nAddressing the multifaceted challenges posed by TBI has seen the development of\ninnovative, data-driven approaches, for this complex condition. Particularly\nnoteworthy is the prevalence of mild TBI (mTBI), which constitutes the majority\nof TBI cases where conventional methods often fall short. As such, we review\nthe state-of-the-art Machine Learning (ML) techniques applied to clinical\ninformation and CT scans in TBI, with a particular focus on mTBI. We categorize\nML applications based on their data sources, and there is a spectrum of ML\ntechniques used to date. Most of these techniques have primarily focused on\ndiagnosis, with relatively few attempts at predicting the prognosis. This\nreview may serve as a source of inspiration for future research studies aimed\nat improving the diagnosis of TBI using data-driven approaches and standard\ndiagnostic data.\n", "date": "2024-01-08", "categories": []}
{"id": "2401.05434", "title": "ECGformer: Leveraging transformer for ECG heartbeat arrhythmia\n  classification", "abstract": "  An arrhythmia, also known as a dysrhythmia, refers to an irregular heartbeat.\nThere are various types of arrhythmias that can originate from different areas\nof the heart, resulting in either a rapid, slow, or irregular heartbeat. An\nelectrocardiogram (ECG) is a vital diagnostic tool used to detect heart\nirregularities and abnormalities, allowing experts to analyze the heart's\nelectrical signals to identify intricate patterns and deviations from the norm.\nOver the past few decades, numerous studies have been conducted to develop\nautomated methods for classifying heartbeats based on ECG data. In recent\nyears, deep learning has demonstrated exceptional capabilities in tackling\nvarious medical challenges, particularly with transformers as a model\narchitecture for sequence processing. By leveraging the transformers, we\ndeveloped the ECGformer model for the classification of various arrhythmias\npresent in electrocardiogram data. We assessed the suggested approach using the\nMIT-BIH and PTB datasets. ECG heartbeat arrhythmia classification results show\nthat the proposed method is highly effective.\n", "date": "2024-01-06", "categories": []}
{"id": "2401.05551", "title": "Useful Blunders: Can Automated Speech Recognition Errors Improve\n  Downstream Dementia Classification?", "abstract": "  \\textbf{Objectives}: We aimed to investigate how errors from automatic speech\nrecognition (ASR) systems affect dementia classification accuracy, specifically\nin the ``Cookie Theft'' picture description task. We aimed to assess whether\nimperfect ASR-generated transcripts could provide valuable information for\ndistinguishing between language samples from cognitively healthy individuals\nand those with Alzheimer's disease (AD).\n  \\textbf{Methods}: We conducted experiments using various ASR models, refining\ntheir transcripts with post-editing techniques. Both these imperfect ASR\ntranscripts and manually transcribed ones were used as inputs for the\ndownstream dementia classification. We conducted comprehensive error analysis\nto compare model performance and assess ASR-generated transcript effectiveness\nin dementia classification.\n  \\textbf{Results}: Imperfect ASR-generated transcripts surprisingly\noutperformed manual transcription for distinguishing between individuals with\nAD and those without in the ``Cookie Theft'' task. These ASR-based models\nsurpassed the previous state-of-the-art approach, indicating that ASR errors\nmay contain valuable cues related to dementia. The synergy between ASR and\nclassification models improved overall accuracy in dementia classification.\n  \\textbf{Conclusion}: Imperfect ASR transcripts effectively capture linguistic\nanomalies linked to dementia, improving accuracy in classification tasks. This\nsynergy between ASR and classification models underscores ASR's potential as a\nvaluable tool in assessing cognitive impairment and related clinical\napplications.\n", "date": "2024-01-10", "categories": []}
{"id": "2309.03851", "title": "CenTime: Event-Conditional Modelling of Censoring in Survival Analysis", "abstract": "  Survival analysis is a valuable tool for estimating the time until specific\nevents, such as death or cancer recurrence, based on baseline observations.\nThis is particularly useful in healthcare to prognostically predict clinically\nimportant events based on patient data. However, existing approaches often have\nlimitations; some focus only on ranking patients by survivability, neglecting\nto estimate the actual event time, while others treat the problem as a\nclassification task, ignoring the inherent time-ordered structure of the\nevents. Furthermore, the effective utilization of censored samples - training\ndata points where the exact event time is unknown - is essential for improving\nthe predictive accuracy of the model. In this paper, we introduce CenTime, a\nnovel approach to survival analysis that directly estimates the time to event.\nOur method features an innovative event-conditional censoring mechanism that\nperforms robustly even when uncensored data is scarce. We demonstrate that our\napproach forms a consistent estimator for the event model parameters, even in\nthe absence of uncensored data. Furthermore, CenTime is easily integrated with\ndeep learning models with no restrictions on batch size or the number of\nuncensored samples. We compare our approach with standard survival analysis\nmethods, including the Cox proportional-hazard model and DeepHit. Our results\nindicate that CenTime offers state-of-the-art performance in predicting\ntime-to-death while maintaining comparable ranking performance. Our\nimplementation is publicly available at\nhttps://github.com/ahmedhshahin/CenTime.\n", "date": "2023-09-07", "categories": []}
{"id": "2401.04938", "title": "Advancing ECG Diagnosis Using Reinforcement Learning on Global Waveform\n  Variations Related to P Wave and PR Interval", "abstract": "  The reliable diagnosis of cardiac conditions through electrocardiogram (ECG)\nanalysis critically depends on accurately detecting P waves and measuring the\nPR interval. However, achieving consistent and generalizable diagnoses across\ndiverse populations presents challenges due to the inherent global variations\nobserved in ECG signals. This paper is focused on applying the Q learning\nreinforcement algorithm to the various ECG datasets available in the\nPhysioNet/Computing in Cardiology Challenge (CinC). Five ECG beats, including\nNormal Sinus Rhythm, Atrial Flutter, Atrial Fibrillation, 1st Degree\nAtrioventricular Block, and Left Atrial Enlargement, are included to study\nvariations of P waves and PR Interval on Lead II and Lead V1. Q-Agent\nclassified 71,672 beat samples in 8,867 patients with an average accuracy of\n90.4% and only 9.6% average hamming loss over misclassification. The average\nclassification time at the 100th episode containing around 40,000 samples is\n0.04 seconds. An average training reward of 344.05 is achieved at an alpha,\ngamma, and SoftMax temperature rate of 0.001, 0.9, and 0.1, respectively.\n", "date": "2024-01-10", "categories": []}
{"id": "2401.04368", "title": "Enhancing Acute Kidney Injury Prediction through Integration of Drug\n  Features in Intensive Care Units", "abstract": "  The relationship between acute kidney injury (AKI) prediction and nephrotoxic\ndrugs, or drugs that adversely affect kidney function, is one that has yet to\nbe explored in the critical care setting. One contributing factor to this gap\nin research is the limited investigation of drug modalities in the intensive\ncare unit (ICU) context, due to the challenges of processing prescription data\ninto the corresponding drug representations and a lack in the comprehensive\nunderstanding of these drug representations. This study addresses this gap by\nproposing a novel approach that leverages patient prescription data as a\nmodality to improve existing models for AKI prediction. We base our research on\nElectronic Health Record (EHR) data, extracting the relevant patient\nprescription information and converting it into the selected drug\nrepresentation for our research, the extended-connectivity fingerprint (ECFP).\nFurthermore, we adopt a unique multimodal approach, developing machine learning\nmodels and 1D Convolutional Neural Networks (CNN) applied to clinical drug\nrepresentations, establishing a procedure which has not been used by any\nprevious studies predicting AKI. The findings showcase a notable improvement in\nAKI prediction through the integration of drug embeddings and other patient\ncohort features. By using drug features represented as ECFP molecular\nfingerprints along with common cohort features such as demographics and lab\ntest values, we achieved a considerable improvement in model performance for\nthe AKI prediction task over the baseline model which does not include the drug\nrepresentations as features, indicating that our distinct approach enhances\nexisting baseline techniques and highlights the relevance of drug data in\npredicting AKI in the ICU setting\n", "date": "2024-01-09", "categories": []}
{"id": "2401.02996", "title": "An AI-enabled Bias-Free Respiratory Disease Diagnosis Model using Cough\n  Audio: A Case Study for COVID-19", "abstract": "  Cough-based diagnosis for Respiratory Diseases (RDs) using Artificial\nIntelligence (AI) has attracted considerable attention, yet many existing\nstudies overlook confounding variables in their predictive models. These\nvariables can distort the relationship between cough recordings (input data)\nand RD status (output variable), leading to biased associations and unrealistic\nmodel performance. To address this gap, we propose the Bias Free Network\n(RBFNet), an end to end solution that effectively mitigates the impact of\nconfounders in the training data distribution. RBFNet ensures accurate and\nunbiased RD diagnosis features, emphasizing its relevance by incorporating a\nCOVID19 dataset in this study. This approach aims to enhance the reliability of\nAI based RD diagnosis models by navigating the challenges posed by confounding\nvariables. A hybrid of a Convolutional Neural Networks (CNN) and Long-Short\nTerm Memory (LSTM) networks is proposed for the feature encoder module of\nRBFNet. An additional bias predictor is incorporated in the classification\nscheme to formulate a conditional Generative Adversarial Network (cGAN) which\nhelps in decorrelating the impact of confounding variables from RD prediction.\nThe merit of RBFNet is demonstrated by comparing classification performance\nwith State of The Art (SoTA) Deep Learning (DL) model (CNN LSTM) after training\non different unbalanced COVID-19 data sets, created by using a large scale\nproprietary cough data set. RBF-Net proved its robustness against extremely\nbiased training scenarios by achieving test set accuracies of 84.1%, 84.6%, and\n80.5% for the following confounding variables gender, age, and smoking status,\nrespectively. RBF-Net outperforms the CNN-LSTM model test set accuracies by\n5.5%, 7.7%, and 8.2%, respectively\n", "date": "2024-01-04", "categories": []}
{"id": "2401.03171", "title": "Exploration of Adolescent Depression Risk Prediction Based on Census\n  Surveys and General Life Issues", "abstract": "  In contemporary society, the escalating pressures of life and work have\npropelled psychological disorders to the forefront of modern health concerns,\nan issue that has been further accentuated by the COVID-19 pandemic. The\nprevalence of depression among adolescents is steadily increasing, and\ntraditional diagnostic methods, which rely on scales or interviews, prove\nparticularly inadequate for detecting depression in young people. Addressing\nthese challenges, numerous AI-based methods for assisting in the diagnosis of\nmental health issues have emerged. However, most of these methods center around\nfundamental issues with scales or use multimodal approaches like facial\nexpression recognition. Diagnosis of depression risk based on everyday habits\nand behaviors has been limited to small-scale qualitative studies. Our research\nleverages adolescent census data to predict depression risk, focusing on\nchildren's experiences with depression and their daily life situations. We\nintroduced a method for managing severely imbalanced high-dimensional data and\nan adaptive predictive approach tailored to data structure characteristics.\nFurthermore, we proposed a cloud-based architecture for automatic online\nlearning and data updates. This study utilized publicly available NSCH youth\ncensus data from 2020 to 2022, encompassing nearly 150,000 data entries. We\nconducted basic data analyses and predictive experiments, demonstrating\nsignificant performance improvements over standard machine learning and deep\nlearning algorithms. This affirmed our data processing method's broad\napplicability in handling imbalanced medical data. Diverging from typical\npredictive method research, our study presents a comprehensive architectural\nsolution, considering a wider array of user needs.\n", "date": "2024-01-06", "categories": []}
{"id": "2401.03722", "title": "From Data to Insights: A Comprehensive Survey on Advanced Applications\n  in Thyroid Cancer Research", "abstract": "  Thyroid cancer, the most prevalent endocrine cancer, has gained significant\nglobal attention due to its impact on public health. Extensive research efforts\nhave been dedicated to leveraging artificial intelligence (AI) methods for the\nearly detection of this disease, aiming to reduce its morbidity rates. However,\na comprehensive understanding of the structured organization of research\napplications in this particular field remains elusive. To address this\nknowledge gap, we conducted a systematic review and developed a comprehensive\ntaxonomy of machine learning-based applications in thyroid cancer pathogenesis,\ndiagnosis, and prognosis. Our primary objective was to facilitate the research\ncommunity's ability to stay abreast of technological advancements and\npotentially lead the emerging trends in this field. This survey presents a\ncoherent literature review framework for interpreting the advanced techniques\nused in thyroid cancer research. A total of 758 related studies were identified\nand scrutinized. To the best of our knowledge, this is the first review that\nprovides an in-depth analysis of the various aspects of AI applications\nemployed in the context of thyroid cancer. Furthermore, we highlight key\nchallenges encountered in this domain and propose future research opportunities\nfor those interested in studying the latest trends or exploring\nless-investigated aspects of thyroid cancer research. By presenting this\ncomprehensive review and taxonomy, we contribute to the existing knowledge in\nthe field, while providing valuable insights for researchers, clinicians, and\nstakeholders in advancing the understanding and management of this disease.\n", "date": "2024-01-08", "categories": []}
{"id": "2208.10993", "title": "Application of federated learning techniques for arrhythmia\n  classification using 12-lead ECG signals", "abstract": "  Artificial Intelligence-based (AI) analysis of large, curated medical\ndatasets is promising for providing early detection, faster diagnosis, and more\neffective treatment using low-power Electrocardiography (ECG) monitoring\ndevices information. However, accessing sensitive medical data from diverse\nsources is highly restricted since improper use, unsafe storage, or data\nleakage could violate a person's privacy. This work uses a Federated Learning\n(FL) privacy-preserving methodology to train AI models over heterogeneous sets\nof high-definition ECG from 12-lead sensor arrays collected from six\nheterogeneous sources. We evaluated the capacity of the resulting models to\nachieve equivalent performance compared to state-of-the-art models trained in a\nCentralized Learning (CL) fashion. Moreover, we assessed the performance of our\nsolution over Independent and Identical distributed (IID) and non-IID federated\ndata. Our methodology involves machine learning techniques based on Deep Neural\nNetworks and Long-Short-Term Memory models. It has a robust data preprocessing\npipeline with feature engineering, selection, and data balancing techniques.\nOur AI models demonstrated comparable performance to models trained using CL,\nIID, and non-IID approaches. They showcased advantages in reduced complexity\nand faster training time, making them well-suited for cloud-edge architectures.\n", "date": "2022-08-23", "categories": []}
{"id": "2401.02661", "title": "Nurse-in-the-Loop Artificial Intelligence for Precision Management of\n  Type 2 Diabetes in a Clinical Trial Utilizing Transfer-Learned Predictive\n  Digital Twin", "abstract": "  Background: Type 2 diabetes (T2D) is a prevalent chronic disease with a\nsignificant risk of serious health complications and negative impacts on the\nquality of life. Given the impact of individual characteristics and lifestyle\non the treatment plan and patient outcomes, it is crucial to develop precise\nand personalized management strategies. Artificial intelligence (AI) provides\ngreat promise in combining patterns from various data sources with nurses'\nexpertise to achieve optimal care. Methods: This is a 6-month ancillary study\namong T2D patients (n = 20, age = 57 +- 10). Participants were randomly\nassigned to an intervention (AI, n=10) group to receive daily AI-generated\nindividualized feedback or a control group without receiving the daily feedback\n(non-AI, n=10) in the last three months. The study developed an online\nnurse-in-the-loop predictive control (ONLC) model that utilizes a predictive\ndigital twin (PDT). The PDT was developed using a transfer-learning-based\nArtificial Neural Network. The PDT was trained on participants self-monitoring\ndata (weight, food logs, physical activity, glucose) from the first three\nmonths, and the online control algorithm applied particle swarm optimization to\nidentify impactful behavioral changes for maintaining the patient's glucose and\nweight levels for the next three months. The ONLC provided the intervention\ngroup with individualized feedback and recommendations via text messages. The\nPDT was re-trained weekly to improve its performance. Findings: The trained\nONLC model achieved >=80% prediction accuracy across all patients while the\nmodel was tuned online. Participants in the intervention group exhibited a\ntrend of improved daily steps and stable or improved total caloric and total\ncarb intake as recommended.\n", "date": "2024-01-05", "categories": []}
{"id": "2401.01620", "title": "Large Language Model Capabilities in Perioperative Risk Prediction and\n  Prognostication", "abstract": "  We investigate whether general-domain large language models such as GPT-4\nTurbo can perform risk stratification and predict post-operative outcome\nmeasures using a description of the procedure and a patient's clinical notes\nderived from the electronic health record. We examine predictive performance on\n8 different tasks: prediction of ASA Physical Status Classification, hospital\nadmission, ICU admission, unplanned admission, hospital mortality, PACU Phase 1\nduration, hospital duration, and ICU duration. Few-shot and chain-of-thought\nprompting improves predictive performance for several of the tasks. We achieve\nF1 scores of 0.50 for ASA Physical Status Classification, 0.81 for ICU\nadmission, and 0.86 for hospital mortality. Performance on duration prediction\ntasks were universally poor across all prompt strategies. Current generation\nlarge language models can assist clinicians in perioperative risk\nstratification on classification tasks and produce high-quality natural\nlanguage summaries and explanations.\n", "date": "2024-01-03", "categories": []}
{"id": "2401.00981", "title": "Machine Learning Classification of Alzheimer's Disease Stages Using\n  Cerebrospinal Fluid Biomarkers Alone", "abstract": "  Early diagnosis of Alzheimer's disease is a challenge because the existing\nmethodologies do not identify the patients in their preclinical stage, which\ncan last up to a decade prior to the onset of clinical symptoms. Several\nresearch studies demonstrate the potential of cerebrospinal fluid biomarkers,\namyloid beta 1-42, T-tau, and P-tau, in early diagnosis of Alzheimer's disease\nstages. In this work, we used machine learning models to classify different\nstages of Alzheimer's disease based on the cerebrospinal fluid biomarker levels\nalone. An electronic health record of patients from the National Alzheimer's\nCoordinating Centre database was analyzed and the patients were subdivided\nbased on mini-mental state scores and clinical dementia ratings. Statistical\nand correlation analyses were performed to identify significant differences\nbetween the Alzheimer's stages. Afterward, machine learning classifiers\nincluding K-Nearest Neighbors, Ensemble Boosted Tree, Ensemble Bagged Tree,\nSupport Vector Machine, Logistic Regression, and Naive Bayes classifiers were\nemployed to classify the Alzheimer's disease stages. The results demonstrate\nthat Ensemble Boosted Tree (84.4%) and Logistic Regression (73.4%) provide the\nhighest accuracy for binary classification, while Ensemble Bagged Tree (75.4%)\ndemonstrates better accuracy for multiclassification. The findings from this\nresearch are expected to help clinicians in making an informed decision\nregarding the early diagnosis of Alzheimer's from the cerebrospinal fluid\nbiomarkers alone, monitoring of the disease progression, and implementation of\nappropriate intervention measures.\n", "date": "2024-01-02", "categories": []}
{"id": "2312.08299", "title": "Conceptualizing Suicidal Behavior: Utilizing Explanations of Predicted\n  Outcomes to Analyze Longitudinal Social Media Data", "abstract": "  The COVID-19 pandemic has escalated mental health crises worldwide, with\nsocial isolation and economic instability contributing to a rise in suicidal\nbehavior. Suicide can result from social factors such as shame, abuse,\nabandonment, and mental health conditions like depression, Post-Traumatic\nStress Disorder (PTSD), Attention-Deficit/Hyperactivity Disorder (ADHD),\nanxiety disorders, and bipolar disorders. As these conditions develop, signs of\nsuicidal ideation may manifest in social media interactions. Analyzing social\nmedia data using artificial intelligence (AI) techniques can help identify\npatterns of suicidal behavior, providing invaluable insights for suicide\nprevention agencies, professionals, and broader community awareness\ninitiatives. Machine learning algorithms for this purpose require large volumes\nof accurately labeled data. Previous research has not fully explored the\npotential of incorporating explanations in analyzing and labeling longitudinal\nsocial media data. In this study, we employed a model explanation method, Layer\nIntegrated Gradients, on top of a fine-tuned state-of-the-art language model,\nto assign each token from Reddit users' posts an attribution score for\npredicting suicidal ideation. By extracting and analyzing attributions of\ntokens from the data, we propose a methodology for preliminary screening of\nsocial media posts for suicidal ideation without using large language models\nduring inference.\n", "date": "2023-12-13", "categories": []}
{"id": "2401.00756", "title": "MPRE: Multi-perspective Patient Representation Extractor for Disease\n  Prediction", "abstract": "  Patient representation learning based on electronic health records (EHR) is a\ncritical task for disease prediction. This task aims to effectively extract\nuseful information on dynamic features. Although various existing works have\nachieved remarkable progress, the model performance can be further improved by\nfully extracting the trends, variations, and the correlation between the trends\nand variations in dynamic features. In addition, sparse visit records limit the\nperformance of deep learning models. To address these issues, we propose the\nMulti-perspective Patient Representation Extractor (MPRE) for disease\nprediction. Specifically, we propose Frequency Transformation Module (FTM) to\nextract the trend and variation information of dynamic features in the\ntime-frequency domain, which can enhance the feature representation. In the 2D\nMulti-Extraction Network (2D MEN), we form the 2D temporal tensor based on\ntrend and variation. Then, the correlations between trend and variation are\ncaptured by the proposed dilated operation. Moreover, we propose the\nFirst-Order Difference Attention Mechanism (FODAM) to calculate the\ncontributions of differences in adjacent variations to the disease diagnosis\nadaptively. To evaluate the performance of MPRE and baseline methods, we\nconduct extensive experiments on two real-world public datasets. The experiment\nresults show that MPRE outperforms state-of-the-art baseline methods in terms\nof AUROC and AUPRC.\n", "date": "2024-01-01", "categories": []}
{"id": "2305.08777", "title": "Question-Answering System Extracts Information on Injection Drug Use\n  from Clinical Notes", "abstract": "  Background: Injection drug use (IDU) is a dangerous health behavior that\nincreases mortality and morbidity. Identifying IDU early and initiating harm\nreduction interventions can benefit individuals at risk. However, extracting\nIDU behaviors from patients' electronic health records (EHR) is difficult\nbecause there is no International Classification of Disease (ICD) code and the\nonly place IDU information can be indicated is unstructured free-text clinical\nnotes. Although natural language processing can efficiently extract this\ninformation from unstructured data, there are no validated tools. Methods: To\naddress this gap in clinical information, we design and demonstrate a\nquestion-answering (QA) framework to extract information on IDU from clinical\nnotes. Our framework involves two main steps: (1) generating a gold-standard QA\ndataset and (2) developing and testing the QA model. We utilize 2323 clinical\nnotes of 1145 patients sourced from the VA Corporate Data Warehouse to\nconstruct the gold-standard dataset for developing and evaluating the QA model.\nWe also demonstrate the QA model's ability to extract IDU-related information\non temporally out-of-distribution data. Results: Here we show that for a strict\nmatch between gold-standard and predicted answers, the QA model achieves 51.65%\nF1 score. For a relaxed match between the gold-standard and predicted answers,\nthe QA model obtains 78.03% F1 score, along with 85.38% Precision and 79.02%\nRecall scores. Moreover, the QA model demonstrates consistent performance when\nsubjected to temporally out-of-distribution data. Conclusions: Our study\nintroduces a QA framework designed to extract IDU information from clinical\nnotes, aiming to enhance the accurate and efficient detection of people who\ninject drugs, extract relevant information, and ultimately facilitate informed\npatient care.\n", "date": "2023-05-15", "categories": []}
{"id": "2309.11646", "title": "An Evaluation of Machine Learning Approaches for Early Diagnosis of\n  Autism Spectrum Disorder", "abstract": "  Autistic Spectrum Disorder (ASD) is a neurological disease characterized by\ndifficulties with social interaction, communication, and repetitive activities.\nWhile its primary origin lies in genetics, early detection is crucial, and\nleveraging machine learning offers a promising avenue for a faster and more\ncost-effective diagnosis. This study employs diverse machine learning methods\nto identify crucial ASD traits, aiming to enhance and automate the diagnostic\nprocess. We study eight state-of-the-art classification models to determine\ntheir effectiveness in ASD detection. We evaluate the models using accuracy,\nprecision, recall, specificity, F1-score, area under the curve (AUC), kappa,\nand log loss metrics to find the best classifier for these binary datasets.\nAmong all the classification models, for the children dataset, the SVM and LR\nmodels achieve the highest accuracy of 100% and for the adult dataset, the LR\nmodel produces the highest accuracy of 97.14%. Our proposed ANN model provides\nthe highest accuracy of 94.24% for the new combined dataset when\nhyperparameters are precisely tuned for each model. As almost all\nclassification models achieve high accuracy which utilize true labels, we\nbecome interested in delving into five popular clustering algorithms to\nunderstand model behavior in scenarios without true labels. We calculate\nNormalized Mutual Information (NMI), Adjusted Rand Index (ARI), and Silhouette\nCoefficient (SC) metrics to select the best clustering models. Our evaluation\nfinds that spectral clustering outperforms all other benchmarking clustering\nmodels in terms of NMI and ARI metrics while demonstrating comparability to the\noptimal SC achieved by k-means. The implemented code is available at GitHub.\n", "date": "2023-09-20", "categories": []}
{"id": "2311.04787", "title": "Why Do Probabilistic Clinical Models Fail To Transport Between Sites?", "abstract": "  The rising popularity of artificial intelligence in healthcare is\nhighlighting the problem that a computational model achieving super-human\nclinical performance at its training sites may perform substantially worse at\nnew sites. In this perspective, we present common sources for this failure to\ntransport, which we divide into sources under the control of the experimenter\nand sources inherent to the clinical data-generating process. Of the inherent\nsources we look a little deeper into site-specific clinical practices that can\naffect the data distribution, and propose a potential solution intended to\nisolate the imprint of those practices on the data from the patterns of disease\ncause and effect that are the usual target of probabilistic clinical models.\n", "date": "2023-11-08", "categories": []}
{"id": "2312.13533", "title": "Automated Clinical Coding for Outpatient Departments", "abstract": "  Computerised clinical coding approaches aim to automate the process of\nassigning a set of codes to medical records. While there is active research\npushing the state of the art on clinical coding for hospitalized patients, the\noutpatient setting -- where doctors tend to non-hospitalised patients -- is\noverlooked. Although both settings can be formalised as a multi-label\nclassification task, they present unique and distinct challenges, which raises\nthe question of whether the success of inpatient clinical coding approaches\ntranslates to the outpatient setting. This paper is the first to investigate\nhow well state-of-the-art deep learning-based clinical coding approaches work\nin the outpatient setting at hospital scale. To this end, we collect a large\noutpatient dataset comprising over 7 million notes documenting over half a\nmillion patients. We adapt four state-of-the-art clinical coding approaches to\nthis setting and evaluate their potential to assist coders. We find evidence\nthat clinical coding in outpatient settings can benefit from more innovations\nin popular inpatient coding benchmarks. A deeper analysis of the factors\ncontributing to the success -- amount and form of data and choice of document\nrepresentation -- reveals the presence of easy-to-solve examples, the coding of\nwhich can be completely automated with a low error rate.\n", "date": "2023-12-21", "categories": []}
{"id": "2312.14996", "title": "Bridging AI and Clinical Practice: Integrating Automated Sleep Scoring\n  Algorithm with Uncertainty-Guided Physician Review", "abstract": "  Purpose: This study aims to enhance the clinical use of automated\nsleep-scoring algorithms by incorporating an uncertainty estimation approach to\nefficiently assist clinicians in the manual review of predicted hypnograms, a\nnecessity due to the notable inter-scorer variability inherent in\npolysomnography (PSG) databases. Our efforts target the extent of review\nrequired to achieve predefined agreement levels, examining both in-domain and\nout-of-domain data, and considering subjects diagnoses. Patients and methods:\nTotal of 19578 PSGs from 13 open-access databases were used to train U-Sleep, a\nstate-of-the-art sleep-scoring algorithm. We leveraged a comprehensive clinical\ndatabase of additional 8832 PSGs, covering a full spectrum of ages and\nsleep-disorders, to refine the U-Sleep, and to evaluate different\nuncertainty-quantification approaches, including our novel confidence network.\nThe ID data consisted of PSGs scored by over 50 physicians, and the two OOD\nsets comprised recordings each scored by a unique senior physician. Results:\nU-Sleep demonstrated robust performance, with Cohen's kappa (K) at 76.2% on ID\nand 73.8-78.8% on OOD data. The confidence network excelled at identifying\nuncertain predictions, achieving AUROC scores of 85.7% on ID and 82.5-85.6% on\nOOD data. Independently of sleep-disorder status, statistical evaluations\nrevealed significant differences in confidence scores between aligning vs\ndiscording predictions, and significant correlations of confidence scores with\nclassification performance metrics. To achieve K of at least 90% with physician\nintervention, examining less than 29.0% of uncertain epochs was required,\nsubstantially reducing physicians workload, and facilitating near-perfect\nagreement.\n", "date": "2023-12-22", "categories": []}
{"id": "2312.14221", "title": "Noninvasive Estimation of Mean Pulmonary Artery Pressure Using MRI,\n  Computer Models, and Machine Learning", "abstract": "  Pulmonary Hypertension (PH) is a severe disease characterized by an elevated\npulmonary artery pressure. The gold standard for PH diagnosis is measurement of\nmean Pulmonary Artery Pressure (mPAP) during an invasive Right Heart\nCatheterization. In this paper, we investigate noninvasive approach to PH\ndetection utilizing Magnetic Resonance Imaging, Computer Models and Machine\nLearning. We show using the ablation study, that physics-informed feature\nengineering based on models of blood circulation increases the performance of\nGradient Boosting Decision Trees-based algorithms for classification of PH and\nregression of values of mPAP. We compare results of regression (with\nthresholding of estimated mPAP) and classification and demonstrate that metrics\nachieved in both experiments are comparable. The predicted mPAP values are more\ninformative to the physicians than the probability of PH returned by\nclassification models. They provide the intuitive explanation of the outcome of\nthe machine learning model (clinicians are accustomed to the mPAP metric,\ncontrary to the PH probability).\n", "date": "2023-12-21", "categories": []}
{"id": "2312.12541", "title": "Blood Glucose Level Prediction: A Graph-based Explainable Method with\n  Federated Learning", "abstract": "  In the UK, approximately 400,000 people with type 1 diabetes (T1D) rely on\ninsulin delivery due to insufficient pancreatic insulin production. Managing\nblood glucose (BG) levels is crucial, with continuous glucose monitoring (CGM)\nplaying a key role. CGM, tracking BG every 5 minutes, enables effective blood\nglucose level prediction (BGLP) by considering factors like carbohydrate intake\nand insulin delivery.\n  Recent research has focused on developing sequential models for BGLP using\nhistorical BG data, incorporating additional attributes such as carbohydrate\nintake, insulin delivery, and time. These methods have shown notable success in\nBGLP, with some providing temporal explanations. However, they often lack clear\ncorrelations between attributes and their impact on BGLP. Additionally, some\nmethods raise privacy concerns by aggregating participant data to learn\npopulation patterns.\n  Addressing these limitations, we introduced a graph attentive memory (GAM)\nmodel, combining a graph attention network (GAT) with a gated recurrent unit\n(GRU). GAT applies graph attention to model attribute correlations, offering\ntransparent, dynamic attribute relationships. Attention weights dynamically\ngauge attribute significance over time. To ensure privacy, we employed\nfederated learning (FL), facilitating secure population pattern analysis.\n  Our method was validated using the OhioT1DM'18 and OhioT1DM'20 datasets from\n12 participants, focusing on 6 key attributes. We demonstrated our model's\nstability and effectiveness through hyperparameter impact analysis.\n", "date": "2023-12-19", "categories": []}
{"id": "2312.12876", "title": "COVID-19 Diagnosis: ULGFBP-ResNet51 approach on the CT and the Chest\n  X-ray Images Classification", "abstract": "  The contagious and pandemic COVID-19 disease is currently considered as the\nmain health concern and posed widespread panic across human-beings. It affects\nthe human respiratory tract and lungs intensely. So that it has imposed\nsignificant threats for premature death. Although, its early diagnosis can play\na vital role in revival phase, the radiography tests with the manual\nintervention are a time-consuming process. Time is also limited for such manual\ninspecting of numerous patients in the hospitals. Thus, the necessity of\nautomatic diagnosis on the chest X-ray or the CT images with a high efficient\nperformance is urgent. Toward this end, we propose a novel method, named as the\nULGFBP-ResNet51 to tackle with the COVID-19 diagnosis in the images. In fact,\nthis method includes Uniform Local Binary Pattern (ULBP), Gabor Filter (GF),\nand ResNet51. According to our results, this method could offer superior\nperformance in comparison with the other methods, and attain maximum accuracy.\n", "date": "2023-12-20", "categories": []}
{"id": "2312.11832", "title": "The Validity of a Machine Learning-Based Video Game in the Objective\n  Screening of Attention Deficit Hyperactivity Disorder in Children Aged 5 to\n  12 Years", "abstract": "  Objective: Early identification of ADHD is necessary to provide the\nopportunity for timely treatment. However, screening the symptoms of ADHD on a\nlarge scale is not easy. This study aimed to validate a video game (FishFinder)\nfor the screening of ADHD using objective measurement of the core symptoms of\nthis disorder. Method: The FishFinder measures attention and impulsivity\nthrough in-game performance and evaluates the child's hyperactivity using\nsmartphone motion sensors. This game was tested on 26 children with ADHD and 26\nhealthy children aged 5 to 12 years. A Support Vector Machine was employed to\ndetect children with ADHD. results: This system showed 92.3% accuracy, 90%\nsensitivity, and 93.7% specificity using a combination of in-game and movement\nfeatures. Conclusions: The FishFinder demonstrated a strong ability to identify\nADHD in children. So, this game can be used as an affordable, accessible, and\nenjoyable method for the objective screening of ADHD.\n", "date": "2023-12-19", "categories": []}
{"id": "2312.10977", "title": "Predict and Interpret Health Risk using EHR through Typical Patients", "abstract": "  Predicting health risks from electronic health records (EHR) is a topic of\nrecent interest. Deep learning models have achieved success by modeling\ntemporal and feature interaction. However, these methods learn insufficient\nrepresentations and lead to poor performance when it comes to patients with few\nvisits or sparse records. Inspired by the fact that doctors may compare the\npatient with typical patients and make decisions from similar cases, we propose\na Progressive Prototypical Network (PPN) to select typical patients as\nprototypes and utilize their information to enhance the representation of the\ngiven patient. In particular, a progressive prototype memory and two prototype\nseparation losses are proposed to update prototypes. Besides, a novel\nintegration is introduced for better fusing information from patients and\nprototypes. Experiments on three real-world datasets demonstrate that our model\nbrings improvement on all metrics. To make our results better understood by\nphysicians, we developed an application at http://ppn.ai-care.top. Our code is\nreleased at https://github.com/yzhHoward/PPN.\n", "date": "2023-12-18", "categories": []}
{"id": "2312.09426", "title": "Deep Learning Models for Arrhythmia Classification Using Stacked\n  Time-frequency Scalogram Images from ECG Signals", "abstract": "  Electrocardiograms (ECGs), a medical monitoring technology recording cardiac\nactivity, are widely used for diagnosing cardiac arrhythmia. The diagnosis is\nbased on the analysis of the deformation of the signal shapes due to irregular\nheart rates associated with heart diseases. Due to the infeasibility of manual\nexamination of large volumes of ECG data, this paper aims to propose an\nautomated AI based system for ECG-based arrhythmia classification. To this\nfront, a deep learning based solution has been proposed for ECG-based\narrhythmia classification. Twelve lead electrocardiograms (ECG) of length 10\nsec from 45, 152 individuals from Shaoxing People's Hospital (SPH) dataset from\nPhysioNet with four different types of arrhythmias were used. The sampling\nfrequency utilized was 500 Hz. Median filtering was used to preprocess the ECG\nsignals. For every 1 sec of ECG signal, the time-frequency (TF) scalogram was\nestimated and stacked row wise to obtain a single image from 12 channels,\nresulting in 10 stacked TF scalograms for each ECG signal. These stacked TF\nscalograms are fed to the pretrained convolutional neural network (CNN), 1D\nCNN, and 1D CNN-LSTM (Long short-term memory) models, for arrhythmia\nclassification. The fine-tuned CNN models obtained the best test accuracy of\nabout 98% followed by 95% test accuracy by basic CNN-LSTM in arrhythmia\nclassification.\n", "date": "2023-12-01", "categories": []}
{"id": "2312.09958", "title": "Distilling Large Language Models for Matching Patients to Clinical\n  Trials", "abstract": "  The recent success of large language models (LLMs) has paved the way for\ntheir adoption in the high-stakes domain of healthcare. Specifically, the\napplication of LLMs in patient-trial matching, which involves assessing patient\neligibility against clinical trial's nuanced inclusion and exclusion criteria,\nhas shown promise. Recent research has shown that GPT-3.5, a widely recognized\nLLM developed by OpenAI, can outperform existing methods with minimal 'variable\nengineering' by simply comparing clinical trial information against patient\nsummaries. However, there are significant challenges associated with using\nclosed-source proprietary LLMs like GPT-3.5 in practical healthcare\napplications, such as cost, privacy and reproducibility concerns. To address\nthese issues, this study presents the first systematic examination of the\nefficacy of both proprietary (GPT-3.5, and GPT-4) and open-source LLMs (LLAMA\n7B,13B, and 70B) for the task of patient-trial matching. Employing a\nmultifaceted evaluation framework, we conducted extensive automated and\nhuman-centric assessments coupled with a detailed error analysis for each\nmodel. To enhance the adaptability of open-source LLMs, we have created a\nspecialized synthetic dataset utilizing GPT-4, enabling effective fine-tuning\nunder constrained data conditions. Our findings reveal that open-source LLMs,\nwhen fine-tuned on this limited and synthetic dataset, demonstrate performance\nparity with their proprietary counterparts. This presents a massive opportunity\nfor their deployment in real-world healthcare applications. To foster further\nresearch and applications in this field, we release both the annotated\nevaluation dataset along with the fine-tuned LLM -- Trial-LLAMA -- for public\nuse.\n", "date": "2023-12-15", "categories": []}
{"id": "2312.06832", "title": "Symptom-based Machine Learning Models for the Early Detection of\n  COVID-19: A Narrative Review", "abstract": "  Despite the widespread testing protocols for COVID-19, there are still\nsignificant challenges in early detection of the disease, which is crucial for\npreventing its spread and optimizing patient outcomes. Owing to the limited\ntesting capacity in resource-strapped settings and the limitations of the\navailable traditional methods of testing, it has been established that a fast\nand efficient strategy is important to fully stop the virus. Machine learning\nmodels can analyze large datasets, incorporating patient-reported symptoms,\nclinical data, and medical imaging. Symptom-based detection methods have been\ndeveloped to predict COVID-19, and they have shown promising results. In this\npaper, we provide an overview of the landscape of symptoms-only machine\nlearning models for predicting COVID-19, including their performance and\nlimitations. The review will also examine the performance of symptom-based\nmodels when compared to image-based models. Because different studies used\nvarying datasets, methodologies, and performance metrics. Selecting the model\nthat performs best relies on the context and objectives of the research.\nHowever, based on the results, we observed that ensemble classifier performed\nexceptionally well in predicting the occurrence of COVID-19 based on patient\nsymptoms with the highest overall accuracy of 97.88%. Gradient Boosting\nAlgorithm achieved an AUC (Area Under the Curve) of 0.90 and identified key\nfeatures contributing to the decision-making process. Image-based models, as\nobserved in the analyzed studies, have consistently demonstrated higher\naccuracy than symptom-based models, often reaching impressive levels ranging\nfrom 96.09% to as high as 99%.\n", "date": "2023-12-08", "categories": []}
{"id": "2307.02028", "title": "EHRSHOT: An EHR Benchmark for Few-Shot Evaluation of Foundation Models", "abstract": "  While the general machine learning (ML) community has benefited from public\ndatasets, tasks, and models, the progress of ML in healthcare has been hampered\nby a lack of such shared assets. The success of foundation models creates new\nchallenges for healthcare ML by requiring access to shared pretrained models to\nvalidate performance benefits. We help address these challenges through three\ncontributions. First, we publish a new dataset, EHRSHOT, which contains\ndeidentified structured data from the electronic health records (EHRs) of 6,739\npatients from Stanford Medicine. Unlike MIMIC-III/IV and other popular EHR\ndatasets, EHRSHOT is longitudinal and not restricted to ICU/ED patients.\nSecond, we publish the weights of CLMBR-T-base, a 141M parameter clinical\nfoundation model pretrained on the structured EHR data of 2.57M patients. We\nare one of the first to fully release such a model for coded EHR data; in\ncontrast, most prior models released for clinical data (e.g. GatorTron,\nClinicalBERT) only work with unstructured text and cannot process the rich,\nstructured data within an EHR. We provide an end-to-end pipeline for the\ncommunity to validate and build upon its performance. Third, we define 15\nfew-shot clinical prediction tasks, enabling evaluation of foundation models on\nbenefits such as sample efficiency and task adaptation. Our model and dataset\nare available via a research data use agreement from our website:\nhttps://ehrshot.stanford.edu. Code to reproduce our results are available at\nour Github repo: https://github.com/som-shahlab/ehrshot-benchmark\n", "date": "2023-07-05", "categories": []}
{"id": "2312.06457", "title": "Large Language Models with Retrieval-Augmented Generation for Zero-Shot\n  Disease Phenotyping", "abstract": "  Identifying disease phenotypes from electronic health records (EHRs) is\ncritical for numerous secondary uses. Manually encoding physician knowledge\ninto rules is particularly challenging for rare diseases due to inadequate EHR\ncoding, necessitating review of clinical notes. Large language models (LLMs)\noffer promise in text understanding but may not efficiently handle real-world\nclinical documentation. We propose a zero-shot LLM-based method enriched by\nretrieval-augmented generation and MapReduce, which pre-identifies\ndisease-related text snippets to be used in parallel as queries for the LLM to\nestablish diagnosis. We show that this method as applied to pulmonary\nhypertension (PH), a rare disease characterized by elevated arterial pressures\nin the lungs, significantly outperforms physician logic rules ($F_1$ score of\n0.62 vs. 0.75). This method has the potential to enhance rare disease cohort\nidentification, expanding the scope of robust clinical research and care gap\nidentification.\n", "date": "2023-12-11", "categories": []}
{"id": "2301.09568", "title": "Interpretable Classification of Early Stage Parkinson's Disease from EEG", "abstract": "  Detecting Parkinson's Disease in its early stages using EEG data presents a\nsignificant challenge. This paper introduces a novel approach, representing EEG\ndata as a 15-variate series of bandpower and peak frequency\nvalues/coefficients. The hypothesis is that this representation captures\nessential information from the noisy EEG signal, improving disease detection.\nStatistical features extracted from this representation are utilised as input\nfor interpretable machine learning models, specifically Decision Tree and\nAdaBoost classifiers. Our classification pipeline is deployed within our\nproposed framework which enables high-importance data types and brain regions\nfor classification to be identified. Interestingly, our analysis reveals that\nwhile there is no significant regional importance, the N1 sleep data type\nexhibits statistically significant predictive power (p < 0.01) for early-stage\nParkinson's Disease classification. AdaBoost classifiers trained on the N1 data\ntype consistently outperform baseline models, achieving over 80% accuracy and\nrecall. Our classification pipeline statistically significantly outperforms\nbaseline models indicating that the model has acquired useful information.\nPaired with the interpretability (ability to view feature importance's) of our\npipeline this enables us to generate meaningful insights into the\nclassification of early stage Parkinson's with our N1 models. In Future, these\nmodels could be deployed in the real world - the results presented in this\npaper indicate that more than 3 in 4 early-stage Parkinson's cases would be\ncaptured with our pipeline.\n", "date": "2023-01-20", "categories": []}
{"id": "2312.04595", "title": "Evaluating The Accuracy of Classification Algorithms for Detecting Heart\n  Disease Risk", "abstract": "  The healthcare industry generates enormous amounts of complex clinical data\nthat make the prediction of disease detection a complicated process. In medical\ninformatics, making effective and efficient decisions is very important. Data\nMining (DM) techniques are mainly used to identify and extract hidden patterns\nand interesting knowledge to diagnose and predict diseases in medical datasets.\nNowadays, heart disease is considered one of the most important problems in the\nhealthcare field. Therefore, early diagnosis leads to a reduction in deaths. DM\ntechniques have proven highly effective for predicting and diagnosing heart\ndiseases. This work utilizes the classification algorithms with a medical\ndataset of heart disease; namely, J48, Random Forest, and Na\\\"ive Bayes to\ndiscover the accuracy of their performance. We also examine the impact of the\nfeature selection method. A comparative and analysis study was performed to\ndetermine the best technique using Waikato Environment for Knowledge Analysis\n(Weka) software, version 3.8.6. The performance of the utilized algorithms was\nevaluated using standard metrics such as accuracy, sensitivity and specificity.\nThe importance of using classification techniques for heart disease diagnosis\nhas been highlighted. We also reduced the number of attributes in the dataset,\nwhich showed a significant improvement in prediction accuracy. The results\nindicate that the best algorithm for predicting heart disease was Random Forest\nwith an accuracy of 99.24%.\n", "date": "2023-12-06", "categories": []}
{"id": "2301.12340", "title": "Incremental Value and Interpretability of Radiomics Features of Both\n  Lung and Epicardial Adipose Tissue for Detecting the Severity of COVID-19\n  Infection", "abstract": "  Epicardial adipose tissue (EAT) is known for its pro-inflammatory properties\nand association with Coronavirus Disease 2019 (COVID-19) severity. However,\ncurrent EAT segmentation methods do not consider positional information.\nAdditionally, the detection of COVID-19 severity lacks consideration for EAT\nradiomics features, which limits interpretability. This study investigates the\nuse of radiomics features from EAT and lungs to detect the severity of COVID-19\ninfections. A retrospective analysis of 515 patients with COVID-19 (Cohort1:\n415, Cohort2: 100) was conducted using a proposed three-stage deep learning\napproach for EAT extraction. Lung segmentation was achieved using a published\nmethod. A hybrid model for detecting the severity of COVID-19 was built in a\nderivation cohort, and its performance and uncertainty were evaluated in\ninternal (125, Cohort1) and external (100, Cohort2) validation cohorts. For EAT\nextraction, the Dice similarity coefficients (DSC) of the two centers were\n0.972 (+-0.011) and 0.968 (+-0.005), respectively. For severity detection, the\nhybrid model with radiomics features of both lungs and EAT showed improvements\nin AUC, net reclassification improvement (NRI), and integrated discrimination\nimprovement (IDI) compared to the model with only lung radiomics features. The\nhybrid model exhibited an increase of 0.1 (p<0.001), 19.3%, and 18.0%\nrespectively, in the internal validation cohort and an increase of 0.09\n(p<0.001), 18.0%, and 18.0%, respectively, in the external validation cohort\nwhile outperforming existing detection methods. Uncertainty quantification and\nradiomics features analysis confirmed the interpretability of case prediction\nafter inclusion of EAT features.\n", "date": "2023-01-29", "categories": []}
{"id": "2312.03742", "title": "Clinical Risk Prediction Using Language Models: Benefits And\n  Considerations", "abstract": "  The utilization of Electronic Health Records (EHRs) for clinical risk\nprediction is on the rise. However, strict privacy regulations limit access to\ncomprehensive health records, making it challenging to apply standard machine\nlearning algorithms in practical real-world scenarios. Previous research has\naddressed this data limitation by incorporating medical ontologies and\nemploying transfer learning methods. In this study, we investigate the\npotential of leveraging language models (LMs) as a means to incorporate\nsupplementary domain knowledge for improving the performance of various\nEHR-based risk prediction tasks. Unlike applying LMs to unstructured EHR data\nsuch as clinical notes, this study focuses on using textual descriptions within\nstructured EHR to make predictions exclusively based on that information. We\nextensively compare against previous approaches across various data types and\nsizes. We find that employing LMs to represent structured EHRs, such as\ndiagnostic histories, leads to improved or at least comparable performance in\ndiverse risk prediction tasks. Furthermore, LM-based approaches offer numerous\nadvantages, including few-shot learning, the capability to handle previously\nunseen medical concepts, and adaptability to various medical vocabularies.\nNevertheless, we underscore, through various experiments, the importance of\nbeing cautious when employing such models, as concerns regarding the\nreliability of LMs persist.\n", "date": "2023-11-29", "categories": []}
{"id": "2312.03796", "title": "Multi-Scale and Multi-Modal Contrastive Learning Network for Biomedical\n  Time Series", "abstract": "  Multi-modal biomedical time series (MBTS) data offers a holistic view of the\nphysiological state, holding significant importance in various bio-medical\napplications. Owing to inherent noise and distribution gaps across different\nmodalities, MBTS can be complex to model. Various deep learning models have\nbeen developed to learn representations of MBTS but still fall short in\nrobustness due to the ignorance of modal-to-modal variations. This paper\npresents a multi-scale and multi-modal biomedical time series representation\nlearning (MBSL) network with contrastive learning to migrate these variations.\nFirstly, MBTS is grouped based on inter-modal distances, then each group with\nminimum intra-modal variations can be effectively modeled by individual\nencoders. Besides, to enhance the multi-scale feature extraction (encoder),\nvarious patch lengths and mask ratios are designed to generate tokens with\nsemantic information at different scales and diverse contextual perspectives\nrespectively. Finally, cross-modal contrastive learning is proposed to maximize\nconsistency among inter-modal groups, maintaining useful information and\neliminating noises. Experiments against four bio-medical applications show that\nMBSL outperforms state-of-the-art models by 33.9% mean average errors (MAE) in\nrespiration rate, by 13.8% MAE in exercise heart rate, by 1.41% accuracy in\nhuman activity recognition, and by 1.14% F1-score in obstructive sleep\napnea-hypopnea syndrome.\n", "date": "2023-12-06", "categories": []}
{"id": "2305.20046", "title": "Assessing Language Disorders using Artificial Intelligence: a Paradigm\n  Shift", "abstract": "  Speech, language, and communication deficits are present in most\nneurodegenerative syndromes. They enable the early detection, diagnosis,\ntreatment planning, and monitoring of neurocognitive disease progression as\npart of traditional neurological assessment. Nevertheless, standard speech and\nlanguage evaluation is time-consuming and resource-intensive for clinicians. We\nargue that using machine learning methodologies, natural language processing,\nand modern artificial intelligence (AI) for Language Assessment is an\nimprovement over conventional manual assessment. Using these methodologies,\nComputational Language Assessment (CLA) accomplishes three goals: (i) provides\na neuro-cognitive evaluation of speech, language, and communication in elderly\nand high-risk individuals for dementia; (ii) facilitates the diagnosis,\nprognosis, and therapy efficacy in at-risk and language-impaired populations;\nand (iii) allows easier extensibility to assess patients from a wide range of\nlanguages. By employing AI models, CLA may inform neurocognitive theory on the\nrelationship between language symptoms and their neural bases. Finally, it\nsignals a paradigm shift by significantly advancing our ability to optimize the\nprevention and treatment of elderly individuals with communication disorders,\nallowing them to age gracefully with social engagement.\n", "date": "2023-05-31", "categories": []}
{"id": "2312.03344", "title": "Interpretable Mechanistic Representations for Meal-level Glycemic\n  Control in the Wild", "abstract": "  Diabetes encompasses a complex landscape of glycemic control that varies\nwidely among individuals. However, current methods do not faithfully capture\nthis variability at the meal level. On the one hand, expert-crafted features\nlack the flexibility of data-driven methods; on the other hand, learned\nrepresentations tend to be uninterpretable which hampers clinical adoption. In\nthis paper, we propose a hybrid variational autoencoder to learn interpretable\nrepresentations of CGM and meal data. Our method grounds the latent space to\nthe inputs of a mechanistic differential equation, producing embeddings that\nreflect physiological quantities, such as insulin sensitivity, glucose\neffectiveness, and basal glucose levels. Moreover, we introduce a novel method\nto infer the glucose appearance rate, making the mechanistic model robust to\nunreliable meal logs. On a dataset of CGM and self-reported meals from\nindividuals with type-2 diabetes and pre-diabetes, our unsupervised\nrepresentation discovers a separation between individuals proportional to their\ndisease severity. Our embeddings produce clusters that are up to 4x better than\nnaive, expert, black-box, and pure mechanistic features. Our method provides a\nnuanced, yet interpretable, embedding space to compare glycemic control within\nand across individuals, directly learnable from in-the-wild data.\n", "date": "2023-12-06", "categories": []}
{"id": "2301.03150", "title": "MOTOR: A Time-To-Event Foundation Model For Structured Medical Records", "abstract": "  We present a self-supervised, time-to-event (TTE) foundation model called\nMOTOR (Many Outcome Time Oriented Representations) which is pretrained on\ntimestamped sequences of events in electronic health records (EHR) and health\ninsurance claims. TTE models are used for estimating the probability\ndistribution of the time until a specific event occurs, which is an important\ntask in medical settings. TTE models provide many advantages over\nclassification using fixed time horizons, including naturally handling censored\nobservations, but are challenging to train with limited labeled data. MOTOR\naddresses this challenge by pretraining on up to 55M patient records (9B\nclinical events). We evaluate MOTOR's transfer learning performance on 19\ntasks, across 3 patient databases (a private EHR system, MIMIC-IV, and Merative\nclaims data). Task-specific models adapted from MOTOR improve time-dependent C\nstatistics by 4.6% over state-of-the-art, improve label efficiency by up to 95%\n,and are more robust to temporal distributional shifts. We further evaluate\ncross-site portability by adapting our MOTOR foundation model for six\nprediction tasks on the MIMIC-IV dataset, where it outperforms all baselines.\nMOTOR is the first foundation model for medical TTE predictions and we release\na 143M parameter pretrained model for research use at [redacted URL].\n", "date": "2023-01-09", "categories": []}
{"id": "2310.09485", "title": "Applying Bayesian Ridge Regression AI Modeling in Virus Severity\n  Prediction", "abstract": "  Artificial intelligence (AI) is a powerful tool for reshaping healthcare\nsystems. In healthcare, AI is invaluable for its capacity to manage vast\namounts of data, which can lead to more accurate and speedy diagnoses,\nultimately easing the workload on healthcare professionals. As a result, AI has\nproven itself to be a power tool across various industries, simplifying complex\ntasks and pattern recognition that would otherwise be overwhelming for humans\nor traditional computer algorithms. In this paper, we review the strengths and\nweaknesses of Bayesian Ridge Regression, an AI model that can be used to bring\ncutting edge virus analysis to healthcare professionals around the world. The\nmodel's accuracy assessment revealed promising results, with room for\nimprovement primarily related to data organization. In addition, the severity\nindex serves as a valuable tool to gain a broad overview of patient care needs,\naligning with healthcare professionals' preference for broader categorizations.\n", "date": "2023-10-14", "categories": []}
{"id": "2311.05435", "title": "Parkinson's Disease Detection through Vocal Biomarkers and Advanced\n  Machine Learning Algorithms", "abstract": "  Parkinson's disease (PD) is a prevalent neurodegenerative disorder known for\nits impact on motor neurons, causing symptoms like tremors, stiffness, and gait\ndifficulties. This study explores the potential of vocal feature alterations in\nPD patients as a means of early disease prediction. This research aims to\npredict the onset of Parkinson's disease. Utilizing a variety of advanced\nmachine-learning algorithms, including XGBoost, LightGBM, Bagging, AdaBoost,\nand Support Vector Machine, among others, the study evaluates the predictive\nperformance of these models using metrics such as accuracy, area under the\ncurve (AUC), sensitivity, and specificity. The findings of this comprehensive\nanalysis highlight LightGBM as the most effective model, achieving an\nimpressive accuracy rate of 96% alongside a matching AUC of 96%. LightGBM\nexhibited a remarkable sensitivity of 100% and specificity of 94.43%,\nsurpassing other machine learning algorithms in accuracy and AUC scores. Given\nthe complexities of Parkinson's disease and its challenges in early diagnosis,\nthis study underscores the significance of leveraging vocal biomarkers coupled\nwith advanced machine-learning techniques for precise and timely PD detection.\n", "date": "2023-11-09", "categories": []}
{"id": "2312.00794", "title": "Informative Priors Improve the Reliability of Multimodal Clinical Data\n  Classification", "abstract": "  Machine learning-aided clinical decision support has the potential to\nsignificantly improve patient care. However, existing efforts in this domain\nfor principled quantification of uncertainty have largely been limited to\napplications of ad-hoc solutions that do not consistently improve reliability.\nIn this work, we consider stochastic neural networks and design a tailor-made\nmultimodal data-driven (M2D2) prior distribution over network parameters. We\nuse simple and scalable Gaussian mean-field variational inference to train a\nBayesian neural network using the M2D2 prior. We train and evaluate the\nproposed approach using clinical time-series data in MIMIC-IV and corresponding\nchest X-ray images in MIMIC-CXR for the classification of acute care\nconditions. Our empirical results show that the proposed method produces a more\nreliable predictive model compared to deterministic and Bayesian neural network\nbaselines.\n", "date": "2023-11-17", "categories": []}
{"id": "2312.00811", "title": "Seizure detection from Electroencephalogram signals via Wavelets and\n  Graph Theory metrics", "abstract": "  Epilepsy is one of the most prevalent neurological conditions, where an\nepileptic seizure is a transient occurrence due to abnormal, excessive and\nsynchronous activity in the brain. Electroencephalogram signals emanating from\nthe brain may be captured, analysed and then play a significant role in\ndetection and prediction of epileptic seizures. In this work we enhance upon a\nprevious approach that relied on the differing properties of the wavelet\ntransform. Here we apply the Maximum Overlap Discrete Wavelet Transform to both\nreduce signal \\textit{noise} and use signal variance exhibited at differing\ninherent frequency levels to develop various metrics of connection between the\nelectrodes placed upon the scalp. %The properties of both the noise reduced\nsignal and the interconnected electrodes differ significantly during the\ndifferent brain states.\n  Using short duration epochs, to approximate close to real time monitoring,\ntogether with simple statistical parameters derived from the reconstructed\nnoise reduced signals we initiate seizure detection. To further improve\nperformance we utilise graph theoretic indicators from derived electrode\nconnectivity. From there we build the attribute space. We utilise open-source\nsoftware and publicly available data to highlight the superior\nRecall/Sensitivity performance of our approach, when compared to existing\npublished methods.\n", "date": "2023-11-28", "categories": []}
{"id": "2312.01029", "title": "RNN-BOF: A Multivariate Global Recurrent Neural Network for Binary\n  Outcome Forecasting of Inpatient Aggression", "abstract": "  Psychometric assessment instruments aid clinicians by providing methods of\nassessing the future risk of adverse events such as aggression. Existing\nmachine learning approaches have treated this as a classification problem,\npredicting the probability of an adverse event in a fixed future time period\nfrom the scores produced by both psychometric instruments and clinical and\ndemographic covariates. We instead propose modelling a patient's future risk\nusing a time series methodology that learns from longitudinal data and produces\na probabilistic binary forecast that indicates the presence of the adverse\nevent in the next time period. Based on the recent success of Deep Neural Nets\nfor globally forecasting across many time series, we introduce a global\nmultivariate Recurrent Neural Network for Binary Outcome Forecasting, that\ntrains from and for a population of patient time series to produce individual\nprobabilistic risk assessments. We use a moving window training scheme on a\nreal world dataset of 83 patients, where the main binary time series represents\nthe presence of aggressive events and covariate time series represent clinical\nor demographic features and psychometric measures. On this dataset our approach\nwas capable of a significant performance increase against both benchmark\npsychometric instruments and previously used machine learning methodologies.\n", "date": "2023-12-02", "categories": []}
{"id": "2312.01093", "title": "Predicting Postoperative Nausea And Vomiting Using Machine Learning: A\n  Model Development and Validation Study", "abstract": "  Background: Postoperative nausea and vomiting (PONV) is a frequently observed\ncomplication in patients undergoing surgery under general anesthesia. Moreover,\nit is a frequent cause of distress and dissatisfaction during the early\npostoperative period. The tools used for predicting PONV at present have not\nyielded satisfactory results. Therefore, prognostic tools for the prediction of\nearly and delayed PONV were developed in this study with the aim of achieving\nsatisfactory predictive performance.\n  Methods: The retrospective data of adult patients admitted to the\npost-anesthesia care unit after undergoing surgical procedures under general\nanesthesia at the Sheba Medical Center, Israel, between September 1, 2018, and\nSeptember 1, 2023, were used in this study. An ensemble model of machine\nlearning algorithms trained on the data of 54848 patients was developed. The\nk-fold cross-validation method was used followed by splitting the data to train\nand test sets that optimally preserve the sociodemographic features of the\npatients, such as age, sex, and smoking habits, using the Bee Colony algorithm.\n  Findings: Among the 54848 patients, early and delayed PONV were observed in\n2706 (4.93%) and 8218 (14.98%) patients, respectively. The proposed PONV\nprediction tools could correctly predict early and delayed PONV in 84.0% and\n77.3% of cases, respectively, outperforming the second-best PONV prediction\ntool (Koivuranta score) by 13.4% and 12.9%, respectively. Feature importance\nanalysis revealed that the performance of the proposed prediction tools aligned\nwith previous clinical knowledge, indicating their utility.\n  Interpretation: The machine learning-based tools developed in this study\nenabled improved PONV prediction, thereby facilitating personalized care and\nimproved patient outcomes.\n", "date": "2023-12-02", "categories": []}
{"id": "2312.01242", "title": "DDxT: Deep Generative Transformer Models for Differential Diagnosis", "abstract": "  Differential Diagnosis (DDx) is the process of identifying the most likely\nmedical condition among the possible pathologies through the process of\nelimination based on evidence. An automated process that narrows a large set of\npathologies down to the most likely pathologies will be of great importance.\nThe primary prior works have relied on the Reinforcement Learning (RL) paradigm\nunder the intuition that it aligns better with how physicians perform DDx. In\nthis paper, we show that a generative approach trained with simpler supervised\nand self-supervised learning signals can achieve superior results on the\ncurrent benchmark. The proposed Transformer-based generative network, named\nDDxT, autoregressively produces a set of possible pathologies, i.e., DDx, and\npredicts the actual pathology using a neural network. Experiments are performed\nusing the DDXPlus dataset. In the case of DDx, the proposed network has\nachieved a mean accuracy of 99.82% and a mean F1 score of 0.9472. Additionally,\nmean accuracy reaches 99.98% with a mean F1 score of 0.9949 while predicting\nground truth pathology. The proposed DDxT outperformed the previous RL-based\napproaches by a big margin. Overall, the automated Transformer-based DDx\ngenerative model has the potential to become a useful tool for a physician in\ntimes of urgency.\n", "date": "2023-12-02", "categories": []}
{"id": "2308.11645", "title": "Neurological Prognostication of Post-Cardiac-Arrest Coma Patients Using\n  EEG Data: A Dynamic Survival Analysis Framework with Competing Risks", "abstract": "  Patients resuscitated from cardiac arrest who enter a coma are at high risk\nof death. Forecasting neurological outcomes of these patients (the task of\nneurological prognostication) could help with treatment decisions. In this\npaper, we propose, to the best of our knowledge, the first dynamic framework\nfor neurological prognostication of post-cardiac-arrest comatose patients using\nEEG data: our framework makes predictions for a patient over time as more EEG\ndata become available, and different training patients' available EEG time\nseries could vary in length. Predictions are phrased in terms of either\ntime-to-event outcomes (time-to-awakening or time-to-death) or as the patient's\nprobability of awakening or of dying across multiple time horizons. Our\nframework uses any dynamic survival analysis model that supports competing\nrisks in the form of estimating patient-level cumulative incidence functions.\nWe consider three competing risks as to what happens first to a patient:\nawakening, being withdrawn from life-sustaining therapies (and thus\ndeterministically dying), or dying (by other causes). We demonstrate our\nframework by benchmarking three existing dynamic survival analysis models that\nsupport competing risks on a real dataset of 922 patients. Our main\nexperimental findings are that: (1) the classical Fine and Gray model which\nonly uses a patient's static features and summary statistics from the patient's\nlatest hour's worth of EEG data is highly competitive, achieving accuracy\nscores as high as the recently developed Dynamic-DeepHit model that uses\nsubstantially more of the patient's EEG data; and (2) in an ablation study, we\nshow that our choice of modeling three competing risks results in a model that\nis at least as accurate while learning more information than simpler models\n(using two competing risks or a standard survival analysis setup with no\ncompeting risks).\n", "date": "2023-08-17", "categories": []}
{"id": "2212.13878", "title": "Using machine learning algorithms to determine the post-COVID state of a\n  person by his rhythmogram", "abstract": "  In this study we applyed machine-learning algorithms to determine the\npost-COVID state of a person. During the study, a marker of the post-COVID\nstate of a person was found in the electrocardiogram data. We have shown that\nthis marker in the patient's ECG signal can be used to diagnose a post-COVID\nstate.\n", "date": "2022-12-16", "categories": []}
{"id": "2311.18245", "title": "Automatic Detection of Alzheimer's Disease with Multi-Modal Fusion of\n  Clinical MRI Scans", "abstract": "  The aging population of the U.S. drives the prevalence of Alzheimer's\ndisease. Brookmeyer et al. forecasts approximately 15 million Americans will\nhave either clinical AD or mild cognitive impairment by 2060. In response to\nthis urgent call, methods for early detection of Alzheimer's disease have been\ndeveloped for prevention and pre-treatment. Notably, literature on the\napplication of deep learning in the automatic detection of the disease has been\nproliferating. This study builds upon previous literature and maintains a focus\non leveraging multi-modal information to enhance automatic detection. We aim to\npredict the stage of the disease - Cognitively Normal (CN), Mildly Cognitive\nImpairment (MCI), and Alzheimer's Disease (AD), based on two different types of\nbrain MRI scans. We design an AlexNet-based deep learning model that learns the\nsynergy of complementary information from both T1 and FLAIR MRI scans.\n", "date": "2023-11-30", "categories": []}
{"id": "2311.18265", "title": "MCI Detection using fMRI time series embeddings of Recurrence plots", "abstract": "  The human brain can be conceptualized as a dynamical system. Utilizing\nresting state fMRI time series imaging, we can study the underlying dynamics at\near-marked Regions of Interest (ROIs) to understand structure or lack thereof.\nThis differential behavior could be key to understanding the neurodegeneration\nand also to classify between healthy and Mild Cognitive Impairment (MCI)\nsubjects. In this study, we consider 6 brain networks spanning over 160 ROIs\nderived from Dosenbach template, where each network consists of 25-30 ROIs.\nRecurrence plot, extensively used to understand evolution of time series, is\nemployed. Representative time series at each ROI is converted to its\ncorresponding recurrence plot visualization, which is subsequently condensed to\nlow-dimensional feature embeddings through Autoencoders. The performance of the\nproposed method is shown on fMRI volumes of 100 subjects (balanced data), taken\nfrom publicly available ADNI dataset. Results obtained show peak classification\naccuracy of 93% among the 6 brain networks, mean accuracy of 89.3% thereby\nillustrating promise in the proposed approach.\n", "date": "2023-11-30", "categories": []}
{"id": "2311.17066", "title": "Cluster trajectory of SOFA score in predicting mortality in sepsis", "abstract": "  Objective: Sepsis is a life-threatening condition. Sequential Organ Failure\nAssessment (SOFA) score is commonly used to assess organ dysfunction and\npredict ICU mortality, but it is taken as a static measurement and fails to\ncapture dynamic changes. This study aims to investigate the relationship\nbetween dynamic changes in SOFA scores over the first 72 hours of ICU admission\nand patient outcomes.\n  Design, setting, and participants: 3,253 patients in the Medical Information\nMart for Intensive Care IV database who met the sepsis-3 criteria and were\nadmitted from the emergency department with at least 72 hours of ICU admission\nand full-active resuscitation status were analysed. Group-based trajectory\nmodelling with dynamic time warping and k-means clustering identified distinct\ntrajectory patterns in dynamic SOFA scores. They were subsequently compared\nusing Python.\n  Main outcome measures: Outcomes including hospital and ICU mortality, length\nof stay in hospital and ICU, and readmission during hospital stay, were\ncollected. Discharge time from ICU to wards and cut-offs at 7-day and 14-day\nwere taken.\n  Results: Four clusters were identified: A (consistently low SOFA scores), B\n(rapid increase followed by a decline in SOFA scores), C (higher baseline\nscores with gradual improvement), and D (persistently elevated scores). Cluster\nD had the longest ICU and hospital stays, highest ICU and hospital mortality.\nDischarge rates from ICU were similar for Clusters A and B, while Cluster C had\ninitially comparable rates but a slower transition to ward.\n  Conclusion: Monitoring dynamic changes in SOFA score is valuable for\nassessing sepsis severity and treatment responsiveness.\n", "date": "2023-11-23", "categories": []}
{"id": "2311.17133", "title": "Deployment of a Robust and Explainable Mortality Prediction Model: The\n  COVID-19 Pandemic and Beyond", "abstract": "  This study investigated the performance, explainability, and robustness of\ndeployed artificial intelligence (AI) models in predicting mortality during the\nCOVID-19 pandemic and beyond. The first study of its kind, we found that\nBayesian Neural Networks (BNNs) and intelligent training techniques allowed our\nmodels to maintain performance amidst significant data shifts. Our results\nemphasize the importance of developing robust AI models capable of matching or\nsurpassing clinician predictions, even under challenging conditions. Our\nexploration of model explainability revealed that stochastic models generate\nmore diverse and personalized explanations thereby highlighting the need for AI\nmodels that provide detailed and individualized insights in real-world clinical\nsettings. Furthermore, we underscored the importance of quantifying uncertainty\nin AI models which enables clinicians to make better-informed decisions based\non reliable predictions. Our study advocates for prioritizing implementation\nscience in AI research for healthcare and ensuring that AI solutions are\npractical, beneficial, and sustainable in real-world clinical environments. By\naddressing unique challenges and complexities in healthcare settings,\nresearchers can develop AI models that effectively improve clinical practice\nand patient outcomes.\n", "date": "2023-11-28", "categories": []}
{"id": "2311.17173", "title": "A personalized Uncertainty Quantification framework for patient survival\n  models: estimating individual uncertainty of patients with metastatic brain\n  tumors in the absence of ground truth", "abstract": "  TodevelopanovelUncertaintyQuantification (UQ) framework to estimate the\nuncertainty of patient survival models in the absence of ground truth, we\ndeveloped and evaluated our approach based on a dataset of 1383 patients\ntreated with stereotactic radiosurgery (SRS) for brain metastases between\nJanuary 2015 and December 2020. Our motivating hypothesis is that a\ntime-to-event prediction of a test patient on inference is more certain given a\nhigher feature-space-similarity to patients in the training set. Therefore, the\nuncertainty for a particular patient-of-interest is represented by the\nconcordance index between a patient similarity rank and a prediction similarity\nrank. Model uncertainty was defined as the increased percentage of the max\nuncertainty-constrained-AUC compared to the model AUC. We evaluated our method\non multiple clinically-relevant endpoints, including time to intracranial\nprogression (ICP), progression-free survival (PFS) after SRS, overall survival\n(OS), and time to ICP and/or death (ICPD), on a variety of both statistical and\nnon-statistical models, including CoxPH, conditional survival forest (CSF), and\nneural multi-task linear regression (NMTLR). Our results show that all models\nhad the lowest uncertainty on ICP (2.21%) and the highest uncertainty (17.28%)\non ICPD. OS models demonstrated high variation in uncertainty performance,\nwhere NMTLR had the lowest uncertainty(1.96%)and CSF had the highest\nuncertainty (14.29%). In conclusion, our method can estimate the uncertainty of\nindividual patient survival modeling results. As expected, our data empirically\ndemonstrate that as model uncertainty measured via our technique increases, the\nsimilarity between a feature-space and its predicted outcome decreases.\n", "date": "2023-11-28", "categories": []}
{"id": "2311.17307", "title": "RoKEPG: RoBERTa and Knowledge Enhancement for Prescription Generation of\n  Traditional Chinese Medicine", "abstract": "  Traditional Chinese medicine (TCM) prescription is the most critical form of\nTCM treatment, and uncovering the complex nonlinear relationship between\nsymptoms and TCM is of great significance for clinical practice and assisting\nphysicians in diagnosis and treatment. Although there have been some studies on\nTCM prescription generation, these studies consider a single factor and\ndirectly model the symptom-prescription generation problem mainly based on\nsymptom descriptions, lacking guidance from TCM knowledge. To this end, we\npropose a RoBERTa and Knowledge Enhancement model for Prescription Generation\nof Traditional Chinese Medicine (RoKEPG). RoKEPG is firstly pre-trained by our\nconstructed TCM corpus, followed by fine-tuning the pre-trained model, and the\nmodel is guided to generate TCM prescriptions by introducing four classes of\nknowledge of TCM through the attention mask matrix. Experimental results on the\npublicly available TCM prescription dataset show that RoKEPG improves the F1\nmetric by about 2% over the baseline model with the best results.\n", "date": "2023-11-29", "categories": []}
{"id": "2303.06980", "title": "Self-supervised learning-based general laboratory progress pretrained\n  model for cardiovascular event detection", "abstract": "  The inherent nature of patient data poses several challenges. Prevalent cases\namass substantial longitudinal data owing to their patient volume and\nconsistent follow-ups, however, longitudinal laboratory data are renowned for\ntheir irregularity, temporality, absenteeism, and sparsity; In contrast,\nrecruitment for rare or specific cases is often constrained due to their\nlimited patient size and episodic observations. This study employed\nself-supervised learning (SSL) to pretrain a generalized laboratory progress\n(GLP) model that captures the overall progression of six common laboratory\nmarkers in prevalent cardiovascular cases, with the intention of transferring\nthis knowledge to aid in the detection of specific cardiovascular event. GLP\nimplemented a two-stage training approach, leveraging the information embedded\nwithin interpolated data and amplify the performance of SSL. After GLP\npretraining, it is transferred for TVR detection. The proposed two-stage\ntraining improved the performance of pure SSL, and the transferability of GLP\nexhibited distinctiveness. After GLP processing, the classification exhibited a\nnotable enhancement, with averaged accuracy rising from 0.63 to 0.90. All\nevaluated metrics demonstrated substantial superiority (p < 0.01) compared to\nprior GLP processing. Our study effectively engages in translational\nengineering by transferring patient progression of cardiovascular laboratory\nparameters from one patient group to another, transcending the limitations of\ndata availability. The transferability of disease progression optimized the\nstrategies of examinations and treatments, and improves patient prognosis while\nusing commonly available laboratory parameters. The potential for expanding\nthis approach to encompass other diseases holds great promise.\n", "date": "2023-03-13", "categories": []}
{"id": "2311.16381", "title": "Deep Learning for Time Series Classification of Parkinson's Disease Eye\n  Tracking Data", "abstract": "  Eye-tracking is an accessible and non-invasive technology that provides\ninformation about a subject's motor and cognitive abilities. As such, it has\nproven to be a valuable resource in the study of neurodegenerative diseases\nsuch as Parkinson's disease. Saccade experiments, in particular, have proven\nuseful in the diagnosis and staging of Parkinson's disease. However, to date,\nno single eye-movement biomarker has been found to conclusively differentiate\npatients from healthy controls. In the present work, we investigate the use of\nstate-of-the-art deep learning algorithms to perform Parkinson's disease\nclassification using eye-tracking data from saccade experiments. In contrast to\nprevious work, instead of using hand-crafted features from the saccades, we use\nraw $\\sim1.5\\,s$ long fixation intervals recorded during the preparatory phase\nbefore each trial. Using these short time series as input we implement two\ndifferent classification models, InceptionTime and ROCKET. We find that the\nmodels are able to learn the classification task and generalize to unseen\nsubjects. InceptionTime achieves $78\\%$ accuracy, while ROCKET achieves $88\\%$\naccuracy. We also employ a novel method for pruning the ROCKET model to improve\ninterpretability and generalizability, achieving an accuracy of $96\\%$. Our\nresults suggest that fixation data has low inter-subject variability and\npotentially carries useful information about brain cognitive and motor\nconditions, making it suitable for use with machine learning in the discovery\nof disease-relevant biomarkers.\n", "date": "2023-11-28", "categories": []}
{"id": "2311.16654", "title": "Elucidating Discrepancy in Explanations of Predictive Models Developed\n  using EMR", "abstract": "  The lack of transparency and explainability hinders the clinical adoption of\nMachine learning (ML) algorithms. While explainable artificial intelligence\n(XAI) methods have been proposed, little research has focused on the agreement\nbetween these methods and expert clinical knowledge. This study applies current\nstate-of-the-art explainability methods to clinical decision support algorithms\ndeveloped for Electronic Medical Records (EMR) data to analyse the concordance\nbetween these factors and discusses causes for identified discrepancies from a\nclinical and technical perspective. Important factors for achieving trustworthy\nXAI solutions for clinical decision support are also discussed.\n", "date": "2023-11-28", "categories": []}
{"id": "2304.07310", "title": "Federated and distributed learning applications for electronic health\n  records and structured medical data: A scoping review", "abstract": "  Federated learning (FL) has gained popularity in clinical research in recent\nyears to facilitate privacy-preserving collaboration. Structured data, one of\nthe most prevalent forms of clinical data, has experienced significant growth\nin volume concurrently, notably with the widespread adoption of electronic\nhealth records in clinical practice. This review examines FL applications on\nstructured medical data, identifies contemporary limitations and discusses\npotential innovations. We searched five databases, SCOPUS, MEDLINE, Web of\nScience, Embase, and CINAHL, to identify articles that applied FL to structured\nmedical data and reported results following the PRISMA guidelines. Each\nselected publication was evaluated from three primary perspectives, including\ndata quality, modeling strategies, and FL frameworks. Out of the 1160 papers\nscreened, 34 met the inclusion criteria, with each article consisting of one or\nmore studies that used FL to handle structured clinical/medical data. Of these,\n24 utilized data acquired from electronic health records, with clinical\npredictions and association studies being the most common clinical research\ntasks that FL was applied to. Only one article exclusively explored the\nvertical FL setting, while the remaining 33 explored the horizontal FL setting,\nwith only 14 discussing comparisons between single-site (local) and FL (global)\nanalysis. The existing FL applications on structured medical data lack\nsufficient evaluations of clinically meaningful benefits, particularly when\ncompared to single-site analyses. Therefore, it is crucial for future FL\napplications to prioritize clinical motivations and develop designs and\nmethodologies that can effectively support and aid clinical practice and\nresearch.\n", "date": "2023-04-14", "categories": []}
{"id": "2308.16781", "title": "StratMed: Relevance Stratification between Biomedical Entities for\n  Sparsity on Medication Recommendation", "abstract": "  With the growing imbalance between limited medical resources and escalating\ndemands, AI-based clinical tasks have become paramount. As a sub-domain,\nmedication recommendation aims to amalgamate longitudinal patient history with\nmedical knowledge, assisting physicians in prescribing safer and more accurate\nmedication combinations. Existing works ignore the inherent long-tailed\ndistribution of medical data, have uneven learning strengths for hot and sparse\ndata, and fail to balance safety and accuracy. To address the above\nlimitations, we propose StratMed, which introduces a stratification strategy\nthat overcomes the long-tailed problem and achieves fuller learning of sparse\ndata. It also utilizes a dual-property network to address the issue of mutual\nconstraints on the safety and accuracy of medication combinations,\nsynergistically enhancing these two properties. Specifically, we construct a\npre-training method using deep learning networks to obtain medication and\ndisease representations. After that, we design a pyramid-like stratification\nmethod based on relevance to strengthen the expressiveness of sparse data.\nBased on this relevance, we design two graph structures to express medication\nsafety and precision at the same level to obtain patient representations.\nFinally, the patient's historical clinical information is fitted to generate\nmedication combinations for the current health condition. We employed the\nMIMIC-III dataset to evaluate our model against state-of-the-art methods in\nthree aspects comprehensively. Compared to the sub-optimal baseline model, our\nmodel reduces safety risk by 15.08\\%, improves accuracy by 0.36\\%, and reduces\ntraining time consumption by 81.66\\%.\n", "date": "2023-08-31", "categories": []}
{"id": "2309.01897", "title": "Inferring Actual Treatment Pathways from Patient Records", "abstract": "  Treatment pathways are step-by-step plans outlining the recommended medical\ncare for specific diseases; they get revised when different treatments are\nfound to improve patient outcomes. Examining health records is an important\npart of this revision process, but inferring patients' actual treatments from\nhealth data is challenging due to complex event-coding schemes and the absence\nof pathway-related annotations. This study aims to infer the actual treatment\nsteps for a particular patient group from administrative health records (AHR) -\na common form of tabular healthcare data - and address several technique- and\nmethodology-based gaps in treatment pathway-inference research. We introduce\nDefrag, a method for examining AHRs to infer the real-world treatment steps for\na particular patient group. Defrag learns the semantic and temporal meaning of\nhealthcare event sequences, allowing it to reliably infer treatment steps from\ncomplex healthcare data. To our knowledge, Defrag is the first\npathway-inference method to utilise a neural network (NN), an approach made\npossible by a novel, self-supervised learning objective. We also developed a\ntesting and validation framework for pathway inference, which we use to\ncharacterise and evaluate Defrag's pathway inference ability and compare\nagainst baselines. We demonstrate Defrag's effectiveness by identifying\nbest-practice pathway fragments for breast cancer, lung cancer, and melanoma in\npublic healthcare records. Additionally, we use synthetic data experiments to\ndemonstrate the characteristics of the Defrag method, and to compare Defrag to\nseveral baselines where it significantly outperforms non-NN-based methods.\nDefrag significantly outperforms several existing pathway-inference methods and\noffers an innovative and effective approach for inferring treatment pathways\nfrom AHRs. Open-source code is provided to encourage further research in this\narea.\n", "date": "2023-09-05", "categories": []}
{"id": "2302.10106", "title": "Towards Understanding the Survival of Patients with High-Grade\n  Gastroenteropancreatic Neuroendocrine Neoplasms: An Investigation of Ensemble\n  Feature Selection in the Prediction of Overall Survival", "abstract": "  Determining the most informative features for predicting the overall survival\nof patients diagnosed with high-grade gastroenteropancreatic neuroendocrine\nneoplasms is crucial to improve individual treatment plans for patients, as\nwell as the biological understanding of the disease. Recently developed\nensemble feature selectors like the Repeated Elastic Net Technique for Feature\nSelection (RENT) and the User-Guided Bayesian Framework for Feature Selection\n(UBayFS) allow the user to identify such features in datasets with low sample\nsizes. While RENT is purely data-driven, UBayFS is capable of integrating\nexpert knowledge a priori in the feature selection process. In this work we\ncompare both feature selectors on a dataset comprising of 63 patients and 134\nfeatures from multiple sources, including basic patient characteristics,\nbaseline blood values, tumor histology, imaging, and treatment information. Our\nexperiments involve data-driven and expert-driven setups, as well as\ncombinations of both. We use findings from clinical literature as a source of\nexpert knowledge. Our results demonstrate that both feature selectors allow\naccurate predictions, and that expert knowledge has a stabilizing effect on the\nfeature set, while the impact on predictive performance is limited. The\nfeatures WHO Performance Status, Albumin, Platelets, Ki-67, Tumor Morphology,\nTotal MTV, Total TLG, and SUVmax are the most stable and predictive features in\nour study.\n", "date": "2023-02-20", "categories": []}
{"id": "2311.13821", "title": "HypUC: Hyperfine Uncertainty Calibration with Gradient-boosted\n  Corrections for Reliable Regression on Imbalanced Electrocardiograms", "abstract": "  The automated analysis of medical time series, such as the electrocardiogram\n(ECG), electroencephalogram (EEG), pulse oximetry, etc, has the potential to\nserve as a valuable tool for diagnostic decisions, allowing for remote\nmonitoring of patients and more efficient use of expensive and time-consuming\nmedical procedures. Deep neural networks (DNNs) have been demonstrated to\nprocess such signals effectively. However, previous research has primarily\nfocused on classifying medical time series rather than attempting to regress\nthe continuous-valued physiological parameters central to diagnosis. One\nsignificant challenge in this regard is the imbalanced nature of the dataset,\nas a low prevalence of abnormal conditions can lead to heavily skewed data that\nresults in inaccurate predictions and a lack of certainty in such predictions\nwhen deployed. To address these challenges, we propose HypUC, a framework for\nimbalanced probabilistic regression in medical time series, making several\ncontributions. (i) We introduce a simple kernel density-based technique to\ntackle the imbalanced regression problem with medical time series. (ii)\nMoreover, we employ a probabilistic regression framework that allows\nuncertainty estimation for the predicted continuous values. (iii) We also\npresent a new approach to calibrate the predicted uncertainty further. (iv)\nFinally, we demonstrate a technique to use calibrated uncertainty estimates to\nimprove the predicted continuous value and show the efficacy of the calibrated\nuncertainty estimates to flag unreliable predictions. HypUC is evaluated on a\nlarge, diverse, real-world dataset of ECGs collected from millions of patients,\noutperforming several conventional baselines on various diagnostic tasks,\nsuggesting a potential use-case for the reliable clinical deployment of deep\nlearning models.\n", "date": "2023-11-23", "categories": []}
{"id": "2311.14086", "title": "Brain MRI Screening Tool with Federated Learning", "abstract": "  In clinical practice, we often see significant delays between MRI scans and\nthe diagnosis made by radiologists, even for severe cases. In some cases, this\nmay be caused by the lack of additional information and clues, so even the\nsevere cases need to wait in the queue for diagnosis. This can be avoided if\nthere is an automatic software tool, which would supplement additional\ninformation, alerting radiologists that the particular patient may be a severe\ncase.\n  We are presenting an automatic brain MRI Screening Tool and we are\ndemonstrating its capabilities for detecting tumor-like pathologies. It is the\nfirst version on the path toward a robust multi-pathology screening solution.\nThe tool supports Federated Learning, so multiple institutions may contribute\nto the model without disclosing their private data.\n", "date": "2023-11-23", "categories": []}
{"id": "2311.14227", "title": "Robust and Interpretable COVID-19 Diagnosis on Chest X-ray Images using\n  Adversarial Training", "abstract": "  The novel 2019 Coronavirus disease (COVID-19) global pandemic is a defining\nhealth crisis. Recent efforts have been increasingly directed towards achieving\nquick and accurate detection of COVID-19 across symptomatic patients to\nmitigate the intensity and spread of the disease. Artificial intelligence (AI)\nalgorithms applied to chest X-ray (CXR) images have emerged as promising\ndiagnostic tools, and previous work has demonstrated impressive classification\nperformances. However, such methods have faced criticisms from physicians due\nto their black-box reasoning process and unpredictable nature. In contrast to\nprofessional radiologist diagnosis, AI systems often lack generalizability,\nexplainability, and robustness in the clinical decision making process. In our\nwork, we address these issues by first proposing an extensive baseline study,\ntraining and evaluating 21 convolutional neural network (CNN) models on a\ndiverse set of 33,000+ CXR images to classify between healthy, COVID-19, and\nnon-COVID-19 pneumonia CXRs. Our resulting models achieved a 3-way\nclassification accuracy, recall, and precision of up to 97.03\\%, 97.97\\%, and\n99.95\\%, respectively. Next, we investigate the effectiveness of adversarial\ntraining on model robustness and explainability via Gradient-weighted Class\nActivation Mapping (Grad-CAM) heatmaps. We find that adversarially trained\nmodels not only significantly outperform their standard counterparts on\nclassifying perturbed images, but also yield saliency maps that 1) better\nspecify clinically relevant features, 2) are robust against extraneous\nartifacts, and 3) agree considerably more with expert radiologist findings.\n", "date": "2023-11-23", "categories": []}
{"id": "2311.12582", "title": "Echocardiogram Foundation Model -- Application 1: Estimating Ejection\n  Fraction", "abstract": "  Cardiovascular diseases stand as the primary global cause of mortality. Among\nthe various imaging techniques available for visualising the heart and\nevaluating its function, echocardiograms emerge as the preferred choice due to\ntheir safety and low cost. Quantifying cardiac function based on\nechocardiograms is very laborious, time-consuming and subject to high\ninteroperator variability. In this work, we introduce EchoAI, an echocardiogram\nfoundation model, that is trained using self-supervised learning (SSL) on 1.5\nmillion echocardiograms. We evaluate our approach by fine-tuning EchoAI to\nestimate the ejection fraction achieving a mean absolute percentage error of\n9.40%. This level of accuracy aligns with the performance of expert\nsonographers.\n", "date": "2023-11-21", "categories": []}
{"id": "2311.12590", "title": "ChronoPscychosis: Temporal Segmentation and Its Impact on Schizophrenia\n  Classification Using Motor Activity Data", "abstract": "  Schizophrenia is a complicated mental illness characterized by a broad\nspectrum of symptoms affecting cognition, behavior, and emotion. The task of\nidentifying reliable biomarkers to classify Schizophrenia accurately continues\nto be a challenge in the field of psychiatry. We investigate the temporal\npatterns within the motor activity data as a potential key to enhancing the\ncategorization of individuals with Schizophrenia, using the dataset having\nmotor activity recordings of 22 Schizophrenia patients and 32 control subjects.\nThe dataset contains per-minute motor activity measurements collected for an\naverage of 12.7 days in a row for each participant. We dissect each day into\nsegments (Twelve, Eight, six, four, three, and two parts) and evaluate their\nimpact on classification. We employ sixteen statistical features within these\ntemporal segments and train them on Seven machine learning models to get deeper\ninsights. LightGBM model outperforms the other six models. Our results indicate\nthat the temporal segmentation significantly improves the classification, with\nAUC-ROC = 0.93, F1 score = 0.84( LightGBM- without any segmentation) and\nAUC-ROC = 0.98, F1 score = 0.93( LightGBM- with segmentation). Distinguishing\nbetween diurnal and nocturnal segments amplifies the differences between\nSchizophrenia patients and controls. However, further subdivisions into smaller\ntime segments do not affect the AUC- ROC significantly. Morning, afternoon,\nevening, and night partitioning gives similar classification performance to\nday-night partitioning. These findings are valuable as they indicate that\nextensive temporal classification beyond distinguishing between day and night\ndoes not yield substantial results, offering an efficient approach for further\nclassification, early diagnosis, and monitoring of Schizophrenia.\n", "date": "2023-11-21", "categories": []}
{"id": "2311.12781", "title": "Quantifying Impairment and Disease Severity Using AI Models Trained on\n  Healthy Subjects", "abstract": "  Automatic assessment of impairment and disease severity is a key challenge in\ndata-driven medicine. We propose a novel framework to address this challenge,\nwhich leverages AI models trained exclusively on healthy individuals. The\nCOnfidence-Based chaRacterization of Anomalies (COBRA) score exploits the\ndecrease in confidence of these models when presented with impaired or diseased\npatients to quantify their deviation from the healthy population. We applied\nthe COBRA score to address a key limitation of current clinical evaluation of\nupper-body impairment in stroke patients. The gold-standard Fugl-Meyer\nAssessment (FMA) requires in-person administration by a trained assessor for\n30-45 minutes, which restricts monitoring frequency and precludes physicians\nfrom adapting rehabilitation protocols to the progress of each patient. The\nCOBRA score, computed automatically in under one minute, is shown to be\nstrongly correlated with the FMA on an independent test cohort for two\ndifferent data modalities: wearable sensors ($\\rho = 0.845$, 95% CI\n[0.743,0.908]) and video ($\\rho = 0.746$, 95% C.I [0.594, 0.847]). To\ndemonstrate the generalizability of the approach to other conditions, the COBRA\nscore was also applied to quantify severity of knee osteoarthritis from\nmagnetic-resonance imaging scans, again achieving significant correlation with\nan independent clinical assessment ($\\rho = 0.644$, 95% C.I [0.585,0.696]).\n", "date": "2023-11-21", "categories": []}
{"id": "2311.10731", "title": "Gender-Based Comparative Study of Type 2 Diabetes Risk Factors in\n  Kolkata, India: A Machine Learning Approach", "abstract": "  Type 2 diabetes mellitus represents a prevalent and widespread global health\nconcern, necessitating a comprehensive assessment of its risk factors. This\nstudy aimed towards learning whether there is any differential impact of age,\nLifestyle, BMI and Waist to height ratio on the risk of Type 2 diabetes\nmellitus in males and females in Kolkata, West Bengal, India based on a sample\nobserved from the out-patient consultation department of Belle Vue Clinic in\nKolkata. Various machine learning models like Logistic Regression, Random\nForest, and Support Vector Classifier, were used to predict the risk of\ndiabetes, and performance was compared based on different predictors. Our\nfindings indicate a significant age-related increase in risk of diabetes for\nboth males and females. Although exercising and BMI was found to have\nsignificant impact on the risk of Type 2 diabetes in males, in females both\nturned out to be statistically insignificant. For both males and females,\npredictive models based on WhtR demonstrated superior performance in risk\nassessment compared to those based on BMI. This study sheds light on the\ngender-specific differences in the risk factors for Type 2 diabetes, offering\nvaluable insights that can be used towards more targeted healthcare\ninterventions and public health strategies.\n", "date": "2023-10-15", "categories": []}
{"id": "2311.10752", "title": "Differentiating patients with obstructive sleep apnea from healthy\n  controls based on heart rate - blood pressure coupling quantified by\n  entropy-based indices", "abstract": "  We introduce an entropy-based classification method for pairs of sequences\n(ECPS) for quantifying mutual dependencies in heart rate and beat-to-beat blood\npressure recordings. The purpose of the method is to build a classifier for\ndata in which each item consists of the two intertwined data series taken for\neach subject. The method is based on ordinal patterns, and uses entropy-like\nindices. Machine learning is used to select a subset of indices most suitable\nfor our classification problem in order to build an optimal yet simple model\nfor distinguishing between patients suffering from obstructive sleep apnea and\na control group.\n", "date": "2023-11-04", "categories": []}
{"id": "2311.10962", "title": "Classification Methods Based on Machine Learning for the Analysis of\n  Fetal Health Data", "abstract": "  The persistent battle to decrease childhood mortality serves as a commonly\nemployed benchmark for gauging advancements in the field of medicine. Globally,\nthe under-5 mortality rate stands at approximately 5 million, with a\nsignificant portion of these deaths being avoidable. Given the significance of\nthis problem, Machine learning-based techniques have emerged as a prominent\ntool for assessing fetal health. In this work, we have analyzed the\nclassification performance of various machine learning models for fetal health\nanalysis. Classification performance of various machine learning models, such\nas support vector machine (SVM), random forest(RF), and attentive interpretable\ntabular learning (TabNet) have been assessed on fetal health. Moreover,\ndimensionality reduction techniques, such as Principal component analysis (PCA)\nand Linear discriminant analysis (LDA) have been implemented to obtain better\nclassification performance with less number of features. A TabNet model on a\nfetal health dataset provides a classification accuracy of 94.36%. In general,\nthis technology empowers doctors and healthcare experts to achieve precise\nfetal health classification and identify the most influential features in the\nprocess.\n", "date": "2023-11-18", "categories": []}
{"id": "2304.12737", "title": "NPRL: Nightly Profile Representation Learning for Early Sepsis Onset\n  Prediction in ICU Trauma Patients", "abstract": "  Sepsis is a syndrome that develops in the body in response to the presence of\nan infection. Characterized by severe organ dysfunction, sepsis is one of the\nleading causes of mortality in Intensive Care Units (ICUs) worldwide. These\ncomplications can be reduced through early application of antibiotics. Hence,\nthe ability to anticipate the onset of sepsis early is crucial to the survival\nand well-being of patients. Current machine learning algorithms deployed inside\nmedical infrastructures have demonstrated poor performance and are insufficient\nfor anticipating sepsis onset early. Recently, deep learning methodologies have\nbeen proposed to predict sepsis, but some fail to capture the time of onset\n(e.g., classifying patients' entire visits as developing sepsis or not) and\nothers are unrealistic for deployment in clinical settings (e.g., creating\ntraining instances using a fixed time to onset, where the time of onset needs\nto be known apriori). In this paper, we first propose a novel but realistic\nprediction framework that predicts each morning whether sepsis onset will occur\nwithin the next 24 hours using the most recent data collected the previous\nnight, when patient-provider ratios are higher due to cross-coverage resulting\nin limited observation to each patient. However, as we increase the prediction\nrate into daily, the number of negative instances will increase, while that of\npositive instances remain the same. This causes a severe class imbalance\nproblem making it hard to capture these rare sepsis cases. To address this, we\npropose a nightly profile representation learning (NPRL) approach. We prove\nthat NPRL can theoretically alleviate the rare event problem and our empirical\nstudy using data from a level-1 trauma center demonstrates the effectiveness of\nour proposal.\n", "date": "2023-04-25", "categories": []}
{"id": "2210.05673", "title": "Performance Deterioration of Deep Learning Models after Clinical\n  Deployment: A Case Study with Auto-segmentation for Definitive Prostate\n  Cancer Radiotherapy", "abstract": "  We evaluated the temporal performance of a deep learning (DL) based\nartificial intelligence (AI) model for auto segmentation in prostate\nradiotherapy, seeking to correlate its efficacy with changes in clinical\nlandscapes. Our study involved 1328 prostate cancer patients who underwent\ndefinitive radiotherapy from January 2006 to August 2022 at the University of\nTexas Southwestern Medical Center. We trained a UNet based segmentation model\non data from 2006 to 2011 and tested it on data from 2012 to 2022 to simulate\nreal world clinical deployment. We measured the model performance using the\nDice similarity coefficient (DSC), visualized the trends in contour quality\nusing exponentially weighted moving average (EMA) curves. Additionally, we\nperformed Wilcoxon Rank Sum Test to analyze the differences in DSC\ndistributions across distinct periods, and multiple linear regression to\ninvestigate the impact of various clinical factors. The model exhibited peak\nperformance in the initial phase (from 2012 to 2014) for segmenting the\nprostate, rectum, and bladder. However, we observed a notable decline in\nperformance for the prostate and rectum after 2015, while bladder contour\nquality remained stable. Key factors that impacted the prostate contour quality\nincluded physician contouring styles, the use of various hydrogel spacer, CT\nscan slice thickness, MRI-guided contouring, and using intravenous (IV)\ncontrast. Rectum contour quality was influenced by factors such as slice\nthickness, physician contouring styles, and the use of various hydrogel\nspacers. The bladder contour quality was primarily affected by using IV\ncontrast. This study highlights the challenges in maintaining AI model\nperformance consistency in a dynamic clinical setting. It underscores the need\nfor continuous monitoring and updating of AI models to ensure their ongoing\neffectiveness and relevance in patient care.\n", "date": "2022-10-11", "categories": []}
{"id": "2311.09329", "title": "A Comparative Analysis of Machine Learning Models for Early Detection of\n  Hospital-Acquired Infections", "abstract": "  As more and more infection-specific machine learning models are developed and\nplanned for clinical deployment, simultaneously running predictions from\ndifferent models may provide overlapping or even conflicting information. It is\nimportant to understand the concordance and behavior of parallel models in\ndeployment. In this study, we focus on two models for the early detection of\nhospital-acquired infections (HAIs): 1) the Infection Risk Index (IRI) and 2)\nthe Ventilator-Associated Pneumonia (VAP) prediction model. The IRI model was\nbuilt to predict all HAIs, whereas the VAP model identifies patients at risk of\ndeveloping ventilator-associated pneumonia. These models could make important\nimprovements in patient outcomes and hospital management of infections through\nearly detection of infections and in turn, enable early interventions. The two\nmodels vary in terms of infection label definition, cohort selection, and\nprediction schema. In this work, we present a comparative analysis between the\ntwo models to characterize concordances and confusions in predicting HAIs by\nthese models. The learnings from this study will provide important findings for\nhow to deploy multiple concurrent disease-specific models in the future.\n", "date": "2023-11-15", "categories": []}
{"id": "2311.09369", "title": "Time-dependent Probabilistic Generative Models for Disease Progression", "abstract": "  Electronic health records contain valuable information for monitoring\npatients' health trajectories over time. Disease progression models have been\ndeveloped to understand the underlying patterns and dynamics of diseases using\nthese data as sequences. However, analyzing temporal data from EHRs is\nchallenging due to the variability and irregularities present in medical\nrecords. We propose a Markovian generative model of treatments developed to (i)\nmodel the irregular time intervals between medical events; (ii) classify\ntreatments into subtypes based on the patient sequence of medical events and\nthe time intervals between them; and (iii) segment treatments into subsequences\nof disease progression patterns. We assume that sequences have an associated\nstructure of latent variables: a latent class representing the different\nsubtypes of treatments; and a set of latent stages indicating the phase of\nprogression of the treatments. We use the Expectation-Maximization algorithm to\nlearn the model, which is efficiently solved with a dynamic programming-based\nmethod. Various parametric models have been employed to model the time\nintervals between medical events during the learning process, including the\ngeometric, exponential, and Weibull distributions. The results demonstrate the\neffectiveness of our model in recovering the underlying model from data and\naccurately modeling the irregular time intervals between medical actions.\n", "date": "2023-11-15", "categories": []}
{"id": "2311.09566", "title": "A Knowledge Distillation Approach for Sepsis Outcome Prediction from\n  Multivariate Clinical Time Series", "abstract": "  Sepsis is a life-threatening condition triggered by an extreme infection\nresponse. Our objective is to forecast sepsis patient outcomes using their\nmedical history and treatments, while learning interpretable state\nrepresentations to assess patients' risks in developing various adverse\noutcomes. While neural networks excel in outcome prediction, their limited\ninterpretability remains a key issue. In this work, we use knowledge\ndistillation via constrained variational inference to distill the knowledge of\na powerful \"teacher\" neural network model with high predictive power to train a\n\"student\" latent variable model to learn interpretable hidden state\nrepresentations to achieve high predictive performance for sepsis outcome\nprediction. Using real-world data from the MIMIC-IV database, we trained an\nLSTM as the \"teacher\" model to predict mortality for sepsis patients, given\ninformation about their recent history of vital signs, lab values and\ntreatments. For our student model, we use an autoregressive hidden Markov model\n(AR-HMM) to learn interpretable hidden states from patients' clinical time\nseries, and use the posterior distribution of the learned state representations\nto predict various downstream outcomes, including hospital mortality, pulmonary\nedema, need for diuretics, dialysis, and mechanical ventilation. Our results\nshow that our approach successfully incorporates the constraint to achieve high\npredictive power similar to the teacher model, while maintaining the generative\nperformance.\n", "date": "2023-11-16", "categories": []}
{"id": "2311.09854", "title": "SurvTimeSurvival: Survival Analysis On The Patient With Multiple\n  Visits/Records", "abstract": "  The accurate prediction of survival times for patients with severe diseases\nremains a critical challenge despite recent advances in artificial\nintelligence. This study introduces \"SurvTimeSurvival: Survival Analysis On\nPatients With Multiple Visits/Records\", utilizing the Transformer model to not\nonly handle the complexities of time-varying covariates but also covariates\ndata. We also tackle the data sparsity issue common to survival analysis\ndatasets by integrating synthetic data generation into the learning process of\nour model. We show that our method outperforms state-of-the-art deep learning\napproaches on both covariates and time-varying covariates datasets. Our\napproach aims not only to enhance the understanding of individual patient\nsurvival trajectories across various medical conditions, thereby improving\nprediction accuracy, but also to play a pivotal role in designing clinical\ntrials and creating new treatments.\n", "date": "2023-11-16", "categories": []}
{"id": "2207.09858", "title": "GenHPF: General Healthcare Predictive Framework with Multi-task\n  Multi-source Learning", "abstract": "  Despite the remarkable progress in the development of predictive models for\nhealthcare, applying these algorithms on a large scale has been challenging.\nAlgorithms trained on a particular task, based on specific data formats\navailable in a set of medical records, tend to not generalize well to other\ntasks or databases in which the data fields may differ. To address this\nchallenge, we propose General Healthcare Predictive Framework (GenHPF), which\nis applicable to any EHR with minimal preprocessing for multiple prediction\ntasks. GenHPF resolves heterogeneity in medical codes and schemas by converting\nEHRs into a hierarchical textual representation while incorporating as many\nfeatures as possible. To evaluate the efficacy of GenHPF, we conduct multi-task\nlearning experiments with single-source and multi-source settings, on three\npublicly available EHR datasets with different schemas for 12 clinically\nmeaningful prediction tasks. Our framework significantly outperforms baseline\nmodels that utilize domain knowledge in multi-source learning, improving\naverage AUROC by 1.2%P in pooled learning and 2.6%P in transfer learning while\nalso showing comparable results when trained on a single EHR dataset.\nFurthermore, we demonstrate that self-supervised pretraining using multi-source\ndatasets is effective when combined with GenHPF, resulting in a 0.6%P AUROC\nimprovement compared to models without pretraining. By eliminating the need for\npreprocessing and feature engineering, we believe that this work offers a solid\nframework for multi-task and multi-source learning that can be leveraged to\nspeed up the scaling and usage of predictive algorithms in healthcare.\n", "date": "2022-07-20", "categories": []}
{"id": "2305.06295", "title": "Extracting Diagnosis Pathways from Electronic Health Records Using Deep\n  Reinforcement Learning", "abstract": "  Clinical diagnosis guidelines aim at specifying the steps that may lead to a\ndiagnosis. Inspired by guidelines, we aim to learn the optimal sequence of\nactions to perform in order to obtain a correct diagnosis from electronic\nhealth records. We apply various deep reinforcement learning algorithms to this\ntask and experiment on a synthetic but realistic dataset to differentially\ndiagnose anemia and its subtypes and particularly evaluate the robustness of\nvarious approaches to noise and missing data. Experimental results show that\nthe deep reinforcement learning algorithms show competitive performance\ncompared to the state-of-the-art methods with the added advantage that they\nenable the progressive generation of a pathway to the suggested diagnosis,\nwhich can both guide and explain the decision process.\n", "date": "2023-05-10", "categories": []}
{"id": "2311.09165", "title": "Approaching adverse event detection utilizing transformers on clinical\n  time-series", "abstract": "  Patients being admitted to a hospital will most often be associated with a\ncertain clinical development during their stay. However, there is always a risk\nof patients being subject to the wrong diagnosis or to a certain treatment not\npertaining to the desired effect, potentially leading to adverse events. Our\nresearch aims to develop an anomaly detection system for identifying deviations\nfrom expected clinical trajectories. To address this goal we analyzed 16 months\nof vital sign recordings obtained from the Nordland Hospital Trust (NHT). We\nemployed an self-supervised framework based on the STraTS transformer\narchitecture to represent the time series data in a latent space. These\nrepresentations were then subjected to various clustering techniques to explore\npotential patient phenotypes based on their clinical progress. While our\npreliminary results from this ongoing research are promising, they underscore\nthe importance of enhancing the dataset with additional demographic information\nfrom patients. This additional data will be crucial for a more comprehensive\nevaluation of the method's performance.\n", "date": "2023-11-15", "categories": []}
{"id": "2310.15742", "title": "Improving Diffusion Models for ECG Imputation with an Augmented Template\n  Prior", "abstract": "  Pulsative signals such as the electrocardiogram (ECG) are extensively\ncollected as part of routine clinical care. However, noisy and poor-quality\nrecordings are a major issue for signals collected using mobile health systems,\ndecreasing the signal quality, leading to missing values, and affecting\nautomated downstream tasks. Recent studies have explored the imputation of\nmissing values in ECG with probabilistic time-series models. Nevertheless, in\ncomparison with the deterministic models, their performance is still limited,\nas the variations across subjects and heart-beat relationships are not\nexplicitly considered in the training objective. In this work, to improve the\nimputation and forecasting accuracy for ECG with probabilistic models, we\npresent a template-guided denoising diffusion probabilistic model (DDPM),\nPulseDiff, which is conditioned on an informative prior for a range of health\nconditions. Specifically, 1) we first extract a subject-level pulsative\ntemplate from the observed values to use as an informative prior of the missing\nvalues, which personalises the prior; 2) we then add beat-level stochastic\nshift terms to augment the prior, which considers variations in the position\nand amplitude of the prior at each beat; 3) we finally design a confidence\nscore to consider the health condition of the subject, which ensures our prior\nis provided safely. Experiments with the PTBXL dataset reveal that PulseDiff\nimproves the performance of two strong DDPM baseline models, CSDI and\nSSSD$^{S4}$, verifying that our method guides the generation of DDPMs while\nmanaging the uncertainty. When combined with SSSD$^{S4}$, PulseDiff outperforms\nthe leading deterministic model for short-interval missing data and is\ncomparable for long-interval data loss.\n", "date": "2023-10-24", "categories": []}
{"id": "2311.07608", "title": "MuST: Multimodal Spatiotemporal Graph-Transformer for Hospital\n  Readmission Prediction", "abstract": "  Hospital readmission prediction is considered an essential approach to\ndecreasing readmission rates, which is a key factor in assessing the quality\nand efficacy of a healthcare system. Previous studies have extensively utilized\nthree primary modalities, namely electronic health records (EHR), medical\nimages, and clinical notes, to predict hospital readmissions. However, the\nmajority of these studies did not integrate information from all three\nmodalities or utilize the spatiotemporal relationships present in the dataset.\nThis study introduces a novel model called the Multimodal Spatiotemporal\nGraph-Transformer (MuST) for predicting hospital readmissions. By employing\nGraph Convolution Networks and temporal transformers, we can effectively\ncapture spatial and temporal dependencies in EHR and chest radiographs. We then\npropose a fusion transformer to combine the spatiotemporal features from the\ntwo modalities mentioned above with the features from clinical notes extracted\nby a pre-trained, domain-specific transformer. We assess the effectiveness of\nour methods using the latest publicly available dataset, MIMIC-IV. The\nexperimental results indicate that the inclusion of multimodal features in MuST\nimproves its performance in comparison to unimodal methods. Furthermore, our\nproposed pipeline outperforms the current leading methods in the prediction of\nhospital readmissions.\n", "date": "2023-11-11", "categories": []}
{"id": "2311.07867", "title": "Mixture of Coupled HMMs for Robust Modeling of Multivariate Healthcare\n  Time Series", "abstract": "  Analysis of multivariate healthcare time series data is inherently\nchallenging: irregular sampling, noisy and missing values, and heterogeneous\npatient groups with different dynamics violating exchangeability. In addition,\ninterpretability and quantification of uncertainty are critically important.\nHere, we propose a novel class of models, a mixture of coupled hidden Markov\nmodels (M-CHMM), and demonstrate how it elegantly overcomes these challenges.\nTo make the model learning feasible, we derive two algorithms to sample the\nsequences of the latent variables in the CHMM: samplers based on (i) particle\nfiltering and (ii) factorized approximation. Compared to existing inference\nmethods, our algorithms are computationally tractable, improve mixing, and\nallow for likelihood estimation, which is necessary to learn the mixture model.\nExperiments on challenging real-world epidemiological and semi-synthetic data\ndemonstrate the advantages of the M-CHMM: improved data fit, capacity to\nefficiently handle missing and noisy measurements, improved prediction\naccuracy, and ability to identify interpretable subsets in the data.\n", "date": "2023-11-14", "categories": []}
{"id": "2309.11373", "title": "Learning and DiSentangling Patient Static Information from Time-series\n  Electronic HEalth Record (STEER)", "abstract": "  Recent work in machine learning for healthcare has raised concerns about\npatient privacy and algorithmic fairness. For example, previous work has shown\nthat patient self-reported race can be predicted from medical data that does\nnot explicitly contain racial information. However, the extent of data\nidentification is unknown, and we lack ways to develop models whose outcomes\nare minimally affected by such information. Here we systematically investigated\nthe ability of time-series electronic health record data to predict patient\nstatic information. We found that not only the raw time-series data, but also\nlearned representations from machine learning models, can be trained to predict\na variety of static information with area under the receiver operating\ncharacteristic curve as high as 0.851 for biological sex, 0.869 for binarized\nage and 0.810 for self-reported race. Such high predictive performance can be\nextended to a wide range of comorbidity factors and exists even when the model\nwas trained for different tasks, using different cohorts, using different model\narchitectures and databases. Given the privacy and fairness concerns these\nfindings pose, we develop a variational autoencoder-based approach that learns\na structured latent space to disentangle patient-sensitive attributes from\ntime-series data. Our work thoroughly investigates the ability of machine\nlearning models to encode patient static information from time-series\nelectronic health records and introduces a general approach to protect\npatient-sensitive attribute information for downstream tasks.\n", "date": "2023-09-20", "categories": []}
{"id": "2309.16521", "title": "Generating Personalized Insulin Treatments Strategies with Deep\n  Conditional Generative Time Series Models", "abstract": "  We propose a novel framework that combines deep generative time series models\nwith decision theory for generating personalized treatment strategies. It\nleverages historical patient trajectory data to jointly learn the generation of\nrealistic personalized treatment and future outcome trajectories through deep\ngenerative time series models. In particular, our framework enables the\ngeneration of novel multivariate treatment strategies tailored to the\npersonalized patient history and trained for optimal expected future outcomes\nbased on conditional expected utility maximization. We demonstrate our\nframework by generating personalized insulin treatment strategies and blood\nglucose predictions for hospitalized diabetes patients, showcasing the\npotential of our approach for generating improved personalized treatment\nstrategies. Keywords: deep generative model, probabilistic decision support,\npersonalized treatment generation, insulin and blood glucose prediction\n", "date": "2023-09-28", "categories": []}
{"id": "2310.06552", "title": "Automated clinical coding using off-the-shelf large language models", "abstract": "  The task of assigning diagnostic ICD codes to patient hospital admissions is\ntypically performed by expert human coders. Efforts towards automated ICD\ncoding are dominated by supervised deep learning models. However, difficulties\nin learning to predict the large number of rare codes remain a barrier to\nadoption in clinical practice. In this work, we leverage off-the-shelf\npre-trained generative large language models (LLMs) to develop a practical\nsolution that is suitable for zero-shot and few-shot code assignment, with no\nneed for further task-specific training. Unsupervised pre-training alone does\nnot guarantee precise knowledge of the ICD ontology and specialist clinical\ncoding task, therefore we frame the task as information extraction, providing a\ndescription of each coded concept and asking the model to retrieve related\nmentions. For efficiency, rather than iterating over all codes, we leverage the\nhierarchical nature of the ICD ontology to sparsely search for relevant codes.\n", "date": "2023-10-10", "categories": []}
{"id": "2311.00567", "title": "A Robust Deep Learning Method with Uncertainty Estimation for the\n  Pathological Classification of Renal Cell Carcinoma based on CT Images", "abstract": "  Objectives To develop and validate a deep learning-based diagnostic model\nincorporating uncertainty estimation so as to facilitate radiologists in the\npreoperative differentiation of the pathological subtypes of renal cell\ncarcinoma (RCC) based on CT images. Methods Data from 668 consecutive patients,\npathologically proven RCC, were retrospectively collected from Center 1. By\nusing five-fold cross-validation, a deep learning model incorporating\nuncertainty estimation was developed to classify RCC subtypes into clear cell\nRCC (ccRCC), papillary RCC (pRCC), and chromophobe RCC (chRCC). An external\nvalidation set of 78 patients from Center 2 further evaluated the model's\nperformance. Results In the five-fold cross-validation, the model's area under\nthe receiver operating characteristic curve (AUC) for the classification of\nccRCC, pRCC, and chRCC was 0.868 (95% CI: 0.826-0.923), 0.846 (95% CI:\n0.812-0.886), and 0.839 (95% CI: 0.802-0.88), respectively. In the external\nvalidation set, the AUCs were 0.856 (95% CI: 0.838-0.882), 0.787 (95% CI:\n0.757-0.818), and 0.793 (95% CI: 0.758-0.831) for ccRCC, pRCC, and chRCC,\nrespectively. Conclusions The developed deep learning model demonstrated robust\nperformance in predicting the pathological subtypes of RCC, while the\nincorporated uncertainty emphasized the importance of understanding model\nconfidence, which is crucial for assisting clinical decision-making for\npatients with renal tumors. Clinical relevance statement Our deep learning\napproach, integrated with uncertainty estimation, offers clinicians a dual\nadvantage: accurate RCC subtype predictions complemented by diagnostic\nconfidence references, promoting informed decision-making for patients with\nRCC.\n", "date": "2023-11-01", "categories": []}
{"id": "2311.06633", "title": "The Pros and Cons of Using Machine Learning and Interpretable Machine\n  Learning Methods in psychiatry detection applications, specifically\n  depression disorder: A Brief Review", "abstract": "  The COVID-19 pandemic has forced many people to limit their social\nactivities, which has resulted in a rise in mental illnesses, particularly\ndepression. To diagnose these illnesses with accuracy and speed, and prevent\nsevere outcomes such as suicide, the use of machine learning has become\nincreasingly important. Additionally, to provide precise and understandable\ndiagnoses for better treatment, AI scientists and researchers must develop\ninterpretable AI-based solutions. This article provides an overview of relevant\narticles in the field of machine learning and interpretable AI, which helps to\nunderstand the advantages and disadvantages of using AI in psychiatry disorder\ndetection applications.\n", "date": "2023-11-11", "categories": []}
{"id": "2210.02490", "title": "Analyzing historical diagnosis code data from NIH N3C and RECOVER\n  Programs using deep learning to determine risk factors for Long Covid", "abstract": "  Post-acute sequelae of SARS-CoV-2 infection (PASC) or Long COVID is an\nemerging medical condition that has been observed in several patients with a\npositive diagnosis for COVID-19. Historical Electronic Health Records (EHR)\nlike diagnosis codes, lab results and clinical notes have been analyzed using\ndeep learning and have been used to predict future clinical events. In this\npaper, we propose an interpretable deep learning approach to analyze historical\ndiagnosis code data from the National COVID Cohort Collective (N3C) to find the\nrisk factors contributing to developing Long COVID. Using our deep learning\napproach, we are able to predict if a patient is suffering from Long COVID from\na temporally ordered list of diagnosis codes up to 45 days post the first COVID\npositive test or diagnosis for each patient, with an accuracy of 70.48\\%. We\nare then able to examine the trained model using Gradient-weighted Class\nActivation Mapping (GradCAM) to give each input diagnoses a score. The highest\nscored diagnosis were deemed to be the most important for making the correct\nprediction for a patient. We also propose a way to summarize these top\ndiagnoses for each patient in our cohort and look at their temporal trends to\ndetermine which codes contribute towards a positive Long COVID diagnosis.\n", "date": "2022-10-05", "categories": []}
{"id": "2311.05665", "title": "Explainable artificial intelligence for Healthcare applications using\n  Random Forest Classifier with LIME and SHAP", "abstract": "  With the advances in computationally efficient artificial Intelligence (AI)\ntechniques and their numerous applications in our everyday life, there is a\npressing need to understand the computational details hidden in black box AI\ntechniques such as most popular machine learning and deep learning techniques;\nthrough more detailed explanations. The origin of explainable AI (xAI) is\ncoined from these challenges and recently gained more attention by the\nresearchers by adding explainability comprehensively in traditional AI systems.\nThis leads to develop an appropriate framework for successful applications of\nxAI in real life scenarios with respect to innovations, risk mitigation,\nethical issues and logical values to the users. In this book chapter, an\nin-depth analysis of several xAI frameworks and methods including LIME (Local\nInterpretable Model-agnostic Explanations) and SHAP (SHapley Additive\nexPlanations) are provided. Random Forest Classifier as black box AI is used on\na publicly available Diabetes symptoms dataset with LIME and SHAP for better\ninterpretations. The results obtained are interesting in terms of transparency,\nvalid and trustworthiness in diabetes disease prediction.\n", "date": "2023-11-09", "categories": []}
{"id": "2311.04937", "title": "Multimodal Clinical Benchmark for Emergency Care (MC-BEC): A\n  Comprehensive Benchmark for Evaluating Foundation Models in Emergency\n  Medicine", "abstract": "  We propose the Multimodal Clinical Benchmark for Emergency Care (MC-BEC), a\ncomprehensive benchmark for evaluating foundation models in Emergency Medicine\nusing a dataset of 100K+ continuously monitored Emergency Department visits\nfrom 2020-2022. MC-BEC focuses on clinically relevant prediction tasks at\ntimescales from minutes to days, including predicting patient decompensation,\ndisposition, and emergency department (ED) revisit, and includes a standardized\nevaluation framework with train-test splits and evaluation metrics. The\nmultimodal dataset includes a wide range of detailed clinical data, including\ntriage information, prior diagnoses and medications, continuously measured\nvital signs, electrocardiogram and photoplethysmograph waveforms, orders placed\nand medications administered throughout the visit, free-text reports of imaging\nstudies, and information on ED diagnosis, disposition, and subsequent revisits.\nWe provide performance baselines for each prediction task to enable the\nevaluation of multimodal, multitask models. We believe that MC-BEC will\nencourage researchers to develop more effective, generalizable, and accessible\nfoundation models for multimodal clinical data.\n", "date": "2023-11-07", "categories": []}
{"id": "2311.04226", "title": "Assessing Upper Limb Motor Function in the Immediate Post-Stroke Perioud\n  Using Accelerometry", "abstract": "  Accelerometry has been extensively studied as an objective means of measuring\nupper limb function in patients post-stroke. The objective of this paper is to\ndetermine whether the accelerometry-derived measurements frequently used in\nmore long-term rehabilitation studies can also be used to monitor and rapidly\ndetect sudden changes in upper limb motor function in more recently\nhospitalized stroke patients. Six binary classification models were created by\ntraining on variable data window times of paretic upper limb accelerometer\nfeature data. The models were assessed on their effectiveness for\ndifferentiating new input data into two classes: severe or moderately severe\nmotor function. The classification models yielded Area Under the Curve (AUC)\nscores that ranged from 0.72 to 0.82 for 15-minute data windows to 0.77 to 0.94\nfor 120-minute data windows. These results served as a preliminary assessment\nand a basis on which to further investigate the efficacy of using accelerometry\nand machine learning to alert healthcare professionals to rapid changes in\nmotor function in the days immediately following a stroke.\n", "date": "2023-11-01", "categories": []}
{"id": "2311.04325", "title": "Extending Machine Learning-Based Early Sepsis Detection to Different\n  Demographics", "abstract": "  Sepsis requires urgent diagnosis, but research is predominantly focused on\nWestern datasets. In this study, we perform a comparative analysis of two\nensemble learning methods, LightGBM and XGBoost, using the public eICU-CRD\ndataset and a private South Korean St. Mary's Hospital's dataset. Our analysis\nreveals the effectiveness of these methods in addressing healthcare data\nimbalance and enhancing sepsis detection. Specifically, LightGBM shows a slight\nedge in computational efficiency and scalability. The study paves the way for\nthe broader application of machine learning in critical care, thereby expanding\nthe reach of predictive analytics in healthcare globally.\n", "date": "2023-11-07", "categories": []}
{"id": "2311.04606", "title": "Accurate Autism Spectrum Disorder prediction using Support Vector\n  Classifier based on Federated Learning (SVCFL)", "abstract": "  The path to an autism diagnosis can be long and difficult, and delays can\nhave serious consequences. Artificial intelligence can completely change the\nway autism is diagnosed, especially when it comes to situations where it is\ndifficult to see the first signs of the disease. AI-based diagnostic tools may\nhelp confirm a diagnosis or highlight the need for further testing by analyzing\nlarge volumes of data and uncovering patterns that may not be immediately\napparent to human evaluators. After a successful and timely diagnosis, autism\ncan be treated through artificial intelligence using various methods. In this\narticle, by using four datasets and gathering them with the federated learning\nmethod and diagnosing them with the support vector classifier method, the early\ndiagnosis of this disorder has been discussed. In this method, we have achieved\n99% accuracy for predicting autism spectrum disorder and we have achieved 13%\nimprovement in the results.\n", "date": "2023-11-08", "categories": []}
{"id": "2311.04770", "title": "Vital Sign Forecasting for Sepsis Patients in ICUs", "abstract": "  Sepsis and septic shock are a critical medical condition affecting millions\nglobally, with a substantial mortality rate. This paper uses state-of-the-art\ndeep learning (DL) architectures to introduce a multi-step forecasting system\nto predict vital signs indicative of septic shock progression in Intensive Care\nUnits (ICUs). Our approach utilizes a short window of historical vital sign\ndata to forecast future physiological conditions. We introduce a DL-based vital\nsign forecasting system that predicts up to 3 hours of future vital signs from\n6 hours of past data. We further adopt the DILATE loss function to capture\nbetter the shape and temporal dynamics of vital signs, which are critical for\nclinical decision-making. We compare three DL models, N-BEATS, N-HiTS, and\nTemporal Fusion Transformer (TFT), using the publicly available eICU\nCollaborative Research Database (eICU-CRD), highlighting their forecasting\ncapabilities in a critical care setting. We evaluate the performance of our\nmodels using mean squared error (MSE) and dynamic time warping (DTW) metrics.\nOur findings show that while TFT excels in capturing overall trends, N-HiTS is\nsuperior in retaining short-term fluctuations within a predefined range. This\npaper demonstrates the potential of deep learning in transforming the\nmonitoring systems in ICUs, potentially leading to significant improvements in\npatient care and outcomes by accurately forecasting vital signs to assist\nhealthcare providers in detecting early signs of physiological instability and\nanticipating septic shock.\n", "date": "2023-11-08", "categories": []}
{"id": "2311.04772", "title": "GCS-ICHNet: Assessment of Intracerebral Hemorrhage Prognosis using\n  Self-Attention with Domain Knowledge Integration", "abstract": "  Intracerebral Hemorrhage (ICH) is a severe condition resulting from damaged\nbrain blood vessel ruptures, often leading to complications and fatalities.\nTimely and accurate prognosis and management are essential due to its high\nmortality rate. However, conventional methods heavily rely on subjective\nclinician expertise, which can lead to inaccurate diagnoses and delays in\ntreatment. Artificial intelligence (AI) models have been explored to assist\nclinicians, but many prior studies focused on model modification without\nconsidering domain knowledge. This paper introduces a novel deep learning\nalgorithm, GCS-ICHNet, which integrates multimodal brain CT image data and the\nGlasgow Coma Scale (GCS) score to improve ICH prognosis. The algorithm utilizes\na transformer-based fusion module for assessment. GCS-ICHNet demonstrates high\nsensitivity 81.03% and specificity 91.59%, outperforming average clinicians and\nother state-of-the-art methods.\n", "date": "2023-11-08", "categories": []}
{"id": "2311.03557", "title": "Spatio-Temporal Similarity Measure based Multi-Task Learning for\n  Predicting Alzheimer's Disease Progression using MRI Data", "abstract": "  Identifying and utilising various biomarkers for tracking Alzheimer's disease\n(AD) progression have received many recent attentions and enable helping\nclinicians make the prompt decisions. Traditional progression models focus on\nextracting morphological biomarkers in regions of interest (ROIs) from MRI/PET\nimages, such as regional average cortical thickness and regional volume. They\nare effective but ignore the relationships between brain ROIs over time, which\nwould lead to synergistic deterioration. For exploring the synergistic\ndeteriorating relationship between these biomarkers, in this paper, we propose\na novel spatio-temporal similarity measure based multi-task learning approach\nfor effectively predicting AD progression and sensitively capturing the\ncritical relationships between biomarkers. Specifically, we firstly define a\ntemporal measure for estimating the magnitude and velocity of biomarker change\nover time, which indicate a changing trend(temporal). Converting this trend\ninto the vector, we then compare this variability between biomarkers in a\nunified vector space(spatial). The experimental results show that compared with\ndirectly ROI based learning, our proposed method is more effective in\npredicting disease progression. Our method also enables performing longitudinal\nstability selection to identify the changing relationships between biomarkers,\nwhich play a key role in disease progression. We prove that the synergistic\ndeteriorating biomarkers between cortical volumes or surface areas have a\nsignificant effect on the cognitive prediction.\n", "date": "2023-11-06", "categories": []}
{"id": "2311.04088", "title": "Personality Style Recognition via Machine Learning: Identifying\n  Anaclitic and Introjective Personality Styles from Patients' Speech", "abstract": "  In disentangling the heterogeneity observed in psychopathology, personality\nof the patients is considered crucial. While it has been demonstrated that\npersonality traits are reflected in the language used by a patient, we\nhypothesize that this enables automatic inference of the personality type\ndirectly from speech utterances, potentially more accurately than through a\ntraditional questionnaire-based approach explicitly designed for personality\nclassification. To validate this hypothesis, we adopt natural language\nprocessing (NLP) and standard machine learning tools for classification. We\ntest this on a dataset of recorded clinical diagnostic interviews (CDI) on a\nsample of 79 patients diagnosed with major depressive disorder (MDD) -- a\ncondition for which differentiated treatment based on personality styles has\nbeen advocated -- and classified into anaclitic and introjective personality\nstyles. We start by analyzing the interviews to see which linguistic features\nare associated with each style, in order to gain a better understanding of the\nstyles. Then, we develop automatic classifiers based on (a) standardized\nquestionnaire responses; (b) basic text features, i.e., TF-IDF scores of words\nand word sequences; (c) more advanced text features, using LIWC (linguistic\ninquiry and word count) and context-aware features using BERT (bidirectional\nencoder representations from transformers); (d) audio features. We find that\nautomated classification with language-derived features (i.e., based on LIWC)\nsignificantly outperforms questionnaire-based classification models.\nFurthermore, the best performance is achieved by combining LIWC with the\nquestionnaire features. This suggests that more work should be put into\ndeveloping linguistically based automated techniques for characterizing\npersonality, however questionnaires still to some extent complement such\nmethods.\n", "date": "2023-11-07", "categories": []}
{"id": "2305.11742", "title": "MedLens: Improve Mortality Prediction Via Medical Signs Selecting and\n  Regression", "abstract": "  Monitoring the health status of patients and predicting mortality in advance\nis vital for providing patients with timely care and treatment. Massive medical\nsigns in electronic health records (EHR) are fitted into advanced machine\nlearning models to make predictions. However, the data-quality problem of\noriginal clinical signs is less discussed in the literature. Based on an\nin-depth measurement of the missing rate and correlation score across various\nmedical signs and a large amount of patient hospital admission records, we\ndiscovered the comprehensive missing rate is extremely high, and a large number\nof useless signs could hurt the performance of prediction models. Then we\nconcluded that only improving data-quality could improve the baseline accuracy\nof different prediction algorithms. We designed MEDLENS, with an automatic\nvital medical signs selection approach via statistics and a flexible\ninterpolation approach for high missing rate time series. After augmenting the\ndata-quality of original medical signs, MEDLENS applies ensemble classifiers to\nboost the accuracy and reduce the computation overhead at the same time. It\nachieves a very high accuracy performance of 0.96 AUC-ROC and 0.81 AUC-PR,\nwhich exceeds the previous benchmark.\n", "date": "2023-05-19", "categories": []}
{"id": "2311.02251", "title": "The Potential of Wearable Sensors for Assessing Patient Acuity in\n  Intensive Care Unit (ICU)", "abstract": "  Acuity assessments are vital in critical care settings to provide timely\ninterventions and fair resource allocation. Traditional acuity scores rely on\nmanual assessments and documentation of physiological states, which can be\ntime-consuming, intermittent, and difficult to use for healthcare providers.\nFurthermore, such scores do not incorporate granular information such as\npatients' mobility level, which can indicate recovery or deterioration in the\nICU. We hypothesized that existing acuity scores could be potentially improved\nby employing Artificial Intelligence (AI) techniques in conjunction with\nElectronic Health Records (EHR) and wearable sensor data. In this study, we\nevaluated the impact of integrating mobility data collected from wrist-worn\naccelerometers with clinical data obtained from EHR for developing an AI-driven\nacuity assessment score. Accelerometry data were collected from 86 patients\nwearing accelerometers on their wrists in an academic hospital setting. The\ndata was analyzed using five deep neural network models: VGG, ResNet,\nMobileNet, SqueezeNet, and a custom Transformer network. These models\noutperformed a rule-based clinical score (SOFA= Sequential Organ Failure\nAssessment) used as a baseline, particularly regarding the precision,\nsensitivity, and F1 score. The results showed that while a model relying solely\non accelerometer data achieved limited performance (AUC 0.50, Precision 0.61,\nand F1-score 0.68), including demographic information with the accelerometer\ndata led to a notable enhancement in performance (AUC 0.69, Precision 0.75, and\nF1-score 0.67). This work shows that the combination of mobility and patient\ninformation can successfully differentiate between stable and unstable states\nin critically ill patients.\n", "date": "2023-11-03", "categories": []}
{"id": "2311.03037", "title": "Validity problems in clinical machine learning by indirect data labeling\n  using consensus definitions", "abstract": "  We demonstrate a validity problem of machine learning in the vital\napplication area of disease diagnosis in medicine. It arises when target labels\nin training data are determined by an indirect measurement, and the fundamental\nmeasurements needed to determine this indirect measurement are included in the\ninput data representation. Machine learning models trained on this data will\nlearn nothing else but to exactly reconstruct the known target definition. Such\nmodels show perfect performance on similarly constructed test data but will\nfail catastrophically on real-world examples where the defining fundamental\nmeasurements are not or only incompletely available. We present a general\nprocedure allowing identification of problematic datasets and black-box machine\nlearning models trained on them, and exemplify our detection procedure on the\ntask of early prediction of sepsis.\n", "date": "2023-11-06", "categories": []}
{"id": "2311.01428", "title": "Identifying Alzheimer Disease Dementia Levels Using Machine Learning\n  Methods", "abstract": "  Dementia, a prevalent neurodegenerative condition, is a major manifestation\nof Alzheimer's disease (AD). As the condition progresses from mild to severe,\nit significantly impairs the individual's ability to perform daily tasks\nindependently, necessitating the need for timely and accurate AD\nclassification. Machine learning or deep learning models have emerged as\neffective tools for this purpose. In this study, we suggested an approach for\nclassifying the four stages of dementia using RF, SVM, and CNN algorithms,\naugmented with watershed segmentation for feature extraction from MRI images.\nOur results reveal that SVM with watershed features achieves an impressive\naccuracy of 96.25%, surpassing other classification methods. The ADNI dataset\nis utilized to evaluate the effectiveness of our method, and we observed that\nthe inclusion of watershed segmentation contributes to the enhanced performance\nof the models.\n", "date": "2023-11-02", "categories": []}
{"id": "2311.01681", "title": "The R.O.A.D. to precision medicine", "abstract": "  We propose a prognostic stratum matching framework that addresses the\ndeficiencies of Randomized trial data subgroup analysis and transforms\nObservAtional Data to be used as if they were randomized, thus paving the road\nfor precision medicine. Our approach counters the effects of unobserved\nconfounding in observational data by correcting the estimated probabilities of\nthe outcome under a treatment through a novel two-step process. These\nprobabilities are then used to train Optimal Policy Trees (OPTs), which are\ndecision trees that optimally assign treatments to subgroups of patients based\non their characteristics. This facilitates the creation of clinically intuitive\ntreatment recommendations. We applied our framework to observational data of\npatients with gastrointestinal stromal tumors (GIST) and validated the OPTs in\nan external cohort using the sensitivity and specificity metrics. We show that\nthese recommendations outperformed those of experts in GIST. We further applied\nthe same framework to randomized clinical trial (RCT) data of patients with\nextremity sarcomas. Remarkably, despite the initial trial results suggesting\nthat all patients should receive treatment, our framework, after addressing\nimbalances in patient distribution due to the trial's small sample size,\nidentified through the OPTs a subset of patients with unique characteristics\nwho may not require treatment. Again, we successfully validated our\nrecommendations in an external cohort.\n", "date": "2023-11-03", "categories": []}
{"id": "2311.00737", "title": "Real-Time Magnetic Tracking and Diagnosis of COVID-19 via Machine\n  Learning", "abstract": "  The COVID-19 pandemic underscored the importance of reliable, noninvasive\ndiagnostic tools for robust public health interventions. In this work, we fused\nmagnetic respiratory sensing technology (MRST) with machine learning (ML) to\ncreate a diagnostic platform for real-time tracking and diagnosis of COVID-19\nand other respiratory diseases. The MRST precisely captures breathing patterns\nthrough three specific breath testing protocols: normal breath, holding breath,\nand deep breath. We collected breath data from both COVID-19 patients and\nhealthy subjects in Vietnam using this platform, which then served to train and\nvalidate ML models. Our evaluation encompassed multiple ML algorithms,\nincluding support vector machines and deep learning models, assessing their\nability to diagnose COVID-19. Our multi-model validation methodology ensures a\nthorough comparison and grants the adaptability to select the most optimal\nmodel, striking a balance between diagnostic precision with model\ninterpretability. The findings highlight the exceptional potential of our\ndiagnostic tool in pinpointing respiratory anomalies, achieving over 90%\naccuracy. This innovative sensor technology can be seamlessly integrated into\nhealthcare settings for patient monitoring, marking a significant enhancement\nfor the healthcare infrastructure.\n", "date": "2023-11-01", "categories": []}
{"id": "2311.00517", "title": "Improving Cardiovascular Disease Prediction Through Comparative Analysis\n  of Machine Learning Models: A Case Study on Myocardial Infarction", "abstract": "  Cardiovascular disease remains a leading cause of mortality in the\ncontemporary world. Its association with smoking, elevated blood pressure, and\ncholesterol levels underscores the significance of these risk factors. This\nstudy addresses the challenge of predicting myocardial illness, a formidable\ntask in medical research. Accurate predictions are pivotal for refining\nhealthcare strategies. This investigation conducts a comparative analysis of\nsix distinct machine learning models: Logistic Regression, Support Vector\nMachine, Decision Tree, Bagging, XGBoost, and LightGBM. The attained outcomes\nexhibit promise, with accuracy rates as follows: Logistic Regression (81.00%),\nSupport Vector Machine (75.01%), XGBoost (92.72%), LightGBM (90.60%), Decision\nTree (82.30%), and Bagging (83.01%). Notably, XGBoost emerges as the\ntop-performing model. These findings underscore its potential to enhance\npredictive precision for coronary infarction. As the prevalence of\ncardiovascular risk factors persists, incorporating advanced machine learning\ntechniques holds the potential to refine proactive medical interventions.\n", "date": "2023-11-01", "categories": []}
{"id": "2307.15367", "title": "Toward Transparent Sequence Models with Model-Based Tree Markov Model", "abstract": "  In this study, we address the interpretability issue in complex, black-box\nMachine Learning models applied to sequence data. We introduce the Model-Based\ntree Hidden Semi-Markov Model (MOB-HSMM), an inherently interpretable model\naimed at detecting high mortality risk events and discovering hidden patterns\nassociated with the mortality risk in Intensive Care Units (ICU). This model\nleverages knowledge distilled from Deep Neural Networks (DNN) to enhance\npredictive performance while offering clear explanations. Our experimental\nresults indicate the improved performance of Model-Based trees (MOB trees) via\nemploying LSTM for learning sequential patterns, which are then transferred to\nMOB trees. Integrating MOB trees with the Hidden Semi-Markov Model (HSMM) in\nthe MOB-HSMM enables uncovering potential and explainable sequences using\navailable information.\n", "date": "2023-07-28", "categories": []}
{"id": "2310.16936", "title": "Diagnosing Alzheimer's Disease using Early-Late Multimodal Data Fusion\n  with Jacobian Maps", "abstract": "  Alzheimer's disease (AD) is a prevalent and debilitating neurodegenerative\ndisorder impacting a large aging population. Detecting AD in all its\npresymptomatic and symptomatic stages is crucial for early intervention and\ntreatment. An active research direction is to explore machine learning methods\nthat harness multimodal data fusion to outperform human inspection of medical\nscans. However, existing multimodal fusion models have limitations, including\nredundant computation, complex architecture, and simplistic handling of missing\ndata. Moreover, the preprocessing pipelines of medical scans remain\ninadequately detailed and are seldom optimized for individual subjects. In this\npaper, we propose an efficient early-late fusion (ELF) approach, which\nleverages a convolutional neural network for automated feature extraction and\nrandom forests for their competitive performance on small datasets.\nAdditionally, we introduce a robust preprocessing pipeline that adapts to the\nunique characteristics of individual subjects and makes use of whole brain\nimages rather than slices or patches. Moreover, to tackle the challenge of\ndetecting subtle changes in brain volume, we transform images into the Jacobian\ndomain (JD) to enhance both accuracy and robustness in our classification.\nUsing MRI and CT images from the OASIS-3 dataset, our experiments demonstrate\nthe effectiveness of the ELF approach in classifying AD into four stages with\nan accuracy of 97.19%.\n", "date": "2023-10-25", "categories": []}
{"id": "2310.18472", "title": "Parameter-Efficient Methods for Metastases Detection from Clinical Notes", "abstract": "  Understanding the progression of cancer is crucial for defining treatments\nfor patients. The objective of this study is to automate the detection of\nmetastatic liver disease from free-style computed tomography (CT) radiology\nreports. Our research demonstrates that transferring knowledge using three\napproaches can improve model performance. First, we utilize generic language\nmodels (LMs), pretrained in a self-supervised manner. Second, we use a\nsemi-supervised approach to train our model by automatically annotating a large\nunlabeled dataset; this approach substantially enhances the model's\nperformance. Finally, we transfer knowledge from related tasks by designing a\nmulti-task transfer learning methodology. We leverage the recent advancement of\nparameter-efficient LM adaptation strategies to improve performance and\ntraining efficiency. Our dataset consists of CT reports collected at Memorial\nSloan Kettering Cancer Center (MSKCC) over the course of 12 years. 2,641\nreports were manually annotated by domain experts; among them, 841 reports have\nbeen annotated for the presence of liver metastases. Our best model achieved an\nF1-score of 73.8%, a precision of 84%, and a recall of 65.8%.\n", "date": "2023-10-27", "categories": []}
{"id": "2310.18688", "title": "Clairvoyance: A Pipeline Toolkit for Medical Time Series", "abstract": "  Time-series learning is the bread and butter of data-driven *clinical\ndecision support*, and the recent explosion in ML research has demonstrated\ngreat potential in various healthcare settings. At the same time, medical\ntime-series problems in the wild are challenging due to their highly\n*composite* nature: They entail design choices and interactions among\ncomponents that preprocess data, impute missing values, select features, issue\npredictions, estimate uncertainty, and interpret models. Despite exponential\ngrowth in electronic patient data, there is a remarkable gap between the\npotential and realized utilization of ML for clinical research and decision\nsupport. In particular, orchestrating a real-world project lifecycle poses\nchallenges in engineering (i.e. hard to build), evaluation (i.e. hard to\nassess), and efficiency (i.e. hard to optimize). Designed to address these\nissues simultaneously, Clairvoyance proposes a unified, end-to-end,\nautoML-friendly pipeline that serves as a (i) software toolkit, (ii) empirical\nstandard, and (iii) interface for optimization. Our ultimate goal lies in\nfacilitating transparent and reproducible experimentation with complex\ninference workflows, providing integrated pathways for (1) personalized\nprediction, (2) treatment-effect estimation, and (3) information acquisition.\nThrough illustrative examples on real-world data in outpatient, general wards,\nand intensive-care settings, we illustrate the applicability of the pipeline\nparadigm on core tasks in the healthcare journey. To the best of our knowledge,\nClairvoyance is the first to demonstrate viability of a comprehensive and\nautomatable pipeline for clinical time-series ML.\n", "date": "2023-10-28", "categories": []}
{"id": "2310.18795", "title": "A Review on the Applications of Machine Learning for Tinnitus Diagnosis\n  Using EEG Signals", "abstract": "  Tinnitus is a prevalent hearing disorder that can be caused by various\nfactors such as age, hearing loss, exposure to loud noises, ear infections or\ntumors, certain medications, head or neck injuries, and psychological\nconditions like anxiety and depression. While not every patient requires\nmedical attention, about 20% of sufferers seek clinical intervention. Early\ndiagnosis is crucial for effective treatment. New developments have been made\nin tinnitus detection to aid in early detection of this illness. Over the past\nfew years, there has been a notable growth in the usage of\nelectroencephalography (EEG) to study variations in oscillatory brain activity\nrelated to tinnitus. However, the results obtained from numerous studies vary\ngreatly, leading to conflicting conclusions. Currently, clinicians rely solely\non their expertise to identify individuals with tinnitus. Researchers in this\nfield have incorporated various data modalities and machine-learning techniques\nto aid clinicians in identifying tinnitus characteristics and classifying\npeople with tinnitus. The purpose of writing this article is to review articles\nthat focus on using machine learning (ML) to identify or predict tinnitus\npatients using EEG signals as input data. We have evaluated 11 articles\npublished between 2016 and 2023 using a systematic literature review (SLR)\nmethod. This article arranges perfect summaries of all the research reviewed\nand compares the significant aspects of each. Additionally, we performed\nstatistical analyses to gain a deeper comprehension of the most recent research\nin this area. Almost all of the reviewed articles followed a five-step\nprocedure to achieve the goal of tinnitus. Disclosure. Finally, we discuss the\nopen affairs and challenges in this method of tinnitus recognition or\nprediction and suggest future directions for research.\n", "date": "2023-10-28", "categories": []}
{"id": "2310.17675", "title": "Early Detection of Tuberculosis with Machine Learning Cough Audio\n  Analysis: Towards More Accessible Global Triaging Usage", "abstract": "  Tuberculosis (TB), a bacterial disease mainly affecting the lungs, is one of\nthe leading infectious causes of mortality worldwide. To prevent TB from\nspreading within the body, which causes life-threatening complications, timely\nand effective anti-TB treatment is crucial. Cough, an objective biomarker for\nTB, is a triage tool that monitors treatment response and regresses with\nsuccessful therapy. Current gold standards for TB diagnosis are slow or\ninaccessible, especially in rural areas where TB is most prevalent. In\naddition, current machine learning (ML) diagnosis research, like utilizing\nchest radiographs, is ineffective and does not monitor treatment progression.\nTo enable effective diagnosis, an ensemble model was developed that analyzes,\nusing a novel ML architecture, coughs' acoustic epidemiologies from\nsmartphones' microphones to detect TB. The architecture includes a 2D-CNN and\nXGBoost that was trained on 724,964 cough audio samples and demographics from 7\ncountries. After feature extraction (Mel-spectrograms) and data augmentation\n(IR-convolution), the model achieved AUROC (area under the receiving operator\ncharacteristic) of 88%, surpassing WHO's requirements for screening tests. The\nresults are available within 15 seconds and can easily be accessible via a\nmobile app. This research helps to improve TB diagnosis through a promising\naccurate, quick, and accessible triaging tool.\n", "date": "2023-10-25", "categories": []}
{"id": "2310.17755", "title": "Alzheimers Disease Diagnosis by Deep Learning Using MRI-Based Approaches", "abstract": "  The most frequent kind of dementia of the nervous system, Alzheimer's\ndisease, weakens several brain processes (such as memory) and eventually\nresults in death. The clinical study uses magnetic resonance imaging to\ndiagnose AD. Deep learning algorithms are capable of pattern recognition and\nfeature extraction from the inputted raw data. As early diagnosis and stage\ndetection are the most crucial elements in enhancing patient care and treatment\noutcomes, deep learning algorithms for MRI images have recently allowed for\ndiagnosing a medical condition at the beginning stage and identifying\nparticular symptoms of Alzheimer's disease. As a result, we aimed to analyze\nfive specific studies focused on AD diagnosis using MRI-based deep learning\nalgorithms between 2021 and 2023 in this study. To completely illustrate the\ndifferences between these techniques and comprehend how deep learning\nalgorithms function, we attempted to explore selected approaches in depth.\n", "date": "2023-10-26", "categories": []}
{"id": "2310.16978", "title": "The Significance of Machine Learning in Clinical Disease Diagnosis: A\n  Review", "abstract": "  The global need for effective disease diagnosis remains substantial, given\nthe complexities of various disease mechanisms and diverse patient symptoms. To\ntackle these challenges, researchers, physicians, and patients are turning to\nmachine learning (ML), an artificial intelligence (AI) discipline, to develop\nsolutions. By leveraging sophisticated ML and AI methods, healthcare\nstakeholders gain enhanced diagnostic and treatment capabilities. However,\nthere is a scarcity of research focused on ML algorithms for enhancing the\naccuracy and computational efficiency. This research investigates the capacity\nof machine learning algorithms to improve the transmission of heart rate data\nin time series healthcare metrics, concentrating particularly on optimizing\naccuracy and efficiency. By exploring various ML algorithms used in healthcare\napplications, the review presents the latest trends and approaches in ML-based\ndisease diagnosis (MLBDD). The factors under consideration include the\nalgorithm utilized, the types of diseases targeted, the data types employed,\nthe applications, and the evaluation metrics. This review aims to shed light on\nthe prospects of ML in healthcare, particularly in disease diagnosis. By\nanalyzing the current literature, the study provides insights into\nstate-of-the-art methodologies and their performance metrics.\n", "date": "2023-10-25", "categories": []}
{"id": "2310.16430", "title": "An Integrative Paradigm for Enhanced Stroke Prediction: Synergizing\n  XGBoost and xDeepFM Algorithms", "abstract": "  Stroke prediction plays a crucial role in preventing and managing this\ndebilitating condition. In this study, we address the challenge of stroke\nprediction using a comprehensive dataset, and propose an ensemble model that\ncombines the power of XGBoost and xDeepFM algorithms. Our work aims to improve\nupon existing stroke prediction models by achieving higher accuracy and\nrobustness. Through rigorous experimentation, we validate the effectiveness of\nour ensemble model using the AUC metric. Through comparing our findings with\nthose of other models in the field, we gain valuable insights into the merits\nand drawbacks of various approaches. This, in turn, contributes significantly\nto the progress of machine learning and deep learning techniques specifically\nin the domain of stroke prediction.\n", "date": "2023-10-25", "categories": []}
{"id": "2308.09312", "title": "Path Signatures for Seizure Forecasting", "abstract": "  Predicting future system behaviour from past observed behaviour (time series)\nis fundamental to science and engineering. In computational neuroscience, the\nprediction of future epileptic seizures from brain activity measurements, using\nEEG data, remains largely unresolved despite much dedicated research effort.\nBased on a longitudinal and state-of-the-art data set using intercranial EEG\nmeasurements from people with epilepsy, we consider the automated discovery of\npredictive features (or biomarkers) to forecast seizures in a patient-specific\nway. To this end, we use the path signature, a recent development in the\nanalysis of data streams, to map from measured time series to seizure\nprediction. The predictor is based on linear classification, here augmented\nwith sparsity constraints, to discern time series with and without an impending\nseizure. This approach may be seen as a step towards a generic pattern\nrecognition pipeline where the main advantages are simplicity and ease of\ncustomisation, while maintaining forecasting performance on par with modern\nmachine learning. Nevertheless, it turns out that although the path signature\nmethod has some powerful theoretical guarantees, appropriate time series\nstatistics can achieve essentially the same results in our context of seizure\nprediction. This suggests that, due to their inherent complexity and\nnon-stationarity, the brain's dynamics are not identifiable from the available\nEEG measurement data, and, more concretely, epileptic episode prediction is not\nreliably achieved using EEG measurement data alone.\n", "date": "2023-08-18", "categories": []}
{"id": "2309.16730", "title": "Explainable machine learning-based prediction model for diabetic\n  nephropathy", "abstract": "  The aim of this study is to analyze the effect of serum metabolites on\ndiabetic nephropathy (DN) and predict the prevalence of DN through a machine\nlearning approach. The dataset consists of 548 patients from April 2018 to\nApril 2019 in Second Affiliated Hospital of Dalian Medical University (SAHDMU).\nWe select the optimal 38 features through a Least absolute shrinkage and\nselection operator (LASSO) regression model and a 10-fold cross-validation. We\ncompare four machine learning algorithms, including eXtreme Gradient Boosting\n(XGB), random forest, decision tree and logistic regression, by AUC-ROC curves,\ndecision curves, calibration curves. We quantify feature importance and\ninteraction effects in the optimal predictive model by Shapley Additive\nexPlanations (SHAP) method. The XGB model has the best performance to screen\nfor DN with the highest AUC value of 0.966. The XGB model also gains more\nclinical net benefits than others and the fitting degree is better. In\naddition, there are significant interactions between serum metabolites and\nduration of diabetes. We develop a predictive model by XGB algorithm to screen\nfor DN. C2, C5DC, Tyr, Ser, Met, C24, C4DC, and Cys have great contribution in\nthe model, and can possibly be biomarkers for DN.\n", "date": "2023-09-27", "categories": []}
{"id": "2310.15853", "title": "Improving Event Time Prediction by Learning to Partition the Event Time\n  Space", "abstract": "  Recently developed survival analysis methods improve upon existing approaches\nby predicting the probability of event occurrence in each of a number\npre-specified (discrete) time intervals. By avoiding placing strong parametric\nassumptions on the event density, this approach tends to improve prediction\nperformance, particularly when data are plentiful. However, in clinical\nsettings with limited available data, it is often preferable to judiciously\npartition the event time space into a limited number of intervals well suited\nto the prediction task at hand. In this work, we develop a method to learn from\ndata a set of cut points defining such a partition. We show that in two\nsimulated datasets, we are able to recover intervals that match the underlying\ngenerative model. We then demonstrate improved prediction performance on three\nreal-world observational datasets, including a large, newly harmonized stroke\nrisk prediction dataset. Finally, we argue that our approach facilitates\nclinical decision-making by suggesting time intervals that are most appropriate\nfor each task, in the sense that they facilitate more accurate risk prediction.\n", "date": "2023-10-24", "categories": []}
{"id": "2310.13701", "title": "Assessment and treatment of visuospatial neglect using active learning\n  with Gaussian processes regression", "abstract": "  Visuospatial neglect is a disorder characterised by impaired awareness for\nvisual stimuli located in regions of space and frames of reference. It is often\nassociated with stroke. Patients can struggle with all aspects of daily living\nand community participation. Assessment methods are limited and show several\nshortcomings, considering they are mainly performed on paper and do not\nimplement the complexity of daily life. Similarly, treatment options are sparse\nand often show only small improvements. We present an artificial intelligence\nsolution designed to accurately assess a patient's visuospatial neglect in a\nthree-dimensional setting. We implement an active learning method based on\nGaussian process regression to reduce the effort it takes a patient to undergo\nan assessment. Furthermore, we describe how this model can be utilised in\npatient oriented treatment and how this opens the way to gamification,\ntele-rehabilitation and personalised healthcare, providing a promising avenue\nfor improving patient engagement and rehabilitation outcomes. To validate our\nassessment module, we conducted clinical trials involving patients in a\nreal-world setting. We compared the results obtained using our AI-based\nassessment with the widely used conventional visuospatial neglect tests\ncurrently employed in clinical practice. The validation process serves to\nestablish the accuracy and reliability of our model, confirming its potential\nas a valuable tool for diagnosing and monitoring visuospatial neglect. Our VR\napplication proves to be more sensitive, while intra-rater reliability remains\nhigh.\n", "date": "2023-09-29", "categories": []}
{"id": "2310.14743", "title": "The Safety Challenges of Deep Learning in Real-World Type 1 Diabetes\n  Management", "abstract": "  Blood glucose simulation allows the effectiveness of type 1 diabetes (T1D)\nmanagement strategies to be evaluated without patient harm. Deep learning\nalgorithms provide a promising avenue for extending simulator capabilities;\nhowever, these algorithms are limited in that they do not necessarily learn\nphysiologically correct glucose dynamics and can learn incorrect and\npotentially dangerous relationships from confounders in training data. This is\nlikely to be more important in real-world scenarios, as data is not collected\nunder strict research protocol. This work explores the implications of using\ndeep learning algorithms trained on real-world data to model glucose dynamics.\nFree-living data was processed from the OpenAPS Data Commons and supplemented\nwith patient-reported tags of challenging diabetes events, constituting one of\nthe most detailed real-world T1D datasets. This dataset was used to train and\nevaluate state-of-the-art glucose simulators, comparing their prediction error\nacross safety critical scenarios and assessing the physiological\nappropriateness of the learned dynamics using Shapley Additive Explanations\n(SHAP). While deep learning prediction accuracy surpassed the widely-used\nmathematical simulator approach, the model deteriorated in safety critical\nscenarios and struggled to leverage self-reported meal and exercise\ninformation. SHAP value analysis also indicated the model had fundamentally\nconfused the roles of insulin and carbohydrates, which is one of the most basic\nT1D management principles. This work highlights the importance of considering\nphysiological appropriateness when using deep learning to model real-world\nsystems in T1D and healthcare more broadly, and provides recommendations for\nbuilding models that are robust to real-world data constraints.\n", "date": "2023-10-23", "categories": []}
{"id": "2302.00225", "title": "The Past, Current, and Future of Neonatal Intensive Care Units with\n  Artificial Intelligence", "abstract": "  Machine learning and deep learning are two subsets of artificial intelligence\nthat involve teaching computers to learn and make decisions from any sort of\ndata. Most recent developments in artificial intelligence are coming from deep\nlearning, which has proven revolutionary in almost all fields, from computer\nvision to health sciences. The effects of deep learning in medicine have\nchanged the conventional ways of clinical application significantly. Although\nsome sub-fields of medicine, such as pediatrics, have been relatively slow in\nreceiving the critical benefits of deep learning, related research in\npediatrics has started to accumulate to a significant level, too. Hence, in\nthis paper, we review recently developed machine learning and deep\nlearning-based solutions for neonatology applications. We systematically\nevaluate the roles of both classical machine learning and deep learning in\nneonatology applications, define the methodologies, including algorithmic\ndevelopments, and describe the remaining challenges in the assessment of\nneonatal diseases by using PRISMA 2020 guidelines. To date, the primary areas\nof focus in neonatology regarding AI applications have included survival\nanalysis, neuroimaging, analysis of vital parameters and biosignals, and\nretinopathy of prematurity diagnosis. We have categorically summarized 106\nresearch articles from 1996 to 2022 and discussed their pros and cons,\nrespectively. In this systematic review, we aimed to further enhance the\ncomprehensiveness of the study. We also discuss possible directions for new AI\nmodels and the future of neonatology with the rising power of AI, suggesting\nroadmaps for the integration of AI into neonatal intensive care units.\n", "date": "2023-02-01", "categories": []}
{"id": "2302.12343", "title": "CHiLL: Zero-shot Custom Interpretable Feature Extraction from Clinical\n  Notes with Large Language Models", "abstract": "  We propose CHiLL (Crafting High-Level Latents), an approach for\nnatural-language specification of features for linear models. CHiLL prompts\nLLMs with expert-crafted queries to generate interpretable features from health\nrecords. The resulting noisy labels are then used to train a simple linear\nclassifier. Generating features based on queries to an LLM can empower\nphysicians to use their domain expertise to craft features that are clinically\nmeaningful for a downstream task of interest, without having to manually\nextract these from raw EHR. We are motivated by a real-world risk prediction\ntask, but as a reproducible proxy, we use MIMIC-III and MIMIC-CXR data and\nstandard predictive tasks (e.g., 30-day readmission) to evaluate this approach.\nWe find that linear models using automatically extracted features are\ncomparably performant to models using reference features, and provide greater\ninterpretability than linear models using \"Bag-of-Words\" features. We verify\nthat learned feature weights align well with clinical expectations.\n", "date": "2023-02-23", "categories": []}
{"id": "2305.04422", "title": "Multivariate Analysis on Performance Gaps of Artificial Intelligence\n  Models in Screening Mammography", "abstract": "  Although deep learning models for abnormality classification can perform well\nin screening mammography, the demographic, imaging, and clinical\ncharacteristics associated with increased risk of model failure remain unclear.\nThis retrospective study uses the Emory BrEast Imaging Dataset(EMBED)\ncontaining mammograms from 115931 patients imaged at Emory Healthcare between\n2013-2020, with BI-RADS assessment, region of interest coordinates for\nabnormalities, imaging features, pathologic outcomes, and patient demographics.\nMultiple deep learning models were trained to distinguish between abnormal\ntissue patches and randomly selected normal tissue patches from screening\nmammograms. We assessed model performance by subgroups defined by age, race,\npathologic outcome, tissue density, and imaging characteristics and\ninvestigated their associations with false negatives (FN) and false positives\n(FP). We also performed multivariate logistic regression to control for\nconfounding between subgroups. The top-performing model, ResNet152V2, achieved\naccuracy of 92.6%(95%CI=92.0-93.2%), and AUC 0.975(95%CI=0.972-0.978). Before\ncontrolling for confounding, nearly all subgroups showed statistically\nsignificant differences in model performance. However, after controlling for\nconfounding, we found lower FN risk associates with Other\nrace(RR=0.828;p=.050), biopsy-proven benign lesions(RR=0.927;p=.011), and\nmass(RR=0.921;p=.010) or asymmetry(RR=0.854;p=.040); higher FN risk associates\nwith architectural distortion (RR=1.037;p<.001). Higher FP risk associates to\nBI-RADS density C(RR=1.891;p<.001) and D(RR=2.486;p<.001). Our results\ndemonstrate subgroup analysis is important in mammogram classifier performance\nevaluation, and controlling for confounding between subgroups elucidates the\ntrue associations between variables and model failure. These results can help\nguide developing future breast cancer detection models.\n", "date": "2023-05-08", "categories": []}
{"id": "2310.07871", "title": "Hierarchical Pretraining on Multimodal Electronic Health Records", "abstract": "  Pretraining has proven to be a powerful technique in natural language\nprocessing (NLP), exhibiting remarkable success in various NLP downstream\ntasks. However, in the medical domain, existing pretrained models on electronic\nhealth records (EHR) fail to capture the hierarchical nature of EHR data,\nlimiting their generalization capability across diverse downstream tasks using\na single pretrained model. To tackle this challenge, this paper introduces a\nnovel, general, and unified pretraining framework called MEDHMP, specifically\ndesigned for hierarchically multimodal EHR data. The effectiveness of the\nproposed MEDHMP is demonstrated through experimental results on eight\ndownstream tasks spanning three levels. Comparisons against eighteen baselines\nfurther highlight the efficacy of our approach.\n", "date": "2023-10-11", "categories": []}
{"id": "2310.13279", "title": "Pathologist-Like Explanations Unveiled: an Explainable Deep Learning\n  System for White Blood Cell Classification", "abstract": "  White blood cells (WBCs) play a crucial role in safeguarding the human body\nagainst pathogens and foreign substances. Leveraging the abundance of WBC\nimaging data and the power of deep learning algorithms, automated WBC analysis\nhas the potential for remarkable accuracy. However, the capability of deep\nlearning models to explain their WBC classification remains largely unexplored.\nIn this study, we introduce HemaX, an explainable deep neural network-based\nmodel that produces pathologist-like explanations using five attributes:\ngranularity, cytoplasm color, nucleus shape, size relative to red blood cells,\nand nucleus to cytoplasm ratio (N:C), along with cell classification,\nlocalization, and segmentation. HemaX is trained and evaluated on a novel\ndataset, LeukoX, comprising 467 blood smear images encompassing ten (10) WBC\ntypes. The proposed model achieves impressive results, with an average\nclassification accuracy of 81.08% and a Jaccard index of 89.16% for cell\nlocalization. Additionally, HemaX performs well in generating the five\nexplanations with a normalized mean square error of 0.0317 for N:C ratio and\nover 80% accuracy for the other four attributes. Comprehensive experiments\ncomparing against multiple state-of-the-art models demonstrate that HemaX's\nclassification accuracy remains unaffected by its ability to provide\nexplanations. Moreover, empirical analyses and validation by expert\nhematologists confirm the faithfulness of explanations predicted by our\nproposed model.\n", "date": "2023-10-20", "categories": []}
{"id": "2310.13654", "title": "An experimental study for early diagnosing Parkinson's disease using\n  machine learning", "abstract": "  One of the most catastrophic neurological disorders worldwide is Parkinson's\nDisease. Along with it, the treatment is complicated and abundantly expensive.\nThe only effective action to control the progression is diagnosing it in the\nearly stage. However, this is challenging because early detection necessitates\na large and complex clinical study. This experimental work used Machine\nLearning techniques to automate the early detection of Parkinson's Disease from\nclinical characteristics, voice features and motor examination. In this study,\nwe develop ML models utilizing a public dataset of 130 individuals, 30 of whom\nare untreated Parkinson's Disease patients, 50 of whom are Rapid Eye Movement\nSleep Behaviour Disorder patients who are at a greater risk of contracting\nParkinson's Disease, and 50 of whom are Healthy Controls. We use MinMax Scaler\nto rescale the data points, Local Outlier Factor to remove outliers, and SMOTE\nto balance existing class frequency. Afterwards, apply a number of Machine\nLearning techniques. We implement the approaches in such a way that data\nleaking and overfitting are not possible. Finally, obtained 100% accuracy in\nclassifying PD and RBD patients, as well as 92% accuracy in classifying PD and\nHC individuals.\n", "date": "2023-10-20", "categories": []}
{"id": "2310.12866", "title": "Predicting Ovarian Cancer Treatment Response in Histopathology using\n  Hierarchical Vision Transformers and Multiple Instance Learning", "abstract": "  For many patients, current ovarian cancer treatments offer limited clinical\nbenefit. For some therapies, it is not possible to predict patients' responses,\npotentially exposing them to the adverse effects of treatment without any\ntherapeutic benefit. As part of the automated prediction of treatment\neffectiveness in ovarian cancer using histopathological images (ATEC23)\nchallenge, we evaluated the effectiveness of deep learning to predict whether a\ncourse of treatment including the antiangiogenic drug bevacizumab could\ncontribute to remission or prevent disease progression for at least 6 months in\na set of 282 histopathology whole slide images (WSIs) from 78 ovarian cancer\npatients. Our approach used a pretrained Hierarchical Image Pyramid Transformer\n(HIPT) to extract region-level features and an attention-based multiple\ninstance learning (ABMIL) model to aggregate features and classify whole\nslides. The optimal HIPT-ABMIL model had an internal balanced accuracy of 60.2%\n+- 2.9% and an AUC of 0.646 +- 0.033. Histopathology-specific model pretraining\nwas found to be beneficial to classification performance, though hierarchical\ntransformers were not, with a ResNet feature extractor achieving similar\nperformance. Due to the dataset being small and highly heterogeneous,\nperformance was variable across 5-fold cross-validation folds, and there were\nsome extreme differences between validation and test set performance within\nfolds. The model did not generalise well to tissue microarrays, with accuracy\nworse than random chance. It is not yet clear whether ovarian cancer WSIs\ncontain information that can be used to accurately predict treatment response,\nwith further validation using larger, higher-quality datasets required.\n", "date": "2023-10-19", "categories": []}
{"id": "2207.03568", "title": "The use of deep learning enables high diagnostic accuracy in detecting\n  syndesmotic instability on weight-bearing CT scanning", "abstract": "  Delayed diagnosis of syndesmosis instability can lead to significant\nmorbidity and accelerated arthritic change in the ankle joint. Weight-bearing\ncomputed tomography (WBCT) has shown promising potential for early and reliable\ndetection of isolated syndesmotic instability using 3D volumetric measurements.\nWhile these measurements have been reported to be highly accurate, they are\nalso experience-dependent, time-consuming, and need a particular 3D measurement\nsoftware tool that leads the clinicians to still show more interest in the\nconventional diagnostic methods for syndesmotic instability. The purpose of\nthis study was to increase accuracy, accelerate analysis time, and reduce\ninter-observer bias by automating 3D volume assessment of syndesmosis anatomy\nusing WBCT scans. We conducted a retrospective study using previously collected\nWBCT scans of patients with unilateral syndesmotic instability. 144 bilateral\nankle WBCT scans were evaluated (48 unstable, 96 control). We developed three\ndeep learning (DL) models for analyzing WBCT scans to recognize syndesmosis\ninstability. These three models included two state-of-the-art models (Model 1 -\n3D convolutional neural network [CNN], and Model 2 - CNN with long short-term\nmemory [LSTM]), and a new model (Model 3 - differential CNN LSTM) that we\nintroduced in this study. Model 1 failed to analyze the WBCT scans (F1-score =\n0). Model 2 only misclassified two cases (F1-score = 0.80). Model 3\noutperformed Model 2 and achieved a nearly perfect performance, misclassifying\nonly one case (F1-score = 0.91) in the control group as unstable while being\nfaster than Model 2.\n", "date": "2022-07-07", "categories": []}
{"id": "2310.12121", "title": "Automatic prediction of mortality in patients with mental illness using\n  electronic health records", "abstract": "  Mental disorders impact the lives of millions of people globally, not only\nimpeding their day-to-day lives but also markedly reducing life expectancy.\nThis paper addresses the persistent challenge of predicting mortality in\npatients with mental diagnoses using predictive machine-learning models with\nelectronic health records (EHR). Data from patients with mental disease\ndiagnoses were extracted from the well-known clinical MIMIC-III data set\nutilizing demographic, prescription, and procedural information. Four machine\nlearning algorithms (Logistic Regression, Random Forest, Support Vector\nMachine, and K-Nearest Neighbors) were used, with results indicating that\nRandom Forest and Support Vector Machine models outperformed others, with AUC\nscores of 0.911. Feature importance analysis revealed that drug prescriptions,\nparticularly Morphine Sulfate, play a pivotal role in prediction. We applied a\nvariety of machine learning algorithms to predict 30-day mortality followed by\nfeature importance analysis. This study can be used to assist hospital workers\nin identifying at-risk patients to reduce excess mortality.\n", "date": "2023-10-18", "categories": []}
{"id": "2310.10203", "title": "Interpretable Predictive Models to Understand Risk Factors for Maternal\n  and Fetal Outcomes", "abstract": "  Although most pregnancies result in a good outcome, complications are not\nuncommon and can be associated with serious implications for mothers and\nbabies. Predictive modeling has the potential to improve outcomes through\nbetter understanding of risk factors, heightened surveillance for high risk\npatients, and more timely and appropriate interventions, thereby helping\nobstetricians deliver better care. We identify and study the most important\nrisk factors for four types of pregnancy complications: (i) severe maternal\nmorbidity, (ii) shoulder dystocia, (iii) preterm preeclampsia, and (iv)\nantepartum stillbirth. We use an Explainable Boosting Machine (EBM), a\nhigh-accuracy glass-box learning method, for prediction and identification of\nimportant risk factors. We undertake external validation and perform an\nextensive robustness analysis of the EBM models. EBMs match the accuracy of\nother black-box ML methods such as deep neural networks and random forests, and\noutperform logistic regression, while being more interpretable. EBMs prove to\nbe robust. The interpretability of the EBM models reveals surprising insights\ninto the features contributing to risk (e.g. maternal height is the second most\nimportant feature for shoulder dystocia) and may have potential for clinical\napplication in the prediction and prevention of serious complications in\npregnancy.\n", "date": "2023-10-16", "categories": []}
{"id": "2310.10928", "title": "Using Audio Data to Facilitate Depression Risk Assessment in Primary\n  Health Care", "abstract": "  Telehealth is a valuable tool for primary health care (PHC), where depression\nis a common condition. PHC is the first point of contact for most people with\ndepression, but about 25% of diagnoses made by PHC physicians are inaccurate.\nMany other barriers also hinder depression detection and treatment in PHC.\nArtificial intelligence (AI) may help reduce depression misdiagnosis in PHC and\nimprove overall diagnosis and treatment outcomes. Telehealth consultations\noften have video issues, such as poor connectivity or dropped calls. Audio-only\ntelehealth is often more practical for lower-income patients who may lack\nstable internet connections. Thus, our study focused on using audio data to\npredict depression risk. The objectives were to: 1) Collect audio data from 24\npeople (12 with depression and 12 without mental health or major health\ncondition diagnoses); 2) Build a machine learning model to predict depression\nrisk. TPOT, an autoML tool, was used to select the best machine learning\nalgorithm, which was the K-nearest neighbors classifier. The selected model had\nhigh performance in classifying depression risk (Precision: 0.98, Recall: 0.93,\nF1-Score: 0.96). These findings may lead to a range of tools to help screen for\nand treat depression. By developing tools to detect depression risk, patients\ncan be routed to AI-driven chatbots for initial screenings. Partnerships with a\nrange of stakeholders are crucial to implementing these solutions. Moreover,\nethical considerations, especially around data privacy and potential biases in\nAI models, need to be at the forefront of any AI-driven intervention in mental\nhealth care.\n", "date": "2023-10-17", "categories": []}
{"id": "2310.08757", "title": "Detection and prediction of clopidogrel treatment failures using\n  longitudinal structured electronic health records", "abstract": "  We propose machine learning algorithms to automatically detect and predict\nclopidogrel treatment failure using longitudinal structured electronic health\nrecords (EHR). By drawing analogies between natural language and structured\nEHR, we introduce various machine learning algorithms used in natural language\nprocessing (NLP) applications to build models for treatment failure detection\nand prediction. In this regard, we generated a cohort of patients with\nclopidogrel prescriptions from UK Biobank and annotated if the patients had\ntreatment failure events within one year of the first clopidogrel prescription;\nout of 502,527 patients, 1,824 patients were identified as treatment failure\ncases, and 6,859 patients were considered as control cases. From the dataset,\nwe gathered diagnoses, prescriptions, and procedure records together per\npatient and organized them into visits with the same date to build models. The\nmodels were built for two different tasks, i.e., detection and prediction, and\nthe experimental results showed that time series models outperform bag-of-words\napproaches in both tasks. In particular, a Transformer-based model, namely\nBERT, could reach 0.928 AUC in detection tasks and 0.729 AUC in prediction\ntasks. BERT also showed competence over other time series models when there is\nnot enough training data, because it leverages the pre-training procedure using\nlarge unlabeled data.\n", "date": "2023-10-12", "categories": []}
{"id": "2310.08817", "title": "Exploring the relationship between response time sequence in scale\n  answering process and severity of insomnia: a machine learning approach", "abstract": "  Objectives: The study aims to investigate the relationship between insomnia\nand response time. Additionally, it aims to develop a machine learning model to\npredict the presence of insomnia in participants using response time data.\nMethods: A mobile application was designed to administer scale tests and\ncollect response time data from 2729 participants. The relationship between\nsymptom severity and response time was explored, and a machine learning model\nwas developed to predict the presence of insomnia. Results: The result revealed\na statistically significant difference (p<.001) in the total response time\nbetween participants with or without insomnia symptoms. A correlation was\nobserved between the severity of specific insomnia aspects and response times\nat the individual questions level. The machine learning model demonstrated a\nhigh predictive accuracy of 0.743 in predicting insomnia symptoms based on\nresponse time data. Conclusions: These findings highlight the potential utility\nof response time data to evaluate cognitive and psychological measures,\ndemonstrating the effectiveness of using response time as a diagnostic tool in\nthe assessment of insomnia.\n", "date": "2023-10-13", "categories": []}
{"id": "2310.07000", "title": "CarDS-Plus ECG Platform: Development and Feasibility Evaluation of a\n  Multiplatform Artificial Intelligence Toolkit for Portable and Wearable\n  Device Electrocardiograms", "abstract": "  In the rapidly evolving landscape of modern healthcare, the integration of\nwearable & portable technology provides a unique opportunity for personalized\nhealth monitoring in the community. Devices like the Apple Watch, FitBit, and\nAliveCor KardiaMobile have revolutionized the acquisition and processing of\nintricate health data streams. Amidst the variety of data collected by these\ngadgets, single-lead electrocardiogram (ECG) recordings have emerged as a\ncrucial source of information for monitoring cardiovascular health. There has\nbeen significant advances in artificial intelligence capable of interpreting\nthese 1-lead ECGs, facilitating clinical diagnosis as well as the detection of\nrare cardiac disorders. This design study describes the development of an\ninnovative multiplatform system aimed at the rapid deployment of AI-based ECG\nsolutions for clinical investigation & care delivery. The study examines design\nconsiderations, aligning them with specific applications, develops data flows\nto maximize efficiency for research & clinical use. This process encompasses\nthe reception of single-lead ECGs from diverse wearable devices, channeling\nthis data into a centralized data lake & facilitating real-time inference\nthrough AI models for ECG interpretation. An evaluation of the platform\ndemonstrates a mean duration from acquisition to reporting of results of 33.0\nto 35.7 seconds, after a standard 30 second acquisition. There were no\nsubstantial differences in acquisition to reporting across two commercially\navailable devices (Apple Watch and KardiaMobile). These results demonstrate the\nsucccessful translation of design principles into a fully integrated &\nefficient strategy for leveraging 1-lead ECGs across platforms & interpretation\nby AI-ECG algorithms. Such a platform is critical to translating AI discoveries\nfor wearable and portable ECG devices to clinical impact through rapid\ndeployment.\n", "date": "2023-10-10", "categories": []}
{"id": "2310.07029", "title": "Brain Age Revisited: Investigating the State vs. Trait Hypotheses of\n  EEG-derived Brain-Age Dynamics with Deep Learning", "abstract": "  The brain's biological age has been considered as a promising candidate for a\nneurologically significant biomarker. However, recent results based on\nlongitudinal magnetic resonance imaging data have raised questions on its\ninterpretation. A central question is whether an increased biological age of\nthe brain is indicative of brain pathology and if changes in brain age\ncorrelate with diagnosed pathology (state hypothesis). Alternatively, could the\ndiscrepancy in brain age be a stable characteristic unique to each individual\n(trait hypothesis)? To address this question, we present a comprehensive study\non brain aging based on clinical EEG, which is complementary to previous\nMRI-based investigations. We apply a state-of-the-art Temporal Convolutional\nNetwork (TCN) to the task of age regression. We train on recordings of the\nTemple University Hospital EEG Corpus (TUEG) explicitly labeled as\nnon-pathological and evaluate on recordings of subjects with non-pathological\nas well as pathological recordings, both with examinations at a single point in\ntime and repeated examinations over time. Therefore, we created four novel\nsubsets of TUEG that include subjects with multiple recordings: I) all labeled\nnon-pathological; II) all labeled pathological; III) at least one recording\nlabeled non-pathological followed by at least one recording labeled\npathological; IV) similar to III) but with opposing transition (first\npathological then non-pathological). The results show that our TCN reaches\nstate-of-the-art performance in age decoding with a mean absolute error of 6.6\nyears. Our extensive analyses demonstrate that the model significantly\nunderestimates the age of non-pathological and pathological subjects (-1 and -5\nyears, paired t-test, p <= 0.18 and p <= 0.0066). Furthermore, the brain age\ngap biomarker is not indicative of pathological EEG.\n", "date": "2023-09-22", "categories": []}
{"id": "2310.07187", "title": "Kernel Cox partially linear regression: building predictive models for\n  cancer patients' survival", "abstract": "  Wide heterogeneity exists in cancer patients' survival, ranging from a few\nmonths to several decades. To accurately predict clinical outcomes, it is vital\nto build an accurate predictive model that relates patients' molecular profiles\nwith patients' survival. With complex relationships between survival and\nhigh-dimensional molecular predictors, it is challenging to conduct\nnon-parametric modeling and irrelevant predictors removing simultaneously. In\nthis paper, we build a kernel Cox proportional hazards semi-parametric model\nand propose a novel regularized garrotized kernel machine (RegGKM) method to\nfit the model. We use the kernel machine method to describe the complex\nrelationship between survival and predictors, while automatically removing\nirrelevant parametric and non-parametric predictors through a LASSO penalty. An\nefficient high-dimensional algorithm is developed for the proposed method.\nComparison with other competing methods in simulation shows that the proposed\nmethod always has better predictive accuracy. We apply this method to analyze a\nmultiple myeloma dataset and predict patients' death burden based on their gene\nexpressions. Our results can help classify patients into groups with different\ndeath risks, facilitating treatment for better clinical outcomes.\n", "date": "2023-10-11", "categories": []}
{"id": "2310.05996", "title": "A novel Network Science Algorithm for Improving Triage of Patients", "abstract": "  Patient triage plays a crucial role in healthcare, ensuring timely and\nappropriate care based on the urgency of patient conditions. Traditional triage\nmethods heavily rely on human judgment, which can be subjective and prone to\nerrors. Recently, a growing interest has been in leveraging artificial\nintelligence (AI) to develop algorithms for triaging patients. This paper\npresents the development of a novel algorithm for triaging patients. It is\nbased on the analysis of patient data to produce decisions regarding their\nprioritization. The algorithm was trained on a comprehensive data set\ncontaining relevant patient information, such as vital signs, symptoms, and\nmedical history. The algorithm was designed to accurately classify patients\ninto triage categories through rigorous preprocessing and feature engineering.\nExperimental results demonstrate that our algorithm achieved high accuracy and\nperformance, outperforming traditional triage methods. By incorporating\ncomputer science into the triage process, healthcare professionals can benefit\nfrom improved efficiency, accuracy, and consistency, prioritizing patients\neffectively and optimizing resource allocation. Although further research is\nneeded to address challenges such as biases in training data and model\ninterpretability, the development of AI-based algorithms for triaging patients\nshows great promise in enhancing healthcare delivery and patient outcomes.\n", "date": "2023-10-09", "categories": []}
{"id": "2310.06075", "title": "Pain Forecasting using Self-supervised Learning and Patient Phenotyping:\n  An attempt to prevent Opioid Addiction", "abstract": "  Sickle Cell Disease (SCD) is a chronic genetic disorder characterized by\nrecurrent acute painful episodes. Opioids are often used to manage these\npainful episodes; the extent of their use in managing pain in this disorder is\nan issue of debate. The risk of addiction and side effects of these opioid\ntreatments can often lead to more pain episodes in the future. Hence, it is\ncrucial to forecast future patient pain trajectories to help patients manage\ntheir SCD to improve their quality of life without compromising their\ntreatment. It is challenging to obtain many pain records to design forecasting\nmodels since it is mainly recorded by patients' self-report. Therefore, it is\nexpensive and painful (due to the need for patient compliance) to solve pain\nforecasting problems in a purely supervised manner. In light of this challenge,\nwe propose to solve the pain forecasting problem using self-supervised learning\nmethods. Also, clustering such time-series data is crucial for patient\nphenotyping, anticipating patients' prognoses by identifying \"similar\"\npatients, and designing treatment guidelines tailored to homogeneous patient\nsubgroups. Hence, we propose a self-supervised learning approach for clustering\ntime-series data, where each cluster comprises patients who share similar\nfuture pain profiles. Experiments on five years of real-world datasets show\nthat our models achieve superior performance over state-of-the-art benchmarks\nand identify meaningful clusters that can be translated into actionable\ninformation for clinical decision-making.\n", "date": "2023-10-09", "categories": []}
{"id": "2310.06283", "title": "Towards More Efficient Depression Risk Recognition via Gait", "abstract": "  Depression, a highly prevalent mental illness, affects over 280 million\nindividuals worldwide. Early detection and timely intervention are crucial for\npromoting remission, preventing relapse, and alleviating the emotional and\nfinancial burdens associated with depression. However, patients with depression\noften go undiagnosed in the primary care setting. Unlike many physiological\nillnesses, depression lacks objective indicators for recognizing depression\nrisk, and existing methods for depression risk recognition are time-consuming\nand often encounter a shortage of trained medical professionals. The\ncorrelation between gait and depression risk has been empirically established.\nGait can serve as a promising objective biomarker, offering the advantage of\nefficient and convenient data collection. However, current methods for\nrecognizing depression risk based on gait have only been validated on small,\nprivate datasets, lacking large-scale publicly available datasets for research\npurposes. Additionally, these methods are primarily limited to hand-crafted\napproaches. Gait is a complex form of motion, and hand-crafted gait features\noften only capture a fraction of the intricate associations between gait and\ndepression risk. Therefore, this study first constructs a large-scale gait\ndatabase, encompassing over 1,200 individuals, 40,000 gait sequences, and\ncovering six perspectives and three types of attire. Two commonly used\npsychological scales are provided as depression risk annotations. Subsequently,\na deep learning-based depression risk recognition model is proposed, overcoming\nthe limitations of hand-crafted approaches. Through experiments conducted on\nthe constructed large-scale database, the effectiveness of the proposed method\nis validated, and numerous instructive insights are presented in the paper,\nhighlighting the significant potential of gait-based depression risk\nrecognition.\n", "date": "2023-10-10", "categories": []}
{"id": "2310.06322", "title": "Predicting Three Types of Freezing of Gait Events Using Deep Learning\n  Models", "abstract": "  Freezing of gait is a Parkinson's Disease symptom that episodically inflicts\na patient with the inability to step or turn while walking. While medical\nexperts have discovered various triggers and alleviating actions for freezing\nof gait, the underlying causes and prediction models are still being explored\ntoday. Current freezing of gait prediction models that utilize machine learning\nachieve high sensitivity and specificity in freezing of gait predictions based\non time-series data; however, these models lack specifications on the type of\nfreezing of gait events. We develop various deep learning models using the\ntransformer encoder architecture plus Bidirectional LSTM layers and different\nfeature sets to predict the three different types of freezing of gait events.\nThe best performing model achieves a score of 0.427 on testing data, which\nwould rank top 5 in Kaggle's Freezing of Gait prediction competition, hosted by\nTHE MICHAEL J. FOX FOUNDATION. However, we also recognize overfitting in\ntraining data that could be potentially improved through pseudo labelling on\nadditional data and model architecture simplification.\n", "date": "2023-10-10", "categories": []}
{"id": "2211.07624", "title": "Semantic Similarity Models for Depression Severity Estimation", "abstract": "  Depressive disorders constitute a severe public health issue worldwide.\nHowever, public health systems have limited capacity for case detection and\ndiagnosis. In this regard, the widespread use of social media has opened up a\nway to access public information on a large scale. Computational methods can\nserve as support tools for rapid screening by exploiting this user-generated\nsocial media content. This paper presents an efficient semantic pipeline to\nstudy depression severity in individuals based on their social media writings.\nWe select test user sentences for producing semantic rankings over an index of\nrepresentative training sentences corresponding to depressive symptoms and\nseverity levels. Then, we use the sentences from those results as evidence for\npredicting users' symptom severity. For that, we explore different aggregation\nmethods to answer one of four Beck Depression Inventory (BDI) options per\nsymptom. We evaluate our methods on two Reddit-based benchmarks, achieving 30\\%\nimprovement over state of the art in terms of measuring depression severity.\n", "date": "2022-11-14", "categories": []}
{"id": "2304.00012", "title": "Multi-Task Learning for Post-transplant Cause of Death Analysis: A Case\n  Study on Liver Transplant", "abstract": "  Organ transplant is the essential treatment method for some end-stage\ndiseases, such as liver failure. Analyzing the post-transplant cause of death\n(CoD) after organ transplant provides a powerful tool for clinical decision\nmaking, including personalized treatment and organ allocation. However,\ntraditional methods like Model for End-stage Liver Disease (MELD) score and\nconventional machine learning (ML) methods are limited in CoD analysis due to\ntwo major data and model-related challenges. To address this, we propose a\nnovel framework called CoD-MTL leveraging multi-task learning to model the\nsemantic relationships between various CoD prediction tasks jointly.\nSpecifically, we develop a novel tree distillation strategy for multi-task\nlearning, which combines the strength of both the tree model and multi-task\nlearning. Experimental results are presented to show the precise and reliable\nCoD predictions of our framework. A case study is conducted to demonstrate the\nclinical importance of our method in the liver transplant.\n", "date": "2023-03-30", "categories": []}
{"id": "2309.07182", "title": "Sleep Stage Classification Using a Pre-trained Deep Learning Model", "abstract": "  One of the common human diseases is sleep disorders. The classification of\nsleep stages plays a fundamental role in diagnosing sleep disorders, monitoring\ntreatment effectiveness, and understanding the relationship between sleep\nstages and various health conditions. A precise and efficient classification of\nthese stages can significantly enhance our understanding of sleep-related\nphenomena and ultimately lead to improved health outcomes and disease\ntreatment.\n  Models others propose are often time-consuming and lack sufficient accuracy,\nespecially in stage N1. The main objective of this research is to present a\nmachine-learning model called \"EEGMobile\". This model utilizes pre-trained\nmodels and learns from electroencephalogram (EEG) spectrograms of brain\nsignals. The model achieved an accuracy of 86.97% on a publicly available\ndataset named \"Sleep-EDF20\", outperforming other models proposed by different\nresearchers. Moreover, it recorded an accuracy of 56.4% in stage N1, which is\nbetter than other models. These findings demonstrate that this model has the\npotential to achieve better results for the treatment of this disease.\n", "date": "2023-09-12", "categories": []}
{"id": "2310.03756", "title": "A Multi-channel EEG Data Analysis for Poor Neuro-prognostication in\n  Comatose Patients with Self and Cross-channel Attention Mechanism", "abstract": "  This work investigates the predictive potential of bipolar\nelectroencephalogram (EEG) recordings towards efficient prediction of poor\nneurological outcomes. A retrospective design using a hybrid deep learning\napproach is utilized to optimize an objective function aiming for high\nspecificity, i.e., true positive rate (TPR) with reduced false positives (<\n0.05). A multi-channel EEG array of 18 bipolar channel pairs from a randomly\nselected 5-minute segment in an hour is kept. In order to determine the outcome\nprediction, a combination of a feature encoder with 1-D convolutional layers,\nlearnable position encoding, a context network with attention mechanisms, and\nfinally, a regressor and classifier blocks are used. The feature encoder\nextricates local temporal and spatial features, while the following position\nencoding and attention mechanisms attempt to capture global temporal\ndependencies. Results: The proposed framework by our team, OUS IVS, when\nvalidated on the challenge hidden validation data, exhibited a score of 0.57.\n", "date": "2023-09-24", "categories": []}
{"id": "2310.03772", "title": "Investigating Alternative Feature Extraction Pipelines For Clinical Note\n  Phenotyping", "abstract": "  A common practice in the medical industry is the use of clinical notes, which\nconsist of detailed patient observations. However, electronic health record\nsystems frequently do not contain these observations in a structured format,\nrendering patient information challenging to assess and evaluate automatically.\nUsing computational systems for the extraction of medical attributes offers\nmany applications, including longitudinal analysis of patients, risk\nassessment, and hospital evaluation. Recent work has constructed successful\nmethods for phenotyping: extracting medical attributes from clinical notes.\nBERT-based models can be used to transform clinical notes into a series of\nrepresentations, which are then condensed into a single document representation\nbased on their CLS embeddings and passed into an LSTM (Mulyar et al., 2020).\nThough this pipeline yields a considerable performance improvement over\nprevious results, it requires extensive convergence time. This method also does\nnot allow for predicting attributes not yet identified in clinical notes.\n  Considering the wide variety of medical attributes that may be present in a\nclinical note, we propose an alternative pipeline utilizing ScispaCy (Neumann\net al., 2019) for the extraction of common diseases. We then train various\nsupervised learning models to associate the presence of these conditions with\npatient attributes. Finally, we replicate a ClinicalBERT (Alsentzer et al.,\n2019) and LSTM-based approach for purposes of comparison. We find that\nalternative methods moderately underperform the replicated LSTM approach. Yet,\nconsidering a complex tradeoff between accuracy and runtime, in addition to the\nfact that the alternative approach also allows for the detection of medical\nconditions that are not already present in a clinical note, its usage may be\nconsidered as a supplement to established methods.\n", "date": "2023-10-05", "categories": []}
{"id": "2306.04018", "title": "PyTrial: Machine Learning Software and Benchmark for Clinical Trial\n  Applications", "abstract": "  Clinical trials are conducted to test the effectiveness and safety of\npotential drugs in humans for regulatory approval. Machine learning (ML) has\nrecently emerged as a new tool to assist in clinical trials. Despite this\nprogress, there have been few efforts to document and benchmark ML4Trial\nalgorithms available to the ML research community. Additionally, the\naccessibility to clinical trial-related datasets is limited, and there is a\nlack of well-defined clinical tasks to facilitate the development of new\nalgorithms.\n  To fill this gap, we have developed PyTrial that provides benchmarks and\nopen-source implementations of a series of ML algorithms for clinical trial\ndesign and operations. In this paper, we thoroughly investigate 34 ML\nalgorithms for clinical trials across 6 different tasks, including patient\noutcome prediction, trial site selection, trial outcome prediction,\npatient-trial matching, trial similarity search, and synthetic data generation.\nWe have also collected and prepared 23 ML-ready datasets as well as their\nworking examples in Jupyter Notebooks for quick implementation and testing.\n  PyTrial defines each task through a simple four-step process: data loading,\nmodel specification, model training, and model evaluation, all achievable with\njust a few lines of code. Furthermore, our modular API architecture empowers\npractitioners to expand the framework to incorporate new algorithms and tasks\neffortlessly. The code is available at https://github.com/RyanWangZf/PyTrial.\n", "date": "2023-06-06", "categories": []}
{"id": "2310.03232", "title": "Deep Representations of First-person Pronouns for Prediction of\n  Depression Symptom Severity", "abstract": "  Prior work has shown that analyzing the use of first-person singular pronouns\ncan provide insight into individuals' mental status, especially depression\nsymptom severity. These findings were generated by counting frequencies of\nfirst-person singular pronouns in text data. However, counting doesn't capture\nhow these pronouns are used. Recent advances in neural language modeling have\nleveraged methods generating contextual embeddings. In this study, we sought to\nutilize the embeddings of first-person pronouns obtained from contextualized\nlanguage representation models to capture ways these pronouns are used, to\nanalyze mental status. De-identified text messages sent during online\npsychotherapy with weekly assessment of depression severity were used for\nevaluation. Results indicate the advantage of contextualized first-person\npronoun embeddings over standard classification token embeddings and\nfrequency-based pronoun analysis results in predicting depression symptom\nseverity. This suggests contextual representations of first-person pronouns can\nenhance the predictive utility of language used by people with depression\nsymptoms.\n", "date": "2023-10-05", "categories": []}
{"id": "2310.03353", "title": "Deep Geometric Learning with Monotonicity Constraints for Alzheimer's\n  Disease Progression", "abstract": "  Alzheimer's disease (AD) is a devastating neurodegenerative condition that\nprecedes progressive and irreversible dementia; thus, predicting its\nprogression over time is vital for clinical diagnosis and treatment. Numerous\nstudies have implemented structural magnetic resonance imaging (MRI) to model\nAD progression, focusing on three integral aspects: (i) temporal variability,\n(ii) incomplete observations, and (iii) temporal geometric characteristics.\nHowever, deep learning-based approaches regarding data variability and sparsity\nhave yet to consider inherent geometrical properties sufficiently. The ordinary\ndifferential equation-based geometric modeling method (ODE-RGRU) has recently\nemerged as a promising strategy for modeling time-series data by intertwining a\nrecurrent neural network and an ODE in Riemannian space. Despite its\nachievements, ODE-RGRU encounters limitations when extrapolating positive\ndefinite symmetric metrics from incomplete samples, leading to feature reverse\noccurrences that are particularly problematic, especially within the clinical\nfacet. Therefore, this study proposes a novel geometric learning approach that\nmodels longitudinal MRI biomarkers and cognitive scores by combining three\nmodules: topological space shift, ODE-RGRU, and trajectory estimation. We have\nalso developed a training algorithm that integrates manifold mapping with\nmonotonicity constraints to reflect measurement transition irreversibility. We\nverify our proposed method's efficacy by predicting clinical labels and\ncognitive scores over time in regular and irregular settings. Furthermore, we\nthoroughly analyze our proposed framework through an ablation study.\n", "date": "2023-10-05", "categories": []}
{"id": "2310.03457", "title": "A Quantitatively Interpretable Model for Alzheimer's Disease Prediction\n  Using Deep Counterfactuals", "abstract": "  Deep learning (DL) for predicting Alzheimer's disease (AD) has provided\ntimely intervention in disease progression yet still demands attentive\ninterpretability to explain how their DL models make definitive decisions.\nRecently, counterfactual reasoning has gained increasing attention in medical\nresearch because of its ability to provide a refined visual explanatory map.\nHowever, such visual explanatory maps based on visual inspection alone are\ninsufficient unless we intuitively demonstrate their medical or neuroscientific\nvalidity via quantitative features. In this study, we synthesize the\ncounterfactual-labeled structural MRIs using our proposed framework and\ntransform it into a gray matter density map to measure its volumetric changes\nover the parcellated region of interest (ROI). We also devised a lightweight\nlinear classifier to boost the effectiveness of constructed ROIs, promoted\nquantitative interpretation, and achieved comparable predictive performance to\nDL methods. Throughout this, our framework produces an ``AD-relatedness index''\nfor each ROI and offers an intuitive understanding of brain status for an\nindividual patient and across patient groups with respect to AD progression.\n", "date": "2023-10-05", "categories": []}
{"id": "2310.02398", "title": "Reducing Intraspecies and Interspecies Covariate Shift in Traumatic\n  Brain Injury EEG of Humans and Mice Using Transfer Euclidean Alignment", "abstract": "  While analytics of sleep electroencephalography (EEG) holds certain\nadvantages over other methods in clinical applications, high variability across\nsubjects poses a significant challenge when it comes to deploying machine\nlearning models for classification tasks in the real world. In such instances,\nmachine learning models that exhibit exceptional performance on a specific\ndataset may not necessarily demonstrate similar proficiency when applied to a\ndistinct dataset for the same task. The scarcity of high-quality biomedical\ndata further compounds this challenge, making it difficult to evaluate the\nmodel's generality comprehensively. In this paper, we introduce Transfer\nEuclidean Alignment - a transfer learning technique to tackle the problem of\nthe dearth of human biomedical data for training deep learning models. We\ntested the robustness of this transfer learning technique on various rule-based\nclassical machine learning models as well as the EEGNet-based deep learning\nmodel by evaluating on different datasets, including human and mouse data in a\nbinary classification task of detecting individuals with versus without\ntraumatic brain injury (TBI). By demonstrating notable improvements with an\naverage increase of 14.42% for intraspecies datasets and 5.53% for interspecies\ndatasets, our findings underscore the importance of the use of transfer\nlearning to improve the performance of machine learning and deep learning\nmodels when using diverse datasets for training.\n", "date": "2023-10-03", "categories": []}
{"id": "2310.02451", "title": "Backdoor Adjustment of Confounding by Provenance for Robust Text\n  Classification of Multi-institutional Clinical Notes", "abstract": "  Natural Language Processing (NLP) methods have been broadly applied to\nclinical tasks. Machine learning and deep learning approaches have been used to\nimprove the performance of clinical NLP. However, these approaches require\nsufficiently large datasets for training, and trained models have been shown to\ntransfer poorly across sites. These issues have led to the promotion of data\ncollection and integration across different institutions for accurate and\nportable models. However, this can introduce a form of bias called confounding\nby provenance. When source-specific data distributions differ at deployment,\nthis may harm model performance. To address this issue, we evaluate the utility\nof backdoor adjustment for text classification in a multi-site dataset of\nclinical notes annotated for mentions of substance abuse. Using an evaluation\nframework devised to measure robustness to distributional shifts, we assess the\nutility of backdoor adjustment. Our results indicate that backdoor adjustment\ncan effectively mitigate for confounding shift.\n", "date": "2023-10-03", "categories": []}
{"id": "2209.02965", "title": "Risk of Bias in Chest Radiography Deep Learning Foundation Models", "abstract": "  Purpose: To analyze a recently published chest radiography foundation model\nfor the presence of biases that could lead to subgroup performance disparities\nacross biological sex and race.\n  Materials and Methods: This retrospective study used 127,118 chest\nradiographs from 42,884 patients (mean age, 63 [SD] 17 years; 23,623 male,\n19,261 female) from the CheXpert dataset collected between October 2002 and\nJuly 2017. To determine the presence of bias in features generated by a chest\nradiography foundation model and baseline deep learning model, dimensionality\nreduction methods together with two-sample Kolmogorov-Smirnov tests were used\nto detect distribution shifts across sex and race. A comprehensive disease\ndetection performance analysis was then performed to associate any biases in\nthe features to specific disparities in classification performance across\npatient subgroups.\n  Results: Ten out of twelve pairwise comparisons across biological sex and\nrace showed statistically significant differences in the studied foundation\nmodel, compared with four significant tests in the baseline model. Significant\ndifferences were found between male and female (P < .001) and Asian and Black\npatients (P < .001) in the feature projections that primarily capture disease.\nCompared with average model performance across all subgroups, classification\nperformance on the 'no finding' label dropped between 6.8% and 7.8% for female\npatients, and performance in detecting 'pleural effusion' dropped between 10.7%\nand 11.6% for Black patients.\n  Conclusion: The studied chest radiography foundation model demonstrated\nracial and sex-related bias leading to disparate performance across patient\nsubgroups and may be unsafe for clinical applications.\n", "date": "2022-09-07", "categories": []}
{"id": "2308.11819", "title": "A Counterfactual Fair Model for Longitudinal Electronic Health Records\n  via Deconfounder", "abstract": "  The fairness issue of clinical data modeling, especially on Electronic Health\nRecords (EHRs), is of utmost importance due to EHR's complex latent structure\nand potential selection bias. It is frequently necessary to mitigate health\ndisparity while keeping the model's overall accuracy in practice. However,\ntraditional methods often encounter the trade-off between accuracy and\nfairness, as they fail to capture the underlying factors beyond observed data.\nTo tackle this challenge, we propose a novel model called Fair Longitudinal\nMedical Deconfounder (FLMD) that aims to achieve both fairness and accuracy in\nlongitudinal Electronic Health Records (EHR) modeling. Drawing inspiration from\nthe deconfounder theory, FLMD employs a two-stage training process. In the\nfirst stage, FLMD captures unobserved confounders for each encounter, which\neffectively represents underlying medical factors beyond observed EHR, such as\npatient genotypes and lifestyle habits. This unobserved confounder is crucial\nfor addressing the accuracy/fairness dilemma. In the second stage, FLMD\ncombines the learned latent representation with other relevant features to make\npredictions. By incorporating appropriate fairness criteria, such as\ncounterfactual fairness, FLMD ensures that it maintains high prediction\naccuracy while simultaneously minimizing health disparities. We conducted\ncomprehensive experiments on two real-world EHR datasets to demonstrate the\neffectiveness of FLMD. Apart from the comparison of baseline methods and FLMD\nvariants in terms of fairness and accuracy, we assessed the performance of all\nmodels on disturbed/imbalanced and synthetic datasets to showcase the\nsuperiority of FLMD across different settings and provide valuable insights\ninto its capabilities.\n", "date": "2023-08-22", "categories": []}
{"id": "2308.13328", "title": "Compressor-Based Classification for Atrial Fibrillation Detection", "abstract": "  Atrial fibrillation (AF) is one of the most common arrhythmias with\nchallenging public health implications. Therefore, automatic detection of AF\nepisodes on ECG is one of the essential tasks in biomedical engineering. In\nthis paper, we applied the recently introduced method of compressor-based text\nclassification with gzip algorithm for AF detection (binary classification\nbetween heart rhythms). We investigated the normalized compression distance\napplied to RR-interval and $\\Delta$RR-interval sequences ($\\Delta$RR-interval\nis the difference between subsequent RR-intervals). Here, the configuration of\nthe k-nearest neighbour classifier, an optimal window length, and the choice of\ndata types for compression were analyzed. We achieved good classification\nresults while learning on the full MIT-BIH Atrial Fibrillation database, close\nto the best specialized AF detection algorithms (avg. sensitivity = 97.1\\%,\navg. specificity = 91.7\\%, best sensitivity of 99.8\\%, best specificity of\n97.6\\% with fivefold cross-validation). In addition, we evaluated the\nclassification performance under the few-shot learning setting. Our results\nsuggest that gzip compression-based classification, originally proposed for\ntexts, is suitable for biomedical data and quantized continuous stochastic\nsequences in general.\n", "date": "2023-08-25", "categories": []}
{"id": "2310.00540", "title": "Machine Learning-driven Analysis of Gastrointestinal Symptoms in\n  Post-COVID-19 Patients", "abstract": "  The COVID-19 pandemic, caused by the novel coronavirus SARS-CoV-2, has posed\nsignificant health challenges worldwide. While respiratory symptoms have been\nthe primary focus, emerging evidence has highlighted the impact of COVID-19 on\nvarious organ systems, including the gastrointestinal (GI) tract. This study,\nbased on data from 913 post-COVID-19 patients in Iraq collected during 2022 and\n2023, investigates the prevalence and patterns of GI symptoms in individuals\nrecovering from COVID-19 and leverages machine learning algorithms to identify\npredictive factors for these symptoms. The research findings reveal that a\nnotable percentage of post-COVID-19 patients experience GI symptoms during\ntheir recovery phase. Diarrhea emerged as the most frequently reported symptom,\nfollowed by abdominal pain and nausea. Machine learning analysis uncovered\nsignificant predictive factors for GI symptoms, including age, gender, disease\nseverity, comorbidities, and the duration of COVID-19 illness. These findings\nunderscore the importance of monitoring and addressing GI symptoms in\npost-COVID-19 care, with machine learning offering valuable tools for early\nidentification and personalized intervention. This study contributes to the\nunderstanding of the long-term consequences of COVID-19 on GI health and\nemphasizes the potential benefits of utilizing machine learning-driven analysis\nin predicting and managing these symptoms. Further research is warranted to\ndelve into the mechanisms underlying GI symptoms in COVID-19 survivors and to\ndevelop targeted interventions for symptom management. Keywords: COVID-19,\ngastrointestinal symptoms, machine learning, predictive factors, post-COVID-19\ncare, long COVID.\n", "date": "2023-09-27", "categories": []}
{"id": "2310.00727", "title": "Review of deep learning in healthcare", "abstract": "  Given the growing complexity of healthcare data over the last several years,\nusing machine learning techniques like Deep Neural Network (DNN) models has\ngained increased appeal. In order to extract hidden patterns and other valuable\ninformation from the huge quantity of health data, which traditional analytics\nare unable to do in a reasonable length of time, machine learning (ML)\ntechniques are used. Deep Learning (DL) algorithms in particular have been\nshown as potential approaches to pattern identification in healthcare systems.\nThis thought has led to the contribution of this research, which examines deep\nlearning methods used in healthcare systems via an examination of cutting-edge\nnetwork designs, applications, and market trends. To connect deep learning\nmethodologies and human healthcare interpretability, the initial objective is\nto provide in-depth insight into the deployment of deep learning models in\nhealthcare solutions. And last, to outline the current unresolved issues and\npotential directions.\n", "date": "2023-10-01", "categories": []}
{"id": "2309.12625", "title": "DRG-LLaMA : Tuning LLaMA Model to Predict Diagnosis-related Group for\n  Hospitalized Patients", "abstract": "  In the U.S. inpatient payment system, the Diagnosis-Related Group (DRG) is\npivotal, but its assignment process is inefficient. The study introduces\nDRG-LLaMA, an advanced large language model (LLM) fine-tuned on clinical notes\nto enhance DRGs assignment. Utilizing LLaMA as the foundational model and\noptimizing it through Low-Rank Adaptation (LoRA) on 236,192 MIMIC-IV discharge\nsummaries, our DRG-LLaMA-7B model exhibited a noteworthy macro-averaged F1\nscore of 0.327, a top-1 prediction accuracy of 52.0%, and a macro-averaged Area\nUnder the Curve (AUC) of 0.986, with a maximum input token length of 512. This\nmodel surpassed the performance of prior leading models in DRG prediction,\nshowing a relative improvement of 40.3% and 35.7% in macro-averaged F1 score\ncompared to ClinicalBERT and CAML, respectively. Applied to base DRG and\ncomplication or comorbidity (CC)/major complication or comorbidity (MCC)\nprediction, DRG-LLaMA achieved a top-1 prediction accuracy of 67.8% and 67.5%,\nrespectively. Additionally, our findings indicate that DRG-LLaMA's performance\ncorrelates with increased model parameters and input context lengths.\n", "date": "2023-09-22", "categories": []}
{"id": "2309.16736", "title": "Cognizance of Post-COVID-19 Multi-Organ Dysfunction through Machine\n  Learning Analysis", "abstract": "  In the year 2022, a total of 466 patients from various cities across Iraq\nwere included in this study. This research paper focuses on the application of\nmachine learning techniques to analyse and predict multi-organ dysfunction in\nindividuals experiencing Post-COVID-19 Syndrome, commonly known as Long COVID.\nPost-COVID-19 Syndrome presents a wide array of persistent symptoms affecting\nvarious organ systems, posing a significant challenge to healthcare. Leveraging\nthe power of artificial intelligence, this study aims to enhance early\ndetection and management of this complex condition. The paper outlines the\nimportance of data collection and preprocessing, feature selection and\nengineering, model development and validation, and ethical considerations in\nconducting research in this field. By improving our understanding of\nPost-COVID-19 Syndrome through machine learning, healthcare providers can\nidentify at-risk individuals and offer timely interventions, potentially\nimproving patient outcomes and quality of life. Further research is essential\nto refine models, validate their clinical utility, and explore treatment\noptions for Long COVID. Keywords: Post-COVID-19 Syndrome, Machine Learning,\nMulti-Organ Dysfunction, Healthcare, Artificial Intelligence.\n", "date": "2023-09-27", "categories": []}
{"id": "2309.16744", "title": "Predicting Long-term Renal Impairment in Post-COVID-19 Patients with\n  Machine Learning Algorithms", "abstract": "  The COVID-19 pandemic has had far-reaching implications for global public\nhealth. As we continue to grapple with its consequences, it becomes\nincreasingly clear that post-COVID-19 complications are a significant concern.\nAmong these complications, renal impairment has garnered particular attention\ndue to its potential long-term health impacts. This study, conducted with a\ncohort of 821 post-COVID-19 patients from diverse regions of Iraq across the\nyears 2021, 2022, and 2023, endeavors to predict the risk of long-term renal\nimpairment using advanced machine learning algorithms. Our findings have the\npotential to revolutionize post-COVID-19 patient care by enabling early\nidentification and intervention for those at risk of renal impairment,\nultimately improving clinical outcomes. This research encompasses comprehensive\ndata collection and preprocessing, feature selection, and the development of\npredictive models using various machine learning algorithms. The study's\nobjectives are to assess the incidence of long-term renal impairment in\npost-COVID-19 patients, identify associated risk factors, create predictive\nmodels, and evaluate their accuracy. We anticipate that our machine learning\nmodels, drawing from a rich dataset, will provide valuable insights into the\nrisk of renal impairment, ultimately enhancing patient care and quality of\nlife. In conclusion, the research presented herein offers a critical\ncontribution to the field of post-COVID-19 care. By harnessing the power of\nmachine learning, we aim to predict long-term renal impairment risk accurately.\nThese predictions have the potential to inform healthcare professionals,\nenabling them to take proactive measures and provide targeted interventions for\npost-COVID-19 patients at risk of renal complications, thus minimizing the\nimpact of this serious health concern.\n", "date": "2023-09-28", "categories": []}
{"id": "2309.17320", "title": "Development of a Deep Learning Method to Identify Acute Ischemic Stroke\n  Lesions on Brain CT", "abstract": "  Computed Tomography (CT) is commonly used to image acute ischemic stroke\n(AIS) patients, but its interpretation by radiologists is time-consuming and\nsubject to inter-observer variability. Deep learning (DL) techniques can\nprovide automated CT brain scan assessment, but usually require annotated\nimages. Aiming to develop a DL method for AIS using labelled but not annotated\nCT brain scans from patients with AIS, we designed a convolutional neural\nnetwork-based DL algorithm using routinely-collected CT brain scans from the\nThird International Stroke Trial (IST-3), which were not acquired using strict\nresearch protocols. The DL model aimed to detect AIS lesions and classify the\nside of the brain affected. We explored the impact of AIS lesion features,\nbackground brain appearances, and timing on DL performance. From 5772 unique CT\nscans of 2347 AIS patients (median age 82), 54% had visible AIS lesions\naccording to expert labelling. Our best-performing DL method achieved 72%\naccuracy for lesion presence and side. Lesions that were larger (80% accuracy)\nor multiple (87% accuracy for two lesions, 100% for three or more), were better\ndetected. Follow-up scans had 76% accuracy, while baseline scans 67% accuracy.\nChronic brain conditions reduced accuracy, particularly non-stroke lesions and\nold stroke lesions (32% and 31% error rates respectively). DL methods can be\ndesigned for AIS lesion detection on CT using the vast quantities of\nroutinely-collected CT brain scan data. Ultimately, this should lead to more\nrobust and widely-applicable methods.\n", "date": "2023-09-29", "categories": []}
{"id": "2308.13104", "title": "Contrastive Learning of Temporal Distinctiveness for Survival Analysis\n  in Electronic Health Records", "abstract": "  Survival analysis plays a crucial role in many healthcare decisions, where\nthe risk prediction for the events of interest can support an informative\noutlook for a patient's medical journey. Given the existence of data censoring,\nan effective way of survival analysis is to enforce the pairwise temporal\nconcordance between censored and observed data, aiming to utilize the time\ninterval before censoring as partially observed time-to-event labels for\nsupervised learning. Although existing studies mostly employed ranking methods\nto pursue an ordering objective, contrastive methods which learn a\ndiscriminative embedding by having data contrast against each other, have not\nbeen explored thoroughly for survival analysis. Therefore, in this paper, we\npropose a novel Ontology-aware Temporality-based Contrastive Survival (OTCSurv)\nanalysis framework that utilizes survival durations from both censored and\nobserved data to define temporal distinctiveness and construct negative sample\npairs with adjustable hardness for contrastive learning. Specifically, we first\nuse an ontological encoder and a sequential self-attention encoder to represent\nthe longitudinal EHR data with rich contexts. Second, we design a temporal\ncontrastive loss to capture varying survival durations in a supervised setting\nthrough a hardness-aware negative sampling mechanism. Last, we incorporate the\ncontrastive task into the time-to-event predictive task with multiple loss\ncomponents. We conduct extensive experiments using a large EHR dataset to\nforecast the risk of hospitalized patients who are in danger of developing\nacute kidney injury (AKI), a critical and urgent medical condition. The\neffectiveness and explainability of the proposed model are validated through\ncomprehensive quantitative and qualitative studies.\n", "date": "2023-08-24", "categories": []}
{"id": "2309.15867", "title": "Identifying factors associated with fast visual field progression in\n  patients with ocular hypertension based on unsupervised machine learning", "abstract": "  Purpose: To identify ocular hypertension (OHT) subtypes with different trends\nof visual field (VF) progression based on unsupervised machine learning and to\ndiscover factors associated with fast VF progression. Participants: A total of\n3133 eyes of 1568 ocular hypertension treatment study (OHTS) participants with\nat least five follow-up VF tests were included in the study. Methods: We used a\nlatent class mixed model (LCMM) to identify OHT subtypes using standard\nautomated perimetry (SAP) mean deviation (MD) trajectories. We characterized\nthe subtypes based on demographic, clinical, ocular, and VF factors at the\nbaseline. We then identified factors driving fast VF progression using\ngeneralized estimating equation (GEE) and justified findings qualitatively and\nquantitatively. Results: The LCMM model discovered four clusters (subtypes) of\neyes with different trajectories of MD worsening. The number of eyes in\nclusters were 794 (25%), 1675 (54%), 531 (17%) and 133 (4%). We labelled the\nclusters as Improvers, Stables, Slow progressors, and Fast progressors based on\ntheir mean of MD decline, which were 0.08, -0.06, -0.21, and -0.45 dB/year,\nrespectively. Eyes with fast VF progression had higher baseline age,\nintraocular pressure (IOP), pattern standard deviation (PSD) and refractive\nerror (RE), but lower central corneal thickness (CCT). Fast progression was\nassociated with calcium channel blockers, being male, heart disease history,\ndiabetes history, African American race, stroke history, and migraine\nheadaches.\n", "date": "2023-09-26", "categories": []}
{"id": "2309.15990", "title": "Machine Learning Based Analytics for the Significance of Gait Analysis\n  in Monitoring and Managing Lower Extremity Injuries", "abstract": "  This study explored the potential of gait analysis as a tool for assessing\npost-injury complications, e.g., infection, malunion, or hardware irritation,\nin patients with lower extremity fractures. The research focused on the\nproficiency of supervised machine learning models predicting complications\nusing consecutive gait datasets. We identified patients with lower extremity\nfractures at an academic center. Patients underwent gait analysis with a\nchest-mounted IMU device. Using software, raw gait data was preprocessed,\nemphasizing 12 essential gait variables. Machine learning models including\nXGBoost, Logistic Regression, SVM, LightGBM, and Random Forest were trained,\ntested, and evaluated. Attention was given to class imbalance, addressed using\nSMOTE. We introduced a methodology to compute the Rate of Change (ROC) for gait\nvariables, independent of the time difference between gait analyses. XGBoost\nwas the optimal model both before and after applying SMOTE. Prior to SMOTE, the\nmodel achieved an average test AUC of 0.90 (95% CI: [0.79, 1.00]) and test\naccuracy of 86% (95% CI: [75%, 97%]). Feature importance analysis attributed\nimportance to the duration between injury and gait analysis. Data patterns\nshowed early physiological compensations, followed by stabilization phases,\nemphasizing prompt gait analysis. This study underscores the potential of\nmachine learning, particularly XGBoost, in gait analysis for orthopedic care.\nPredicting post-injury complications, early gait assessment becomes vital,\nrevealing intervention points. The findings support a shift in orthopedics\ntowards a data-informed approach, enhancing patient outcomes.\n", "date": "2023-09-27", "categories": []}
{"id": "2309.16055", "title": "Identifying Risk Factors for Post-COVID-19 Mental Health Disorders: A\n  Machine Learning Perspective", "abstract": "  In this study, we leveraged machine learning techniques to identify risk\nfactors associated with post-COVID-19 mental health disorders. Our analysis,\nbased on data collected from 669 patients across various provinces in Iraq,\nyielded valuable insights. We found that age, gender, and geographical region\nof residence were significant demographic factors influencing the likelihood of\ndeveloping mental health disorders in post-COVID-19 patients. Additionally,\ncomorbidities and the severity of COVID-19 illness were important clinical\npredictors. Psychosocial factors, such as social support, coping strategies,\nand perceived stress levels, also played a substantial role. Our findings\nemphasize the complex interplay of multiple factors in the development of\nmental health disorders following COVID-19 recovery. Healthcare providers and\npolicymakers should consider these risk factors when designing targeted\ninterventions and support systems for individuals at risk. Machine\nlearning-based approaches can provide a valuable tool for predicting and\npreventing adverse mental health outcomes in post-COVID-19 patients. Further\nresearch and prospective studies are needed to validate these findings and\nenhance our understanding of the long-term psychological impact of the COVID-19\npandemic. This study contributes to the growing body of knowledge regarding the\nmental health consequences of the COVID-19 pandemic and underscores the\nimportance of a multidisciplinary approach to address the diverse needs of\nindividuals on the path to recovery. Keywords: COVID-19, mental health, risk\nfactors, machine learning, Iraq\n", "date": "2023-09-27", "categories": []}
{"id": "2309.16059", "title": "Predicting Cardiovascular Complications in Post-COVID-19 Patients Using\n  Data-Driven Machine Learning Models", "abstract": "  The COVID-19 pandemic has globally posed numerous health challenges, notably\nthe emergence of post-COVID-19 cardiovascular complications. This study\naddresses this by utilizing data-driven machine learning models to predict such\ncomplications in 352 post-COVID-19 patients from Iraq. Clinical data, including\ndemographics, comorbidities, lab results, and imaging, were collected and used\nto construct predictive models. These models, leveraging various machine\nlearning algorithms, demonstrated commendable performance in identifying\npatients at risk. Early detection through these models promises timely\ninterventions and improved outcomes. In conclusion, this research underscores\nthe potential of data-driven machine learning for predicting post-COVID-19\ncardiovascular complications, emphasizing the need for continued validation and\nresearch in diverse clinical settings.\n", "date": "2023-09-27", "categories": []}
{"id": "2309.16335", "title": "End-to-end Risk Prediction of Atrial Fibrillation from the 12-Lead ECG\n  by Deep Neural Networks", "abstract": "  Background: Atrial fibrillation (AF) is one of the most common cardiac\narrhythmias that affects millions of people each year worldwide and it is\nclosely linked to increased risk of cardiovascular diseases such as stroke and\nheart failure. Machine learning methods have shown promising results in\nevaluating the risk of developing atrial fibrillation from the\nelectrocardiogram. We aim to develop and evaluate one such algorithm on a large\nCODE dataset collected in Brazil.\n  Results: The deep neural network model identified patients without indication\nof AF in the presented ECG but who will develop AF in the future with an AUC\nscore of 0.845. From our survival model, we obtain that patients in the\nhigh-risk group (i.e. with the probability of a future AF case being greater\nthan 0.7) are 50% more likely to develop AF within 40 weeks, while patients\nbelonging to the minimal-risk group (i.e. with the probability of a future AF\ncase being less than or equal to 0.1) have more than 85% chance of remaining AF\nfree up until after seven years.\n  Conclusion: We developed and validated a model for AF risk prediction. If\napplied in clinical practice, the model possesses the potential of providing\nvaluable and useful information in decision-making and patient management\nprocesses.\n", "date": "2023-09-28", "categories": []}
{"id": "2309.15529", "title": "Missing-modality Enabled Multi-modal Fusion Architecture for Medical\n  Data", "abstract": "  Fusing multi-modal data can improve the performance of deep learning models.\nHowever, missing modalities are common for medical data due to patients'\nspecificity, which is detrimental to the performance of multi-modal models in\napplications. Therefore, it is critical to adapt the models to missing\nmodalities. This study aimed to develop an efficient multi-modal fusion\narchitecture for medical data that was robust to missing modalities and further\nimproved the performance on disease diagnosis.X-ray chest radiographs for the\nimage modality, radiology reports for the text modality, and structured value\ndata for the tabular data modality were fused in this study. Each modality pair\nwas fused with a Transformer-based bi-modal fusion module, and the three\nbi-modal fusion modules were then combined into a tri-modal fusion framework.\nAdditionally, multivariate loss functions were introduced into the training\nprocess to improve model's robustness to missing modalities in the inference\nprocess. Finally, we designed comparison and ablation experiments for\nvalidating the effectiveness of the fusion, the robustness to missing\nmodalities and the enhancements from each key component. Experiments were\nconducted on MIMIC-IV, MIMIC-CXR with the 14-label disease diagnosis task.\nAreas under the receiver operating characteristic curve (AUROC), the area under\nthe precision-recall curve (AUPRC) were used to evaluate models' performance.\nThe experimental results demonstrated that our proposed multi-modal fusion\narchitecture effectively fused three modalities and showed strong robustness to\nmissing modalities. This method is hopeful to be scaled to more modalities to\nenhance the clinical practicality of the model.\n", "date": "2023-09-27", "categories": []}
{"id": "2309.15803", "title": "ANNCRIPS: Artificial Neural Networks for Cancer Research In Prediction &\n  Survival", "abstract": "  Prostate cancer is a prevalent malignancy among men aged 50 and older.\nCurrent diagnostic methods primarily rely on blood tests, PSA:Prostate-Specific\nAntigen levels, and Digital Rectal Examinations (DRE). However, these methods\nsuffer from a significant rate of false positive results. This study focuses on\nthe development and validation of an intelligent mathematical model utilizing\nArtificial Neural Networks (ANNs) to enhance the early detection of prostate\ncancer. The primary objective of this research paper is to present a novel\nmathematical model designed to aid in the early detection of prostate cancer,\nfacilitating prompt intervention by healthcare professionals. The model's\nimplementation demonstrates promising potential in reducing the incidence of\nfalse positives, thereby improving patient outcomes. Furthermore, we envision\nthat, with further refinement, extensive testing, and validation, this model\ncan evolve into a robust, marketable solution for prostate cancer detection.\nThe long-term goal is to make this solution readily available for deployment in\nvarious screening centers, hospitals, and research institutions, ultimately\ncontributing to more effective cancer screening and patient care.\n", "date": "2023-09-26", "categories": []}
{"id": "2309.15838", "title": "Automated Detection of Persistent Inflammatory Biomarkers in\n  Post-COVID-19 Patients Using Machine Learning Techniques", "abstract": "  The COVID-19 pandemic has left a lasting impact on individuals, with many\nexperiencing persistent symptoms, including inflammation, in the post-acute\nphase of the disease. Detecting and monitoring these inflammatory biomarkers is\ncritical for timely intervention and improved patient outcomes. This study\nemploys machine learning techniques to automate the identification of\npersistent inflammatory biomarkers in 290 post-COVID-19 patients, based on\nmedical data collected from hospitals in Iraq. The data encompassed a wide\narray of clinical parameters, such as C-reactive protein and interleukin-6\nlevels, patient demographics, comorbidities, and treatment histories. Rigorous\ndata preprocessing and feature selection processes were implemented to optimize\nthe dataset for machine learning analysis. Various machine learning algorithms,\nincluding logistic regression, random forests, support vector machines, and\ngradient boosting, were deployed to construct predictive models. These models\nexhibited promising results, showcasing high accuracy and precision in the\nidentification of patients with persistent inflammation. The findings of this\nstudy underscore the potential of machine learning in automating the detection\nof persistent inflammatory biomarkers in post-COVID-19 patients. These models\ncan serve as valuable tools for healthcare providers, facilitating early\ndiagnosis and personalized treatment strategies for individuals at risk of\npersistent inflammation, ultimately contributing to improved post-acute\nCOVID-19 care and patient well-being. Keywords: COVID-19, post-COVID-19,\ninflammation, biomarkers, machine learning, early detection.\n", "date": "2023-09-26", "categories": []}
{"id": "2307.03332", "title": "ACDNet: Attention-guided Collaborative Decision Network for Effective\n  Medication Recommendation", "abstract": "  Medication recommendation using Electronic Health Records (EHR) is\nchallenging due to complex medical data. Current approaches extract\nlongitudinal information from patient EHR to personalize recommendations.\nHowever, existing models often lack sufficient patient representation and\noverlook the importance of considering the similarity between a patient's\nmedication records and specific medicines. Therefore, an Attention-guided\nCollaborative Decision Network (ACDNet) for medication recommendation is\nproposed in this paper. Specifically, ACDNet utilizes attention mechanism and\nTransformer to effectively capture patient health conditions and medication\nrecords by modeling their historical visits at both global and local levels.\nACDNet also employs a collaborative decision framework, utilizing the\nsimilarity between medication records and medicine representation to facilitate\nthe recommendation process. The experimental results on two extensive medical\ndatasets, MIMIC-III and MIMIC-IV, clearly demonstrate that ACDNet outperforms\nstate-of-the-art models in terms of Jaccard, PR-AUC, and F1 score, reaffirming\nits superiority. Moreover, the ablation experiments provide solid evidence of\nthe effectiveness of each module in ACDNet, validating their contribution to\nthe overall performance. Furthermore, a detailed case study reinforces the\neffectiveness of ACDNet in medication recommendation based on EHR data,\nshowcasing its practical value in real-world healthcare scenarios.\n", "date": "2023-07-06", "categories": []}
{"id": "2309.14397", "title": "Predicting environment effects on breast cancer by implementing machine\n  learning", "abstract": "  The biggest Breast cancer is increasingly a major factor in female\nfatalities, overtaking heart disease. While genetic factors are important in\nthe growth of breast cancer, new research indicates that environmental factors\nalso play a substantial role in its occurrence and progression. The literature\non the various environmental factors that may affect breast cancer risk,\nincidence, and outcomes is thoroughly reviewed in this study report. The study\nstarts by looking at how lifestyle decisions, such as eating habits, exercise\nroutines, and alcohol consumption, may affect hormonal imbalances and\ninflammation, two important factors driving the development of breast cancer.\nAdditionally, it explores the part played by environmental contaminants such\npesticides, endocrine-disrupting chemicals (EDCs), and industrial emissions,\nall of which have been linked to a higher risk of developing breast cancer due\nto their interference with hormone signaling and DNA damage. Algorithms for\nmachine learning are used to express predictions. Logistic Regression, Random\nForest, KNN Algorithm, SVC and extra tree classifier. Metrics including the\nconfusion matrix correlation coefficient, F1-score, Precision, Recall, and ROC\ncurve were used to evaluate the models. The best accuracy among all the\nclassifiers is Random Forest with 0.91% accuracy and ROC curve 0.901% of\nLogistic Regression. The accuracy of the multiple algorithms for machine\nlearning utilized in this research was good, which is important and indicates\nthat these techniques could serve as replacement forecasting techniques in\nbreast cancer survival analysis, notably in the Asia region.\n", "date": "2023-09-25", "categories": []}
{"id": "2309.13049", "title": "AI-Driven Personalised Offloading Device Prescriptions: A Cutting-Edge\n  Approach to Preventing Diabetes-Related Plantar Forefoot Ulcers and\n  Complications", "abstract": "  Diabetes-related foot ulcers and complications are a significant concern for\nindividuals with diabetes, leading to severe health implications such as\nlower-limb amputation and reduced quality of life. This chapter discusses\napplying AI-driven personalised offloading device prescriptions as an advanced\nsolution for preventing such conditions. By harnessing the capabilities of\nartificial intelligence, this cutting-edge approach enables the prescription of\noffloading devices tailored to each patient's specific requirements. This\nincludes the patient's preferences on offloading devices such as footwear and\nfoot orthotics and their adaptations that suit the patient's intention of use\nand lifestyle. Through a series of studies, real-world data analysis and\nmachine learning algorithms, high-risk areas can be identified, facilitating\nthe recommendation of precise offloading strategies, including custom orthotic\ninsoles, shoe adaptations, or specialised footwear. By including\npatient-specific factors to promote adherence, proactively addressing pressure\npoints and promoting optimal foot mechanics, these personalised offloading\ndevices have the potential to minimise the occurrence of foot ulcers and\nassociated complications. This chapter proposes an AI-powered Clinical Decision\nSupport System (CDSS) to recommend personalised prescriptions of offloading\ndevices (footwear and insoles) for patients with diabetes who are at risk of\nfoot complications. This innovative approach signifies a transformative leap in\ndiabetic foot care, offering promising opportunities for preventive healthcare\ninterventions.\n", "date": "2023-09-06", "categories": []}
{"id": "2309.13636", "title": "Development of an intelligent system for the detection of corona virus\n  using artificial neural network", "abstract": "  This paper presents the development of an intelligent system for the\ndetection of coronavirus using artificial neural network. This was done after\nseries of literature review which indicated that high fever accounts for 87.9%\nof the COVID-19 symptoms. 683 temperature data of COVID-19 patients at >= 38C^o\nwere collected from Colliery hospital Enugu, Nigeria and used to train an\nartificial neural network detective model for the detection of COVID-19. The\nreference model generated was used converted into Verilog codes using Hardware\nDescription Language (HDL) and then burn into a Field Programming Gate Array\n(FPGA) controller using FPGA tool in Matlab. The performance of the model when\nevaluated using confusion matrix, regression and means square error (MSE)\nshowed that the regression value is 0.967; the accuracy is 97% and then MSE is\n0.00100Mu. These results all implied that the new detection system for is\nreliable and very effective for the detection of COVID-19.\n", "date": "2023-09-24", "categories": []}
{"id": "2308.09735", "title": "CTP:A Causal Interpretable Model for Non-Communicable Disease\n  Progression Prediction", "abstract": "  Non-communicable disease is the leading cause of death, emphasizing the need\nfor accurate prediction of disease progression and informed clinical\ndecision-making. Machine learning (ML) models have shown promise in this domain\nby capturing non-linear patterns within patient features. However, existing\nML-based models cannot provide causal interpretable predictions and estimate\ntreatment effects, limiting their decision-making perspective. In this study,\nwe propose a novel model called causal trajectory prediction (CTP) to tackle\nthe limitation. The CTP model combines trajectory prediction and causal\ndiscovery to enable accurate prediction of disease progression trajectories and\nuncover causal relationships between features. By incorporating a causal graph\ninto the prediction process, CTP ensures that ancestor features are not\ninfluenced by the treatment of descendant features, thereby enhancing the\ninterpretability of the model. By estimating the bounds of treatment effects,\neven in the presence of unmeasured confounders, the CTP provides valuable\ninsights for clinical decision-making. We evaluate the performance of the CTP\nusing simulated and real medical datasets. Experimental results demonstrate\nthat our model achieves satisfactory performance, highlighting its potential to\nassist clinical decisions. Source code is in\n\\href{https://github.com/DanielSun94/CFPA}{here}.\n", "date": "2023-08-18", "categories": []}
{"id": "2309.11995", "title": "Identification of pneumonia on chest x-ray images through machine\n  learning", "abstract": "  Pneumonia is the leading infectious cause of infant death in the world. When\nidentified early, it is possible to alter the prognosis of the patient, one\ncould use imaging exams to help in the diagnostic confirmation. Performing and\ninterpreting the exams as soon as possible is vital for a good treatment, with\nthe most common exam for this pathology being chest X-ray. The objective of\nthis study was to develop a software that identify the presence or absence of\npneumonia in chest radiographs. The software was developed as a computational\nmodel based on machine learning using transfer learning technique. For the\ntraining process, images were collected from a database available online with\nchildren's chest X-rays images taken at a hospital in China. After training,\nthe model was then exposed to new images, achieving relevant results on\nidentifying such pathology, reaching 98% sensitivity and 97.3% specificity for\nthe sample used for testing. It can be concluded that it is possible to develop\na software that identifies pneumonia in chest X-ray images.\n", "date": "2023-09-21", "categories": []}
{"id": "2309.12202", "title": "Empowering Precision Medicine: AI-Driven Schizophrenia Diagnosis via EEG\n  Signals: A Comprehensive Review from 2002-2023", "abstract": "  Schizophrenia (SZ) is a prevalent mental disorder characterized by cognitive,\nemotional, and behavioral changes. Symptoms of SZ include hallucinations,\nillusions, delusions, lack of motivation, and difficulties in concentration.\nDiagnosing SZ involves employing various tools, including clinical interviews,\nphysical examinations, psychological evaluations, the Diagnostic and\nStatistical Manual of Mental Disorders (DSM), and neuroimaging techniques.\nElectroencephalography (EEG) recording is a significant functional neuroimaging\nmodality that provides valuable insights into brain function during SZ.\nHowever, EEG signal analysis poses challenges for neurologists and scientists\ndue to the presence of artifacts, long-term recordings, and the utilization of\nmultiple channels. To address these challenges, researchers have introduced\nartificial intelligence (AI) techniques, encompassing conventional machine\nlearning (ML) and deep learning (DL) methods, to aid in SZ diagnosis. This\nstudy reviews papers focused on SZ diagnosis utilizing EEG signals and AI\nmethods. The introduction section provides a comprehensive explanation of SZ\ndiagnosis methods and intervention techniques. Subsequently, review papers in\nthis field are discussed, followed by an introduction to the AI methods\nemployed for SZ diagnosis and a summary of relevant papers presented in tabular\nform. Additionally, this study reports on the most significant challenges\nencountered in SZ diagnosis, as identified through a review of papers in this\nfield. Future directions to overcome these challenges are also addressed. The\ndiscussion section examines the specific details of each paper, culminating in\nthe presentation of conclusions and findings.\n", "date": "2023-09-14", "categories": []}
{"id": "2309.09993", "title": "Long-term Neurological Sequelae in Post-COVID-19 Patients: A Machine\n  Learning Approach to Predict Outcomes", "abstract": "  The COVID-19 pandemic has brought to light a concerning aspect of long-term\nneurological complications in post-recovery patients. This study delved into\nthe investigation of such neurological sequelae in a cohort of 500\npost-COVID-19 patients, encompassing individuals with varying illness severity.\nThe primary aim was to predict outcomes using a machine learning approach based\non diverse clinical data and neuroimaging parameters. The results revealed that\n68% of the post-COVID-19 patients reported experiencing neurological symptoms,\nwith fatigue, headache, and anosmia being the most common manifestations.\nMoreover, 22% of the patients exhibited more severe neurological complications,\nincluding encephalopathy and stroke. The application of machine learning models\nshowed promising results in predicting long-term neurological outcomes.\nNotably, the Random Forest model achieved an accuracy of 85%, sensitivity of\n80%, and specificity of 90% in identifying patients at risk of developing\nneurological sequelae. These findings underscore the importance of continuous\nmonitoring and follow-up care for post-COVID-19 patients, particularly in\nrelation to potential neurological complications. The integration of machine\nlearning-based outcome prediction offers a valuable tool for early intervention\nand personalized treatment strategies, aiming to improve patient care and\nclinical decision-making. In conclusion, this study sheds light on the\nprevalence of long-term neurological complications in post-COVID-19 patients\nand demonstrates the potential of machine learning in predicting outcomes,\nthereby contributing to enhanced patient management and better health outcomes.\nFurther research and larger studies are warranted to validate and refine these\npredictive models and to gain deeper insights into the underlying mechanisms of\npost-COVID-19 neurological sequelae.\n", "date": "2023-09-15", "categories": []}
{"id": "2309.08623", "title": "Balance Measures Derived from Insole Sensor Differentiate Prodromal\n  Dementia with Lewy Bodies", "abstract": "  Dementia with Lewy bodies is the second most common type of neurodegenerative\ndementia, and identification at the prodromal stage$-$i.e., mild cognitive\nimpairment due to Lewy bodies (MCI-LB)$-$is important for providing appropriate\ncare. However, MCI-LB is often underrecognized because of its diversity in\nclinical manifestations and similarities with other conditions such as mild\ncognitive impairment due to Alzheimer's disease (MCI-AD). In this study, we\npropose a machine learning-based automatic pipeline that helps identify MCI-LB\nby exploiting balance measures acquired with an insole sensor during a 30-s\nstanding task. An experiment with 98 participants (14 MCI-LB, 38 MCI-AD, 46\ncognitively normal) showed that the resultant models could discriminate MCI-LB\nfrom the other groups with up to 78.0% accuracy (AUC: 0.681), which was 6.8%\nbetter than the accuracy of a reference model based on demographic and clinical\nneuropsychological measures. Our findings may open up a new approach for timely\nidentification of MCI-LB, enabling better care for patients.\n", "date": "2023-09-11", "categories": []}
{"id": "2304.07097", "title": "Interpretable Weighted Siamese Network to Predict the Time to Onset of\n  Alzheimer's Disease from MRI Images", "abstract": "  Alzheimer's Disease (AD) is a progressive disease preceded by Mild Cognitive\nImpairment (MCI). Early detection of AD is crucial for making treatment\ndecisions. However, most of the literature on computer-assisted detection of AD\nfocuses on classifying brain images into one of three major categories:\nhealthy, MCI, and AD; or categorizing MCI patients into (1) progressive: those\nwho progress from MCI to AD at a future examination time, and (2) stable: those\nwho stay as MCI and never progress to AD. This misses the opportunity to\naccurately identify the trajectory of progressive MCI patients. In this paper,\nwe revisit the brain image classification task for AD identification and\nre-frame it as an ordinal classification task to predict how close a patient is\nto the severe AD stage. To this end, we select progressive MCI patients from\nthe Alzheimer's Disease Neuroimaging Initiative (ADNI) dataset and construct an\nordinal dataset with a prediction target that indicates the time to progression\nto AD. We train a Siamese network model to predict the time to onset of AD\nbased on MRI brain images. We also propose a Weighted variety of Siamese\nnetwork and compare its performance to a baseline model. Our evaluations show\nthat incorporating a weighting factor to Siamese networks brings considerable\nperformance gain at predicting how close input brain MRI images are to\nprogressing to AD. Moreover, we complement our results with an interpretation\nof the learned embedding space of the Siamese networks using a model\nexplainability technique.\n", "date": "2023-04-14", "categories": []}
{"id": "2309.07134", "title": "Entropy-based machine learning model for diagnosis and monitoring of\n  Parkinson's Disease in smart IoT environment", "abstract": "  The study presents the concept of a computationally efficient machine\nlearning (ML) model for diagnosing and monitoring Parkinson's disease (PD) in\nan Internet of Things (IoT) environment using rest-state EEG signals (rs-EEG).\nWe computed different types of entropy from EEG signals and found that Fuzzy\nEntropy performed the best in diagnosing and monitoring PD using rs-EEG. We\nalso investigated different combinations of signal frequency ranges and EEG\nchannels to accurately diagnose PD. Finally, with a fewer number of features\n(11 features), we achieved a maximum classification accuracy (ARKF) of ~99.9%.\nThe most prominent frequency range of EEG signals has been identified, and we\nhave found that high classification accuracy depends on low-frequency signal\ncomponents (0-4 Hz). Moreover, the most informative signals were mainly\nreceived from the right hemisphere of the head (F8, P8, T8, FC6). Furthermore,\nwe assessed the accuracy of the diagnosis of PD using three different lengths\nof EEG data (150-1000 samples). Because the computational complexity is reduced\nby reducing the input data. As a result, we have achieved a maximum mean\naccuracy of 99.9% for a sample length (LEEG) of 1000 (~7.8 seconds), 98.2% with\na LEEG of 800 (~6.2 seconds), and 79.3% for LEEG = 150 (~1.2 seconds). By\nreducing the number of features and segment lengths, the computational cost of\nclassification can be reduced. Lower-performance smart ML sensors can be used\nin IoT environments for enhances human resilience to PD.\n", "date": "2023-08-28", "categories": []}
{"id": "2303.13722", "title": "Natural language processing to automatically extract the presence and\n  severity of esophagitis in notes of patients undergoing radiotherapy", "abstract": "  Radiotherapy (RT) toxicities can impair survival and quality-of-life, yet\nremain under-studied. Real-world evidence holds potential to improve our\nunderstanding of toxicities, but toxicity information is often only in clinical\nnotes. We developed natural language processing (NLP) models to identify the\npresence and severity of esophagitis from notes of patients treated with\nthoracic RT. We fine-tuned statistical and pre-trained BERT-based models for\nthree esophagitis classification tasks: Task 1) presence of esophagitis, Task\n2) severe esophagitis or not, and Task 3) no esophagitis vs. grade 1 vs. grade\n2-3. Transferability was tested on 345 notes from patients with esophageal\ncancer undergoing RT.\n  Fine-tuning PubmedBERT yielded the best performance. The best macro-F1 was\n0.92, 0.82, and 0.74 for Task 1, 2, and 3, respectively. Selecting the most\ninformative note sections during fine-tuning improved macro-F1 by over 2% for\nall tasks. Silver-labeled data improved the macro-F1 by over 3% across all\ntasks. For the esophageal cancer notes, the best macro-F1 was 0.73, 0.74, and\n0.65 for Task 1, 2, and 3, respectively, without additional fine-tuning.\n  To our knowledge, this is the first effort to automatically extract\nesophagitis toxicity severity according to CTCAE guidelines from clinic notes.\nThe promising performance provides proof-of-concept for NLP-based automated\ndetailed toxicity monitoring in expanded domains.\n", "date": "2023-03-24", "categories": []}
{"id": "2309.06989", "title": "Remote Inference of Cognitive Scores in ALS Patients Using a Picture\n  Description", "abstract": "  Amyotrophic lateral sclerosis is a fatal disease that not only affects\nmovement, speech, and breath but also cognition. Recent studies have focused on\nthe use of language analysis techniques to detect ALS and infer scales for\nmonitoring functional progression. In this paper, we focused on another\nimportant aspect, cognitive impairment, which affects 35-50% of the ALS\npopulation. In an effort to reach the ALS population, which frequently exhibits\nmobility limitations, we implemented the digital version of the Edinburgh\nCognitive and Behavioral ALS Screen (ECAS) test for the first time. This test\nwhich is designed to measure cognitive impairment was remotely performed by 56\nparticipants from the EverythingALS Speech Study. As part of the study,\nparticipants (ALS and non-ALS) were asked to describe weekly one picture from a\npool of many pictures with complex scenes displayed on their computer at home.\nWe analyze the descriptions performed within +/- 60 days from the day the ECAS\ntest was administered and extract different types of linguistic and acoustic\nfeatures. We input those features into linear regression models to infer 5 ECAS\nsub-scores and the total score. Speech samples from the picture description are\nreliable enough to predict the ECAS subs-scores, achieving statistically\nsignificant Spearman correlation values between 0.32 and 0.51 for the model's\nperformance using 10-fold cross-validation.\n", "date": "2023-09-13", "categories": []}
{"id": "2309.05675", "title": "SHAPE: A Sample-adaptive Hierarchical Prediction Network for Medication\n  Recommendation", "abstract": "  Effectively medication recommendation with complex multimorbidity conditions\nis a critical task in healthcare. Most existing works predicted medications\nbased on longitudinal records, which assumed the information transmitted\npatterns of learning longitudinal sequence data are stable and intra-visit\nmedical events are serialized. However, the following conditions may have been\nignored: 1) A more compact encoder for intra-relationship in the intra-visit\nmedical event is urgent; 2) Strategies for learning accurate representations of\nthe variable longitudinal sequences of patients are different. In this paper,\nwe proposed a novel Sample-adaptive Hierarchical medicAtion Prediction nEtwork,\ntermed SHAPE, to tackle the above challenges in the medication recommendation\ntask. Specifically, we design a compact intra-visit set encoder to encode the\nrelationship in the medical event for obtaining visit-level representation and\nthen develop an inter-visit longitudinal encoder to learn the patient-level\nlongitudinal representation efficiently. To endow the model with the capability\nof modeling the variable visit length, we introduce a soft curriculum learning\nmethod to assign the difficulty of each sample automatically by the visit\nlength. Extensive experiments on a benchmark dataset verify the superiority of\nour model compared with several state-of-the-art baselines.\n", "date": "2023-09-09", "categories": []}
{"id": "2309.05857", "title": "Radiomics Boosts Deep Learning Model for IPMN Classification", "abstract": "  Intraductal Papillary Mucinous Neoplasm (IPMN) cysts are pre-malignant\npancreas lesions, and they can progress into pancreatic cancer. Therefore,\ndetecting and stratifying their risk level is of ultimate importance for\neffective treatment planning and disease control. However, this is a highly\nchallenging task because of the diverse and irregular shape, texture, and size\nof the IPMN cysts as well as the pancreas. In this study, we propose a novel\ncomputer-aided diagnosis pipeline for IPMN risk classification from\nmulti-contrast MRI scans. Our proposed analysis framework includes an efficient\nvolumetric self-adapting segmentation strategy for pancreas delineation,\nfollowed by a newly designed deep learning-based classification scheme with a\nradiomics-based predictive approach. We test our proposed decision-fusion model\nin multi-center data sets of 246 multi-contrast MRI scans and obtain superior\nperformance to the state of the art (SOTA) in this field. Our ablation studies\ndemonstrate the significance of both radiomics and deep learning modules for\nachieving the new SOTA performance compared to international guidelines and\npublished studies (81.9\\% vs 61.3\\% in accuracy). Our findings have important\nimplications for clinical decision-making. In a series of rigorous experiments\non multi-center data sets (246 MRI scans from five centers), we achieved\nunprecedented performance (81.9\\% accuracy).\n", "date": "2023-09-11", "categories": []}
{"id": "2302.07854", "title": "Benchmarking Continuous Time Models for Predicting Multiple Sclerosis\n  Progression", "abstract": "  Multiple sclerosis is a disease that affects the brain and spinal cord, it\ncan lead to severe disability and has no known cure. The majority of prior work\nin machine learning for multiple sclerosis has been centered around using\nMagnetic Resonance Imaging scans or laboratory tests; these modalities are both\nexpensive to acquire and can be unreliable. In a recent paper it was shown that\ndisease progression can be predicted effectively using performance outcome\nmeasures and demographic data. In our work we build on this to investigate the\nmodeling side, using continuous time models to predict progression. We\nbenchmark four continuous time models using a publicly available multiple\nsclerosis dataset. We find that the best continuous model is often able to\noutperform the best benchmarked discrete time model. We also carry out an\nextensive ablation to discover the sources of performance gains, we find that\nstandardizing existing features leads to a larger performance increase than\ninterpolating missing features.\n", "date": "2023-02-15", "categories": []}
{"id": "2309.01007", "title": "Comparative Analysis of Deep Learning Architectures for Breast Cancer\n  Diagnosis Using the BreaKHis Dataset", "abstract": "  Cancer is an extremely difficult and dangerous health problem because it\nmanifests in so many different ways and affects so many different organs and\ntissues. The primary goal of this research was to evaluate deep learning\nmodels' ability to correctly identify breast cancer cases using the BreakHis\ndataset. The BreakHis dataset covers a wide range of breast cancer subtypes\nthrough its huge collection of histopathological pictures. In this study, we\nuse and compare the performance of five well-known deep learning models for\ncancer classification: VGG, ResNet, Xception, Inception, and InceptionResNet.\nThe results placed the Xception model at the top, with an F1 score of 0.9 and\nan accuracy of 89%. At the same time, the Inception and InceptionResNet models\nboth hit accuracy of 87% . However, the F1 score for the Inception model was\n87, while that for the InceptionResNet model was 86. These results demonstrate\nthe importance of deep learning methods in making correct breast cancer\ndiagnoses. This highlights the potential to provide improved diagnostic\nservices to patients. The findings of this study not only improve current\nmethods of cancer diagnosis, but also make significant contributions to the\ncreation of new and improved cancer treatment strategies. In a nutshell, the\nresults of this study represent a major advancement in the direction of\nachieving these vital healthcare goals.\n", "date": "2023-09-02", "categories": []}
{"id": "2309.02884", "title": "Aligning Large Language Models for Clinical Tasks", "abstract": "  Large Language Models (LLMs) have demonstrated remarkable adaptability,\nshowcasing their capacity to excel in tasks for which they were not explicitly\ntrained. However, despite their impressive natural language processing (NLP)\ncapabilities, effective alignment of LLMs remains a crucial challenge when\ndeploying them for specific clinical applications. The ability to generate\nresponses with factually accurate content and to engage in non-trivial\nreasoning steps are crucial for the LLMs to be eligible for applications in\nclinical medicine. Employing a combination of techniques including\ninstruction-tuning and in-prompt strategies like few-shot and chain-of-thought\nprompting has significantly enhanced the performance of LLMs. Our proposed\nalignment strategy for medical question-answering, known as\n'expand-guess-refine', offers a parameter and data-efficient solution. A\npreliminary analysis of this method demonstrated outstanding performance,\nachieving a score of 70.63% on a subset of questions sourced from the USMLE\ndataset.\n", "date": "2023-09-06", "categories": []}
{"id": "2309.03331", "title": "Expert Uncertainty and Severity Aware Chest X-Ray Classification by\n  Multi-Relationship Graph Learning", "abstract": "  Patients undergoing chest X-rays (CXR) often endure multiple lung diseases.\nWhen evaluating a patient's condition, due to the complex pathologies, subtle\ntexture changes of different lung lesions in images, and patient condition\ndifferences, radiologists may make uncertain even when they have experienced\nlong-term clinical training and professional guidance, which makes much noise\nin extracting disease labels based on CXR reports. In this paper, we re-extract\ndisease labels from CXR reports to make them more realistic by considering\ndisease severity and uncertainty in classification. Our contributions are as\nfollows: 1. We re-extracted the disease labels with severity and uncertainty by\na rule-based approach with keywords discussed with clinical experts. 2. To\nfurther improve the explainability of chest X-ray diagnosis, we designed a\nmulti-relationship graph learning method with an expert uncertainty-aware loss\nfunction. 3. Our multi-relationship graph learning method can also interpret\nthe disease classification results. Our experimental results show that models\nconsidering disease severity and uncertainty outperform previous\nstate-of-the-art methods.\n", "date": "2023-09-06", "categories": []}
{"id": "2309.03386", "title": "Community-Based Hierarchical Positive-Unlabeled (PU) Model Fusion for\n  Chronic Disease Prediction", "abstract": "  Positive-Unlabeled (PU) Learning is a challenge presented by binary\nclassification problems where there is an abundance of unlabeled data along\nwith a small number of positive data instances, which can be used to address\nchronic disease screening problem. State-of-the-art PU learning methods have\nresulted in the development of various risk estimators, yet they neglect the\ndifferences among distinct populations. To address this issue, we present a\nnovel Positive-Unlabeled Learning Tree (PUtree) algorithm. PUtree is designed\nto take into account communities such as different age or income brackets, in\ntasks of chronic disease prediction. We propose a novel approach for binary\ndecision-making, which hierarchically builds community-based PU models and then\naggregates their deliverables. Our method can explicate each PU model on the\ntree for the optimized non-leaf PU node splitting. Furthermore, a mask-recovery\ndata augmentation strategy enables sufficient training of the model in\nindividual communities. Additionally, the proposed approach includes an\nadversarial PU risk estimator to capture hierarchical PU-relationships, and a\nmodel fusion network that integrates data from each tree path, resulting in\nrobust binary classification results. We demonstrate the superior performance\nof PUtree as well as its variants on two benchmarks and a new\ndiabetes-prediction dataset.\n", "date": "2023-09-06", "categories": []}
{"id": "2309.03664", "title": "Alzheimer Disease Detection from Raman Spectroscopy of the Cerebrospinal\n  Fluid via Topological Machine Learning", "abstract": "  The cerebrospinal fluid (CSF) of 19 subjects who received a clinical\ndiagnosis of Alzheimer's disease (AD) as well as of 5 pathological controls\nhave been collected and analysed by Raman spectroscopy (RS). We investigated\nwhether the raw and preprocessed Raman spectra could be used to distinguish AD\nfrom controls. First, we applied standard Machine Learning (ML) methods\nobtaining unsatisfactory results. Then, we applied ML to a set of topological\ndescriptors extracted from raw spectra, achieving a very good classification\naccuracy (>87%). Although our results are preliminary, they indicate that RS\nand topological analysis together may provide an effective combination to\nconfirm or disprove a clinical diagnosis of AD. The next steps will include\nenlarging the dataset of CSF samples to validate the proposed method better\nand, possibly, to understand if topological data analysis could support the\ncharacterization of AD subtypes.\n", "date": "2023-09-07", "categories": []}
{"id": "2309.03759", "title": "M(otion)-mode Based Prediction of Ejection Fraction using\n  Echocardiograms", "abstract": "  Early detection of cardiac dysfunction through routine screening is vital for\ndiagnosing cardiovascular diseases. An important metric of cardiac function is\nthe left ventricular ejection fraction (EF), where lower EF is associated with\ncardiomyopathy. Echocardiography is a popular diagnostic tool in cardiology,\nwith ultrasound being a low-cost, real-time, and non-ionizing technology.\nHowever, human assessment of echocardiograms for calculating EF is\ntime-consuming and expertise-demanding, raising the need for an automated\napproach. In this work, we propose using the M(otion)-mode of echocardiograms\nfor estimating the EF and classifying cardiomyopathy. We generate multiple\nartificial M-mode images from a single echocardiogram and combine them using\noff-the-shelf model architectures. Additionally, we extend contrastive learning\n(CL) to cardiac imaging to learn meaningful representations from exploiting\nstructures in unlabeled data allowing the model to achieve high accuracy, even\nwith limited annotations. Our experiments show that the supervised setting\nconverges with only ten modes and is comparable to the baseline method while\nbypassing its cumbersome training process and being computationally much more\nefficient. Furthermore, CL using M-mode images is helpful for limited data\nscenarios, such as having labels for only 200 patients, which is common in\nmedical applications.\n", "date": "2023-09-07", "categories": []}
{"id": "2305.19347", "title": "Machine Learning Based IoT Adaptive Architecture for Epilepsy Seizure\n  Detection: Anatomy and Analysis", "abstract": "  A seizure tracking system is crucial for monitoring and evaluating epilepsy\ntreatments. Caretaker seizure diaries are used in epilepsy care today, but\nclinical seizure monitoring may miss seizures. Monitoring devices that can be\nworn may be better tolerated and more suitable for long-term ambulatory use.\nMany techniques and methods are proposed for seizure detection; However,\nsimplicity and affordability are key concepts for daily use while preserving\nthe accuracy of the detection. In this study, we propose a versal, affordable\nnoninvasive based on a simple real-time k-Nearest-Neighbors (kNN) machine\nlearning that can be customized and adapted to individual users in less than\nfour seconds of training time; the system was verified and validated using 500\nsubjects, with seizure detection data sampled at 178 Hz, the operated with a\nmean accuracy of (94.5%).\n", "date": "2023-05-30", "categories": []}
{"id": "2309.02467", "title": "Developing A Fair Individualized Polysocial Risk Score (iPsRS) for\n  Identifying Increased Social Risk of Hospitalizations in Patients with Type 2\n  Diabetes (T2D)", "abstract": "  Background: Racial and ethnic minority groups and individuals facing social\ndisadvantages, which often stem from their social determinants of health\n(SDoH), bear a disproportionate burden of type 2 diabetes (T2D) and its\ncomplications. It is therefore crucial to implement effective social risk\nmanagement strategies at the point of care. Objective: To develop an EHR-based\nmachine learning (ML) analytical pipeline to identify the unmet social needs\nassociated with hospitalization risk in patients with T2D. Methods: We\nidentified 10,192 T2D patients from the EHR data (from 2012 to 2022) from the\nUniversity of Florida Health Integrated Data Repository, including contextual\nSDoH (e.g., neighborhood deprivation) and individual-level SDoH (e.g., housing\nstability). We developed an electronic health records (EHR)-based machine\nlearning (ML) analytic pipeline, namely individualized polysocial risk score\n(iPsRS), to identify high social risk associated with hospitalizations in T2D\npatients, along with explainable AI (XAI) techniques and fairness assessment\nand optimization. Results: Our iPsRS achieved a C statistic of 0.72 in\npredicting 1-year hospitalization after fairness optimization across\nracial-ethnic groups. The iPsRS showed excellent utility for capturing\nindividuals at high hospitalization risk; the actual 1-year hospitalization\nrate in the top 5% of iPsRS was ~13 times as high as the bottom decile.\nConclusion: Our ML pipeline iPsRS can fairly and accurately screen for patients\nwho have increased social risk leading to hospitalization in T2D patients.\n", "date": "2023-09-05", "categories": []}
{"id": "2309.02562", "title": "Recurrence-Free Survival Prediction for Anal Squamous Cell Carcinoma\n  Chemoradiotherapy using Planning CT-based Radiomics Model", "abstract": "  Objectives: Approximately 30% of non-metastatic anal squamous cell carcinoma\n(ASCC) patients will experience recurrence after chemoradiotherapy (CRT), and\ncurrently available clinical variables are poor predictors of treatment\nresponse. We aimed to develop a model leveraging information extracted from\nradiation pretreatment planning CT to predict recurrence-free survival (RFS) in\nASCC patients after CRT. Methods: Radiomics features were extracted from\nplanning CT images of 96 ASCC patients. Following pre-feature selection, the\noptimal feature set was selected via step-forward feature selection with a\nmultivariate Cox proportional hazard model. The RFS prediction was generated\nfrom a radiomics-clinical combined model based on an optimal feature set with\nfive repeats of five-fold cross validation. The risk stratification ability of\nthe proposed model was evaluated with Kaplan-Meier analysis. Results: Shape-\nand texture-based radiomics features significantly predicted RFS. Compared to a\nclinical-only model, radiomics-clinical combined model achieves better\nperformance in the testing cohort with higher C-index (0.80 vs 0.73) and AUC\n(0.84 vs 0.79 for 1-year RFS, 0.84 vs 0.78 for 2-year RFS, and 0.86 vs 0.83 for\n3-year RFS), leading to distinctive high- and low-risk of recurrence groups\n(p<0.001). Conclusions: A treatment planning CT based radiomics and clinical\ncombined model had improved prognostic performance in predicting RFS for ASCC\npatients treated with CRT as compared to a model using clinical features only.\n", "date": "2023-09-05", "categories": []}
{"id": "2309.02604", "title": "Screening of Pneumonia and Urinary Tract Infection at Triage using\n  TriNet", "abstract": "  Due to the steady rise in population demographics and longevity, emergency\ndepartment visits are increasing across North America. As more patients visit\nthe emergency department, traditional clinical workflows become overloaded and\ninefficient, leading to prolonged wait-times and reduced healthcare quality.\nOne of such workflows is the triage medical directive, impeded by limited human\nworkload, inaccurate diagnoses and invasive over-testing. To address this\nissue, we propose TriNet: a machine learning model for medical directives that\nautomates first-line screening at triage for conditions requiring downstream\ntesting for diagnosis confirmation. To verify screening potential, TriNet was\ntrained on hospital triage data and achieved high positive predictive values in\ndetecting pneumonia (0.86) and urinary tract infection (0.93). These models\noutperform current clinical benchmarks, indicating that machine-learning\nmedical directives can offer cost-free, non-invasive screening with high\nspecificity for common conditions, reducing the risk of over-testing while\nincreasing emergency department efficiency.\n", "date": "2023-09-05", "categories": []}
{"id": "2210.05881", "title": "Deterioration Prediction using Time-Series of Three Vital Signs and\n  Current Clinical Features Amongst COVID-19 Patients", "abstract": "  Unrecognized patient deterioration can lead to high morbidity and mortality.\nMost existing deterioration prediction models require a large number of\nclinical information, typically collected in hospital settings, such as medical\nimages or comprehensive laboratory tests. This is infeasible for telehealth\nsolutions and highlights a gap in deterioration prediction models that are\nbased on minimal data, which can be recorded at a large scale in any clinic,\nnursing home, or even at the patient's home. In this study, we propose and\ndevelop a prognostic model that predicts if a patient will experience\ndeterioration in the forthcoming 3-24 hours. The model sequentially processes\nroutine triadic vital signs: (a) oxygen saturation, (b) heart rate, and (c)\ntemperature. The model is also provided with basic patient information,\nincluding sex, age, vaccination status, vaccination date, and status of\nobesity, hypertension, or diabetes. We train and evaluate the model using data\ncollected from 37,006 COVID-19 patients at NYU Langone Health in New York, USA.\nThe model achieves an area under the receiver operating characteristic curve\n(AUROC) of 0.808-0.880 for 3-24 hour deterioration prediction. We also conduct\nocclusion experiments to evaluate the importance of each input feature, where\nthe results reveal the significance of continuously monitoring the variations\nof the vital signs. Our results show the prospect of accurate deterioration\nforecast using a minimum feature set that can be relatively easily obtained\nusing wearable devices and self-reported patient information.\n", "date": "2022-10-12", "categories": []}
{"id": "2308.06763", "title": "Discovering the Symptom Patterns of COVID-19 from Recovered and Deceased\n  Patients Using Apriori Association Rule Mining", "abstract": "  The COVID-19 pandemic has a devastating impact globally, claiming millions of\nlives and causing significant social and economic disruptions. In order to\noptimize decision-making and allocate limited resources, it is essential to\nidentify COVID-19 symptoms and determine the severity of each case. Machine\nlearning algorithms offer a potent tool in the medical field, particularly in\nmining clinical datasets for useful information and guiding scientific\ndecisions. Association rule mining is a machine learning technique for\nextracting hidden patterns from data. This paper presents an application of\nassociation rule mining based Apriori algorithm to discover symptom patterns\nfrom COVID-19 patients. The study, using 2875 patient's records, identified the\nmost common signs and symptoms as apnea (72%), cough (64%), fever (59%),\nweakness (18%), myalgia (14.5%), and sore throat (12%). The proposed method\nprovides clinicians with valuable insight into disease that can assist them in\nmanaging and treating it effectively.\n", "date": "2023-08-13", "categories": []}
{"id": "2308.11773", "title": "Identifying depression-related topics in smartphone-collected\n  free-response speech recordings using an automatic speech recognition system\n  and a deep learning topic model", "abstract": "  Language use has been shown to correlate with depression, but large-scale\nvalidation is needed. Traditional methods like clinic studies are expensive.\nSo, natural language processing has been employed on social media to predict\ndepression, but limitations remain-lack of validated labels, biased user\nsamples, and no context. Our study identified 29 topics in 3919\nsmartphone-collected speech recordings from 265 participants using the Whisper\ntool and BERTopic model. Six topics with a median PHQ-8 greater than or equal\nto 10 were regarded as risk topics for depression: No Expectations, Sleep,\nMental Therapy, Haircut, Studying, and Coursework. To elucidate the topic\nemergence and associations with depression, we compared behavioral (from\nwearables) and linguistic characteristics across identified topics. The\ncorrelation between topic shifts and changes in depression severity over time\nwas also investigated, indicating the importance of longitudinally monitoring\nlanguage use. We also tested the BERTopic model on a similar smaller dataset\n(356 speech recordings from 57 participants), obtaining some consistent\nresults. In summary, our findings demonstrate specific speech topics may\nindicate depression severity. The presented data-driven workflow provides a\npractical approach to collecting and analyzing large-scale speech data from\nreal-world settings for digital health research.\n", "date": "2023-08-22", "categories": []}
{"id": "2308.16067", "title": "Consensus of state of the art mortality prediction models: From\n  all-cause mortality to sudden death prediction", "abstract": "  Worldwide, many millions of people die suddenly and unexpectedly each year,\neither with or without a prior history of cardiovascular disease. Such events\nare sparse (once in a lifetime), many victims will not have had prior\ninvestigations for cardiac disease and many different definitions of sudden\ndeath exist. Accordingly, sudden death is hard to predict.\n  This analysis used NHS Electronic Health Records (EHRs) for people aged\n$\\geq$50 years living in the Greater Glasgow and Clyde (GG\\&C) region in 2010\n(n = 380,000) to try to overcome these challenges. We investigated whether\nmedical history, blood tests, prescription of medicines, and hospitalisations\nmight, in combination, predict a heightened risk of sudden death.\n  We compared the performance of models trained to predict either sudden death\nor all-cause mortality. We built six models for each outcome of interest: three\ntaken from state-of-the-art research (BEHRT, Deepr and Deep Patient), and three\nof our own creation. We trained these using two different data representations:\na language-based representation, and a sparse temporal matrix.\n  We used global interpretability to understand the most important features of\neach model, and compare how much agreement there was amongst models using Rank\nBiased Overlap. It is challenging to account for correlated variables without\nincreasing the complexity of the interpretability technique. We overcame this\nby clustering features into groups and comparing the most important groups for\neach model. We found the agreement between models to be much higher when\naccounting for correlated variables.\n  Our analysis emphasises the challenge of predicting sudden death and\nemphasises the need for better understanding and interpretation of machine\nlearning models applied to healthcare applications.\n", "date": "2023-08-30", "categories": []}
{"id": "2309.01783", "title": "Survival Prediction from Imbalance colorectal cancer dataset using\n  hybrid sampling methods and tree-based classifiers", "abstract": "  Background and Objective: Colorectal cancer is a high mortality cancer.\nClinical data analysis plays a crucial role in predicting the survival of\ncolorectal cancer patients, enabling clinicians to make informed treatment\ndecisions. However, utilizing clinical data can be challenging, especially when\ndealing with imbalanced outcomes. This paper focuses on developing algorithms\nto predict 1-, 3-, and 5-year survival of colorectal cancer patients using\nclinical datasets, with particular emphasis on the highly imbalanced 1-year\nsurvival prediction task. To address this issue, we propose a method that\ncreates a pipeline of some of standard balancing techniques to increase the\ntrue positive rate. Evaluation is conducted on a colorectal cancer dataset from\nthe SEER database. Methods: The pre-processing step consists of removing\nrecords with missing values and merging categories. The minority class of\n1-year and 3-year survival tasks consists of 10% and 20% of the data,\nrespectively. Edited Nearest Neighbor, Repeated edited nearest neighbor (RENN),\nSynthetic Minority Over-sampling Techniques (SMOTE), and pipelines of SMOTE and\nRENN approaches were used and compared for balancing the data with tree-based\nclassifiers. Decision Trees, Random Forest, Extra Tree, eXtreme Gradient\nBoosting, and Light Gradient Boosting (LGBM) are used in this article. Method.\nResults: The performance evaluation utilizes a 5-fold cross-validation\napproach. In the case of highly imbalanced datasets (1-year), our proposed\nmethod with LGBM outperforms other sampling methods with the sensitivity of\n72.30%. For the task of imbalance (3-year survival), the combination of RENN\nand LGBM achieves a sensitivity of 80.81%, indicating that our proposed method\nworks best for highly imbalanced datasets. Conclusions: Our proposed method\nsignificantly improves mortality prediction for the minority class of\ncolorectal cancer patients.\n", "date": "2023-09-04", "categories": []}
{"id": "2309.02237", "title": "Sample Size in Natural Language Processing within Healthcare Research", "abstract": "  Sample size calculation is an essential step in most data-based disciplines.\nLarge enough samples ensure representativeness of the population and determine\nthe precision of estimates. This is true for most quantitative studies,\nincluding those that employ machine learning methods, such as natural language\nprocessing, where free-text is used to generate predictions and classify\ninstances of text. Within the healthcare domain, the lack of sufficient corpora\nof previously collected data can be a limiting factor when determining sample\nsizes for new studies. This paper tries to address the issue by making\nrecommendations on sample sizes for text classification tasks in the healthcare\ndomain.\n  Models trained on the MIMIC-III database of critical care records from Beth\nIsrael Deaconess Medical Center were used to classify documents as having or\nnot having Unspecified Essential Hypertension, the most common diagnosis code\nin the database. Simulations were performed using various classifiers on\ndifferent sample sizes and class proportions. This was repeated for a\ncomparatively less common diagnosis code within the database of diabetes\nmellitus without mention of complication.\n  Smaller sample sizes resulted in better results when using a K-nearest\nneighbours classifier, whereas larger sample sizes provided better results with\nsupport vector machines and BERT models. Overall, a sample size larger than\n1000 was sufficient to provide decent performance metrics.\n  The simulations conducted within this study provide guidelines that can be\nused as recommendations for selecting appropriate sample sizes and class\nproportions, and for predicting expected performance, when building classifiers\nfor textual healthcare data. The methodology used here can be modified for\nsample size estimates calculations with other datasets.\n", "date": "2023-09-05", "categories": []}
{"id": "2309.00330", "title": "Multitask Deep Learning for Accurate Risk Stratification and Prediction\n  of Next Steps for Coronary CT Angiography Patients", "abstract": "  Diagnostic investigation has an important role in risk stratification and\nclinical decision making of patients with suspected and documented Coronary\nArtery Disease (CAD). However, the majority of existing tools are primarily\nfocused on the selection of gatekeeper tests, whereas only a handful of systems\ncontain information regarding the downstream testing or treatment. We propose a\nmulti-task deep learning model to support risk stratification and down-stream\ntest selection for patients undergoing Coronary Computed Tomography Angiography\n(CCTA). The analysis included 14,021 patients who underwent CCTA between 2006\nand 2017. Our novel multitask deep learning framework extends the state-of-the\nart Perceiver model to deal with real-world CCTA report data. Our model\nachieved an Area Under the receiver operating characteristic Curve (AUC) of\n0.76 in CAD risk stratification, and 0.72 AUC in predicting downstream tests.\nOur proposed deep learning model can accurately estimate the likelihood of CAD\nand provide recommended downstream tests based on prior CCTA data. In clinical\npractice, the utilization of such an approach could bring a paradigm shift in\nrisk stratification and downstream management. Despite significant progress\nusing deep learning models for tabular data, they do not outperform gradient\nboosting decision trees, and further research is required in this area.\nHowever, neural networks appear to benefit more readily from multi-task\nlearning than tree-based models. This could offset the shortcomings of using\nsingle task learning approach when working with tabular data.\n", "date": "2023-09-01", "categories": []}
{"id": "2308.14657", "title": "DeepHealthNet: Adolescent Obesity Prediction System Based on a Deep\n  Learning Framework", "abstract": "  Childhood and adolescent obesity rates are a global concern because obesity\nis associated with chronic diseases and long-term health risks. Artificial\nintelligence technology has emerged as a promising solution to accurately\npredict obesity rates and provide personalized feedback to adolescents. This\nstudy emphasizes the importance of early identification and prevention of\nobesity-related health issues. Factors such as height, weight, waist\ncircumference, calorie intake, physical activity levels, and other relevant\nhealth information need to be considered for developing robust algorithms for\nobesity rate prediction and delivering personalized feedback. Hence, by\ncollecting health datasets from 321 adolescents, we proposed an adolescent\nobesity prediction system that provides personalized predictions and assists\nindividuals in making informed health decisions. Our proposed deep learning\nframework, DeepHealthNet, effectively trains the model using data augmentation\ntechniques, even when daily health data are limited, resulting in improved\nprediction accuracy (acc: 0.8842). Additionally, the study revealed variations\nin the prediction of the obesity rate between boys (acc: 0.9320) and girls\n(acc: 0.9163), allowing the identification of disparities and the determination\nof the optimal time to provide feedback. The proposed system shows significant\npotential in effectively addressing childhood and adolescent obesity.\n", "date": "2023-08-28", "categories": []}
{"id": "2308.16585", "title": "Development and validation of an interpretable machine learning-based\n  calculator for predicting 5-year weight trajectories after bariatric surgery:\n  a multinational retrospective cohort SOPHIA study", "abstract": "  Background Weight loss trajectories after bariatric surgery vary widely\nbetween individuals, and predicting weight loss before the operation remains\nchallenging. We aimed to develop a model using machine learning to provide\nindividual preoperative prediction of 5-year weight loss trajectories after\nsurgery. Methods In this multinational retrospective observational study we\nenrolled adult participants (aged $\\ge$18 years) from ten prospective cohorts\n(including ABOS [NCT01129297], BAREVAL [NCT02310178], the Swedish Obese\nSubjects study, and a large cohort from the Dutch Obesity Clinic [Nederlandse\nObesitas Kliniek]) and two randomised trials (SleevePass [NCT00793143] and\nSM-BOSS [NCT00356213]) in Europe, the Americas, and Asia, with a 5 year\nfollowup after Roux-en-Y gastric bypass, sleeve gastrectomy, or gastric band.\nPatients with a previous history of bariatric surgery or large delays between\nscheduled and actual visits were excluded. The training cohort comprised\npatients from two centres in France (ABOS and BAREVAL). The primary outcome was\nBMI at 5 years. A model was developed using least absolute shrinkage and\nselection operator to select variables and the classification and regression\ntrees algorithm to build interpretable regression trees. The performances of\nthe model were assessed through the median absolute deviation (MAD) and root\nmean squared error (RMSE) of BMI. Findings10 231 patients from 12 centres in\nten countries were included in the analysis, corresponding to 30 602\npatient-years. Among participants in all 12 cohorts, 7701 (75$\\bullet$3%) were\nfemale, 2530 (24$\\bullet$7%) were male. Among 434 baseline attributes available\nin the training cohort, seven variables were selected: height, weight,\nintervention type, age, diabetes status, diabetes duration, and smoking status.\nAt 5 years, across external testing cohorts the overall mean MAD BMI was\n2$\\bullet$8 kg/m${}^2$ (95% CI 2$\\bullet$6-3$\\bullet$0) and mean RMSE BMI was\n4$\\bullet$7 kg/m${}^2$ (4$\\bullet$4-5$\\bullet$0), and the mean difference\nbetween predicted and observed BMI was-0$\\bullet$3 kg/m${}^2$ (SD 4$\\bullet$7).\nThis model is incorporated in an easy to use and interpretable web-based\nprediction tool to help inform clinical decision before surgery.\nInterpretationWe developed a machine learning-based model, which is\ninternationally validated, for predicting individual 5-year weight loss\ntrajectories after three common bariatric interventions.\n", "date": "2023-08-31", "categories": []}
{"id": "2303.11338", "title": "Towards Domain Generalization for ECG and EEG Classification: Algorithms\n  and Benchmarks", "abstract": "  Despite their immense success in numerous fields, machine and deep learning\nsystems have not yet been able to firmly establish themselves in\nmission-critical applications in healthcare. One of the main reasons lies in\nthe fact that when models are presented with previously unseen,\nOut-of-Distribution samples, their performance deteriorates significantly. This\nis known as the Domain Generalization (DG) problem. Our objective in this work\nis to propose a benchmark for evaluating DG algorithms, in addition to\nintroducing a novel architecture for tackling DG in biosignal classification.\nIn this paper, we describe the Domain Generalization problem for biosignals,\nfocusing on electrocardiograms (ECG) and electroencephalograms (EEG) and\npropose and implement an open-source biosignal DG evaluation benchmark.\nFurthermore, we adapt state-of-the-art DG algorithms from computer vision to\nthe problem of 1D biosignal classification and evaluate their effectiveness.\nFinally, we also introduce a novel neural network architecture that leverages\nmulti-layer representations for improved model generalizability. By\nimplementing the above DG setup we are able to experimentally demonstrate the\npresence of the DG problem in ECG and EEG datasets. In addition, our proposed\nmodel demonstrates improved effectiveness compared to the baseline algorithms,\nexceeding the state-of-the-art in both datasets. Recognizing the significance\nof the distribution shift present in biosignal datasets, the presented\nbenchmark aims at urging further research into the field of biomedical DG by\nsimplifying the evaluation process of proposed algorithms. To our knowledge,\nthis is the first attempt at developing an open-source framework for evaluating\nECG and EEG DG algorithms.\n", "date": "2023-03-20", "categories": []}
{"id": "2308.15291", "title": "Towards quantitative precision for ECG analysis: Leveraging state space\n  models, self-supervision and patient metadata", "abstract": "  Deep learning has emerged as the preferred modeling approach for automatic\nECG analysis. In this study, we investigate three elements aimed at improving\nthe quantitative accuracy of such systems. These components consistently\nenhance performance beyond the existing state-of-the-art, which is\npredominantly based on convolutional models. Firstly, we explore more\nexpressive architectures by exploiting structured state space models (SSMs).\nThese models have shown promise in capturing long-term dependencies in time\nseries data. By incorporating SSMs into our approach, we not only achieve\nbetter performance, but also gain insights into long-standing questions in the\nfield. Specifically, for standard diagnostic tasks, we find no advantage in\nusing higher sampling rates such as 500Hz compared to 100Hz. Similarly,\nextending the input size of the model beyond 3 seconds does not lead to\nsignificant improvements. Secondly, we demonstrate that self-supervised\nlearning using contrastive predictive coding can further improve the\nperformance of SSMs. By leveraging self-supervision, we enable the model to\nlearn more robust and representative features, leading to improved analysis\naccuracy. Lastly, we depart from synthetic benchmarking scenarios and\nincorporate basic demographic metadata alongside the ECG signal as input. This\ninclusion of patient metadata departs from the conventional practice of relying\nsolely on the signal itself. Remarkably, this addition consistently yields\npositive effects on predictive performance. We firmly believe that all three\ncomponents should be considered when developing next-generation ECG analysis\nalgorithms.\n", "date": "2023-08-29", "categories": []}
{"id": "2308.15339", "title": "AI Framework for Early Diagnosis of Coronary Artery Disease: An\n  Integration of Borderline SMOTE, Autoencoders and Convolutional Neural\n  Networks Approach", "abstract": "  The accuracy of coronary artery disease (CAD) diagnosis is dependent on a\nvariety of factors, including demographic, symptom, and medical examination,\nECG, and echocardiography data, among others. In this context, artificial\nintelligence (AI) can help clinicians identify high-risk patients early in the\ndiagnostic process, by synthesizing information from multiple factors. To this\naim, Machine Learning algorithms are used to classify patients based on their\nCAD disease risk. In this study, we contribute to this research filed by\ndeveloping a methodology for balancing and augmenting data for more accurate\nprediction when the data is imbalanced and the sample size is small. The\nmethodology can be used in a variety of other situations, particularly when\ndata collection is expensive and the sample size is small. The experimental\nresults revealed that the average accuracy of our proposed method for CAD\nprediction was 95.36, and was higher than random forest (RF), decision tree\n(DT), support vector machine (SVM), logistic regression (LR), and artificial\nneural network (ANN).\n", "date": "2023-08-29", "categories": []}
{"id": "2308.15469", "title": "Multimodal Contrastive Learning and Tabular Attention for Automated\n  Alzheimer's Disease Prediction", "abstract": "  Alongside neuroimaging such as MRI scans and PET, Alzheimer's disease (AD)\ndatasets contain valuable tabular data including AD biomarkers and clinical\nassessments. Existing computer vision approaches struggle to utilize this\nadditional information. To address these needs, we propose a generalizable\nframework for multimodal contrastive learning of image data and tabular data, a\nnovel tabular attention module for amplifying and ranking salient features in\ntables, and the application of these techniques onto Alzheimer's disease\nprediction. Experimental evaulations demonstrate the strength of our framework\nby detecting Alzheimer's disease (AD) from over 882 MR image slices from the\nADNI database. We take advantage of the high interpretability of tabular data\nand our novel tabular attention approach and through attribution of the\nattention scores for each row of the table, we note and rank the most\npredominant features. Results show that the model is capable of an accuracy of\nover 83.8%, almost a 10% increase from previous state of the art.\n", "date": "2023-08-29", "categories": []}
{"id": "2303.15699", "title": "Enhancing Breast Cancer Risk Prediction by Incorporating Prior Images", "abstract": "  Recently, deep learning models have shown the potential to predict breast\ncancer risk and enable targeted screening strategies, but current models do not\nconsider the change in the breast over time. In this paper, we present a new\nmethod, PRIME+, for breast cancer risk prediction that leverages prior\nmammograms using a transformer decoder, outperforming a state-of-the-art risk\nprediction method that only uses mammograms from a single time point. We\nvalidate our approach on a dataset with 16,113 exams and further demonstrate\nthat it effectively captures patterns of changes from prior mammograms, such as\nchanges in breast density, resulting in improved short-term and long-term\nbreast cancer risk prediction. Experimental results show that our model\nachieves a statistically significant improvement in performance over the\nstate-of-the-art based model, with a C-index increase from 0.68 to 0.73 (p <\n0.05) on held-out test sets.\n", "date": "2023-03-28", "categories": []}
{"id": "2305.05105", "title": "TinyML Design Contest for Life-Threatening Ventricular Arrhythmia\n  Detection", "abstract": "  The first ACM/IEEE TinyML Design Contest (TDC) held at the 41st International\nConference on Computer-Aided Design (ICCAD) in 2022 is a challenging,\nmulti-month, research and development competition. TDC'22 focuses on real-world\nmedical problems that require the innovation and implementation of artificial\nintelligence/machine learning (AI/ML) algorithms on implantable devices. The\nchallenge problem of TDC'22 is to develop a novel AI/ML-based real-time\ndetection algorithm for life-threatening ventricular arrhythmia over low-power\nmicrocontrollers utilized in Implantable Cardioverter-Defibrillators (ICDs).\nThe dataset contains more than 38,000 5-second intracardiac electrograms\n(IEGMs) segments over 8 different types of rhythm from 90 subjects. The\ndedicated hardware platform is NUCLEO-L432KC manufactured by\nSTMicroelectronics. TDC'22, which is open to multi-person teams world-wide,\nattracted more than 150 teams from over 50 organizations. This paper first\npresents the medical problem, dataset, and evaluation procedure in detail. It\nfurther demonstrates and discusses the designs developed by the leading teams\nas well as representative results. This paper concludes with the direction of\nimprovement for the future TinyML design for health monitoring applications.\n", "date": "2023-05-09", "categories": []}
{"id": "2308.13592", "title": "AI in Thyroid Cancer Diagnosis: Techniques, Trends, and Future\n  Directions", "abstract": "  There has been a growing interest in creating intelligent diagnostic systems\nto assist medical professionals in analyzing and processing big data for the\ntreatment of incurable diseases. One of the key challenges in this field is\ndetecting thyroid cancer, where advancements have been made using machine\nlearning (ML) and big data analytics to evaluate thyroid cancer prognosis and\ndetermine a patient's risk of malignancy. This review paper summarizes a large\ncollection of articles related to artificial intelligence (AI)-based techniques\nused in the diagnosis of thyroid cancer. Accordingly, a new classification was\nintroduced to classify these techniques based on the AI algorithms used, the\npurpose of the framework, and the computing platforms used. Additionally, this\nstudy compares existing thyroid cancer datasets based on their features. The\nfocus of this study is on how AI-based tools can support the diagnosis and\ntreatment of thyroid cancer, through supervised, unsupervised, or hybrid\ntechniques. It also highlights the progress made and the unresolved challenges\nin this field. Finally, the future trends and areas of focus in this field are\ndiscussed.\n", "date": "2023-08-25", "categories": []}
{"id": "2308.13714", "title": "Uncovering Promises and Challenges of Federated Learning to Detect\n  Cardiovascular Diseases: A Scoping Literature Review", "abstract": "  Cardiovascular diseases (CVD) are the leading cause of death globally, and\nearly detection can significantly improve outcomes for patients. Machine\nlearning (ML) models can help diagnose CVDs early, but their performance is\nlimited by the data available for model training. Privacy concerns in\nhealthcare make it harder to acquire data to train accurate ML models.\nFederated learning (FL) is an emerging approach to machine learning that allows\nmodels to be trained on data from multiple sources without compromising the\nprivacy of the individual data owners. This survey paper provides an overview\nof the current state-of-the-art in FL for CVD detection. We review the\ndifferent FL models proposed in various papers and discuss their advantages and\nchallenges. We also compare FL with traditional centralized learning approaches\nand highlight the differences in terms of model accuracy, privacy, and data\ndistribution handling capacity. Finally, we provide a critical analysis of FL's\ncurrent challenges and limitations for CVD detection and discuss potential\navenues for future research. Overall, this survey paper aims to provide a\ncomprehensive overview of the current state-of-the-art in FL for CVD detection\nand to highlight its potential for improving the accuracy and privacy of CVD\ndetection models.\n", "date": "2023-08-26", "categories": []}
{"id": "2308.14050", "title": "PECon: Contrastive Pretraining to Enhance Feature Alignment between CT\n  and EHR Data for Improved Pulmonary Embolism Diagnosis", "abstract": "  Previous deep learning efforts have focused on improving the performance of\nPulmonary Embolism(PE) diagnosis from Computed Tomography (CT) scans using\nConvolutional Neural Networks (CNN). However, the features from CT scans alone\nare not always sufficient for the diagnosis of PE. CT scans along with\nelectronic heath records (EHR) can provide a better insight into the patients\ncondition and can lead to more accurate PE diagnosis. In this paper, we propose\nPulmonary Embolism Detection using Contrastive Learning (PECon), a supervised\ncontrastive pretraining strategy that employs both the patients CT scans as\nwell as the EHR data, aiming to enhance the alignment of feature\nrepresentations between the two modalities and leverage information to improve\nthe PE diagnosis. In order to achieve this, we make use of the class labels and\npull the sample features of the same class together, while pushing away those\nof the other class. Results show that the proposed work outperforms the\nexisting techniques and achieves state-of-the-art performance on the RadFusion\ndataset with an F1-score of 0.913, accuracy of 0.90 and an AUROC of 0.943.\nFurthermore, we also explore the explainability of our approach in comparison\nto other methods. Our code is publicly available at\nhttps://github.com/BioMedIA-MBZUAI/PECon.\n", "date": "2023-08-27", "categories": []}
{"id": "2308.14667", "title": "Neural Network-Based Histologic Remission Prediction In Ulcerative\n  Colitis", "abstract": "  BACKGROUND & AIMS: Histological remission (HR) is advocated and considered as\na new therapeutic target in ulcerative colitis (UC). Diagnosis of histologic\nremission currently relies on biopsy; during this process, patients are at risk\nfor bleeding, infection, and post-biopsy fibrosis. In addition, histologic\nresponse scoring is complex and time-consuming, and there is heterogeneity\namong pathologists. Endocytoscopy (EC) is a novel ultra-high magnification\nendoscopic technique that can provide excellent in vivo assessment of glands.\nBased on the EC technique, we propose a neural network model that can assess\nhistological disease activity in UC using EC images to address the above\nissues. The experiment results demonstrate that the proposed method can assist\npatients in precise treatment and prognostic assessment.\n  METHODS: We construct a neural network model for UC evaluation. A total of\n5105 images of 154 intestinal segments from 87 patients undergoing EC treatment\nat a center in China between March 2022 and March 2023 are scored according to\nthe Geboes score. Subsequently, 103 intestinal segments are used as the\ntraining set, 16 intestinal segments are used as the validation set for neural\nnetwork training, and the remaining 35 intestinal segments are used as the test\nset to measure the model performance together with the validation set.\n  RESULTS: By treating HR as a negative category and histologic activity as a\npositive category, the proposed neural network model can achieve an accuracy of\n0.9, a specificity of 0.95, a sensitivity of 0.75, and an area under the curve\n(AUC) of 0.81.\n  CONCLUSION: We develop a specific neural network model that can distinguish\nhistologic remission/activity in EC images of UC, which helps to accelerate\nclinical histological diagnosis.\n  keywords: ulcerative colitis; Endocytoscopy; Geboes score; neural network.\n", "date": "2023-08-28", "categories": []}
{"id": "2304.09270", "title": "Coarse race data conceals disparities in clinical risk score performance", "abstract": "  Healthcare data in the United States often records only a patient's coarse\nrace group: for example, both Indian and Chinese patients are typically coded\nas \"Asian.\" It is unknown, however, whether this coarse coding conceals\nmeaningful disparities in the performance of clinical risk scores across\ngranular race groups. Here we show that it does. Using data from 418K emergency\ndepartment visits, we assess clinical risk score performance disparities across\n26 granular groups for three outcomes, five risk scores, and four performance\nmetrics. Across outcomes and metrics, we show that the risk scores exhibit\nsignificant granular performance disparities within coarse race groups. In\nfact, variation in performance within coarse groups often *exceeds* the\nvariation between coarse groups. We explore why these disparities arise,\nfinding that outcome rates, feature distributions, and the relationships\nbetween features and outcomes all vary significantly across granular groups.\nOur results suggest that healthcare providers, hospital systems, and machine\nlearning researchers should strive to collect, release, and use granular race\ndata in place of coarse race data, and that existing analyses may significantly\nunderestimate racial disparities in performance.\n", "date": "2023-04-18", "categories": []}
{"id": "2308.12675", "title": "A Study of Age and Sex Bias in Multiple Instance Learning based\n  Classification of Acute Myeloid Leukemia Subtypes", "abstract": "  Accurate classification of Acute Myeloid Leukemia (AML) subtypes is crucial\nfor clinical decision-making and patient care. In this study, we investigate\nthe potential presence of age and sex bias in AML subtype classification using\nMultiple Instance Learning (MIL) architectures. To that end, we train multiple\nMIL models using different levels of sex imbalance in the training set and\nexcluding certain age groups. To assess the sex bias, we evaluate the\nperformance of the models on male and female test sets. For age bias, models\nare tested against underrepresented age groups in the training data. We find a\nsignificant effect of sex and age bias on the performance of the model for AML\nsubtype classification. Specifically, we observe that females are more likely\nto be affected by sex imbalance dataset and certain age groups, such as\npatients with 72 to 86 years of age with the RUNX1::RUNX1T1 genetic subtype,\nare significantly affected by an age bias present in the training data.\nEnsuring inclusivity in the training data is thus essential for generating\nreliable and equitable outcomes in AML genetic subtype classification,\nultimately benefiting diverse patient populations.\n", "date": "2023-08-24", "categories": []}
{"id": "2308.12800", "title": "ICU Mortality Prediction Using Long Short-Term Memory Networks", "abstract": "  Extensive bedside monitoring in Intensive Care Units (ICUs) has resulted in\ncomplex temporal data regarding patient physiology, which presents an upscale\ncontext for clinical data analysis. In the other hand, identifying the\ntime-series patterns within these data may provide a high aptitude to predict\nclinical events. Hence, we investigate, during this work, the implementation of\nan automatic data-driven system, which analyzes large amounts of multivariate\ntemporal data derived from Electronic Health Records (EHRs), and extracts\nhigh-level information so as to predict in-hospital mortality and Length of\nStay (LOS) early. Practically, we investigate the applicability of LSTM network\nby reducing the time-frame to 6-hour so as to enhance clinical tasks. The\nexperimental results highlight the efficiency of LSTM model with rigorous\nmultivariate time-series measurements for building real-world prediction\nengines.\n", "date": "2023-08-18", "categories": []}
{"id": "2308.12188", "title": "Development and external validation of a lung cancer risk estimation\n  tool using gradient-boosting", "abstract": "  Lung cancer is a significant cause of mortality worldwide, emphasizing the\nimportance of early detection for improved survival rates. In this study, we\npropose a machine learning (ML) tool trained on data from the PLCO Cancer\nScreening Trial and validated on the NLST to estimate the likelihood of lung\ncancer occurrence within five years. The study utilized two datasets, the PLCO\n(n=55,161) and NLST (n=48,595), consisting of comprehensive information on risk\nfactors, clinical measurements, and outcomes related to lung cancer. Data\npreprocessing involved removing patients who were not current or former smokers\nand those who had died of causes unrelated to lung cancer. Additionally, a\nfocus was placed on mitigating bias caused by censored data. Feature selection,\nhyper-parameter optimization, and model calibration were performed using\nXGBoost, an ensemble learning algorithm that combines gradient boosting and\ndecision trees. The ML model was trained on the pre-processed PLCO dataset and\ntested on the NLST dataset. The model incorporated features such as age,\ngender, smoking history, medical diagnoses, and family history of lung cancer.\nThe model was well-calibrated (Brier score=0.044). ROC-AUC was 82% on the PLCO\ndataset and 70% on the NLST dataset. PR-AUC was 29% and 11% respectively. When\ncompared to the USPSTF guidelines for lung cancer screening, our model provided\nthe same recall with a precision of 13.1% vs. 9.3% on the PLCO dataset and 3.2%\nvs. 3.1% on the NLST dataset. The developed ML tool provides a freely available\nweb application for estimating the likelihood of developing lung cancer within\nfive years. By utilizing risk factors and clinical data, individuals can assess\ntheir risk and make informed decisions regarding lung cancer screening. This\nresearch contributes to the efforts in early detection and prevention\nstrategies, aiming to reduce lung cancer-related mortality rates.\n", "date": "2023-08-23", "categories": []}
{"id": "2308.11013", "title": "Personalized Event Prediction for Electronic Health Records", "abstract": "  Clinical event sequences consist of hundreds of clinical events that\nrepresent records of patient care in time. Developing accurate predictive\nmodels of such sequences is of a great importance for supporting a variety of\nmodels for interpreting/classifying the current patient condition, or\npredicting adverse clinical events and outcomes, all aimed to improve patient\ncare. One important challenge of learning predictive models of clinical\nsequences is their patient-specific variability. Based on underlying clinical\nconditions, each patient's sequence may consist of different sets of clinical\nevents (observations, lab results, medications, procedures). Hence, simple\npopulation-wide models learned from event sequences for many different patients\nmay not accurately predict patient-specific dynamics of event sequences and\ntheir differences. To address the problem, we propose and investigate multiple\nnew event sequence prediction models and methods that let us better adjust the\nprediction for individual patients and their specific conditions. The methods\ndeveloped in this work pursue refinement of population-wide models to\nsubpopulations, self-adaptation, and a meta-level model switching that is able\nto adaptively select the model with the best chance to support the immediate\nprediction. We analyze and test the performance of these models on clinical\nevent sequences of patients in MIMIC-III database.\n", "date": "2023-08-21", "categories": []}
{"id": "2308.11220", "title": "Federated Learning on Patient Data for Privacy-Protecting Polycystic\n  Ovary Syndrome Treatment", "abstract": "  The field of women's endocrinology has trailed behind data-driven medical\nsolutions, largely due to concerns over the privacy of patient data. Valuable\ndatapoints about hormone levels or menstrual cycling could expose patients who\nsuffer from comorbidities or terminate a pregnancy, violating their privacy. We\nexplore the application of Federated Learning (FL) to predict the optimal drug\nfor patients with polycystic ovary syndrome (PCOS). PCOS is a serious hormonal\ndisorder impacting millions of women worldwide, yet it's poorly understood and\nits research is stunted by a lack of patient data. We demonstrate that a\nvariety of FL approaches succeed on a synthetic PCOS patient dataset. Our\nproposed FL models are a tool to access massive quantities of diverse data and\nidentify the most effective treatment option while providing PCOS patients with\nprivacy guarantees.\n", "date": "2023-08-22", "categories": []}
{"id": "2303.04630", "title": "Mining the contribution of intensive care clinical course to outcome\n  after traumatic brain injury", "abstract": "  Existing methods to characterise the evolving condition of traumatic brain\ninjury (TBI) patients in the intensive care unit (ICU) do not capture the\ncontext necessary for individualising treatment. Here, we integrate all\nheterogenous data stored in medical records (1,166 pre-ICU and ICU variables)\nto model the individualised contribution of clinical course to six-month\nfunctional outcome on the Glasgow Outcome Scale - Extended (GOSE). On a\nprospective cohort (n=1,550, 65 centres) of TBI patients, we train recurrent\nneural network models to map a token-embedded time series representation of all\nvariables (including missing values) to an ordinal GOSE prognosis every two\nhours. The full range of variables explains up to 52% (95% CI: 50%-54%) of the\nordinal variance in functional outcome. Up to 91% (95% CI: 90%-91%) of this\nexplanation is derived from pre-ICU and admission information (i.e., static\nvariables). Information collected in the ICU (i.e., dynamic variables)\nincreases explanation (by up to 5% [95% CI: 4%-6%]), though not enough to\ncounter poorer overall performance in longer-stay (>5.75 days) patients.\nHighest-contributing variables include physician-based prognoses, CT features,\nand markers of neurological function. Whilst static information currently\naccounts for the majority of functional outcome explanation after TBI,\ndata-driven analysis highlights investigative avenues to improve dynamic\ncharacterisation of longer-stay patients. Moreover, our modelling strategy\nproves useful for converting large patient records into interpretable time\nseries with missing data integration and minimal processing.\n", "date": "2023-03-08", "categories": []}
{"id": "2308.02180", "title": "Scaling Clinical Trial Matching Using Large Language Models: A Case\n  Study in Oncology", "abstract": "  Clinical trial matching is a key process in health delivery and discovery. In\npractice, it is plagued by overwhelming unstructured data and unscalable manual\nprocessing. In this paper, we conduct a systematic study on scaling clinical\ntrial matching using large language models (LLMs), with oncology as the focus\narea. Our study is grounded in a clinical trial matching system currently in\ntest deployment at a large U.S. health network. Initial findings are promising:\nout of box, cutting-edge LLMs, such as GPT-4, can already structure elaborate\neligibility criteria of clinical trials and extract complex matching logic\n(e.g., nested AND/OR/NOT). While still far from perfect, LLMs substantially\noutperform prior strong baselines and may serve as a preliminary solution to\nhelp triage patient-trial candidates with humans in the loop. Our study also\nreveals a few significant growth areas for applying LLMs to end-to-end clinical\ntrial matching, such as context limitation and accuracy, especially in\nstructuring patient information from longitudinal medical records.\n", "date": "2023-08-04", "categories": []}
{"id": "2308.10368", "title": "Prediction of Pneumonia and COVID-19 Using Deep Neural Networks", "abstract": "  Pneumonia, caused by bacteria and viruses, is a rapidly spreading viral\ninfection with global implications. Prompt identification of infected\nindividuals is crucial for containing its transmission. This study explores the\npotential of medical image analysis to address this challenge. We propose\nmachine-learning techniques for predicting Pneumonia from chest X-ray images.\nChest X-ray imaging is vital for Pneumonia diagnosis due to its accessibility\nand cost-effectiveness. However, interpreting X-rays for Pneumonia detection\ncan be complex, as radiographic features can overlap with other respiratory\nconditions. We evaluate the performance of different machine learning models,\nincluding DenseNet121, Inception Resnet-v2, Inception Resnet-v3, Resnet50, and\nXception, using chest X-ray images of pneumonia patients. Performance measures\nand confusion matrices are employed to assess and compare the models. The\nfindings reveal that DenseNet121 outperforms other models, achieving an\naccuracy rate of 99.58%. This study underscores the significance of machine\nlearning in the accurate detection of Pneumonia, leveraging chest X-ray images.\nOur study offers insights into the potential of technology to mitigate the\nspread of pneumonia through precise diagnostics.\n", "date": "2023-08-20", "categories": []}
{"id": "2211.07643", "title": "Secure and Privacy-Preserving Automated Machine Learning Operations into\n  End-to-End Integrated IoT-Edge-Artificial Intelligence-Blockchain Monitoring\n  System for Diabetes Mellitus Prediction", "abstract": "  Diabetes Mellitus, one of the leading causes of death worldwide, has no cure\nto date and can lead to severe health complications, such as retinopathy, limb\namputation, cardiovascular diseases, and neuronal disease, if left untreated.\nConsequently, it becomes crucial to take precautionary measures to\navoid/predict the occurrence of diabetes. Machine learning approaches have been\nproposed and evaluated in the literature for diabetes prediction. This paper\nproposes an IoT-edge-Artificial Intelligence (AI)-blockchain system for\ndiabetes prediction based on risk factors. The proposed system is underpinned\nby the blockchain to obtain a cohesive view of the risk factors data from\npatients across different hospitals and to ensure security and privacy of the\nuser's data. Furthermore, we provide a comparative analysis of different\nmedical sensors, devices, and methods to measure and collect the risk factors\nvalues in the system. Numerical experiments and comparative analysis were\ncarried out between our proposed system, using the most accurate random forest\n(RF) model, and the two most used state-of-the-art machine learning approaches,\nLogistic Regression (LR) and Support Vector Machine (SVM), using three\nreal-life diabetes datasets. The results show that the proposed system using RF\npredicts diabetes with 4.57% more accuracy on average compared to LR and SVM,\nwith 2.87 times more execution time. Data balancing without feature selection\ndoes not show significant improvement. The performance is improved by 1.14% and\n0.02% after feature selection for PIMA Indian and Sylhet datasets respectively,\nwhile it reduces by 0.89% for MIMIC III.\n", "date": "2022-11-13", "categories": []}
{"id": "2308.08660", "title": "Large Language Models for Granularized Barrett's Esophagus Diagnosis\n  Classification", "abstract": "  Diagnostic codes for Barrett's esophagus (BE), a precursor to esophageal\ncancer, lack granularity and precision for many research or clinical use cases.\nLaborious manual chart review is required to extract key diagnostic phenotypes\nfrom BE pathology reports. We developed a generalizable transformer-based\nmethod to automate data extraction. Using pathology reports from Columbia\nUniversity Irving Medical Center with gastroenterologist-annotated targets, we\nperformed binary dysplasia classification as well as granularized multi-class\nBE-related diagnosis classification. We utilized two clinically pre-trained\nlarge language models, with best model performance comparable to a highly\ntailored rule-based system developed using the same data. Binary dysplasia\nextraction achieves 0.964 F1-score, while the multi-class model achieves 0.911\nF1-score. Our method is generalizable and faster to implement as compared to a\ntailored rule-based approach.\n", "date": "2023-08-16", "categories": []}
{"id": "2308.09360", "title": "Multi-feature concatenation and multi-classifier stacking: an\n  interpretable and generalizable machine learning method for MDD\n  discrimination with rsfMRI", "abstract": "  Major depressive disorder is a serious and heterogeneous psychiatric disorder\nthat needs accurate diagnosis. Resting-state functional MRI (rsfMRI), which\ncaptures multiple perspectives on brain structure, function, and connectivity,\nis increasingly applied in the diagnosis and pathological research of mental\ndiseases. Different machine learning algorithms are then developed to exploit\nthe rich information in rsfMRI and discriminate MDD patients from normal\ncontrols. Despite recent advances reported, the discrimination accuracy has\nroom for further improvement. The generalizability and interpretability of the\nmethod are not sufficiently addressed either. Here, we propose a machine\nlearning method (MFMC) for MDD discrimination by concatenating multiple\nfeatures and stacking multiple classifiers. MFMC is tested on the REST-meta-MDD\ndata set that contains 2428 subjects collected from 25 different sites. MFMC\nyields 96.9% MDD discrimination accuracy, demonstrating a significant\nimprovement over existing methods. In addition, the generalizability of MFMC is\nvalidated by the good performance when the training and testing subjects are\nfrom independent sites. The use of XGBoost as the meta classifier allows us to\nprobe the decision process of MFMC. We identify 13 feature values related to 9\nbrain regions including the posterior cingulate gyrus, superior frontal gyrus\norbital part, and angular gyrus, which contribute most to the classification\nand also demonstrate significant differences at the group level. The use of\nthese 13 feature values alone can reach 87% of MFMC's full performance when\ntaking all feature values. These features may serve as clinically useful\ndiagnostic and prognostic biomarkers for mental disorders in the future.\n", "date": "2023-08-18", "categories": []}
{"id": "2304.13017", "title": "DuETT: Dual Event Time Transformer for Electronic Health Records", "abstract": "  Electronic health records (EHRs) recorded in hospital settings typically\ncontain a wide range of numeric time series data that is characterized by high\nsparsity and irregular observations. Effective modelling for such data must\nexploit its time series nature, the semantic relationship between different\ntypes of observations, and information in the sparsity structure of the data.\nSelf-supervised Transformers have shown outstanding performance in a variety of\nstructured tasks in NLP and computer vision. But multivariate time series data\ncontains structured relationships over two dimensions: time and recorded event\ntype, and straightforward applications of Transformers to time series data do\nnot leverage this distinct structure. The quadratic scaling of self-attention\nlayers can also significantly limit the input sequence length without\nappropriate input engineering. We introduce the DuETT architecture, an\nextension of Transformers designed to attend over both time and event type\ndimensions, yielding robust representations from EHR data. DuETT uses an\naggregated input where sparse time series are transformed into a regular\nsequence with fixed length; this lowers the computational complexity relative\nto previous EHR Transformer models and, more importantly, enables the use of\nlarger and deeper neural networks. When trained with self-supervised prediction\ntasks, that provide rich and informative signals for model pre-training, our\nmodel outperforms state-of-the-art deep learning models on multiple downstream\ntasks from the MIMIC-IV and PhysioNet-2012 EHR datasets.\n", "date": "2023-04-25", "categories": []}
{"id": "2308.07927", "title": "Predictive Modeling of Menstrual Cycle Length: A Time Series Forecasting\n  Approach", "abstract": "  A proper forecast of the menstrual cycle is meaningful for women's health, as\nit allows individuals to take preventive actions to minimize cycle-associated\ndiscomforts. In addition, precise prediction can be useful for planning\nimportant events in a woman's life, such as family planning. In this work, we\nexplored the use of machine learning techniques to predict regular and\nirregular menstrual cycles. We implemented some time series forecasting\nalgorithm approaches, such as AutoRegressive Integrated Moving Average, Huber\nRegression, Lasso Regression, Orthogonal Matching Pursuit, and Long Short-Term\nMemory Network. Moreover, we generated synthetic data to achieve our purposes.\nThe results showed that it is possible to accurately predict the onset and\nduration of menstrual cycles using machine learning techniques.\n", "date": "2023-06-11", "categories": []}
{"id": "2308.07436", "title": "A Hybrid Deep Spatio-Temporal Attention-Based Model for Parkinson's\n  Disease Diagnosis Using Resting State EEG Signals", "abstract": "  Parkinson's disease (PD), a severe and progressive neurological illness,\naffects millions of individuals worldwide. For effective treatment and\nmanagement of PD, an accurate and early diagnosis is crucial. This study\npresents a deep learning-based model for the diagnosis of PD using resting\nstate electroencephalogram (EEG) signal. The objective of the study is to\ndevelop an automated model that can extract complex hidden nonlinear features\nfrom EEG and demonstrate its generalizability on unseen data. The model is\ndesigned using a hybrid model, consists of convolutional neural network (CNN),\nbidirectional gated recurrent unit (Bi-GRU), and attention mechanism. The\nproposed method is evaluated on three public datasets (Uc San Diego Dataset,\nPRED-CT, and University of Iowa (UI) dataset), with one dataset used for\ntraining and the other two for evaluation. The results show that the proposed\nmodel can accurately diagnose PD with high performance on both the training and\nhold-out datasets. The model also performs well even when some part of the\ninput information is missing. The results of this work have significant\nimplications for patient treatment and for ongoing investigations into the\nearly detection of Parkinson's disease. The suggested model holds promise as a\nnon-invasive and reliable technique for PD early detection utilizing resting\nstate EEG.\n", "date": "2023-08-14", "categories": []}
{"id": "2305.12622", "title": "Evaluating the Impact of Social Determinants on Health Prediction in the\n  Intensive Care Unit", "abstract": "  Social determinants of health (SDOH) -- the conditions in which people live,\ngrow, and age -- play a crucial role in a person's health and well-being. There\nis a large, compelling body of evidence in population health studies showing\nthat a wide range of SDOH is strongly correlated with health outcomes. Yet, a\nmajority of the risk prediction models based on electronic health records (EHR)\ndo not incorporate a comprehensive set of SDOH features as they are often noisy\nor simply unavailable. Our work links a publicly available EHR database,\nMIMIC-IV, to well-documented SDOH features. We investigate the impact of such\nfeatures on common EHR prediction tasks across different patient populations.\nWe find that community-level SDOH features do not improve model performance for\na general patient population, but can improve data-limited model fairness for\nspecific subpopulations. We also demonstrate that SDOH features are vital for\nconducting thorough audits of algorithmic biases beyond protective attributes.\nWe hope the new integrated EHR-SDOH database will enable studies on the\nrelationship between community health and individual outcomes and provide new\nbenchmarks to study algorithmic biases beyond race, gender, and age.\n", "date": "2023-05-22", "categories": []}
{"id": "2308.06933", "title": "Radiomics-Informed Deep Learning for Classification of Atrial\n  Fibrillation Sub-Types from Left-Atrium CT Volumes", "abstract": "  Atrial Fibrillation (AF) is characterized by rapid, irregular heartbeats, and\ncan lead to fatal complications such as heart failure. The disease is divided\ninto two sub-types based on severity, which can be automatically classified\nthrough CT volumes for disease screening of severe cases. However, existing\nclassification approaches rely on generic radiomic features that may not be\noptimal for the task, whilst deep learning methods tend to over-fit to the\nhigh-dimensional volume inputs. In this work, we propose a novel\nradiomics-informed deep-learning method, RIDL, that combines the advantages of\ndeep learning and radiomic approaches to improve AF sub-type classification.\nUnlike existing hybrid techniques that mostly rely on na\\\"ive feature\nconcatenation, we observe that radiomic feature selection methods can serve as\nan information prior, and propose supplementing low-level deep neural network\n(DNN) features with locally computed radiomic features. This reduces DNN\nover-fitting and allows local variations between radiomic features to be better\ncaptured. Furthermore, we ensure complementary information is learned by deep\nand radiomic features by designing a novel feature de-correlation loss.\nCombined, our method addresses the limitations of deep learning and radiomic\napproaches and outperforms state-of-the-art radiomic, deep learning, and hybrid\napproaches, achieving 86.9% AUC for the AF sub-type classification task. Code\nis available at https://github.com/xmed-lab/RIDL.\n", "date": "2023-08-14", "categories": []}
{"id": "2308.07052", "title": "Diagnosis of Scalp Disorders using Machine Learning and Deep Learning\n  Approach -- A Review", "abstract": "  The morbidity of scalp diseases is minuscule compared to other diseases, but\nthe impact on the patient's life is enormous. It is common for people to\nexperience scalp problems that include Dandruff, Psoriasis, Tinea-Capitis,\nAlopecia and Atopic-Dermatitis. In accordance with WHO research, approximately\n70% of adults have problems with their scalp. It has been demonstrated in\ndescriptive research that hair quality is impaired by impaired scalp, but these\nimpacts are reversible with early diagnosis and treatment. Deep Learning\nadvances have demonstrated the effectiveness of CNN paired with FCN in\ndiagnosing scalp and skin disorders. In one proposed Deep-Learning-based scalp\ninspection and diagnosis system, an imaging microscope and a trained model are\ncombined with an app that classifies scalp disorders accurately with an average\nprecision of 97.41%- 99.09%. Another research dealt with classifying the\nPsoriasis using the CNN with an accuracy of 82.9%. As part of another study, an\nML based algorithm was also employed. It accurately classified the healthy\nscalp and alopecia areata with 91.4% and 88.9% accuracy with SVM and KNN\nalgorithms. Using deep learning models to diagnose scalp related diseases has\nimproved due to advancements i computation capabilities and computer vision,\nbut there remains a wide horizon for further improvements.\n", "date": "2023-08-14", "categories": []}
{"id": "2303.12364", "title": "ExBEHRT: Extended Transformer for Electronic Health Records to Predict\n  Disease Subtypes & Progressions", "abstract": "  In this study, we introduce ExBEHRT, an extended version of BEHRT (BERT\napplied to electronic health records), and apply different algorithms to\ninterpret its results. While BEHRT considers only diagnoses and patient age, we\nextend the feature space to several multimodal records, namely demographics,\nclinical characteristics, vital signs, smoking status, diagnoses, procedures,\nmedications, and laboratory tests, by applying a novel method to unify the\nfrequencies and temporal dimensions of the different features. We show that\nadditional features significantly improve model performance for various\ndownstream tasks in different diseases. To ensure robustness, we interpret\nmodel predictions using an adaptation of expected gradients, which has not been\npreviously applied to transformers with EHR data and provides more granular\ninterpretations than previous approaches such as feature and token importances.\nFurthermore, by clustering the model representations of oncology patients, we\nshow that the model has an implicit understanding of the disease and is able to\nclassify patients with the same cancer type into different risk groups. Given\nthe additional features and interpretability, ExBEHRT can help make informed\ndecisions about disease trajectories, diagnoses, and risk factors of various\ndiseases.\n", "date": "2023-03-22", "categories": []}
{"id": "2307.11211", "title": "The Effect of Epidemiological Cohort Creation on the Machine Learning\n  Prediction of Homelessness and Police Interaction Outcomes Using\n  Administrative Health Care Data", "abstract": "  Background: Mental illness can lead to adverse outcomes such as homelessness\nand police interaction and understanding of the events leading up to these\nadverse outcomes is important. Predictive models may help identify individuals\nat risk of such adverse outcomes. Using a fixed observation window cohort with\nlogistic regression (LR) or machine learning (ML) models can result in lower\nperformance when compared with adaptive and parcellated windows.\n  Method: An administrative healthcare dataset was used, comprising of 240,219\nindividuals in Calgary, Alberta, Canada who were diagnosed with addiction or\nmental health (AMH) between April 1, 2013, and March 31, 2018. The cohort was\nfollowed for 2 years to identify factors associated with homelessness and\npolice interactions. To understand the benefit of flexible windows to\npredictive models, an alternative cohort was created. Then LR and ML models,\nincluding random forests (RF), and extreme gradient boosting (XGBoost) were\ncompared in the two cohorts.\n  Results: Among 237,602 individuals, 0.8% (1,800) experienced first\nhomelessness, while 0.32% (759) reported initial police interaction among\n237,141 individuals. Male sex (AORs: H=1.51, P=2.52), substance disorder (AORs:\nH=3.70, P=2.83), psychiatrist visits (AORs: H=1.44, P=1.49), and drug abuse\n(AORs: H=2.67, P=1.83) were associated with initial homelessness (H) and police\ninteraction (P). XGBoost showed superior performance using the flexible method\n(sensitivity =91%, AUC =90% for initial homelessness, and sensitivity =90%,\nAUC=89% for initial police interaction)\n  Conclusion: This study identified key features associated with initial\nhomelessness and police interaction and demonstrated that flexible windows can\nimprove predictive modeling.\n", "date": "2023-07-20", "categories": []}
{"id": "2308.05133", "title": "Analyzing the Effect of Data Impurity on the Detection Performances of\n  Mental Disorders", "abstract": "  The primary method for identifying mental disorders automatically has\ntraditionally involved using binary classifiers. These classifiers are trained\nusing behavioral data obtained from an interview setup. In this training\nprocess, data from individuals with the specific disorder under consideration\nare categorized as the positive class, while data from all other participants\nconstitute the negative class. In practice, it is widely recognized that\ncertain mental disorders share similar symptoms, causing the collected\nbehavioral data to encompass a variety of attributes associated with multiple\ndisorders. Consequently, attributes linked to the targeted mental disorder\nmight also be present within the negative class. This data impurity may lead to\nsub-optimal training of the classifier for a mental disorder of interest. In\nthis study, we investigate this hypothesis in the context of major depressive\ndisorder (MDD) and post-traumatic stress disorder detection (PTSD). The results\nshow that upon removal of such data impurity, MDD and PTSD detection\nperformances are significantly improved.\n", "date": "2023-08-09", "categories": []}
{"id": "2308.05176", "title": "Comparative Analysis of Epileptic Seizure Prediction: Exploring Diverse\n  Pre-Processing Techniques and Machine Learning Models", "abstract": "  Epilepsy is a prevalent neurological disorder characterized by recurrent and\nunpredictable seizures, necessitating accurate prediction for effective\nmanagement and patient care. Application of machine learning (ML) on\nelectroencephalogram (EEG) recordings, along with its ability to provide\nvaluable insights into brain activity during seizures, is able to make accurate\nand robust seizure prediction an indispensable component in relevant studies.\nIn this research, we present a comprehensive comparative analysis of five\nmachine learning models - Random Forest (RF), Decision Tree (DT), Extra Trees\n(ET), Logistic Regression (LR), and Gradient Boosting (GB) - for the prediction\nof epileptic seizures using EEG data. The dataset underwent meticulous\npreprocessing, including cleaning, normalization, outlier handling, and\noversampling, ensuring data quality and facilitating accurate model training.\nThese preprocessing techniques played a crucial role in enhancing the models'\nperformance. The results of our analysis demonstrate the performance of each\nmodel in terms of accuracy. The LR classifier achieved an accuracy of 56.95%,\nwhile GB and DT both attained 97.17% accuracy. RT achieved a higher accuracy of\n98.99%, while the ET model exhibited the best performance with an accuracy of\n99.29%. Our findings reveal that the ET model outperformed not only the other\nmodels in the comparative analysis but also surpassed the state-of-the-art\nresults from previous research. The superior performance of the ET model makes\nit a compelling choice for accurate and robust epileptic seizure prediction\nusing EEG data.\n", "date": "2023-08-06", "categories": []}
{"id": "2308.05344", "title": "Prostate Age Gap (PAG): An MRI surrogate marker of aging for prostate\n  cancer detection", "abstract": "  Background: Prostate cancer (PC) MRI-based risk calculators are commonly\nbased on biological (e.g. PSA), MRI markers (e.g. volume), and patient age.\nWhilst patient age measures the amount of years an individual has existed,\nbiological age (BA) might better reflect the physiology of an individual.\nHowever, surrogates from prostate MRI and linkage with clinically significant\nPC (csPC) remain to be explored. Purpose: To obtain and evaluate Prostate Age\nGap (PAG) as an MRI marker tool for csPC risk. Study type: Retrospective.\nPopulation: A total of 7243 prostate MRI slices from 468 participants who had\nundergone prostate biopsies. A deep learning model was trained on 3223 MRI\nslices cropped around the gland from 81 low-grade PC (ncsPC, Gleason score <=6)\nand 131 negative cases and tested on the remaining 256 participants.\nAssessment: Chronological age was defined as the age of the participant at the\ntime of the visit and used to train the deep learning model to predict the age\nof the patient. Following, we obtained PAG, defined as the model predicted age\nminus the patient's chronological age. Multivariate logistic regression models\nwere used to estimate the association through odds ratio (OR) and predictive\nvalue of PAG and compared against PSA levels and PI-RADS>=3. Statistical tests:\nT-test, Mann-Whitney U test, Permutation test and ROC curve analysis. Results:\nThe multivariate adjusted model showed a significant difference in the odds of\nclinically significant PC (csPC, Gleason score >=7) (OR =3.78, 95% confidence\ninterval (CI):2.32-6.16, P <.001). PAG showed a better predictive ability when\ncompared to PI-RADS>=3 and adjusted by other risk factors, including PSA\nlevels: AUC =0.981 vs AUC =0.704, p<.001. Conclusion: PAG was significantly\nassociated with the risk of clinically significant PC and outperformed other\nwell-established PC risk factors.\n", "date": "2023-08-10", "categories": []}
{"id": "2308.05619", "title": "Updating Clinical Risk Stratification Models Using Rank-Based\n  Compatibility: Approaches for Evaluating and Optimizing Clinician-Model Team\n  Performance", "abstract": "  As data shift or new data become available, updating clinical machine\nlearning models may be necessary to maintain or improve performance over time.\nHowever, updating a model can introduce compatibility issues when the behavior\nof the updated model does not align with user expectations, resulting in poor\nuser-model team performance. Existing compatibility measures depend on model\ndecision thresholds, limiting their applicability in settings where models are\nused to generate rankings based on estimated risk. To address this limitation,\nwe propose a novel rank-based compatibility measure, $C^R$, and a new loss\nfunction that aims to optimize discriminative performance while encouraging\ngood compatibility. Applied to a case study in mortality risk stratification\nleveraging data from MIMIC, our approach yields more compatible models while\nmaintaining discriminative performance compared to existing model selection\ntechniques, with an increase in $C^R$ of $0.019$ ($95\\%$ confidence interval:\n$0.005$, $0.035$). This work provides new tools to analyze and update risk\nstratification models used in clinical care.\n", "date": "2023-08-10", "categories": []}
{"id": "2211.09317", "title": "Explainable, Domain-Adaptive, and Federated Artificial Intelligence in\n  Medicine", "abstract": "  Artificial intelligence (AI) continues to transform data analysis in many\ndomains. Progress in each domain is driven by a growing body of annotated data,\nincreased computational resources, and technological innovations. In medicine,\nthe sensitivity of the data, the complexity of the tasks, the potentially high\nstakes, and a requirement of accountability give rise to a particular set of\nchallenges. In this review, we focus on three key methodological approaches\nthat address some of the particular challenges in AI-driven medical decision\nmaking. (1) Explainable AI aims to produce a human-interpretable justification\nfor each output. Such models increase confidence if the results appear\nplausible and match the clinicians expectations. However, the absence of a\nplausible explanation does not imply an inaccurate model. Especially in highly\nnon-linear, complex models that are tuned to maximize accuracy, such\ninterpretable representations only reflect a small portion of the\njustification. (2) Domain adaptation and transfer learning enable AI models to\nbe trained and applied across multiple domains. For example, a classification\ntask based on images acquired on different acquisition hardware. (3) Federated\nlearning enables learning large-scale models without exposing sensitive\npersonal health information. Unlike centralized AI learning, where the\ncentralized learning machine has access to the entire training data, the\nfederated learning process iteratively updates models across multiple sites by\nexchanging only parameter updates, not personal health data. This narrative\nreview covers the basic concepts, highlights relevant corner-stone and\nstate-of-the-art research in the field, and discusses perspectives.\n", "date": "2022-11-17", "categories": []}
{"id": "2305.05927", "title": "Deep Learning for Predicting Progression of Patellofemoral\n  Osteoarthritis Based on Lateral Knee Radiographs, Demographic Data and\n  Symptomatic Assessments", "abstract": "  In this study, we propose a novel framework that utilizes deep learning (DL)\nand attention mechanisms to predict the radiographic progression of\npatellofemoral osteoarthritis (PFOA) over a period of seven years. This study\nincluded subjects (1832 subjects, 3276 knees) from the baseline of the MOST\nstudy. PF joint regions-of-interest were identified using an automated landmark\ndetection tool (BoneFinder) on lateral knee X-rays. An end-to-end DL method was\ndeveloped for predicting PFOA progression based on imaging data in a 5-fold\ncross-validation setting. A set of baselines based on known risk factors were\ndeveloped and analyzed using gradient boosting machine (GBM). Risk factors\nincluded age, sex, BMI and WOMAC score, and the radiographic osteoarthritis\nstage of the tibiofemoral joint (KL score). Finally, we trained an ensemble\nmodel using both imaging and clinical data. Among the individual models, the\nperformance of our deep convolutional neural network attention model achieved\nthe best performance with an AUC of 0.856 and AP of 0.431; slightly\noutperforming the deep learning approach without attention (AUC=0.832, AP= 0.4)\nand the best performing reference GBM model (AUC=0.767, AP= 0.334). The\ninclusion of imaging data and clinical variables in an ensemble model allowed\nstatistically more powerful prediction of PFOA progression (AUC = 0.865,\nAP=0.447), although the clinical significance of this minor performance gain\nremains unknown. This study demonstrated the potential of machine learning\nmodels to predict the progression of PFOA using imaging and clinical variables.\nThese models could be used to identify patients who are at high risk of\nprogression and prioritize them for new treatments. However, even though the\naccuracy of the models were excellent in this study using the MOST dataset,\nthey should be still validated using external patient cohorts in the future.\n", "date": "2023-05-10", "categories": []}
{"id": "2308.02571", "title": "ADRNet: A Generalized Collaborative Filtering Framework Combining\n  Clinical and Non-Clinical Data for Adverse Drug Reaction Prediction", "abstract": "  Adverse drug reaction (ADR) prediction plays a crucial role in both health\ncare and drug discovery for reducing patient mortality and enhancing drug\nsafety. Recently, many studies have been devoted to effectively predict the\ndrug-ADRs incidence rates. However, these methods either did not effectively\nutilize non-clinical data, i.e., physical, chemical, and biological information\nabout the drug, or did little to establish a link between content-based and\npure collaborative filtering during the training phase. In this paper, we first\nformulate the prediction of multi-label ADRs as a drug-ADR collaborative\nfiltering problem, and to the best of our knowledge, this is the first work to\nprovide extensive benchmark results of previous collaborative filtering methods\non two large publicly available clinical datasets. Then, by exploiting the easy\naccessible drug characteristics from non-clinical data, we propose ADRNet, a\ngeneralized collaborative filtering framework combining clinical and\nnon-clinical data for drug-ADR prediction. Specifically, ADRNet has a shallow\ncollaborative filtering module and a deep drug representation module, which can\nexploit the high-dimensional drug descriptors to further guide the learning of\nlow-dimensional ADR latent embeddings, which incorporates both the benefits of\ncollaborative filtering and representation learning. Extensive experiments are\nconducted on two publicly available real-world drug-ADR clinical datasets and\ntwo non-clinical datasets to demonstrate the accuracy and efficiency of the\nproposed ADRNet. The code is available at\nhttps://github.com/haoxuanli-pku/ADRnet.\n", "date": "2023-08-03", "categories": []}
{"id": "2308.02597", "title": "Designing a Deep Learning-Driven Resource-Efficient Diagnostic System\n  for Metastatic Breast Cancer: Reducing Long Delays of Clinical Diagnosis and\n  Improving Patient Survival in Developing Countries", "abstract": "  Breast cancer is one of the leading causes of cancer mortality. Breast cancer\npatients in developing countries, especially sub-Saharan Africa, South Asia,\nand South America, suffer from the highest mortality rate in the world. One\ncrucial factor contributing to the global disparity in mortality rate is long\ndelay of diagnosis due to a severe shortage of trained pathologists, which\nconsequently has led to a large proportion of late-stage presentation at\ndiagnosis. The delay between the initial development of symptoms and the\nreceipt of a diagnosis could stretch upwards 15 months. To tackle this critical\nhealthcare disparity, this research has developed a deep learning-based\ndiagnosis system for metastatic breast cancer that can achieve high diagnostic\naccuracy as well as computational efficiency. Based on our evaluation, the\nMobileNetV2-based diagnostic model outperformed the more complex VGG16,\nResNet50 and ResNet101 models in diagnostic accuracy, model generalization, and\nmodel training efficiency. The visual comparisons between the model prediction\nand ground truth have demonstrated that the MobileNetV2 diagnostic models can\nidentify very small cancerous nodes embedded in a large area of normal cells\nwhich is challenging for manual image analysis. Equally Important, the light\nweighted MobleNetV2 models were computationally efficient and ready for mobile\ndevices or devices of low computational power. These advances empower the\ndevelopment of a resource-efficient and high performing AI-based metastatic\nbreast cancer diagnostic system that can adapt to under-resourced healthcare\nfacilities in developing countries. This research provides an innovative\ntechnological solution to address the long delays in metastatic breast cancer\ndiagnosis and the consequent disparity in patient survival outcome in\ndeveloping countries.\n", "date": "2023-08-04", "categories": []}
{"id": "2308.02796", "title": "OBESEYE: Interpretable Diet Recommender for Obesity Management using\n  Machine Learning and Explainable AI", "abstract": "  Obesity, the leading cause of many non-communicable diseases, occurs mainly\nfor eating more than our body requirements and lack of proper activity. So,\nbeing healthy requires heathy diet plans, especially for patients with\ncomorbidities. But it is difficult to figure out the exact quantity of each\nnutrient because nutrients requirement varies based on physical and disease\nconditions. In our study we proposed a novel machine learning based system to\npredict the amount of nutrients one individual requires for being healthy. We\napplied different machine learning algorithms: linear regression, support\nvector machine (SVM), decision tree, random forest, XGBoost, LightGBM on fluid\nand 3 other major micronutrients: carbohydrate, protein, fat consumption\nprediction. We achieved high accuracy with low root mean square error (RMSE) by\nusing linear regression in fluid prediction, random forest in carbohydrate\nprediction and LightGBM in protein and fat prediction. We believe our diet\nrecommender system, OBESEYE, is the only of its kind which recommends diet with\nthe consideration of comorbidities and physical conditions and promote\nencouragement to get rid of obesity.\n", "date": "2023-08-05", "categories": []}
{"id": "2308.03076", "title": "Study for Performance of MobileNetV1 and MobileNetV2 Based on Breast\n  Cancer", "abstract": "  Artificial intelligence is constantly evolving and can provide effective help\nin all aspects of people's lives. The experiment is mainly to study the use of\nartificial intelligence in the field of medicine. The purpose of this\nexperiment was to compare which of MobileNetV1 and MobileNetV2 models was\nbetter at detecting histopathological images of the breast downloaded at\nKaggle. When the doctor looks at the pathological image, there may be errors\nthat lead to errors in judgment, and the observation speed is slow. Rational\nuse of artificial intelligence can effectively reduce the error of doctor\ndiagnosis in breast cancer judgment and speed up doctor diagnosis. The dataset\nwas downloaded from Kaggle and then normalized. The basic principle of the\nexperiment is to let the neural network model learn the downloaded data set.\nThen find the pattern and be able to judge on your own whether breast tissue is\ncancer. In the dataset, benign tumor pictures and malignant tumor pictures have\nbeen classified, of which 198738 are benign tumor pictures and 78, 786 are\nmalignant tumor pictures. After calling MobileNetV1 and MobileNetV2, the\ndataset is trained separately, the training accuracy and validation accuracy\nrate are obtained, and the image is drawn. It can be observed that MobileNetV1\nhas better validation accuracy and overfit during MobileNetV2 training. From\nthe experimental results, it can be seen that in the case of processing this\ndataset, MobileNetV1 is much better than MobileNetV2.\n", "date": "2023-08-06", "categories": []}
{"id": "2308.01929", "title": "A Transformer-based Prediction Method for Depth of Anesthesia During\n  Target-controlled Infusion of Propofol and Remifentanil", "abstract": "  Accurately predicting anesthetic effects is essential for target-controlled\ninfusion systems. The traditional (PK-PD) models for Bispectral index (BIS)\nprediction require manual selection of model parameters, which can be\nchallenging in clinical settings. Recently proposed deep learning methods can\nonly capture general trends and may not predict abrupt changes in BIS. To\naddress these issues, we propose a transformer-based method for predicting the\ndepth of anesthesia (DOA) using drug infusions of propofol and remifentanil.\nOur method employs long short-term memory (LSTM) and gate residual network\n(GRN) networks to improve the efficiency of feature fusion and applies an\nattention mechanism to discover the interactions between the drugs. We also use\nlabel distribution smoothing and reweighting losses to address data imbalance.\nExperimental results show that our proposed method outperforms traditional\nPK-PD models and previous deep learning methods, effectively predicting\nanesthetic depth under sudden and deep anesthesia conditions.\n", "date": "2023-08-02", "categories": []}
{"id": "2308.01930", "title": "Machine Learning-Based Diabetes Detection Using Photoplethysmography\n  Signal Features", "abstract": "  Diabetes is a prevalent chronic condition that compromises the health of\nmillions of people worldwide. Minimally invasive methods are needed to prevent\nand control diabetes but most devices for measuring glucose levels are invasive\nand not amenable for continuous monitoring. Here, we present an alternative\nmethod to overcome these shortcomings based on non-invasive optical\nphotoplethysmography (PPG) for detecting diabetes. We classify non-Diabetic and\nDiabetic patients using the PPG signal and metadata for training Logistic\nRegression (LR) and eXtreme Gradient Boosting (XGBoost) algorithms. We used PPG\nsignals from a publicly available dataset. To prevent overfitting, we divided\nthe data into five folds for cross-validation. By ensuring that patients in the\ntraining set are not in the testing set, the model's performance can be\nevaluated on unseen subjects' data, providing a more accurate assessment of its\ngeneralization. Our model achieved an F1-Score and AUC of $58.8\\pm20.0\\%$ and\n$79.2\\pm15.0\\%$ for LR and $51.7\\pm16.5\\%$ and $73.6\\pm17.0\\%$ for XGBoost,\nrespectively. Feature analysis suggested that PPG morphological features\ncontains diabetes-related information alongside metadata. Our findings are\nwithin the same range reported in the literature, indicating that machine\nlearning methods are promising for developing remote, non-invasive, and\ncontinuous measurement devices for detecting and preventing diabetes.\n", "date": "2023-08-02", "categories": []}
{"id": "2308.02081", "title": "Target specification bias, counterfactual prediction, and algorithmic\n  fairness in healthcare", "abstract": "  Bias in applications of machine learning (ML) to healthcare is usually\nattributed to unrepresentative or incomplete data, or to underlying health\ndisparities. This article identifies a more pervasive source of bias that\naffects the clinical utility of ML-enabled prediction tools: target\nspecification bias. Target specification bias arises when the\noperationalization of the target variable does not match its definition by\ndecision makers. The mismatch is often subtle, and stems from the fact that\ndecision makers are typically interested in predicting the outcomes of\ncounterfactual, rather than actual, healthcare scenarios. Target specification\nbias persists independently of data limitations and health disparities. When\nleft uncorrected, it gives rise to an overestimation of predictive accuracy, to\ninefficient utilization of medical resources, and to suboptimal decisions that\ncan harm patients. Recent work in metrology - the science of measurement -\nsuggests ways of counteracting target specification bias and avoiding its\nharmful consequences.\n", "date": "2023-08-03", "categories": []}
{"id": "2308.01433", "title": "COVID-VR: A Deep Learning COVID-19 Classification Model Using\n  Volume-Rendered Computer Tomography", "abstract": "  The COVID-19 pandemic presented numerous challenges to healthcare systems\nworldwide. Given that lung infections are prevalent among COVID-19 patients,\nchest Computer Tomography (CT) scans have frequently been utilized as an\nalternative method for identifying COVID-19 conditions and various other types\nof pulmonary diseases. Deep learning architectures have emerged to automate the\nidentification of pulmonary disease types by leveraging CT scan slices as\ninputs for classification models. This paper introduces COVID-VR, a novel\napproach for classifying pulmonary diseases based on volume rendering images of\nthe lungs captured from multiple angles, thereby providing a comprehensive view\nof the entire lung in each image. To assess the effectiveness of our proposal,\nwe compared it against competing strategies utilizing both private data\nobtained from partner hospitals and a publicly available dataset. The results\ndemonstrate that our approach effectively identifies pulmonary lesions and\nperforms competitively when compared to slice-based methods.\n", "date": "2023-08-02", "categories": []}
{"id": "2308.01834", "title": "The Capability of Large Language Models to Measure Psychiatric\n  Functioning", "abstract": "  The current work investigates the capability of Large language models (LLMs)\nthat are explicitly trained on large corpuses of medical knowledge (Med-PaLM 2)\nto predict psychiatric functioning from patient interviews and clinical\ndescriptions without being trained to do so. To assess this, n = 145 depression\nand n =115 PTSD assessments and n = 46 clinical case studies across high\nprevalence/high comorbidity disorders (Depressive, Anxiety, Psychotic, trauma\nand stress, Addictive disorders) were analyzed using prompts to extract\nestimated clinical scores and diagnoses. Results demonstrate that Med-PaLM 2 is\ncapable of assessing psychiatric functioning across a range of psychiatric\nconditions with the strongest performance being the prediction of depression\nscores based on standardized assessments (Accuracy range= 0.80 - 0.84) which\nwere statistically indistinguishable from human clinical raters t(1,144) =\n1.20; p = 0.23. Results show the potential for general clinical language models\nto flexibly predict psychiatric risk based on free descriptions of functioning\nfrom both patients and clinicians.\n", "date": "2023-08-03", "categories": []}
{"id": "2308.01057", "title": "MammoDG: Generalisable Deep Learning Breaks the Limits of Cross-Domain\n  Multi-Center Breast Cancer Screening", "abstract": "  Breast cancer is a major cause of cancer death among women, emphasising the\nimportance of early detection for improved treatment outcomes and quality of\nlife. Mammography, the primary diagnostic imaging test, poses challenges due to\nthe high variability and patterns in mammograms. Double reading of mammograms\nis recommended in many screening programs to improve diagnostic accuracy but\nincreases radiologists' workload. Researchers explore Machine Learning models\nto support expert decision-making. Stand-alone models have shown comparable or\nsuperior performance to radiologists, but some studies note decreased\nsensitivity with multiple datasets, indicating the need for high generalisation\nand robustness models. This work devises MammoDG, a novel deep-learning\nframework for generalisable and reliable analysis of cross-domain multi-center\nmammography data. MammoDG leverages multi-view mammograms and a novel\ncontrastive mechanism to enhance generalisation capabilities. Extensive\nvalidation demonstrates MammoDG's superiority, highlighting the critical\nimportance of domain generalisation for trustworthy mammography analysis in\nimaging protocol variations.\n", "date": "2023-08-02", "categories": []}
{"id": "2307.06060", "title": "Interpreting deep embeddings for disease progression clustering", "abstract": "  We propose a novel approach for interpreting deep embeddings in the context\nof patient clustering. We evaluate our approach on a dataset of participants\nwith type 2 diabetes from the UK Biobank, and demonstrate clinically meaningful\ninsights into disease progression patterns.\n", "date": "2023-07-12", "categories": []}
{"id": "2307.15719", "title": "Identifying acute illness phenotypes via deep temporal interpolation and\n  clustering network on physiologic signatures", "abstract": "  Initial hours of hospital admission impact clinical trajectory, but early\nclinical decisions often suffer due to data paucity. With clustering analysis\nfor vital signs within six hours of admission, patient phenotypes with distinct\npathophysiological signatures and outcomes may support early clinical\ndecisions. We created a single-center, longitudinal EHR dataset for 75,762\nadults admitted to a tertiary care center for 6+ hours. We proposed a deep\ntemporal interpolation and clustering network to extract latent representations\nfrom sparse, irregularly sampled vital sign data and derived distinct patient\nphenotypes in a training cohort (n=41,502). Model and hyper-parameters were\nchosen based on a validation cohort (n=17,415). Test cohort (n=16,845) was used\nto analyze reproducibility and correlation with biomarkers. The training,\nvalidation, and testing cohorts had similar distributions of age (54-55 yrs),\nsex (55% female), race, comorbidities, and illness severity. Four clusters were\nidentified. Phenotype A (18%) had most comorbid disease with higher rate of\nprolonged respiratory insufficiency, acute kidney injury, sepsis, and\nthree-year mortality. Phenotypes B (33%) and C (31%) had diffuse patterns of\nmild organ dysfunction. Phenotype B had favorable short-term outcomes but\nsecond-highest three-year mortality. Phenotype C had favorable clinical\noutcomes. Phenotype D (17%) had early/persistent hypotension, high rate of\nearly surgery, and substantial biomarker rate of inflammation but second-lowest\nthree-year mortality. After comparing phenotypes' SOFA scores, clustering\nresults did not simply repeat other acuity assessments. In a heterogeneous\ncohort, four phenotypes with distinct categories of disease and outcomes were\nidentified by a deep temporal interpolation and clustering network. This tool\nmay impact triage decisions and clinical decision-support under time\nconstraints.\n", "date": "2023-07-27", "categories": []}
{"id": "2307.14067", "title": "Machine Learning Applications In Healthcare: The State Of Knowledge and\n  Future Directions", "abstract": "  Detection of easily missed hidden patterns with fast processing power makes\nmachine learning (ML) indispensable to today's healthcare system. Though many\nML applications have already been discovered and many are still under\ninvestigation, only a few have been adopted by current healthcare systems. As a\nresult, there exists an enormous opportunity in healthcare system for ML but\ndistributed information, scarcity of properly arranged and easily explainable\ndocumentation in related sector are major impede which are making ML\napplications difficult to healthcare professionals. This study aimed to gather\nML applications in different areas of healthcare concisely and more effectively\nso that necessary information can be accessed immediately with relevant\nreferences. We divided our study into five major groups: community level work,\nrisk management/ preventive care, healthcare operation management, remote care,\nand early detection. Dividing these groups into subgroups, we provided relevant\nreferences with description in tabular form for quick access. Our objective is\nto inform people about ML applicability in healthcare industry, reduce the\nknowledge gap of clinicians about the ML applications and motivate healthcare\nprofessionals towards more machine learning based healthcare system.\n", "date": "2023-07-26", "categories": []}
{"id": "2307.11952", "title": "Pathology-and-genomics Multimodal Transformer for Survival Outcome\n  Prediction", "abstract": "  Survival outcome assessment is challenging and inherently associated with\nmultiple clinical factors (e.g., imaging and genomics biomarkers) in cancer.\nEnabling multimodal analytics promises to reveal novel predictive patterns of\npatient outcomes. In this study, we propose a multimodal transformer\n(PathOmics) integrating pathology and genomics insights into colon-related\ncancer survival prediction. We emphasize the unsupervised pretraining to\ncapture the intrinsic interaction between tissue microenvironments in gigapixel\nwhole slide images (WSIs) and a wide range of genomics data (e.g.,\nmRNA-sequence, copy number variant, and methylation). After the multimodal\nknowledge aggregation in pretraining, our task-specific model finetuning could\nexpand the scope of data utility applicable to both multi- and single-modal\ndata (e.g., image- or genomics-only). We evaluate our approach on both TCGA\ncolon and rectum cancer cohorts, showing that the proposed approach is\ncompetitive and outperforms state-of-the-art studies. Finally, our approach is\ndesirable to utilize the limited number of finetuned samples towards\ndata-efficient analytics for survival outcome prediction. The code is available\nat https://github.com/Cassie07/PathOmics.\n", "date": "2023-07-22", "categories": []}
{"id": "2307.11487", "title": "A New Deep State-Space Analysis Framework for Patient Latent State\n  Estimation and Classification from EHR Time Series Data", "abstract": "  Many diseases, including cancer and chronic conditions, require extended\ntreatment periods and long-term strategies. Machine learning and AI research\nfocusing on electronic health records (EHRs) have emerged to address this need.\nEffective treatment strategies involve more than capturing sequential changes\nin patient test values. It requires an explainable and clinically interpretable\nmodel by capturing the patient's internal state over time.\n  In this study, we propose the \"deep state-space analysis framework,\" using\ntime-series unsupervised learning of EHRs with a deep state-space model. This\nframework enables learning, visualizing, and clustering of temporal changes in\npatient latent states related to disease progression.\n  We evaluated our framework using time-series laboratory data from 12,695\ncancer patients. By estimating latent states, we successfully discover latent\nstates related to prognosis. By visualization and cluster analysis, the\ntemporal transition of patient status and test items during state transitions\ncharacteristic of each anticancer drug were identified. Our framework surpasses\nexisting methods in capturing interpretable latent space. It can be expected to\nenhance our comprehension of disease progression from EHRs, aiding treatment\nadjustments and prognostic determinations.\n", "date": "2023-07-21", "categories": []}
{"id": "2210.00255", "title": "Cascaded Multi-Modal Mixing Transformers for Alzheimer's Disease\n  Classification with Incomplete Data", "abstract": "  Accurate medical classification requires a large number of multi-modal data,\nand in many cases, different feature types. Previous studies have shown\npromising results when using multi-modal data, outperforming single-modality\nmodels when classifying diseases such as Alzheimer's Disease (AD). However,\nthose models are usually not flexible enough to handle missing modalities.\nCurrently, the most common workaround is discarding samples with missing\nmodalities which leads to considerable data under-utilization. Adding to the\nfact that labeled medical images are already scarce, the performance of\ndata-driven methods like deep learning can be severely hampered. Therefore, a\nmulti-modal method that can handle missing data in various clinical settings is\nhighly desirable. In this paper, we present Multi-Modal Mixing Transformer\n(3MAT), a disease classification transformer that not only leverages\nmulti-modal data but also handles missing data scenarios. In this work, we test\n3MT for AD and Cognitively normal (CN) classification and mild cognitive\nimpairment (MCI) conversion prediction to progressive MCI (pMCI) or stable MCI\n(sMCI) using clinical and neuroimaging data. The model uses a novel Cascaded\nModality Transformer architecture with cross-attention to incorporate\nmulti-modal information for more informed predictions. We propose a novel\nmodality dropout mechanism to ensure an unprecedented level of modality\nindependence and robustness to handle missing data scenarios. The result is a\nversatile network that enables the mixing of arbitrary numbers of modalities\nwith different feature types and also ensures full data utilization missing\ndata scenarios. The model is trained and evaluated on the ADNI dataset with the\nSOTRA performance and further evaluated with the AIBL dataset with missing\ndata.\n", "date": "2022-10-01", "categories": []}
{"id": "2305.13426", "title": "Evaluating Model Performance in Medical Datasets Over Time", "abstract": "  Machine learning (ML) models deployed in healthcare systems must face data\ndrawn from continually evolving environments. However, researchers proposing\nsuch models typically evaluate them in a time-agnostic manner, splitting\ndatasets according to patients sampled randomly throughout the entire study\ntime period. This work proposes the Evaluation on Medical Datasets Over Time\n(EMDOT) framework, which evaluates the performance of a model class across\ntime. Inspired by the concept of backtesting, EMDOT simulates possible training\nprocedures that practitioners might have been able to execute at each point in\ntime and evaluates the resulting models on all future time points. Evaluating\nboth linear and more complex models on six distinct medical data sources\n(tabular and imaging), we show how depending on the dataset, using all\nhistorical data may be ideal in many cases, whereas using a window of the most\nrecent data could be advantageous in others. In datasets where models suffer\nfrom sudden degradations in performance, we investigate plausible explanations\nfor these shocks. We release the EMDOT package to help facilitate further works\nin deployment-oriented evaluation over time.\n", "date": "2023-05-22", "categories": []}
{"id": "2307.09018", "title": "Multimodal LLMs for health grounded in individual-specific data", "abstract": "  Foundation large language models (LLMs) have shown an impressive ability to\nsolve tasks across a wide range of fields including health. To effectively\nsolve personalized health tasks, LLMs need the ability to ingest a diversity of\ndata modalities that are relevant to an individual's health status. In this\npaper, we take a step towards creating multimodal LLMs for health that are\ngrounded in individual-specific data by developing a framework (HeLM: Health\nLarge Language Model for Multimodal Understanding) that enables LLMs to use\nhigh-dimensional clinical modalities to estimate underlying disease risk. HeLM\nencodes complex data modalities by learning an encoder that maps them into the\nLLM's token embedding space and for simple modalities like tabular data by\nserializing the data into text. Using data from the UK Biobank, we show that\nHeLM can effectively use demographic and clinical features in addition to\nhigh-dimensional time-series data to estimate disease risk. For example, HeLM\nachieves an AUROC of 0.75 for asthma prediction when combining tabular and\nspirogram data modalities compared with 0.49 when only using tabular data.\nOverall, we find that HeLM outperforms or performs at parity with classical\nmachine learning approaches across a selection of eight binary traits.\nFurthermore, we investigate the downstream uses of this model such as its\ngeneralizability to out-of-distribution traits and its ability to power\nconversations around individual health and wellness.\n", "date": "2023-07-18", "categories": []}
{"id": "2307.10923", "title": "Sequential Multi-Dimensional Self-Supervised Learning for Clinical Time\n  Series", "abstract": "  Self-supervised learning (SSL) for clinical time series data has received\nsignificant attention in recent literature, since these data are highly rich\nand provide important information about a patient's physiological state.\nHowever, most existing SSL methods for clinical time series are limited in that\nthey are designed for unimodal time series, such as a sequence of structured\nfeatures (e.g., lab values and vitals signs) or an individual high-dimensional\nphysiological signal (e.g., an electrocardiogram). These existing methods\ncannot be readily extended to model time series that exhibit multimodality,\nwith structured features and high-dimensional data being recorded at each\ntimestep in the sequence. In this work, we address this gap and propose a new\nSSL method -- Sequential Multi-Dimensional SSL -- where a SSL loss is applied\nboth at the level of the entire sequence and at the level of the individual\nhigh-dimensional data points in the sequence in order to better capture\ninformation at both scales. Our strategy is agnostic to the specific form of\nloss function used at each level -- it can be contrastive, as in SimCLR, or\nnon-contrastive, as in VICReg. We evaluate our method on two real-world\nclinical datasets, where the time series contains sequences of (1)\nhigh-frequency electrocardiograms and (2) structured data from lab values and\nvitals signs. Our experimental results indicate that pre-training with our\nmethod and then fine-tuning on downstream tasks improves performance over\nbaselines on both datasets, and in several settings, can lead to improvements\nacross different self-supervised loss functions.\n", "date": "2023-07-20", "categories": []}
{"id": "2307.09823", "title": "Multi-modal Learning based Prediction for Disease", "abstract": "  Non alcoholic fatty liver disease (NAFLD) is the most common cause of chronic\nliver disease, which can be predicted accurately to prevent advanced fibrosis\nand cirrhosis. While, a liver biopsy, the gold standard for NAFLD diagnosis, is\ninvasive, expensive, and prone to sampling errors. Therefore, non-invasive\nstudies are extremely promising, yet they are still in their infancy due to the\nlack of comprehensive research data and intelligent methods for multi-modal\ndata. This paper proposes a NAFLD diagnosis system (DeepFLDDiag) combining a\ncomprehensive clinical dataset (FLDData) and a multi-modal learning based NAFLD\nprediction method (DeepFLD). The dataset includes over 6000 participants\nphysical examinations, laboratory and imaging studies, extensive\nquestionnaires, and facial images of partial participants, which is\ncomprehensive and valuable for clinical studies. From the dataset, we\nquantitatively analyze and select clinical metadata that most contribute to\nNAFLD prediction. Furthermore, the proposed DeepFLD, a deep neural network\nmodel designed to predict NAFLD using multi-modal input, including metadata and\nfacial images, outperforms the approach that only uses metadata. Satisfactory\nperformance is also verified on other unseen datasets. Inspiringly, DeepFLD can\nachieve competitive results using only facial images as input rather than\nmetadata, paving the way for a more robust and simpler non-invasive NAFLD\ndiagnosis.\n", "date": "2023-07-19", "categories": []}
{"id": "2207.10603", "title": "Unsupervised pre-training of graph transformers on patient population\n  graphs", "abstract": "  Pre-training has shown success in different areas of machine learning, such\nas Computer Vision, Natural Language Processing (NLP), and medical imaging.\nHowever, it has not been fully explored for clinical data analysis. An immense\namount of clinical records are recorded, but still, data and labels can be\nscarce for data collected in small hospitals or dealing with rare diseases. In\nsuch scenarios, pre-training on a larger set of unlabelled clinical data could\nimprove performance. In this paper, we propose novel unsupervised pre-training\ntechniques designed for heterogeneous, multi-modal clinical data for patient\noutcome prediction inspired by masked language modeling (MLM), by leveraging\ngraph deep learning over population graphs. To this end, we further propose a\ngraph-transformer-based network, designed to handle heterogeneous clinical\ndata. By combining masking-based pre-training with a transformer-based network,\nwe translate the success of masking-based pre-training in other domains to\nheterogeneous clinical data. We show the benefit of our pre-training method in\na self-supervised and a transfer learning setting, utilizing three medical\ndatasets TADPOLE, MIMIC-III, and a Sepsis Prediction Dataset. We find that our\nproposed pre-training methods help in modeling the data at a patient and\npopulation level and improve performance in different fine-tuning tasks on all\ndatasets.\n", "date": "2022-07-21", "categories": []}
{"id": "2307.08576", "title": "A Study on the Performance of Generative Pre-trained Transformer (GPT)\n  in Simulating Depressed Individuals on the Standardized Depressive Symptom\n  Scale", "abstract": "  Background: Depression is a common mental disorder with societal and economic\nburden. Current diagnosis relies on self-reports and assessment scales, which\nhave reliability issues. Objective approaches are needed for diagnosing\ndepression. Objective: Evaluate the potential of GPT technology in diagnosing\ndepression. Assess its ability to simulate individuals with depression and\ninvestigate the influence of depression scales. Methods: Three\ndepression-related assessment tools (HAMD-17, SDS, GDS-15) were used. Two\nexperiments simulated GPT responses to normal individuals and individuals with\ndepression. Compare GPT's responses with expected results, assess its\nunderstanding of depressive symptoms, and performance differences under\ndifferent conditions. Results: GPT's performance in depression assessment was\nevaluated. It aligned with scoring criteria for both individuals with\ndepression and normal individuals. Some performance differences were observed\nbased on depression severity. GPT performed better on scales with higher\nsensitivity. Conclusion: GPT accurately simulates individuals with depression\nand normal individuals during depression-related assessments. Deviations occur\nwhen simulating different degrees of depression, limiting understanding of mild\nand moderate cases. GPT performs better on scales with higher sensitivity,\nindicating potential for developing more effective depression scales. GPT has\nimportant potential in depression assessment, supporting clinicians and\npatients.\n", "date": "2023-07-17", "categories": []}
{"id": "2307.07051", "title": "Making the Most Out of the Limited Context Length: Predictive Power\n  Varies with Clinical Note Type and Note Section", "abstract": "  Recent advances in large language models have led to renewed interest in\nnatural language processing in healthcare using the free text of clinical\nnotes. One distinguishing characteristic of clinical notes is their long time\nspan over multiple long documents. The unique structure of clinical notes\ncreates a new design choice: when the context length for a language model\npredictor is limited, which part of clinical notes should we choose as the\ninput? Existing studies either choose the inputs with domain knowledge or\nsimply truncate them. We propose a framework to analyze the sections with high\npredictive power. Using MIMIC-III, we show that: 1) predictive power\ndistribution is different between nursing notes and discharge notes and 2)\ncombining different types of notes could improve performance when the context\nlength is large. Our findings suggest that a carefully selected sampling\nfunction could enable more efficient information extraction from clinical\nnotes.\n", "date": "2023-07-13", "categories": []}
{"id": "2307.07177", "title": "TriFormer: A Multi-modal Transformer Framework For Mild Cognitive\n  Impairment Conversion Prediction", "abstract": "  The prediction of mild cognitive impairment (MCI) conversion to Alzheimer's\ndisease (AD) is important for early treatment to prevent or slow the\nprogression of AD. To accurately predict the MCI conversion to stable MCI or\nprogressive MCI, we propose Triformer, a novel transformer-based framework with\nthree specialized transformers to incorporate multi-model data. Triformer uses\nI) an image transformer to extract multi-view image features from medical\nscans, II) a clinical transformer to embed and correlate multi-modal clinical\ndata, and III) a modality fusion transformer that produces an accurate\nprediction based on fusing the outputs from the image and clinical\ntransformers. Triformer is evaluated on the Alzheimer's Disease Neuroimaging\nInitiative (ANDI)1 and ADNI2 datasets and outperforms previous state-of-the-art\nsingle and multi-modal methods.\n", "date": "2023-07-14", "categories": []}
{"id": "2307.07423", "title": "Enhancing ECG Analysis of Implantable Cardiac Monitor Data: An Efficient\n  Pipeline for Multi-Label Classification", "abstract": "  Implantable Cardiac Monitor (ICM) devices are demonstrating as of today, the\nfastest-growing market for implantable cardiac devices. As such, they are\nbecoming increasingly common in patients for measuring heart electrical\nactivity. ICMs constantly monitor and record a patient's heart rhythm and when\ntriggered - send it to a secure server where health care professionals (denote\nHCPs from here on) can review it. These devices employ a relatively simplistic\nrule-based algorithm (due to energy consumption constraints) to alert for\nabnormal heart rhythms. This algorithm is usually parameterized to an\nover-sensitive mode in order to not miss a case (resulting in relatively high\nfalse-positive rate) and this, combined with the device's nature of constantly\nmonitoring the heart rhythm and its growing popularity, results in HCPs having\nto analyze and diagnose an increasingly growing amount of data. In order to\nreduce the load on the latter, automated methods for ECG analysis are nowadays\nbecoming a great tool to assist HCPs in their analysis. While state-of-the-art\nalgorithms are data-driven rather than rule-based, training data for ICMs often\nconsist of specific characteristics which make its analysis unique and\nparticularly challenging. This study presents the challenges and solutions in\nautomatically analyzing ICM data and introduces a method for its classification\nthat outperforms existing methods on such data. As such, it could be used in\nnumerous ways such as aiding HCPs in the analysis of ECGs originating from ICMs\nby e.g. suggesting a rhythm type.\n", "date": "2023-07-12", "categories": []}
{"id": "2306.02121", "title": "Identifying Subgroups of ICU Patients Using End-to-End Multivariate\n  Time-Series Clustering Algorithm Based on Real-World Vital Signs Data", "abstract": "  This study employed the MIMIC-IV database as data source to investigate the\nuse of dynamic, high-frequency, multivariate time-series vital signs data,\nincluding temperature, heart rate, mean blood pressure, respiratory rate, and\nSpO2, monitored first 8 hours data in the ICU stay. Various clustering\nalgorithms were compared, and an end-to-end multivariate time series clustering\nsystem called Time2Feat, combined with K-Means, was chosen as the most\neffective method to cluster patients in the ICU. In clustering analysis, data\nof 8,080 patients admitted between 2008 and 2016 was used for model development\nand 2,038 patients admitted between 2017 and 2019 for model validation. By\nanalyzing the differences in clinical mortality prognosis among different\ncategories, varying risks of ICU mortality and hospital mortality were found\nbetween different subgroups. Furthermore, the study visualized the trajectory\nof vital signs changes. The findings of this study provide valuable insights\ninto the potential use of multivariate time-series clustering systems in\npatient management and monitoring in the ICU setting.\n", "date": "2023-06-03", "categories": []}
{"id": "2307.04770", "title": "Predicting Outcomes in Long COVID Patients with Spatiotemporal Attention", "abstract": "  Long COVID is a general term of post-acute sequelae of COVID-19. Patients\nwith long COVID can endure long-lasting symptoms including fatigue, headache,\ndyspnea and anosmia, etc. Identifying the cohorts with severe long-term\ncomplications in COVID-19 could benefit the treatment planning and resource\narrangement. However, due to the heterogeneous phenotype presented in long\nCOVID patients, it is difficult to predict their outcomes from their\nlongitudinal data. In this study, we proposed a spatiotemporal attention\nmechanism to weigh feature importance jointly from the temporal dimension and\nfeature space. Considering that medical examinations can have interchangeable\norders in adjacent time points, we restricted the learning of short-term\ndependency with a Local-LSTM and the learning of long-term dependency with the\njoint spatiotemporal attention. We also compared the proposed method with\nseveral state-of-the-art methods and a method in clinical practice. The methods\nare evaluated on a hard-to-acquire clinical dataset of patients with long\nCOVID. Experimental results show the Local-LSTM with joint spatiotemporal\nattention outperformed related methods in outcome prediction. The proposed\nmethod provides a clinical tool for the severity assessment of long COVID.\n", "date": "2023-07-07", "categories": []}
{"id": "2307.04777", "title": "MentalHealthAI: Utilizing Personal Health Device Data to Optimize\n  Psychiatry Treatment", "abstract": "  Mental health disorders remain a significant challenge in modern healthcare,\nwith diagnosis and treatment often relying on subjective patient descriptions\nand past medical history. To address this issue, we propose a personalized\nmental health tracking and mood prediction system that utilizes patient\nphysiological data collected through personal health devices. Our system\nleverages a decentralized learning mechanism that combines transfer and\nfederated machine learning concepts using smart contracts, allowing data to\nremain on users' devices and enabling effective tracking of mental health\nconditions for psychiatric treatment and management in a privacy-aware and\naccountable manner. We evaluate our model using a popular mental health dataset\nthat demonstrates promising results. By utilizing connected health systems and\nmachine learning models, our approach offers a novel solution to the challenge\nof providing psychiatrists with further insight into their patients' mental\nhealth outside of traditional office visits.\n", "date": "2023-07-09", "categories": []}
{"id": "2307.03757", "title": "A Fully Automated and Explainable Algorithm for the Prediction of\n  Malignant Transformation in Oral Epithelial Dysplasia", "abstract": "  Oral epithelial dysplasia (OED) is a premalignant histopathological diagnosis\ngiven to lesions of the oral cavity. Its grading suffers from significant\ninter-/intra- observer variability, and does not reliably predict malignancy\nprogression, potentially leading to suboptimal treatment decisions. To address\nthis, we developed a novel artificial intelligence algorithm that can assign an\nOral Malignant Transformation (OMT) risk score, based on histological patterns\nin the in Haematoxylin and Eosin stained whole slide images, to quantify the\nrisk of OED progression. The algorithm is based on the detection and\nsegmentation of nuclei within (and around) the epithelium using an in-house\nsegmentation model. We then employed a shallow neural network fed with\ninterpretable morphological/spatial features, emulating histological markers.\nWe conducted internal cross-validation on our development cohort (Sheffield; n\n= 193 cases) followed by independent validation on two external cohorts\n(Birmingham and Belfast; n = 92 cases). The proposed OMTscore yields an AUROC =\n0.74 in predicting whether an OED progresses to malignancy or not. Survival\nanalyses showed the prognostic value of our OMTscore for predicting malignancy\ntransformation, when compared to the manually-assigned WHO and binary grades.\nAnalysis of the correctly predicted cases elucidated the presence of\nperi-epithelial and epithelium-infiltrating lymphocytes in the most predictive\npatches of cases that transformed (p < 0.0001). This is the first study to\npropose a completely automated algorithm for predicting OED transformation\nbased on interpretable nuclear features, whilst being validated on external\ndatasets. The algorithm shows better-than-human-level performance for\nprediction of OED malignant transformation and offers a promising solution to\nthe challenges of grading OED in routine clinical practice.\n", "date": "2023-07-06", "categories": []}
{"id": "2209.06947", "title": "Metrics to guide development of machine learning algorithms for malaria\n  diagnosis", "abstract": "  Automated malaria diagnosis is a difficult but high-value target for machine\nlearning (ML), and effective algorithms could save many thousands of children's\nlives. However, current ML efforts largely neglect crucial use case constraints\nand are thus not clinically useful. Two factors in particular are crucial to\ndeveloping algorithms translatable to clinical field settings: (i) Clear\nunderstanding of the clinical needs that ML solutions must accommodate; and\n(ii) task-relevant metrics for guiding and evaluating ML models. Neglect of\nthese factors has seriously hampered past ML work on malaria, because the\nresulting algorithms do not align with clinical needs.\n  In this paper we address these two issues in the context of automated malaria\ndiagnosis via microscopy on Giemsa-stained blood films. First, we describe why\ndomain expertise is crucial to effectively apply ML to malaria, and list\ntechnical documents and other resources that provide this domain knowledge.\nSecond, we detail performance metrics tailored to the clinical requirements of\nmalaria diagnosis, to guide development of ML models and evaluate model\nperformance through the lens of clinical needs (versus a generic ML lens). We\nhighlight the importance of a patient-level perspective, interpatient\nvariability, false positive rates, limit of detection, and different types of\nerror. We also discuss reasons why ROC curves, AUC, and F1, as commonly used in\nML work, are poorly suited to this context. These findings also apply to other\ndiseases involving parasite loads, including neglected tropical diseases (NTDs)\nsuch as schistosomiasis.\n", "date": "2022-09-14", "categories": []}
{"id": "2307.00995", "title": "Towards Suicide Prevention from Bipolar Disorder with Temporal\n  Symptom-Aware Multitask Learning", "abstract": "  Bipolar disorder (BD) is closely associated with an increased risk of\nsuicide. However, while the prior work has revealed valuable insight into\nunderstanding the behavior of BD patients on social media, little attention has\nbeen paid to developing a model that can predict the future suicidality of a BD\npatient. Therefore, this study proposes a multi-task learning model for\npredicting the future suicidality of BD patients by jointly learning current\nsymptoms. We build a novel BD dataset clinically validated by psychiatrists,\nincluding 14 years of posts on bipolar-related subreddits written by 818 BD\npatients, along with the annotations of future suicidality and BD symptoms. We\nalso suggest a temporal symptom-aware attention mechanism to determine which\nsymptoms are the most influential for predicting future suicidality over time\nthrough a sequence of BD posts. Our experiments demonstrate that the proposed\nmodel outperforms the state-of-the-art models in both BD symptom identification\nand future suicidality prediction tasks. In addition, the proposed temporal\nsymptom-aware attention provides interpretable attention weights, helping\nclinicians to apprehend BD patients more comprehensively and to provide timely\nintervention by tracking mental state progression.\n", "date": "2023-07-03", "categories": []}
{"id": "2307.01238", "title": "Learning Difference Equations with Structured Grammatical Evolution for\n  Postprandial Glycaemia Prediction", "abstract": "  People with diabetes must carefully monitor their blood glucose levels,\nespecially after eating. Blood glucose regulation requires a proper combination\nof food intake and insulin boluses. Glucose prediction is vital to avoid\ndangerous post-meal complications in treating individuals with diabetes.\nAlthough traditional methods, such as artificial neural networks, have shown\nhigh accuracy rates, sometimes they are not suitable for developing\npersonalised treatments by physicians due to their lack of interpretability. In\nthis study, we propose a novel glucose prediction method emphasising\ninterpretability: Interpretable Sparse Identification by Grammatical Evolution.\nCombined with a previous clustering stage, our approach provides finite\ndifference equations to predict postprandial glucose levels up to two hours\nafter meals. We divide the dataset into four-hour segments and perform\nclustering based on blood glucose values for the twohour window before the\nmeal. Prediction models are trained for each cluster for the two-hour windows\nafter meals, allowing predictions in 15-minute steps, yielding up to eight\npredictions at different time horizons. Prediction safety was evaluated based\non Parkes Error Grid regions. Our technique produces safe predictions through\nexplainable expressions, avoiding zones D (0.2% average) and E (0%) and\nreducing predictions on zone C (6.2%). In addition, our proposal has slightly\nbetter accuracy than other techniques, including sparse identification of\nnon-linear dynamics and artificial neural networks. The results demonstrate\nthat our proposal provides interpretable solutions without sacrificing\nprediction accuracy, offering a promising approach to glucose prediction in\ndiabetes management that balances accuracy, interpretability, and computational\nefficiency.\n", "date": "2023-07-03", "categories": []}
{"id": "2307.02023", "title": "Using Random Effects Machine Learning Algorithms to Identify\n  Vulnerability to Depression", "abstract": "  Background: Reliable prediction of clinical progression over time can improve\nthe outcomes of depression. Little work has been done integrating various risk\nfactors for depression, to determine the combinations of factors with the\ngreatest utility for identifying which individuals are at the greatest risk.\nMethod: This study demonstrates that data-driven machine learning (ML) methods\nsuch as RE-EM (Random Effects/Expectation Maximization) trees and MERF (Mixed\nEffects Random Forest) can be applied to reliably identify variables that have\nthe greatest utility for classifying subgroups at greatest risk for depression.\n185 young adults completed measures of depression risk, including rumination,\nworry, negative cognitive styles, cognitive and coping flexibilities, and\nnegative life events, along with symptoms of depression. We trained RE-EM trees\nand MERF algorithms and compared them to traditional linear mixed models (LMMs)\npredicting depressive symptoms prospectively and concurrently with\ncross-validation. Results: Our results indicated that the RE-EM tree and MERF\nmethods model complex interactions, identify subgroups of individuals and\npredict depression severity comparable to LMM. Further, machine learning models\ndetermined that brooding, negative life events, negative cognitive styles, and\nperceived control were the most relevant predictors of future depression\nlevels. Conclusions: Random effects machine learning models have the potential\nfor high clinical utility and can be leveraged for interventions to reduce\nvulnerability to depression.\n", "date": "2023-07-05", "categories": []}
{"id": "2307.00936", "title": "OpenAPMax: Abnormal Patterns-based Model for Real-World Alzheimer's\n  Disease Diagnosis", "abstract": "  Alzheimer's disease (AD) cannot be reversed, but early diagnosis will\nsignificantly benefit patients' medical treatment and care. In recent works, AD\ndiagnosis has the primary assumption that all categories are known a prior -- a\nclosed-set classification problem, which contrasts with the open-set\nrecognition problem. This assumption hinders the application of the model in\nnatural clinical settings. Although many open-set recognition technologies have\nbeen proposed in other fields, they are challenging to use for AD diagnosis\ndirectly since 1) AD is a degenerative disease of the nervous system with\nsimilar symptoms at each stage, and it is difficult to distinguish from its\npre-state, and 2) diversified strategies for AD diagnosis are challenging to\nmodel uniformly. In this work, inspired by the concerns of clinicians during\ndiagnosis, we propose an open-set recognition model, OpenAPMax, based on the\nanomaly pattern to address AD diagnosis in real-world settings. OpenAPMax first\nobtains the abnormal pattern of each patient relative to each known category\nthrough statistics or a literature search, clusters the patients' abnormal\npattern, and finally, uses extreme value theory (EVT) to model the distance\nbetween each patient's abnormal pattern and the center of their category and\nmodify the classification probability. We evaluate the performance of the\nproposed method with recent open-set recognition, where we obtain\nstate-of-the-art results.\n", "date": "2023-07-03", "categories": []}
{"id": "2307.00965", "title": "OpenClinicalAI: An Open and Dynamic Model for Alzheimer's Disease\n  Diagnosis", "abstract": "  Although Alzheimer's disease (AD) cannot be reversed or cured, timely\ndiagnosis can significantly reduce the burden of treatment and care. Current\nresearch on AD diagnosis models usually regards the diagnosis task as a typical\nclassification task with two primary assumptions: 1) All target categories are\nknown a priori; 2) The diagnostic strategy for each patient is consistent, that\nis, the number and type of model input data for each patient are the same.\nHowever, real-world clinical settings are open, with complexity and uncertainty\nin terms of both subjects and the resources of the medical institutions. This\nmeans that diagnostic models may encounter unseen disease categories and need\nto dynamically develop diagnostic strategies based on the subject's specific\ncircumstances and available medical resources. Thus, the AD diagnosis task is\ntangled and coupled with the diagnosis strategy formulation. To promote the\napplication of diagnostic systems in real-world clinical settings, we propose\nOpenClinicalAI for direct AD diagnosis in complex and uncertain clinical\nsettings. This is the first powerful end-to-end model to dynamically formulate\ndiagnostic strategies and provide diagnostic results based on the subject's\nconditions and available medical resources. OpenClinicalAI combines\nreciprocally coupled deep multiaction reinforcement learning (DMARL) for\ndiagnostic strategy formulation and multicenter meta-learning (MCML) for\nopen-set recognition. The experimental results show that OpenClinicalAI\nachieves better performance and fewer clinical examinations than the\nstate-of-the-art model. Our method provides an opportunity to embed the AD\ndiagnostic system into the current health care system to cooperate with\nclinicians to improve current health care.\n", "date": "2023-07-03", "categories": []}
{"id": "2306.17202", "title": "An end-to-end framework for gene expression classification by\n  integrating a background knowledge graph: application to cancer prognosis\n  prediction", "abstract": "  Biological data may be separated into primary data, such as gene expression,\nand secondary data, such as pathways and protein-protein interactions. Methods\nusing secondary data to enhance the analysis of primary data are promising,\nbecause secondary data have background information that is not included in\nprimary data. In this study, we proposed an end-to-end framework to integrally\nhandle secondary data to construct a classification model for primary data. We\napplied this framework to cancer prognosis prediction using gene expression\ndata and a biological network. Cross-validation results indicated that our\nmodel achieved higher accuracy compared with a deep neural network model\nwithout background biological network information. Experiments conducted in\npatient groups by cancer type showed improvement in ROC-area under the curve\nfor many groups. Visualizations of high accuracy cancer types identified\ncontributing genes and pathways by enrichment analysis. Known biomarkers and\nnovel biomarker candidates were identified through these experiments.\n", "date": "2023-06-29", "categories": []}
{"id": "2306.17257", "title": "Prediction of COVID-19 Patients' Emergency Room Revisit using\n  Multi-Source Transfer Learning", "abstract": "  The coronavirus disease 2019 (COVID-19) has led to a global pandemic of\nsignificant severity. In addition to its high level of contagiousness, COVID-19\ncan have a heterogeneous clinical course, ranging from asymptomatic carriers to\nsevere and potentially life-threatening health complications. Many patients\nhave to revisit the emergency room (ER) within a short time after discharge,\nwhich significantly increases the workload for medical staff. Early\nidentification of such patients is crucial for helping physicians focus on\ntreating life-threatening cases. In this study, we obtained Electronic Health\nRecords (EHRs) of 3,210 encounters from 13 affiliated ERs within the University\nof Pittsburgh Medical Center between March 2020 and January 2021. We leveraged\na Natural Language Processing technique, ScispaCy, to extract clinical concepts\nand used the 1001 most frequent concepts to develop 7-day revisit models for\nCOVID-19 patients in ERs. The research data we collected from 13 ERs may have\ndistributional differences that could affect the model development. To address\nthis issue, we employed a classic deep transfer learning method called the\nDomain Adversarial Neural Network (DANN) and evaluated different modeling\nstrategies, including the Multi-DANN algorithm, the Single-DANN algorithm, and\nthree baseline methods. Results showed that the Multi-DANN models outperformed\nthe Single-DANN models and baseline models in predicting revisits of COVID-19\npatients to the ER within 7 days after discharge. Notably, the Multi-DANN\nstrategy effectively addressed the heterogeneity among multiple source domains\nand improved the adaptation of source data to the target domain. Moreover, the\nhigh performance of Multi-DANN models indicates that EHRs are informative for\ndeveloping a prediction model to identify COVID-19 patients who are very likely\nto revisit an ER within 7 days after discharge.\n", "date": "2023-06-29", "categories": []}
{"id": "2306.17337", "title": "Diagnosis Uncertain Models For Medical Risk Prediction", "abstract": "  We consider a patient risk models which has access to patient features such\nas vital signs, lab values, and prior history but does not have access to a\npatient's diagnosis. For example, this occurs in a model deployed at intake\ntime for triage purposes. We show that such `all-cause' risk models have good\ngeneralization across diagnoses but have a predictable failure mode. When the\nsame lab/vital/history profiles can result from diagnoses with different risk\nprofiles (e.g. E.coli vs. MRSA) the risk estimate is a probability weighted\naverage of these two profiles. This leads to an under-estimation of risk for\nrare but highly risky diagnoses. We propose a fix for this problem by\nexplicitly modeling the uncertainty in risk prediction coming from uncertainty\nin patient diagnoses. This gives practitioners an interpretable way to\nunderstand patient risk beyond a single risk number.\n", "date": "2023-06-29", "categories": []}
{"id": "2304.07396", "title": "Improving Patient Pre-screening for Clinical Trials: Assisting\n  Physicians with Large Language Models", "abstract": "  Physicians considering clinical trials for their patients are met with the\nlaborious process of checking many text based eligibility criteria. Large\nLanguage Models (LLMs) have shown to perform well for clinical information\nextraction and clinical reasoning, including medical tests, but not yet in\nreal-world scenarios. This paper investigates the use of InstructGPT to assist\nphysicians in determining eligibility for clinical trials based on a patient's\nsummarised medical profile. Using a prompting strategy combining one-shot,\nselection-inference and chain-of-thought techniques, we investigate the\nperformance of LLMs on 10 synthetically created patient profiles. Performance\nis evaluated at four levels: ability to identify screenable eligibility\ncriteria from a trial given a medical profile; ability to classify for each\nindividual criterion whether the patient qualifies; the overall classification\nwhether a patient is eligible for a clinical trial and the percentage of\ncriteria to be screened by physician. We evaluated against 146 clinical trials\nand a total of 4,135 eligibility criteria. The LLM was able to correctly\nidentify the screenability of 72% (2,994/4,135) of the criteria. Additionally,\n72% (341/471) of the screenable criteria were evaluated correctly. The\nresulting trial level classification as eligible or ineligible resulted in a\nrecall of 0.5. By leveraging LLMs with a physician-in-the-loop, a recall of 1.0\nand precision of 0.71 on clinical trial level can be achieved while reducing\nthe amount of criteria to be checked by an estimated 90%. LLMs can be used to\nassist physicians with pre-screening of patients for clinical trials. By\nforcing instruction-tuned LLMs to produce chain-of-thought responses, the\nreasoning can be made transparent to and the decision process becomes amenable\nby physicians, thereby making such a system feasible for use in real-world\nscenarios.\n", "date": "2023-04-14", "categories": []}
{"id": "2306.16531", "title": "Prediction of Rapid Early Progression and Survival Risk with\n  Pre-Radiation MRI in WHO Grade 4 Glioma Patients", "abstract": "  Recent clinical research describes a subset of glioblastoma patients that\nexhibit REP prior to start of radiation therapy. Current literature has thus\nfar described this population using clinicopathologic features. To our\nknowledge, this study is the first to investigate the potential of conventional\nra-diomics, sophisticated multi-resolution fractal texture features, and\ndifferent molecular features (MGMT, IDH mutations) as a diagnostic and\nprognostic tool for prediction of REP from non-REP cases using computational\nand statistical modeling methods. Radiation-planning T1 post-contrast (T1C) MRI\nsequences of 70 patients are analyzed. Ensemble method with 5-fold cross\nvalidation over 1000 iterations offers AUC of 0.793 with standard deviation of\n0.082 for REP and non-REP classification. In addition, copula-based modeling\nunder dependent censoring (where a subset of the patients may not be followed\nup until death) identifies significant features (p-value <0.05) for survival\nprobability and prognostic grouping of patient cases. The prediction of\nsurvival for the patients cohort produces precision of 0.881 with standard\ndeviation of 0.056. The prognostic index (PI) calculated using the fused\nfeatures suggests that 84.62% of REP cases fall under the bad prognostic group,\nsuggesting potentiality of fused features to predict a higher percentage of REP\ncases. The experimental result further shows that mul-ti-resolution fractal\ntexture features perform better than conventional radiomics features for REP\nand survival outcomes.\n", "date": "2023-06-28", "categories": []}
{"id": "2306.16823", "title": "Length of Stay prediction for Hospital Management using Domain\n  Adaptation", "abstract": "  Inpatient length of stay (LoS) is an important managerial metric which if\nknown in advance can be used to efficiently plan admissions, allocate resources\nand improve care. Using historical patient data and machine learning\ntechniques, LoS prediction models can be developed. Ethically, these models can\nnot be used for patient discharge in lieu of unit heads but are of utmost\nnecessity for hospital management systems in charge of effective hospital\nplanning. Therefore, the design of the prediction system should be adapted to\nwork in a true hospital setting. In this study, we predict early hospital LoS\nat the granular level of admission units by applying domain adaptation to\nleverage information learned from a potential source domain. Time-varying data\nfrom 110,079 and 60,492 patient stays to 8 and 9 intensive care units were\nrespectively extracted from eICU-CRD and MIMIC-IV. These were fed into a\nLong-Short Term Memory and a Fully connected network to train a source domain\nmodel, the weights of which were transferred either partially or fully to\ninitiate training in target domains. Shapley Additive exPlanations (SHAP)\nalgorithms were used to study the effect of weight transfer on model\nexplanability. Compared to the benchmark, the proposed weight transfer model\nshowed statistically significant gains in prediction accuracy (between 1% and\n5%) as well as computation time (up to 2hrs) for some target domains. The\nproposed method thus provides an adapted clinical decision support system for\nhospital management that can ease processes of data access via ethical\ncommittee, computation infrastructures and time.\n", "date": "2023-06-29", "categories": []}
{"id": "2301.03465", "title": "Shorter Latency of Real-time Epileptic Seizure Detection via\n  Probabilistic Prediction", "abstract": "  Although recent studies have proposed seizure detection algorithms with good\nsensitivity performance, there is a remained challenge that they were hard to\nachieve significantly short detection latency in real-time scenarios. In this\nmanuscript, we propose a novel deep learning framework intended for shortening\nepileptic seizure detection latency via probabilistic prediction. We are the\nfirst to convert the seizure detection task from traditional binary\nclassification to probabilistic prediction by introducing a crossing period\nfrom seizure-oriented EEG recording and proposing a labeling rule using\nsoft-label for crossing period samples. And, a novel multiscale STFT-based\nfeature extraction method combined with 3D-CNN architecture is proposed to\naccurately capture predictive probabilities of samples. Furthermore, we also\npropose rectified weighting strategy to enhance predictive probabilities, and\naccumulative decision-making rule to achieve significantly shorter detection\nlatency. We implement the proposed framework on two prevalent datasets --\nCHB-MIT scalp EEG dataset and SWEC-ETHZ intracranial EEG dataset in\npatient-specific leave-one-seizure-out cross-validation scheme. Eventually, the\nproposed algorithm successfully detected 94 out of 99 seizures during crossing\nperiod and 100% seizures detected after EEG onset, averaged 14.84% rectified\npredictive ictal probability (RPIP) errors of crossing samples, 2.3 s detection\nlatency, 0.08/h false detection rate (FDR) on CHB-MIT dataset. Meanwhile, 84\nout of 89 detected seizures during crossing period, 100% detected seizures\nafter EEG onset, 16.17% RPIP errors, 4.7 s detection latency, and 0.08/h FDR\nare achieved on SWEC-ETHZ dataset. The obtained detection latencies are at\nleast 50% shorter than state-of-the-art results reported in previous studies.\n", "date": "2023-01-04", "categories": []}
{"id": "2209.13860", "title": "Natural Language Processing Methods to Identify Oncology Patients at\n  High Risk for Acute Care with Clinical Notes", "abstract": "  Clinical notes are an essential component of a health record. This paper\nevaluates how natural language processing (NLP) can be used to identify the\nrisk of acute care use (ACU) in oncology patients, once chemotherapy starts.\nRisk prediction using structured health data (SHD) is now standard, but\npredictions using free-text formats are complex. This paper explores the use of\nfree-text notes for the prediction of ACU instead of SHD. Deep Learning models\nwere compared to manually engineered language features. Results show that SHD\nmodels minimally outperform NLP models; an l1-penalised logistic regression\nwith SHD achieved a C-statistic of 0.748 (95%-CI: 0.735, 0.762), while the same\nmodel with language features achieved 0.730 (95%-CI: 0.717, 0.745) and a\ntransformer-based model achieved 0.702 (95%-CI: 0.688, 0.717). This paper shows\nhow language models can be used in clinical applications and underlines how\nrisk bias is different for diverse patient groups, even using only free-text\ndata.\n", "date": "2022-09-28", "categories": []}
{"id": "2207.00610", "title": "A Temporal Fusion Transformer for Long-term Explainable Prediction of\n  Emergency Department Overcrowding", "abstract": "  Emergency Departments (EDs) are a fundamental element of the Portuguese\nNational Health Service, serving as an entry point for users with diverse and\nvery serious medical problems. Due to the inherent characteristics of the ED;\nforecasting the number of patients using the services is particularly\nchallenging. And a mismatch between the affluence and the number of medical\nprofessionals can lead to a decrease in the quality of the services provided\nand create problems that have repercussions for the entire hospital, with the\nrequisition of health care workers from other departments and the postponement\nof surgeries. ED overcrowding is driven, in part, by non-urgent patients, that\nresort to emergency services despite not having a medical emergency and which\nrepresent almost half of the total number of daily patients. This paper\ndescribes a novel deep learning architecture, the Temporal Fusion Transformer,\nthat uses calendar and time-series covariates to forecast prediction intervals\nand point predictions for a 4 week period. We have concluded that patient\nvolume can be forecasted with a Mean Absolute Percentage Error (MAPE) of 5.90%\nfor Portugal's Health Regional Areas (HRA) and a Root Mean Squared Error (RMSE)\nof 84.4102 people/day. The paper shows empirical evidence supporting the use of\na multivariate approach with static and time-series covariates while surpassing\nother models commonly found in the literature.\n", "date": "2022-07-01", "categories": []}
{"id": "2212.04936", "title": "Deep conv-attention model for diagnosing left bundle branch block from\n  12-lead electrocardiograms", "abstract": "  Cardiac resynchronization therapy (CRT) is a treatment that is used to\ncompensate for irregularities in the heartbeat. Studies have shown that this\ntreatment is more effective in heart patients with left bundle branch block\n(LBBB) arrhythmia. Therefore, identifying this arrhythmia is an important\ninitial step in determining whether or not to use CRT. On the other hand,\ntraditional methods for detecting LBBB on electrocardiograms (ECG) are often\nassociated with errors. Thus, there is a need for an accurate method to\ndiagnose this arrhythmia from ECG data. Machine learning, as a new field of\nstudy, has helped to increase human systems' performance. Deep learning, as a\nnewer subfield of machine learning, has more power to analyze data and increase\nsystems accuracy. This study presents a deep learning model for the detection\nof LBBB arrhythmia from 12-lead ECG data. This model consists of 1D dilated\nconvolutional layers. Attention mechanism has also been used to identify\nimportant input data features and classify inputs more accurately. The proposed\nmodel is trained and validated on a database containing 10344 12-lead ECG\nsamples using the 10-fold cross-validation method. The final results obtained\nby the model on the 12-lead ECG data are as follows. Accuracy: 98.80+-0.08%,\nspecificity: 99.33+-0.11 %, F1 score: 73.97+-1.8%, and area under the receiver\noperating characteristics curve (AUC): 0.875+-0.0192. These results indicate\nthat the proposed model in this study can effectively diagnose LBBB with good\nefficiency and, if used in medical centers, will greatly help diagnose this\narrhythmia and early treatment.\n", "date": "2022-12-07", "categories": []}
{"id": "2306.10484", "title": "The STOIC2021 COVID-19 AI challenge: applying reusable training\n  methodologies to private data", "abstract": "  Challenges drive the state-of-the-art of automated medical image analysis.\nThe quantity of public training data that they provide can limit the\nperformance of their solutions. Public access to the training methodology for\nthese solutions remains absent. This study implements the Type Three (T3)\nchallenge format, which allows for training solutions on private data and\nguarantees reusable training methodologies. With T3, challenge organizers train\na codebase provided by the participants on sequestered training data. T3 was\nimplemented in the STOIC2021 challenge, with the goal of predicting from a\ncomputed tomography (CT) scan whether subjects had a severe COVID-19 infection,\ndefined as intubation or death within one month. STOIC2021 consisted of a\nQualification phase, where participants developed challenge solutions using\n2000 publicly available CT scans, and a Final phase, where participants\nsubmitted their training methodologies with which solutions were trained on CT\nscans of 9724 subjects. The organizers successfully trained six of the eight\nFinal phase submissions. The submitted codebases for training and running\ninference were released publicly. The winning solution obtained an area under\nthe receiver operating characteristic curve for discerning between severe and\nnon-severe COVID-19 of 0.815. The Final phase solutions of all finalists\nimproved upon their Qualification phase solutions.HSUXJM-TNZF9CHSUXJM-TNZF9C\n", "date": "2023-06-18", "categories": []}
{"id": "2306.11837", "title": "Brain Anatomy Prior Modeling to Forecast Clinical Progression of\n  Cognitive Impairment with Structural MRI", "abstract": "  Brain structural MRI has been widely used to assess the future progression of\ncognitive impairment (CI). Previous learning-based studies usually suffer from\nthe issue of small-sized labeled training data, while there exist a huge amount\nof structural MRIs in large-scale public databases. Intuitively, brain\nanatomical structures derived from these public MRIs (even without\ntask-specific label information) can be used to boost CI progression trajectory\nprediction. However, previous studies seldom take advantage of such brain\nanatomy prior. To this end, this paper proposes a brain anatomy prior modeling\n(BAPM) framework to forecast the clinical progression of cognitive impairment\nwith small-sized target MRIs by exploring anatomical brain structures.\nSpecifically, the BAPM consists of a pretext model and a downstream model, with\na shared brain anatomy-guided encoder to model brain anatomy prior explicitly.\nBesides the encoder, the pretext model also contains two decoders for two\nauxiliary tasks (i.e., MRI reconstruction and brain tissue segmentation), while\nthe downstream model relies on a predictor for classification. The brain\nanatomy-guided encoder is pre-trained with the pretext model on 9,344 auxiliary\nMRIs without diagnostic labels for anatomy prior modeling. With this encoder\nfrozen, the downstream model is then fine-tuned on limited target MRIs for\nprediction. We validate the BAPM on two CI-related studies with T1-weighted\nMRIs from 448 subjects. Experimental results suggest the effectiveness of BAPM\nin (1) four CI progression prediction tasks, (2) MR image reconstruction, and\n(3) brain tissue segmentation, compared with several state-of-the-art methods.\n", "date": "2023-06-20", "categories": []}
{"id": "2306.14016", "title": "Interpreting Forecasted Vital Signs Using N-BEATS in Sepsis Patients", "abstract": "  Detecting and predicting septic shock early is crucial for the best possible\noutcome for patients. Accurately forecasting the vital signs of patients with\nsepsis provides valuable insights to clinicians for timely interventions, such\nas administering stabilizing drugs or optimizing infusion strategies. Our\nresearch examines N-BEATS, an interpretable deep-learning forecasting model\nthat can forecast 3 hours of vital signs for sepsis patients in intensive care\nunits (ICUs). In this work, we use the N-BEATS interpretable configuration to\nforecast the vital sign trends and compare them with the actual trend to\nunderstand better the patient's changing condition and the effects of infused\ndrugs on their vital signs. We evaluate our approach using the publicly\navailable eICU Collaborative Research Database dataset and rigorously evaluate\nthe vital sign forecasts using out-of-sample evaluation criteria. We present\nthe performance of our model using error metrics, including mean squared error\n(MSE), mean average percentage error (MAPE), and dynamic time warping (DTW),\nwhere the best scores achieved are 18.52e-4, 7.60, and 17.63e-3, respectively.\nWe analyze the samples where the forecasted trend does not match the actual\ntrend and study the impact of infused drugs on changing the actual vital signs\ncompared to the forecasted trend. Additionally, we examined the mortality rates\nof patients where the actual trend and the forecasted trend did not match. We\nobserved that the mortality rate was higher (92%) when the actual and\nforecasted trends closely matched, compared to when they were not similar\n(84%).\n", "date": "2023-06-24", "categories": []}
{"id": "2306.14169", "title": "A Web-based Mpox Skin Lesion Detection System Using State-of-the-art\n  Deep Learning Models Considering Racial Diversity", "abstract": "  The recent 'Mpox' outbreak, formerly known as 'Monkeypox', has become a\nsignificant public health concern and has spread to over 110 countries\nglobally. The challenge of clinically diagnosing mpox early on is due, in part,\nto its similarity to other types of rashes. Computer-aided screening tools have\nbeen proven valuable in cases where Polymerase Chain Reaction (PCR) based\ndiagnosis is not immediately available. Deep learning methods are powerful in\nlearning complex data representations, but their efficacy largely depends on\nadequate training data. To address this challenge, we present the \"Mpox Skin\nLesion Dataset Version 2.0 (MSLD v2.0)\" as a follow-up to the previously\nreleased openly accessible dataset, one of the first datasets containing mpox\nlesion images. This dataset contains images of patients with mpox and five\nother non-mpox classes (chickenpox, measles, hand-foot-mouth disease, cowpox,\nand healthy). We benchmark the performance of several state-of-the-art deep\nlearning models, including VGG16, ResNet50, DenseNet121, MobileNetV2,\nEfficientNetB3, InceptionV3, and Xception, to classify mpox and other\ninfectious skin diseases. In order to reduce the impact of racial bias, we\nutilize a color space data augmentation method to increase skin color\nvariability during training. Additionally, by leveraging transfer learning\nimplemented with pre-trained weights generated from the HAM10000 dataset, an\nextensive collection of pigmented skin lesion images, we achieved the best\noverall accuracy of $83.59\\pm2.11\\%$. Finally, the developed models are\nincorporated within a prototype web application to analyze uploaded skin images\nby a user and determine whether a subject is a suspected mpox patient.\n", "date": "2023-06-25", "categories": []}
{"id": "2211.04924", "title": "Bayesian Networks for the robust and unbiased prediction of depression\n  and its symptoms utilizing speech and multimodal data", "abstract": "  Predicting the presence of major depressive disorder (MDD) using behavioural\nand cognitive signals is a highly non-trivial task. The heterogeneous clinical\nprofile of MDD means that any given speech, facial expression and/or observed\ncognitive pattern may be associated with a unique combination of depressive\nsymptoms. Conventional discriminative machine learning models potentially lack\nthe complexity to robustly model this heterogeneity. Bayesian networks,\nhowever, may instead be well-suited to such a scenario. These networks are\nprobabilistic graphical models that efficiently describe the joint probability\ndistribution over a set of random variables by explicitly capturing their\nconditional dependencies. This framework provides further advantages over\nstandard discriminative modelling by offering the possibility to incorporate\nexpert opinion in the graphical structure of the models, generating explainable\nmodel predictions, informing about the uncertainty of predictions, and\nnaturally handling missing data. In this study, we apply a Bayesian framework\nto capture the relationships between depression, depression symptoms, and\nfeatures derived from speech, facial expression and cognitive game data\ncollected at thymia.\n", "date": "2022-11-09", "categories": []}
{"id": "2211.16566", "title": "Relative Sparsity for Medical Decision Problems", "abstract": "  Existing statistical methods can estimate a policy, or a mapping from\ncovariates to decisions, which can then instruct decision makers (e.g., whether\nto administer hypotension treatment based on covariates blood pressure and\nheart rate). There is great interest in using such data-driven policies in\nhealthcare. However, it is often important to explain to the healthcare\nprovider, and to the patient, how a new policy differs from the current\nstandard of care. This end is facilitated if one can pinpoint the aspects of\nthe policy (i.e., the parameters for blood pressure and heart rate) that change\nwhen moving from the standard of care to the new, suggested policy. To this\nend, we adapt ideas from Trust Region Policy Optimization (TRPO). In our work,\nhowever, unlike in TRPO, the difference between the suggested policy and\nstandard of care is required to be sparse, aiding with interpretability. This\nyields ``relative sparsity,\" where, as a function of a tuning parameter,\n$\\lambda$, we can approximately control the number of parameters in our\nsuggested policy that differ from their counterparts in the standard of care\n(e.g., heart rate only). We propose a criterion for selecting $\\lambda$,\nperform simulations, and illustrate our method with a real, observational\nhealthcare dataset, deriving a policy that is easy to explain in the context of\nthe current standard of care. Our work promotes the adoption of data-driven\ndecision aids, which have great potential to improve health outcomes.\n", "date": "2022-11-29", "categories": []}
{"id": "2306.12432", "title": "Interpretation of immunofluorescence slides by deep learning techniques:\n  anti-nuclear antibodies case study", "abstract": "  Nowadays, diseases are increasing in numbers and severity by the hour.\nImmunity diseases, affecting 8\\% of the world population in 2017 according to\nthe World Health Organization (WHO), is a field in medicine worth attention due\nto the high rate of disease occurrence classified under this category. This\nwork presents an up-to-date review of state-of-the-art immune diseases\nhealthcare solutions. We focus on tackling the issue with modern solutions such\nas Deep Learning to detect anomalies in the early stages hence providing health\npractitioners with efficient tools. We rely on advanced deep learning\ntechniques such as Convolutional Neural Networks (CNN) to fulfill our objective\nof providing an efficient tool while providing a proficient analysis of this\nsolution. The proposed solution was tested and evaluated by the immunology\ndepartment in the Principal Military Hospital of Instruction of Tunis, which\nconsidered it a very helpful tool.\n", "date": "2023-06-09", "categories": []}
{"id": "2306.12507", "title": "Investigating Poor Performance Regions of Black Boxes: LIME-based\n  Exploration in Sepsis Detection", "abstract": "  Interpreting machine learning models remains a challenge, hindering their\nadoption in clinical settings. This paper proposes leveraging Local\nInterpretable Model-Agnostic Explanations (LIME) to provide interpretable\ndescriptions of black box classification models in high-stakes sepsis\ndetection. By analyzing misclassified instances, significant features\ncontributing to suboptimal performance are identified. The analysis reveals\nregions where the classifier performs poorly, allowing the calculation of error\nrates within these regions. This knowledge is crucial for cautious\ndecision-making in sepsis detection and other critical applications. The\nproposed approach is demonstrated using the eICU dataset, effectively\nidentifying and visualizing regions where the classifier underperforms. By\nenhancing interpretability, our method promotes the adoption of machine\nlearning models in clinical practice, empowering informed decision-making and\nmitigating risks in critical scenarios.\n", "date": "2023-06-21", "categories": []}
{"id": "2306.09989", "title": "Ensemble Framework for Cardiovascular Disease Prediction", "abstract": "  Heart disease is the major cause of non-communicable and silent death\nworldwide. Heart diseases or cardiovascular diseases are classified into four\ntypes: coronary heart disease, heart failure, congenital heart disease, and\ncardiomyopathy. It is vital to diagnose heart disease early and accurately in\norder to avoid further injury and save patients' lives. As a result, we need a\nsystem that can predict cardiovascular disease before it becomes a critical\nsituation. Machine learning has piqued the interest of researchers in the field\nof medical sciences. For heart disease prediction, researchers implement a\nvariety of machine learning methods and approaches. In this work, to the best\nof our knowledge, we have used the dataset from IEEE Data Port which is one of\nthe online available largest datasets for cardiovascular diseases individuals.\nThe dataset isa combination of Hungarian, Cleveland, Long Beach VA, Switzerland\n& Statlog datasets with important features such as Maximum Heart Rate Achieved,\nSerum Cholesterol, Chest Pain Type, Fasting blood sugar, and so on. To assess\nthe efficacy and strength of the developed model, several performance measures\nare used, such as ROC, AUC curve, specificity, F1-score, sensitivity, MCC, and\naccuracy. In this study, we have proposed a framework with a stacked ensemble\nclassifier using several machine learning algorithms including ExtraTrees\nClassifier, Random Forest, XGBoost, and so on. Our proposed framework attained\nan accuracy of 92.34% which is higher than the existing literature.\n", "date": "2023-06-16", "categories": []}
{"id": "2306.11985", "title": "Evaluation of Popular XAI Applied to Clinical Prediction Models: Can\n  They be Trusted?", "abstract": "  The absence of transparency and explainability hinders the clinical adoption\nof Machine learning (ML) algorithms. Although various methods of explainable\nartificial intelligence (XAI) have been suggested, there is a lack of\nliterature that delves into their practicality and assesses them based on\ncriteria that could foster trust in clinical environments. To address this gap\nthis study evaluates two popular XAI methods used for explaining predictive\nmodels in the healthcare context in terms of whether they (i) generate\ndomain-appropriate representation, i.e. coherent with respect to the\napplication task, (ii) impact clinical workflow and (iii) are consistent. To\nthat end, explanations generated at the cohort and patient levels were\nanalysed. The paper reports the first benchmarking of the XAI methods applied\nto risk prediction models obtained by evaluating the concordance between\ngenerated explanations and the trigger of a future clinical deterioration\nepisode recorded by the data collection system. We carried out an analysis\nusing two Electronic Medical Records (EMR) datasets sourced from Australian\nmajor hospitals. The findings underscore the limitations of state-of-the-art\nXAI methods in the clinical context and their potential benefits. We discuss\nthese limitations and contribute to the theoretical development of trustworthy\nXAI solutions where clinical decision support guides the choice of intervention\nby suggesting the pattern or drivers for clinical deterioration in the future.\n", "date": "2023-06-21", "categories": []}
{"id": "2306.12242", "title": "Concurrent ischemic lesion age estimation and segmentation of CT brain\n  using a Transformer-based network", "abstract": "  The cornerstone of stroke care is expedient management that varies depending\non the time since stroke onset. Consequently, clinical decision making is\ncentered on accurate knowledge of timing and often requires a radiologist to\ninterpret Computed Tomography (CT) of the brain to confirm the occurrence and\nage of an event. These tasks are particularly challenging due to the subtle\nexpression of acute ischemic lesions and the dynamic nature of their\nappearance. Automation efforts have not yet applied deep learning to estimate\nlesion age and treated these two tasks independently, so, have overlooked their\ninherent complementary relationship. To leverage this, we propose a novel\nend-to-end multi-task transformer-based network optimized for concurrent\nsegmentation and age estimation of cerebral ischemic lesions. By utilizing\ngated positional self-attention and CT-specific data augmentation, the proposed\nmethod can capture long-range spatial dependencies while maintaining its\nability to be trained from scratch under low-data regimes commonly found in\nmedical imaging. Furthermore, to better combine multiple predictions, we\nincorporate uncertainty by utilizing quantile loss to facilitate estimating a\nprobability density function of lesion age. The effectiveness of our model is\nthen extensively evaluated on a clinical dataset consisting of 776 CT images\nfrom two medical centers. Experimental results demonstrate that our method\nobtains promising performance, with an area under the curve (AUC) of 0.933 for\nclassifying lesion ages <=4.5 hours compared to 0.858 using a conventional\napproach, and outperforms task-specific state-of-the-art algorithms.\n", "date": "2023-06-21", "categories": []}
{"id": "2306.10334", "title": "A Machine Learning Approach for Predicting Deterioration in Alzheimer's\n  Disease", "abstract": "  This paper explores deterioration in Alzheimers Disease using Machine\nLearning. Subjects were split into two datasets based on baseline diagnosis\n(Cognitively Normal, Mild Cognitive Impairment), with outcome of deterioration\nat final visit (a binomial essentially yes/no categorisation) using data from\nthe Alzheimers Disease Neuroimaging Initiative (demographics, genetics, CSF,\nimaging, and neuropsychological testing etc). Six machine learning models,\nincluding gradient boosting, were built, and evaluated on these datasets using\na nested crossvalidation procedure, with the best performing models being put\nthrough repeated nested cross-validation at 100 iterations. We were able to\ndemonstrate good predictive ability using CART predicting which of those in the\ncognitively normal group deteriorated and received a worse diagnosis (AUC =\n0.88). For the mild cognitive impairment group, we were able to achieve good\npredictive ability for deterioration with Elastic Net (AUC = 0.76).\n", "date": "2023-06-17", "categories": []}
{"id": "2303.18005", "title": "Artificial Intelligence in Ovarian Cancer Histopathology: A Systematic\n  Review", "abstract": "  Purpose - To characterise and assess the quality of published research\nevaluating artificial intelligence (AI) methods for ovarian cancer diagnosis or\nprognosis using histopathology data. Methods - A search of PubMed, Scopus, Web\nof Science, CENTRAL, and WHO-ICTRP was conducted up to 19/05/2023. The\ninclusion criteria required that research evaluated AI on histopathology images\nfor diagnostic or prognostic inferences in ovarian cancer. The risk of bias was\nassessed using PROBAST. Information about each model of interest was tabulated\nand summary statistics were reported. PRISMA 2020 reporting guidelines were\nfollowed. Results - 1573 records were identified, of which 45 were eligible for\ninclusion. There were 80 models of interest, including 37 diagnostic models, 22\nprognostic models, and 21 models with other diagnostically relevant outcomes.\nModels were developed using 1-1375 slides from 1-776 ovarian cancer patients.\nModel outcomes included treatment response (11/80), malignancy status (10/80),\nstain quantity (9/80), and histological subtype (7/80). All models were found\nto be at high or unclear risk of bias overall, with most research having a high\nrisk of bias in the analysis and a lack of clarity regarding participants and\npredictors in the study. Research frequently suffered from insufficient\nreporting and limited validation using small sample sizes. Conclusion - Limited\nresearch has been conducted on the application of AI to histopathology images\nfor diagnostic or prognostic purposes in ovarian cancer, and none of the\nassociated models have been demonstrated to be ready for real-world\nimplementation. Key aspects to help ensure clinical translation include more\ntransparent and comprehensive reporting of data provenance and modelling\napproaches, as well as improved quantitative performance evaluation using\ncross-validation and external validations.\n", "date": "2023-03-31", "categories": []}
{"id": "2306.09877", "title": "Revealing the impact of social circumstances on the selection of cancer\n  therapy through natural language processing of social work notes", "abstract": "  We aimed to investigate the impact of social circumstances on cancer therapy\nselection using natural language processing to derive insights from social\nworker documentation. We developed and employed a Bidirectional Encoder\nRepresentations from Transformers (BERT) based approach, using a hierarchical\nmulti-step BERT model (BERT-MS) to predict the prescription of targeted cancer\ntherapy to patients based solely on documentation by clinical social workers.\nOur corpus included free-text clinical social work notes, combined with\nmedication prescription information, for all patients treated for breast\ncancer. We conducted a feature importance analysis to pinpoint the specific\nsocial circumstances that impact cancer therapy selection. Using only social\nwork notes, we consistently predicted the administration of targeted therapies,\nsuggesting systematic differences in treatment selection exist due to\nnon-clinical factors. The UCSF-BERT model, pretrained on clinical text at UCSF,\noutperformed other publicly available language models with an AUROC of 0.675\nand a Macro F1 score of 0.599. The UCSF BERT-MS model, capable of leveraging\nmultiple pieces of notes, surpassed the UCSF-BERT model in both AUROC and\nMacro-F1. Our feature importance analysis identified several clinically\nintuitive social determinants of health (SDOH) that potentially contribute to\ndisparities in treatment. Our findings indicate that significant disparities\nexist among breast cancer patients receiving different types of therapies based\non social determinants of health. Social work reports play a crucial role in\nunderstanding these disparities in clinical decision-making.\n", "date": "2023-06-16", "categories": []}
{"id": "2306.06914", "title": "Enhancing COVID-19 Diagnosis through Vision Transformer-Based Analysis\n  of Chest X-ray Images", "abstract": "  The advent of 2019 Coronavirus (COVID-19) has engendered a momentous global\nhealth crisis, necessitating the identification of the ailment in individuals\nthrough diverse diagnostic modalities. Radiological imaging, particularly the\ndeployment of X-ray imaging, has been recognized as a pivotal instrument in the\ndetection and characterization of COVID-19. Recent investigations have unveiled\ninvaluable insights pertaining to the virus within X-ray images, instigating\nthe exploration of methodologies aimed at augmenting diagnostic accuracy\nthrough the utilization of artificial intelligence (AI) techniques. The current\nresearch endeavor posits an innovative framework for the automated diagnosis of\nCOVID-19, harnessing raw chest X-ray images, specifically by means of\nfine-tuning pre-trained Vision Transformer (ViT) models. The developed models\nwere appraised in terms of their binary classification performance, discerning\nCOVID-19 from Normal cases, as well as their ternary classification\nperformance, discriminating COVID-19 from Pneumonia and Normal instances, and\nlastly, their quaternary classification performance, discriminating COVID-19\nfrom Bacterial Pneumonia, Viral Pneumonia, and Normal conditions, employing\ndistinct datasets. The proposed model evinced extraordinary precision,\nregistering results of 99.92% and 99.84% for binary classification, 97.95% and\n86.48% for ternary classification, and 86.81% for quaternary classification,\nrespectively, on the respective datasets.\n", "date": "2023-06-12", "categories": []}
{"id": "2302.12044", "title": "Exploring Social Media for Early Detection of Depression in COVID-19\n  Patients", "abstract": "  The COVID-19 pandemic has caused substantial damage to global health. Even\nthough three years have passed, the world continues to struggle with the virus.\nConcerns are growing about the impact of COVID-19 on the mental health of\ninfected individuals, who are more likely to experience depression, which can\nhave long-lasting consequences for both the affected individuals and the world.\nDetection and intervention at an early stage can reduce the risk of depression\nin COVID-19 patients. In this paper, we investigated the relationship between\nCOVID-19 infection and depression through social media analysis. Firstly, we\nmanaged a dataset of COVID-19 patients that contains information about their\nsocial media activity both before and after infection. Secondly,We conducted an\nextensive analysis of this dataset to investigate the characteristic of\nCOVID-19 patients with a higher risk of depression. Thirdly, we proposed a deep\nneural network for early prediction of depression risk. This model considers\ndaily mood swings as a psychiatric signal and incorporates textual and\nemotional characteristics via knowledge distillation. Experimental results\ndemonstrate that our proposed framework outperforms baselines in detecting\ndepression risk, with an AUROC of 0.9317 and an AUPRC of 0.8116. Our model has\nthe potential to enable public health organizations to initiate prompt\nintervention with high-risk patients\n", "date": "2023-02-23", "categories": []}
{"id": "2306.04551", "title": "Multi-Task Training with In-Domain Language Models for Diagnostic\n  Reasoning", "abstract": "  Generative artificial intelligence (AI) is a promising direction for\naugmenting clinical diagnostic decision support and reducing diagnostic errors,\na leading contributor to medical errors. To further the development of clinical\nAI systems, the Diagnostic Reasoning Benchmark (DR.BENCH) was introduced as a\ncomprehensive generative AI framework, comprised of six tasks representing key\ncomponents in clinical reasoning. We present a comparative analysis of\nin-domain versus out-of-domain language models as well as multi-task versus\nsingle task training with a focus on the problem summarization task in DR.BENCH\n(Gao et al., 2023). We demonstrate that a multi-task, clinically trained\nlanguage model outperforms its general domain counterpart by a large margin,\nestablishing a new state-of-the-art performance, with a ROUGE-L score of 28.55.\nThis research underscores the value of domain-specific training for optimizing\nclinical diagnostic reasoning tasks.\n", "date": "2023-06-07", "categories": []}
{"id": "2305.10201", "title": "Echoes of Biases: How Stigmatizing Language Affects AI Performance", "abstract": "  Electronic health records (EHRs) serve as an essential data source for the\nenvisioned artificial intelligence (AI)-driven transformation in healthcare.\nHowever, clinician biases reflected in EHR notes can lead to AI models\ninheriting and amplifying these biases, perpetuating health disparities. This\nstudy investigates the impact of stigmatizing language (SL) in EHR notes on\nmortality prediction using a Transformer-based deep learning model and\nexplainable AI (XAI) techniques. Our findings demonstrate that SL written by\nclinicians adversely affects AI performance, particularly so for black\npatients, highlighting SL as a source of racial disparity in AI model\ndevelopment. To explore an operationally efficient way to mitigate SL's impact,\nwe investigate patterns in the generation of SL through a clinicians'\ncollaborative network, identifying central clinicians as having a stronger\nimpact on racial disparity in the AI model. We find that removing SL written by\ncentral clinicians is a more efficient bias reduction strategy than eliminating\nall SL in the entire corpus of data. This study provides actionable insights\nfor responsible AI development and contributes to understanding clinician\nbehavior and EHR note writing in healthcare.\n", "date": "2023-05-17", "categories": []}
{"id": "2306.06325", "title": "Explaining a machine learning decision to physicians via counterfactuals", "abstract": "  Machine learning models perform well on several healthcare tasks and can help\nreduce the burden on the healthcare system. However, the lack of explainability\nis a major roadblock to their adoption in hospitals. \\textit{How can the\ndecision of an ML model be explained to a physician?} The explanations\nconsidered in this paper are counterfactuals (CFs), hypothetical scenarios that\nwould have resulted in the opposite outcome. Specifically, time-series CFs are\ninvestigated, inspired by the way physicians converse and reason out decisions\n`I would have given the patient a vasopressor if their blood pressure was lower\nand falling'. Key properties of CFs that are particularly meaningful in\nclinical settings are outlined: physiological plausibility, relevance to the\ntask and sparse perturbations. Past work on CF generation does not satisfy\nthese properties, specifically plausibility in that realistic time-series CFs\nare not generated. A variational autoencoder (VAE)-based approach is proposed\nthat captures these desired properties. The method produces CFs that improve on\nprior approaches quantitatively (more plausible CFs as evaluated by their\nlikelihood w.r.t original data distribution, and 100$\\times$ faster at\ngenerating CFs) and qualitatively (2$\\times$ more plausible and relevant) as\nevaluated by three physicians.\n", "date": "2023-06-10", "categories": []}
{"id": "2209.00439", "title": "Ensembling Neural Networks for Improved Prediction and Privacy in Early\n  Diagnosis of Sepsis", "abstract": "  Ensembling neural networks is a long-standing technique for improving the\ngeneralization error of neural networks by combining networks with orthogonal\nproperties via a committee decision. We show that this technique is an ideal\nfit for machine learning on medical data: First, ensembles are amenable to\nparallel and asynchronous learning, thus enabling efficient training of\npatient-specific component neural networks. Second, building on the idea of\nminimizing generalization error by selecting uncorrelated patient-specific\nnetworks, we show that one can build an ensemble of a few selected\npatient-specific models that outperforms a single model trained on much larger\npooled datasets. Third, the non-iterative ensemble combination step is an\noptimal low-dimensional entry point to apply output perturbation to guarantee\nthe privacy of the patient-specific networks. We exemplify our framework of\ndifferentially private ensembles on the task of early prediction of sepsis,\nusing real-life intensive care unit data labeled by clinical experts.\n", "date": "2022-09-01", "categories": []}
{"id": "2304.03392", "title": "Personalising Digital Health Behaviour Change Interventions using\n  Machine Learning and Domain Knowledge", "abstract": "  We are developing a virtual coaching system that helps patients adhere to\nbehavior change interventions (BCI). Our proposed system predicts whether a\npatient will perform the targeted behaviour and uses counterfactual examples\nwith feature control to guide personalisation of BCI. We use simulated patient\ndata with varying levels of receptivity to intervention to arrive at the study\ndesign which would enable evaluation of our system.\n", "date": "2023-04-06", "categories": []}
{"id": "2306.03980", "title": "Counterfactual Explanations and Predictive Models to Enhance Clinical\n  Decision-Making in Schizophrenia using Digital Phenotyping", "abstract": "  Clinical practice in psychiatry is burdened with the increased demand for\nhealthcare services and the scarce resources available. New paradigms of health\ndata powered with machine learning techniques could open the possibility to\nimprove clinical workflow in critical stages of clinical assessment and\ntreatment in psychiatry. In this work, we propose a machine learning system\ncapable of predicting, detecting, and explaining individual changes in symptoms\nof patients with Schizophrenia by using behavioral digital phenotyping data. We\nforecast symptoms of patients with an error rate below 10%. The system detects\ndecreases in symptoms using changepoint algorithms and uses counterfactual\nexplanations as a recourse in a simulated continuous monitoring scenario in\nhealthcare. Overall, this study offers valuable insights into the performance\nand potential of counterfactual explanations, predictive models, and\nchange-point detection within a simulated clinical workflow. These findings lay\nthe foundation for further research to explore additional facets of the\nworkflow, aiming to enhance its effectiveness and applicability in real-world\nhealthcare settings. By leveraging these components, the goal is to develop an\nactionable, interpretable, and trustworthy integrative decision support system\nthat combines real-time clinical assessments with sensor-based inputs.\n", "date": "2023-06-06", "categories": []}
{"id": "2210.12156", "title": "Improving Medical Predictions by Irregular Multimodal Electronic Health\n  Records Modeling", "abstract": "  Health conditions among patients in intensive care units (ICUs) are monitored\nvia electronic health records (EHRs), composed of numerical time series and\nlengthy clinical note sequences, both taken at irregular time intervals.\nDealing with such irregularity in every modality, and integrating irregularity\ninto multimodal representations to improve medical predictions, is a\nchallenging problem. Our method first addresses irregularity in each single\nmodality by (1) modeling irregular time series by dynamically incorporating\nhand-crafted imputation embeddings into learned interpolation embeddings via a\ngating mechanism, and (2) casting a series of clinical note representations as\nmultivariate irregular time series and tackling irregularity via a time\nattention mechanism. We further integrate irregularity in multimodal fusion\nwith an interleaved attention mechanism across temporal steps. To the best of\nour knowledge, this is the first work to thoroughly model irregularity in\nmultimodalities for improving medical predictions. Our proposed methods for two\nmedical prediction tasks consistently outperforms state-of-the-art (SOTA)\nbaselines in each single modality and multimodal fusion scenarios.\nSpecifically, we observe relative improvements of 6.5\\%, 3.6\\%, and 4.3\\% in F1\nfor time series, clinical notes, and multimodal fusion, respectively. These\nresults demonstrate the effectiveness of our methods and the importance of\nconsidering irregularity in multimodal EHRs.\n", "date": "2022-10-18", "categories": []}
{"id": "2303.01513", "title": "Safe AI for health and beyond -- Monitoring to transform a health\n  service", "abstract": "  Machine learning techniques are effective for building predictive models\nbecause they identify patterns in large datasets. Development of a model for\ncomplex real-life problems often stop at the point of publication, proof of\nconcept or when made accessible through some mode of deployment. However, a\nmodel in the medical domain risks becoming obsolete as patient demographics,\nsystems and clinical practices change. The maintenance and monitoring of\npredictive model performance post-publication is crucial to enable their safe\nand effective long-term use. We will assess the infrastructure required to\nmonitor the outputs of a machine learning algorithm, and present two scenarios\nwith examples of monitoring and updates of models, firstly on a breast cancer\nprognosis model trained on public longitudinal data, and secondly on a\nneurodegenerative stratification algorithm that is currently being developed\nand tested in clinic.\n", "date": "2023-03-02", "categories": []}
{"id": "2306.00698", "title": "Prediction of Post-Operative Renal and Pulmonary Complications Using\n  Transformers", "abstract": "  Postoperative complications pose a significant challenge in the healthcare\nindustry, resulting in elevated healthcare expenses and prolonged hospital\nstays, and in rare instances, patient mortality. To improve patient outcomes\nand reduce healthcare costs, healthcare providers rely on various perioperative\nrisk scores to guide clinical decisions and prioritize care. In recent years,\nmachine learning techniques have shown promise in predicting postoperative\ncomplications and fatality, with deep learning models achieving remarkable\nsuccess in healthcare applications. However, research on the application of\ndeep learning models to intra-operative anesthesia management data is limited.\nIn this paper, we evaluate the performance of transformer-based models in\npredicting postoperative acute renal failure, postoperative pulmonary\ncomplications, and postoperative in-hospital mortality. We compare our method's\nperformance with state-of-the-art tabular data prediction models, including\ngradient boosting trees and sequential attention models, on a clinical dataset.\nOur results demonstrate that transformer-based models can achieve superior\nperformance in predicting postoperative complications and outperform\ntraditional machine learning models. This work highlights the potential of deep\nlearning techniques, specifically transformer-based models, in revolutionizing\nthe healthcare industry's approach to postoperative care.\n", "date": "2023-06-01", "categories": []}
{"id": "2208.05844", "title": "Adaptive Identification of Populations with Treatment Benefit in\n  Clinical Trials: Machine Learning Challenges and Solutions", "abstract": "  We study the problem of adaptively identifying patient subpopulations that\nbenefit from a given treatment during a confirmatory clinical trial. This type\nof adaptive clinical trial has been thoroughly studied in biostatistics, but\nhas been allowed only limited adaptivity so far. Here, we aim to relax\nclassical restrictions on such designs and investigate how to incorporate ideas\nfrom the recent machine learning literature on adaptive and online\nexperimentation to make trials more flexible and efficient. We find that the\nunique characteristics of the subpopulation selection problem -- most\nimportantly that (i) one is usually interested in finding subpopulations with\nany treatment benefit (and not necessarily the single subgroup with largest\neffect) given a limited budget and that (ii) effectiveness only has to be\ndemonstrated across the subpopulation on average -- give rise to interesting\nchallenges and new desiderata when designing algorithmic solutions. Building on\nthese findings, we propose AdaGGI and AdaGCPI, two meta-algorithms for\nsubpopulation construction. We empirically investigate their performance across\na range of simulation scenarios and derive insights into their (dis)advantages\nacross different settings.\n", "date": "2022-08-11", "categories": []}
{"id": "2306.01752", "title": "Handling Label Uncertainty on the Example of Automatic Detection of\n  Shepherd's Crook RCA in Coronary CT Angiography", "abstract": "  Coronary artery disease (CAD) is often treated minimally invasively with a\ncatheter being inserted into the diseased coronary vessel. If a patient\nexhibits a Shepherd's Crook (SC) Right Coronary Artery (RCA) - an anatomical\nnorm variant of the coronary vasculature - the complexity of this procedure is\nincreased. Automated reporting of this variant from coronary CT angiography\nscreening would ease prior risk assessment. We propose a 1D convolutional\nneural network which leverages a sequence of residual dilated convolutions to\nautomatically determine this norm variant from a prior extracted vessel\ncenterline. As the SC RCA is not clearly defined with respect to concrete\nmeasurements, labeling also includes qualitative aspects. Therefore, 4.23%\nsamples in our dataset of 519 RCA centerlines were labeled as unsure SC RCAs,\nwith 5.97% being labeled as sure SC RCAs. We explore measures to handle this\nlabel uncertainty, namely global/model-wise random assignment, exclusion, and\nsoft label assignment. Furthermore, we evaluate how this uncertainty can be\nleveraged for the determination of a rejection class. With our best\nconfiguration, we reach an area under the receiver operating characteristic\ncurve (AUC) of 0.938 on confident labels. Moreover, we observe an increase of\nup to 0.020 AUC when rejecting 10% of the data and leveraging the labeling\nuncertainty information in the exclusion process.\n", "date": "2023-05-22", "categories": []}
{"id": "2306.01817", "title": "Heart Diseases Prediction Using Block-chain and Machine Learning", "abstract": "  Most people around the globe are dying due to heart disease. The main reason\nbehind the rapid increase in the death rate due to heart disease is that there\nis no infrastructure developed for the healthcare department that can provide a\nsecure way of data storage and transmission. Due to redundancy in the patient\ndata, it is difficult for cardiac Professionals to predict the disease early\non. This rapid increase in the death rate due to heart disease can be\ncontrolled by monitoring and eliminating some of the key attributes in the\nearly stages such as blood pressure, cholesterol level, body weight, and\naddiction to smoking. Patient data can be monitored by cardiac Professionals\n(Cp) by using the advanced framework in the healthcare departments. Blockchain\nis the world's most reliable provider. The use of advanced systems in the\nhealthcare departments providing new ways of dealing with diseases has been\ndeveloped as well. In this article Machine Learning (ML) algorithm known as a\nsine-cosine weighted k-nearest neighbor (SCA-WKNN) is used for predicting the\nHearth disease with the maximum accuracy among the existing approaches.\nBlockchain technology has been used in the research to secure the data\nthroughout the session and can give more accurate results using this\ntechnology. The performance of the system can be improved by using this\nalgorithm and the dataset proposed has been improved by using different\nresources as well.\n", "date": "2023-06-02", "categories": []}
{"id": "2306.01818", "title": "Beta Thalassemia Carriers detection empowered federated Learning", "abstract": "  Thalassemia is a group of inherited blood disorders that happen when\nhemoglobin, the protein in red blood cells that carries oxygen, is not made\nenough. It is found all over the body and is needed for survival. If both\nparents have thalassemia, a child's chance of getting it increases. Genetic\ncounselling and early diagnosis are essential for treating thalassemia and\nstopping it from being passed on to future generations. It may be hard for\nhealthcare professionals to differentiate between people with thalassemia\ncarriers and those without. The current blood tests for beta thalassemia\ncarriers are too expensive, take too long, and require too much screening\nequipment. The World Health Organization says there is a high death rate for\npeople with thalassemia. Therefore, it is essential to find thalassemia\ncarriers to act quickly. High-performance liquid chromatography (HPLC), the\nstandard test method, has problems such as cost, time, and equipment needs. So,\nthere must be a quick and cheap way to find people carrying the thalassemia\ngene. Using federated learning (FL) techniques, this study shows a new way to\nfind people with the beta-thalassemia gene. FL allows data to be collected and\nprocessed on-site while following privacy rules, making it an excellent choice\nfor sensitive health data. Researchers used FL to train a model for\nbeta-thalassemia carriers by looking at the complete blood count results and\nred blood cell indices. The model was 92.38 % accurate at telling the\ndifference between beta-thalassemia carriers and people who did not have the\ndisease. The proposed FL model is better than other published methods in terms\nof how well it works, how reliable it is, and how private it is. This research\nshows a promising, quick, accurate, and low-cost way to find thalassemia\ncarriers and opens the door for screening them on a large scale.\n", "date": "2023-06-02", "categories": []}
{"id": "2306.01970", "title": "Temporal-spatial Correlation Attention Network for Clinical Data\n  Analysis in Intensive Care Unit", "abstract": "  In recent years, medical information technology has made it possible for\nelectronic health record (EHR) to store fairly complete clinical data. This has\nbrought health care into the era of \"big data\". However, medical data are often\nsparse and strongly correlated, which means that medical problems cannot be\nsolved effectively. With the rapid development of deep learning in recent\nyears, it has provided opportunities for the use of big data in healthcare. In\nthis paper, we propose a temporal-saptial correlation attention network (TSCAN)\nto handle some clinical characteristic prediction problems, such as predicting\ndeath, predicting length of stay, detecting physiologic decline, and\nclassifying phenotypes. Based on the design of the attention mechanism model,\nour approach can effectively remove irrelevant items in clinical data and\nirrelevant nodes in time according to different tasks, so as to obtain more\naccurate prediction results. Our method can also find key clinical indicators\nof important outcomes that can be used to improve treatment options. Our\nexperiments use information from the Medical Information Mart for Intensive\nCare (MIMIC-IV) database, which is open to the public. Finally, we have\nachieved significant performance benefits of 2.0\\% (metric) compared to other\nSOTA prediction methods. We achieved a staggering 90.7\\% on mortality rate,\n45.1\\% on length of stay. The source code can be find:\n\\url{https://github.com/yuyuheintju/TSCAN}.\n", "date": "2023-06-03", "categories": []}
{"id": "2306.02449", "title": "The Power Of Simplicity: Why Simple Linear Models Outperform Complex\n  Machine Learning Techniques -- Case Of Breast Cancer Diagnosis", "abstract": "  This research paper investigates the effectiveness of simple linear models\nversus complex machine learning techniques in breast cancer diagnosis,\nemphasizing the importance of interpretability and computational efficiency in\nthe medical domain. We focus on Logistic Regression (LR), Decision Trees (DT),\nand Support Vector Machines (SVM) and optimize their performance using the UCI\nMachine Learning Repository dataset. Our findings demonstrate that the simpler\nlinear model, LR, outperforms the more complex DT and SVM techniques, with a\ntest score mean of 97.28%, a standard deviation of 1.62%, and a computation\ntime of 35.56 ms. In comparison, DT achieved a test score mean of 93.73%, and\nSVM had a test score mean of 96.44%. The superior performance of LR can be\nattributed to its simplicity and interpretability, which provide a clear\nunderstanding of the relationship between input features and the outcome. This\nis particularly valuable in the medical domain, where interpretability is\ncrucial for decision-making. Moreover, the computational efficiency of LR\noffers advantages in terms of scalability and real-world applicability. The\nresults of this study highlight the power of simplicity in the context of\nbreast cancer diagnosis and suggest that simpler linear models like LR can be\nmore effective, interpretable, and computationally efficient than their complex\ncounterparts, making them a more suitable choice for medical applications.\n", "date": "2023-06-04", "categories": []}
{"id": "2306.02549", "title": "Evaluation of AI Chatbots for Patient-Specific EHR Questions", "abstract": "  This paper investigates the use of artificial intelligence chatbots for\npatient-specific question answering (QA) from clinical notes using several\nlarge language model (LLM) based systems: ChatGPT (versions 3.5 and 4), Google\nBard, and Claude. We evaluate the accuracy, relevance, comprehensiveness, and\ncoherence of the answers generated by each model using a 5-point Likert scale\non a set of patient-specific questions.\n", "date": "2023-06-05", "categories": []}
{"id": "2306.02955", "title": "A Simple and Flexible Modeling for Mental Disorder Detection by Learning\n  from Clinical Questionnaires", "abstract": "  Social media is one of the most highly sought resources for analyzing\ncharacteristics of the language by its users. In particular, many researchers\nutilized various linguistic features of mental health problems from social\nmedia. However, existing approaches to detecting mental disorders face critical\nchallenges, such as the scarcity of high-quality data or the trade-off between\naddressing the complexity of models and presenting interpretable results\ngrounded in expert domain knowledge. To address these challenges, we design a\nsimple but flexible model that preserves domain-based interpretability. We\npropose a novel approach that captures the semantic meanings directly from the\ntext and compares them to symptom-related descriptions. Experimental results\ndemonstrate that our model outperforms relevant baselines on various mental\ndisorder detection tasks. Our detailed analysis shows that the proposed model\nis effective at leveraging domain knowledge, transferable to other mental\ndisorders, and providing interpretable detection results.\n", "date": "2023-06-05", "categories": []}
{"id": "2306.01668", "title": "XAI Renaissance: Redefining Interpretability in Medical Diagnostic\n  Models", "abstract": "  As machine learning models become increasingly prevalent in medical\ndiagnostics, the need for interpretability and transparency becomes paramount.\nThe XAI Renaissance signifies a significant shift in the field, aiming to\nredefine the interpretability of medical diagnostic models. This paper explores\nthe innovative approaches and methodologies within the realm of Explainable AI\n(XAI) that are revolutionizing the interpretability of medical diagnostic\nmodels. By shedding light on the underlying decision-making process, XAI\ntechniques empower healthcare professionals to understand, trust, and\neffectively utilize these models for accurate and reliable medical diagnoses.\nThis review highlights the key advancements in XAI for medical diagnostics and\ntheir potential to transform the healthcare landscape, ultimately improving\npatient outcomes and fostering trust in AI-driven diagnostic systems.\n", "date": "2023-06-02", "categories": []}
{"id": "2306.00005", "title": "A Two-Stage Decoder for Efficient ICD Coding", "abstract": "  Clinical notes in healthcare facilities are tagged with the International\nClassification of Diseases (ICD) code; a list of classification codes for\nmedical diagnoses and procedures. ICD coding is a challenging multilabel text\nclassification problem due to noisy clinical document inputs and long-tailed\nlabel distribution. Recent automated ICD coding efforts improve performance by\nencoding medical notes and codes with additional data and knowledge bases.\nHowever, most of them do not reflect how human coders generate the code: first,\nthe coders select general code categories and then look for specific\nsubcategories that are relevant to a patient's condition. Inspired by this, we\npropose a two-stage decoding mechanism to predict ICD codes. Our model uses the\nhierarchical properties of the codes to split the prediction into two steps: At\nfirst, we predict the parent code and then predict the child code based on the\nprevious prediction. Experiments on the public MIMIC-III data set show that our\nmodel performs well in single-model settings without external data or\nknowledge.\n", "date": "2023-05-27", "categories": []}
{"id": "2306.00023", "title": "Predicting Heart Disease and Reducing Survey Time Using Machine Learning\n  Algorithms", "abstract": "  Currently, many researchers and analysts are working toward medical diagnosis\nenhancement for various diseases. Heart disease is one of the common diseases\nthat can be considered a significant cause of mortality worldwide. Early\ndetection of heart disease significantly helps in reducing the risk of heart\nfailure. Consequently, the Centers for Disease Control and Prevention (CDC)\nconducts a health-related telephone survey yearly from over 400,000\nparticipants. However, several concerns arise regarding the reliability of the\ndata in predicting heart disease and whether all of the survey questions are\nstrongly related. This study aims to utilize several machine learning\ntechniques, such as support vector machines and logistic regression, to\ninvestigate the accuracy of the CDC's heart disease survey in the United\nStates. Furthermore, we use various feature selection methods to identify the\nmost relevant subset of questions that can be utilized to forecast heart\nconditions. To reach a robust conclusion, we perform stability analysis by\nrandomly sampling the data 300 times. The experimental results show that the\nsurvey data can be useful up to 80% in terms of predicting heart disease, which\nsignificantly improves the diagnostic process before bloodwork and tests. In\naddition, the amount of time spent conducting the survey can be reduced by 77%\nwhile maintaining the same level of performance.\n", "date": "2023-05-30", "categories": []}
{"id": "2306.00034", "title": "Diagnosis and Prognosis of Head and Neck Cancer Patients using\n  Artificial Intelligence", "abstract": "  Cancer is one of the most life-threatening diseases worldwide, and head and\nneck (H&N) cancer is a prevalent type with hundreds of thousands of new cases\nrecorded each year. Clinicians use medical imaging modalities such as computed\ntomography and positron emission tomography to detect the presence of a tumor,\nand they combine that information with clinical data for patient prognosis. The\nprocess is mostly challenging and time-consuming. Machine learning and deep\nlearning can automate these tasks to help clinicians with highly promising\nresults. This work studies two approaches for H&N tumor segmentation: (i)\nexploration and comparison of vision transformer (ViT)-based and convolutional\nneural network-based models; and (ii) proposal of a novel 2D perspective to\nworking with 3D data. Furthermore, this work proposes two new architectures for\nthe prognosis task. An ensemble of several models predicts patient outcomes\n(which won the HECKTOR 2021 challenge prognosis task), and a ViT-based\nframework concurrently performs patient outcome prediction and tumor\nsegmentation, which outperforms the ensemble model.\n", "date": "2023-05-31", "categories": []}
{"id": "2305.19373", "title": "Mining Themes in Clinical Notes to Identify Phenotypes and to Predict\n  Length of Stay in Patients admitted with Heart Failure", "abstract": "  Heart failure is a syndrome which occurs when the heart is not able to pump\nblood and oxygen to support other organs in the body. Identifying the\nunderlying themes in the diagnostic codes and procedure reports of patients\nadmitted for heart failure could reveal the clinical phenotypes associated with\nheart failure and to group patients based on their similar characteristics\nwhich could also help in predicting patient outcomes like length of stay. These\nclinical phenotypes usually have a probabilistic latent structure and hence, as\nthere has been no previous work on identifying phenotypes in clinical notes of\nheart failure patients using a probabilistic framework and to predict length of\nstay of these patients using data-driven artificial intelligence-based methods,\nwe apply natural language processing technique, topic modeling, to identify the\nthemes present in diagnostic codes and in procedure reports of 1,200 patients\nadmitted for heart failure at the University of Illinois Hospital and Health\nSciences System (UI Health). Topic modeling identified twelve themes each in\ndiagnostic codes and procedure reports which revealed information about\ndifferent phenotypes related to various perspectives about heart failure, to\nstudy patients' profiles and to discover new relationships among medical\nconcepts. Each theme had a set of keywords and each clinical note was labeled\nwith two themes - one corresponding to its diagnostic code and the other\ncorresponding to its procedure reports along with their percentage\ncontribution. We used these themes and their percentage contribution to predict\nlength of stay. We found that the themes discovered in diagnostic codes and\nprocedure reports using topic modeling together were able to predict length of\nstay of the patients with an accuracy of 61.1% and an Area under the Receiver\nOperating Characteristic Curve (ROC AUC) value of 0.828.\n", "date": "2023-05-30", "categories": []}
{"id": "2305.19636", "title": "Explainable AI for Malnutrition Risk Prediction from m-Health and\n  Clinical Data", "abstract": "  Malnutrition is a serious and prevalent health problem in the older\npopulation, and especially in hospitalised or institutionalised subjects.\nAccurate and early risk detection is essential for malnutrition management and\nprevention. M-health services empowered with Artificial Intelligence (AI) may\nlead to important improvements in terms of a more automatic, objective, and\ncontinuous monitoring and assessment. Moreover, the latest Explainable AI (XAI)\nmethodologies may make AI decisions interpretable and trustworthy for end\nusers. This paper presents a novel AI framework for early and explainable\nmalnutrition risk detection based on heterogeneous m-health data. We performed\nan extensive model evaluation including both subject-independent and\npersonalised predictions, and the obtained results indicate Random Forest (RF)\nand Gradient Boosting as the best performing classifiers, especially when\nincorporating body composition assessment data. We also investigated several\nbenchmark XAI methods to extract global model explanations. Model-specific\nexplanation consistency assessment indicates that each selected model\nprivileges similar subsets of the most relevant predictors, with the highest\nagreement shown between SHapley Additive ExPlanations (SHAP) and feature\npermutation method. Furthermore, we performed a preliminary clinical validation\nto verify that the learned feature-output trends are compliant with the current\nevidence-based assessment.\n", "date": "2023-05-31", "categories": []}
{"id": "2305.19937", "title": "Breast Cancer Detection and Diagnosis: A comparative study of\n  state-of-the-arts deep learning architectures", "abstract": "  Breast cancer is a prevalent form of cancer among women, with over 1.5\nmillion women being diagnosed each year. Unfortunately, the survival rates for\nbreast cancer patients in certain third-world countries, like South Africa, are\nalarmingly low, with only 40% of diagnosed patients surviving beyond five\nyears. The inadequate availability of resources, including qualified\npathologists, delayed diagnoses, and ineffective therapy planning, contribute\nto this low survival rate. To address this pressing issue, medical specialists\nand researchers have turned to domain-specific AI approaches, specifically deep\nlearning models, to develop end-to-end solutions that can be integrated into\ncomputer-aided diagnosis (CAD) systems. By improving the workflow of\npathologists, these AI models have the potential to enhance the detection and\ndiagnosis of breast cancer. This research focuses on evaluating the performance\nof various cutting-edge convolutional neural network (CNN) architectures in\ncomparison to a relatively new model called the Vision Trans-former (ViT). The\nobjective is to determine the superiority of these models in terms of their\naccuracy and effectiveness. The experimental results reveal that the ViT models\noutperform the other selected state-of-the-art CNN architectures, achieving an\nimpressive accuracy rate of 95.15%. This study signifies a significant\nadvancement in the field, as it explores the utilization of data augmentation\nand other relevant preprocessing techniques in conjunction with deep learning\nmodels for the detection and diagnosis of breast cancer using datasets of\nBreast Cancer Histopathological Image Classification.\n", "date": "2023-05-31", "categories": []}
{"id": "2207.13700", "title": "Remote Medication Status Prediction for Individuals with Parkinson's\n  Disease using Time-series Data from Smartphones", "abstract": "  Medication for neurological diseases such as the Parkinson's disease usually\nhappens remotely away from hospitals. Such out-of-lab environments pose\nchallenges in collecting timely and accurate health status data. Individual\ndifferences in behavioral signals collected from wearable sensors also lead to\ndifficulties in adopting current general machine learning analysis pipelines.\nTo address these challenges, we present a method for predicting the medication\nstatus of Parkinson's disease patients using the public mPower dataset, which\ncontains 62,182 remote multi-modal test records collected on smartphones from\n487 patients. The proposed method shows promising results in predicting three\nmedication statuses objectively: Before Medication (AUC=0.95), After Medication\n(AUC=0.958), and Another Time (AUC=0.976) by examining patient-wise historical\nrecords with the attention weights learned through a Transformer model. Our\nmethod provides an innovative way for personalized remote health sensing in a\ntimely and objective fashion which could benefit a broad range of similar\napplications.\n", "date": "2022-07-26", "categories": []}
{"id": "2301.00122", "title": "Hair and Scalp Disease Detection using Machine Learning and Image\n  Processing", "abstract": "  Almost 80 million Americans suffer from hair loss due to aging, stress,\nmedication, or genetic makeup. Hair and scalp-related diseases often go\nunnoticed in the beginning. Sometimes, a patient cannot differentiate between\nhair loss and regular hair fall. Diagnosing hair-related diseases is\ntime-consuming as it requires professional dermatologists to perform visual and\nmedical tests. Because of that, the overall diagnosis gets delayed, which\nworsens the severity of the illness. Due to the image-processing ability,\nneural network-based applications are used in various sectors, especially\nhealthcare and health informatics, to predict deadly diseases like cancers and\ntumors. These applications assist clinicians and patients and provide an\ninitial insight into early-stage symptoms. In this study, we used a deep\nlearning approach that successfully predicts three main types of hair loss and\nscalp-related diseases: alopecia, psoriasis, and folliculitis. However, limited\nstudy in this area, unavailability of a proper dataset, and degree of variety\namong the images scattered over the internet made the task challenging. 150\nimages were obtained from various sources and then preprocessed by denoising,\nimage equalization, enhancement, and data balancing, thereby minimizing the\nerror rate. After feeding the processed data into the 2D convolutional neural\nnetwork (CNN) model, we obtained overall training accuracy of 96.2%, with a\nvalidation accuracy of 91.1%. The precision and recall score of alopecia,\npsoriasis, and folliculitis are 0.895, 0.846, and 1.0, respectively. We also\ncreated a dataset of the scalp images for future prospective researchers.\n", "date": "2022-12-31", "categories": []}
{"id": "2305.18489", "title": "A Transfer Learning and Explainable Solution to Detect mpox from\n  Smartphones images", "abstract": "  In recent months, the monkeypox (mpox) virus -- previously endemic in a\nlimited area of the world -- has started spreading in multiple countries until\nbeing declared a ``public health emergency of international concern'' by the\nWorld Health Organization. The alert was renewed in February 2023 due to a\npersisting sustained incidence of the virus in several countries and worries\nabout possible new outbreaks. Low-income countries with inadequate\ninfrastructures for vaccine and testing administration are particularly at\nrisk.\n  A symptom of mpox infection is the appearance of skin rashes and eruptions,\nwhich can drive people to seek medical advice. A technology that might help\nperform a preliminary screening based on the aspect of skin lesions is the use\nof Machine Learning for image classification. However, to make this technology\nsuitable on a large scale, it should be usable directly on mobile devices of\npeople, with a possible notification to a remote medical expert.\n  In this work, we investigate the adoption of Deep Learning to detect mpox\nfrom skin lesion images. The proposal leverages Transfer Learning to cope with\nthe scarce availability of mpox image datasets. As a first step, a homogenous,\nunpolluted, dataset is produced by manual selection and preprocessing of\navailable image data. It will also be released publicly to researchers in the\nfield. Then, a thorough comparison is conducted amongst several Convolutional\nNeural Networks, based on a 10-fold stratified cross-validation. The best\nmodels are then optimized through quantization for use on mobile devices;\nmeasures of classification quality, memory footprint, and processing times\nvalidate the feasibility of our proposal. Additionally, the use of eXplainable\nAI is investigated as a suitable instrument to both technically and clinically\nvalidate classification outcomes.\n", "date": "2023-05-29", "categories": []}
{"id": "2305.18576", "title": "TreeMAN: Tree-enhanced Multimodal Attention Network for ICD Coding", "abstract": "  ICD coding is designed to assign the disease codes to electronic health\nrecords (EHRs) upon discharge, which is crucial for billing and clinical\nstatistics. In an attempt to improve the effectiveness and efficiency of manual\ncoding, many methods have been proposed to automatically predict ICD codes from\nclinical notes. However, most previous works ignore the decisive information\ncontained in structured medical data in EHRs, which is hard to be captured from\nthe noisy clinical notes. In this paper, we propose a Tree-enhanced Multimodal\nAttention Network (TreeMAN) to fuse tabular features and textual features into\nmultimodal representations by enhancing the text representations with\ntree-based features via the attention mechanism. Tree-based features are\nconstructed according to decision trees learned from structured multimodal\nmedical data, which capture the decisive information about ICD coding. We can\napply the same multi-label classifier from previous text models to the\nmultimodal representations to predict ICD codes. Experiments on two MIMIC\ndatasets show that our method outperforms prior state-of-the-art ICD coding\napproaches. The code is available at https://github.com/liu-zichen/TreeMAN.\n", "date": "2023-05-29", "categories": []}
{"id": "2305.17156", "title": "An Improved Model Ensembled of Different Hyper-parameter Tuned Machine\n  Learning Algorithms for Fetal Health Prediction", "abstract": "  Fetal health is a critical concern during pregnancy as it can impact the\nwell-being of both the mother and the baby. Regular monitoring and timely\ninterventions are necessary to ensure the best possible outcomes. While there\nare various methods to monitor fetal health in the mother's womb, the use of\nartificial intelligence (AI) can improve the accuracy, efficiency, and speed of\ndiagnosis. In this study, we propose a robust ensemble model called ensemble of\ntuned Support Vector Machine and ExtraTrees (ETSE) for predicting fetal health.\nInitially, we employed various data preprocessing techniques such as outlier\nrejection, missing value imputation, data standardization, and data sampling.\nThen, seven machine learning (ML) classifiers including Support Vector Machine\n(SVM), XGBoost (XGB), Light Gradient Boosting Machine (LGBM), Decision Tree\n(DT), Random Forest (RF), ExtraTrees (ET), and K-Neighbors were implemented.\nThese models were evaluated and then optimized by hyperparameter tuning using\nthe grid search technique. Finally, we analyzed the performance of our proposed\nETSE model. The performance analysis of each model revealed that our proposed\nETSE model outperformed the other models with 100% precision, 100% recall, 100%\nF1-score, and 99.66% accuracy. This indicates that the ETSE model can\neffectively predict fetal health, which can aid in timely interventions and\nimprove outcomes for both the mother and the baby.\n", "date": "2023-05-26", "categories": []}
{"id": "2305.16346", "title": "Artificial Intelligence-Based Methods for Precision Medicine: Diabetes\n  Risk Prediction", "abstract": "  The rising prevalence of type 2 diabetes mellitus (T2DM) necessitates the\ndevelopment of predictive models for T2DM risk assessment. Artificial\nintelligence (AI) models are being extensively used for this purpose, but a\ncomprehensive review of their advancements and challenges is lacking. This\nscoping review analyzes existing literature on AI-based models for T2DM risk\nprediction. Forty studies were included, mainly published in the past four\nyears. Traditional machine learning models were more prevalent than deep\nlearning models. Electronic health records were the most commonly used data\nsource. Unimodal AI models relying on EHR data were prominent, while only a few\nutilized multimodal models. Both unimodal and multimodal models showed\npromising performance, with the latter outperforming the former. Internal\nvalidation was common, while external validation was limited. Interpretability\nmethods were reported in half of the studies. Few studies reported novel\nbiomarkers, and open-source code availability was limited. This review provides\ninsights into the current state and limitations of AI-based T2DM risk\nprediction models and highlights challenges for their development and clinical\nimplementation.\n", "date": "2023-05-24", "categories": []}
{"id": "2305.16492", "title": "Image Classification of Stroke Blood Clot Origin using Deep\n  Convolutional Neural Networks and Visual Transformers", "abstract": "  Stroke is one of two main causes of death worldwide. Many individuals suffer\nfrom ischemic stroke every year. Only in US more over 700,000 individuals meet\nischemic stroke due to blood clot blocking an artery to the brain every year.\nThe paper describes particular approach how to apply Artificial Intelligence\nfor purposes of separating two major acute ischemic stroke (AIS) etiology\nsubtypes: cardiac and large artery atherosclerosis. Four deep neural network\narchitectures and simple ensemble method are used in the approach.\n", "date": "2023-05-25", "categories": []}
{"id": "2206.01542", "title": "Detecting the Severity of Major Depressive Disorder from Speech: A Novel\n  HARD-Training Methodology", "abstract": "  Major Depressive Disorder (MDD) is a common worldwide mental health issue\nwith high associated socioeconomic costs. The prediction and automatic\ndetection of MDD can, therefore, make a huge impact on society. Speech, as a\nnon-invasive, easy to collect signal, is a promising marker to aid the\ndiagnosis and assessment of MDD. In this regard, speech samples were collected\nas part of the Remote Assessment of Disease and Relapse in Major Depressive\nDisorder (RADAR-MDD) research programme. RADAR-MDD was an observational cohort\nstudy in which speech and other digital biomarkers were collected from a cohort\nof individuals with a history of MDD in Spain, United Kingdom and the\nNetherlands. In this paper, the RADAR-MDD speech corpus was taken as an\nexperimental framework to test the efficacy of a Sequence-to-Sequence model\nwith a local attention mechanism in a two-class depression severity\nclassification paradigm. Additionally, a novel training method, HARD-Training,\nis proposed. It is a methodology based on the selection of more ambiguous\nsamples for the model training, and inspired by the curriculum learning\nparadigm. HARD-Training was found to consistently improve - with an average\nincrement of 8.6% - the performance of our classifiers for both of two speech\nelicitation tasks used and each collection site of the RADAR-MDD speech corpus.\nWith this novel methodology, our Sequence-to-Sequence model was able to\neffectively detect MDD severity regardless of language. Finally, recognising\nthe need for greater awareness of potential algorithmic bias, we conduct an\nadditional analysis of our results separately for each gender.\n", "date": "2022-06-02", "categories": []}
{"id": "2206.08019", "title": "Multi-View Imputation and Cross-Attention Network Based on Incomplete\n  Longitudinal and Multimodal Data for Conversion Prediction of Mild Cognitive\n  Impairment", "abstract": "  Predicting whether subjects with mild cognitive impairment (MCI) will convert\nto Alzheimer's disease is a significant clinical challenge. Longitudinal\nvariations and complementary information inherent in longitudinal and\nmultimodal data are crucial for MCI conversion prediction, but persistent issue\nof missing data in these data may hinder their effective application.\nAdditionally, conversion prediction should be achieved in the early stages of\ndisease progression in clinical practice, specifically at baseline visit (BL).\nTherefore, longitudinal data should only be incorporated during training to\ncapture disease progression information. To address these challenges, a\nmulti-view imputation and cross-attention network (MCNet) was proposed to\nintegrate data imputation and MCI conversion prediction in a unified framework.\nFirst, a multi-view imputation method combined with adversarial learning was\npresented to handle various missing data scenarios and reduce imputation\nerrors. Second, two cross-attention blocks were introduced to exploit the\npotential associations in longitudinal and multimodal data. Finally, a\nmulti-task learning model was established for data imputation, longitudinal\nclassification, and conversion prediction tasks. When the model was\nappropriately trained, the disease progression information learned from\nlongitudinal data can be leveraged by BL data to improve MCI conversion\nprediction at BL. MCNet was tested on two independent testing sets and\nsingle-modal BL data to verify its effectiveness and flexibility in MCI\nconversion prediction. Results showed that MCNet outperformed several\ncompetitive methods. Moreover, the interpretability of MCNet was demonstrated.\nThus, our MCNet may be a valuable tool in longitudinal and multimodal data\nanalysis for MCI conversion prediction. Codes are available at\nhttps://github.com/Meiyan88/MCNET.\n", "date": "2022-06-16", "categories": []}
{"id": "2209.01321", "title": "Deep Stable Representation Learning on Electronic Health Records", "abstract": "  Deep learning models have achieved promising disease prediction performance\nof the Electronic Health Records (EHR) of patients. However, most models\ndeveloped under the I.I.D. hypothesis fail to consider the agnostic\ndistribution shifts, diminishing the generalization ability of deep learning\nmodels to Out-Of-Distribution (OOD) data. In this setting, spurious statistical\ncorrelations that may change in different environments will be exploited, which\ncan cause sub-optimal performances of deep learning models. The unstable\ncorrelation between procedures and diagnoses existed in the training\ndistribution can cause spurious correlation between historical EHR and future\ndiagnosis. To address this problem, we propose to use a causal representation\nlearning method called Causal Healthcare Embedding (CHE). CHE aims at\neliminating the spurious statistical relationship by removing the dependencies\nbetween diagnoses and procedures. We introduce the Hilbert-Schmidt Independence\nCriterion (HSIC) to measure the degree of independence between the embedded\ndiagnosis and procedure features. Based on causal view analyses, we perform the\nsample weighting technique to get rid of such spurious relationship for the\nstable learning of EHR across different environments. Moreover, our proposed\nCHE method can be used as a flexible plug-and-play module that can enhance\nexisting deep learning models on EHR. Extensive experiments on two public\ndatasets and five state-of-the-art baselines unequivocally show that CHE can\nimprove the prediction accuracy of deep learning models on out-of-distribution\ndata by a large margin. In addition, the interpretability study shows that CHE\ncould successfully leverage causal structures to reflect a more reasonable\ncontribution of historical records for predictions.\n", "date": "2022-09-03", "categories": []}
{"id": "2212.02762", "title": "Automated Identification of Eviction Status from Electronic Health\n  Record Notes", "abstract": "  Objective: Evictions are important social and behavioral determinants of\nhealth. Evictions are associated with a cascade of negative events that can\nlead to unemployment, housing insecurity/homelessness, long-term poverty, and\nmental health problems. In this study, we developed a natural language\nprocessing system to automatically detect eviction status from electronic\nhealth record (EHR) notes.\n  Materials and Methods: We first defined eviction status (eviction presence\nand eviction period) and then annotated eviction status in 5000 EHR notes from\nthe Veterans Health Administration (VHA). We developed a novel model, KIRESH,\nthat has shown to substantially outperform other state-of-the-art models such\nas fine-tuning pre-trained language models like BioBERT and BioClinicalBERT.\nMoreover, we designed a novel prompt to further improve the model performance\nby using the intrinsic connection between the two sub-tasks of eviction\npresence and period prediction. Finally, we used the Temperature Scaling-based\nCalibration on our KIRESH-Prompt method to avoid over-confidence issues arising\nfrom the imbalance dataset.\n  Results: KIRESH-Prompt substantially outperformed strong baseline models\nincluding fine-tuning the BioClinicalBERT model to achieve 0.74672 MCC, 0.71153\nMacro-F1, and 0.83396 Micro-F1 in predicting eviction period and 0.66827 MCC,\n0.62734 Macro-F1, and 0.7863 Micro-F1 in predicting eviction presence. We also\nconducted additional experiments on a benchmark social determinants of health\n(SBDH) dataset to demonstrate the generalizability of our methods.\n  Conclusion and Future Work: KIRESH-Prompt has substantially improved eviction\nstatus classification. We plan to deploy KIRESH-Prompt to the VHA EHRs as an\neviction surveillance system to help address the US Veterans' housing\ninsecurity.\n", "date": "2022-12-06", "categories": []}
{"id": "2305.13275", "title": "A Machine Learning Approach to Detect Dehydration in Afghan Children", "abstract": "  Child dehydration is a significant health concern, especially among children\nunder 5 years of age who are more susceptible to diarrhea and vomiting. In\nAfghanistan, severe diarrhea contributes to child mortality due to dehydration.\nHowever, there is no evidence of research exploring the potential of machine\nlearning techniques in diagnosing dehydration in Afghan children under five. To\nfill this gap, this study leveraged various classifiers such as Random Forest,\nMultilayer Perceptron, Support Vector Machine, J48, and Logistic Regression to\ndevelop a predictive model using a dataset of sick children retrieved from the\nAfghanistan Demographic and Health Survey (ADHS). The primary objective was to\ndetermine the dehydration status of children under 5 years. Among all the\nclassifiers, Random Forest proved to be the most effective, achieving an\naccuracy of 91.46%, precision of 91%, and AUC of 94%. This model can\npotentially assist healthcare professionals in promptly and accurately\nidentifying dehydration in under five children, leading to timely\ninterventions, and reducing the risk of severe health complications. Our study\ndemonstrates the potential of machine learning techniques in improving the\nearly diagnosis of dehydration in Afghan children.\n", "date": "2023-05-22", "categories": []}
{"id": "2305.11199", "title": "At-Admission Prediction of Mortality and Pulmonary Embolism in COVID-19\n  Patients Using Statistical and Machine Learning Methods: An International\n  Cohort Study", "abstract": "  By September, 2022, more than 600 million cases of SARS-CoV-2 infection have\nbeen reported globally, resulting in over 6.5 million deaths. COVID-19\nmortality risk estimators are often, however, developed with small\nunrepresentative samples and with methodological limitations. It is highly\nimportant to develop predictive tools for pulmonary embolism (PE) in COVID-19\npatients as one of the most severe preventable complications of COVID-19. Using\na dataset of more than 800,000 COVID-19 patients from an international cohort,\nwe propose a cost-sensitive gradient-boosted machine learning model that\npredicts occurrence of PE and death at admission. Logistic regression, Cox\nproportional hazards models, and Shapley values were used to identify key\npredictors for PE and death. Our prediction model had a test AUROC of 75.9% and\n74.2%, and sensitivities of 67.5% and 72.7% for PE and all-cause mortality\nrespectively on a highly diverse and held-out test set. The PE prediction model\nwas also evaluated on patients in UK and Spain separately with test results of\n74.5% AUROC, 63.5% sensitivity and 78.9% AUROC, 95.7% sensitivity. Age, sex,\nregion of admission, comorbidities (chronic cardiac and pulmonary disease,\ndementia, diabetes, hypertension, cancer, obesity, smoking), and symptoms (any,\nconfusion, chest pain, fatigue, headache, fever, muscle or joint pain,\nshortness of breath) were the most important clinical predictors at admission.\nOur machine learning model developed from an international cohort can serve to\nbetter regulate hospital risk prioritisation of at-risk patients.\n", "date": "2023-05-18", "categories": []}
{"id": "2305.11407", "title": "LATTE: Label-efficient Incident Phenotyping from Longitudinal Electronic\n  Health Records", "abstract": "  Electronic health record (EHR) data are increasingly used to support\nreal-world evidence (RWE) studies. Yet its ability to generate reliable RWE is\nlimited by the lack of readily available precise information on the timing of\nclinical events such as the onset time of heart failure. We propose a\nLAbel-efficienT incidenT phEnotyping (LATTE) algorithm to accurately annotate\nthe timing of clinical events from longitudinal EHR data. By leveraging the\npre-trained semantic embedding vectors from large-scale EHR data as prior\nknowledge, LATTE selects predictive EHR features in a concept re-weighting\nmodule by mining their relationship to the target event and compresses their\ninformation into longitudinal visit embeddings through a visit attention\nlearning network. LATTE employs a recurrent neural network to capture the\nsequential dependency between the target event and visit embeddings\nbefore/after it. To improve label efficiency, LATTE constructs highly\ninformative longitudinal silver-standard labels from large-scale unlabeled\npatients to perform unsupervised pre-training and semi-supervised joint\ntraining. Finally, LATTE enhances cross-site portability via contrastive\nrepresentation learning. LATTE is evaluated on three analyses: the onset of\ntype-2 diabetes, heart failure, and the onset and relapses of multiple\nsclerosis. We use various evaluation metrics present in the literature\nincluding the $ABC_{gain}$, the proportion of reduction in the area between the\nobserved event indicator and the predicted cumulative incidences in reference\nto the prediction per incident prevalence. LATTE consistently achieves\nsubstantial improvement over benchmark methods such as SAMGEP and RETAIN in all\nsettings.\n", "date": "2023-05-19", "categories": []}
{"id": "2305.10450", "title": "Understanding of Normal and Abnormal Hearts by Phase Space Analysis and\n  Convolutional Neural Networks", "abstract": "  Cardiac diseases are one of the leading mortality factors in modern,\nindustrialized societies, which cause high expenses in public health systems.\nDue to high costs, developing analytical methods to improve cardiac diagnostics\nis essential. The heart's electric activity was first modeled using a set of\nnonlinear differential equations. Following this, variations of cardiac spectra\noriginating from deterministic dynamics are investigated. Analyzing a normal\nhuman heart's power spectra offers His-Purkinje network, which possesses a\nfractal-like structure. Phase space trajectories are extracted from the time\nseries electrocardiogram (ECG) graph with third-order derivate Taylor Series.\nHere in this study, phase space analysis and Convolutional Neural Networks\n(CNNs) method are applied to 44 records via the MIT-BIH database recorded with\nMLII. In order to increase accuracy, a straight line is drawn between the\nhighest Q-R distance in the phase space images of the records. Binary CNN\nclassification is used to determine healthy or unhealthy hearts. With a 90.90%\naccuracy rate, this model could classify records according to their heart\nstatus.\n", "date": "2023-05-16", "categories": []}
{"id": "2305.09756", "title": "Clinical Note Owns its Hierarchy: Multi-Level Hypergraph Neural Networks\n  for Patient-Level Representation Learning", "abstract": "  Leveraging knowledge from electronic health records (EHRs) to predict a\npatient's condition is essential to the effective delivery of appropriate care.\nClinical notes of patient EHRs contain valuable information from healthcare\nprofessionals, but have been underused due to their difficult contents and\ncomplex hierarchies. Recently, hypergraph-based methods have been proposed for\ndocument classifications. Directly adopting existing hypergraph methods on\nclinical notes cannot sufficiently utilize the hierarchy information of the\npatient, which can degrade clinical semantic information by (1) frequent\nneutral words and (2) hierarchies with imbalanced distribution. Thus, we\npropose a taxonomy-aware multi-level hypergraph neural network (TM-HGNN), where\nmulti-level hypergraphs assemble useful neutral words with rare keywords via\nnote and taxonomy level hyperedges to retain the clinical semantic information.\nThe constructed patient hypergraphs are fed into hierarchical message passing\nlayers for learning more balanced multi-level knowledge at the note and\ntaxonomy levels. We validate the effectiveness of TM-HGNN by conducting\nextensive experiments with MIMIC-III dataset on benchmark in-hospital-mortality\nprediction.\n", "date": "2023-05-16", "categories": []}
{"id": "2305.10115", "title": "An Ensemble Deep Learning Approach for COVID-19 Severity Prediction\n  Using Chest CT Scans", "abstract": "  Chest X-rays have been widely used for COVID-19 screening; however, 3D\ncomputed tomography (CT) is a more effective modality. We present our findings\non COVID-19 severity prediction from chest CT scans using the STOIC dataset. We\ndeveloped an ensemble deep learning based model that incorporates multiple\nneural networks to improve predictions. To address data imbalance, we used\nslicing functions and data augmentation. We further improved performance using\ntest time data augmentation. Our approach which employs a simple yet effective\nensemble of deep learning-based models with strong test time augmentations,\nachieved results comparable to more complex methods and secured the fourth\nposition in the STOIC2021 COVID-19 AI Challenge. Our code is available on\nonline: at: https://github.com/aleemsidra/stoic2021- baseline-finalphase-main.\n", "date": "2023-05-17", "categories": []}
{"id": "2305.10421", "title": "Evolving Tsukamoto Neuro Fuzzy Model for Multiclass Covid 19\n  Classification with Chest X Ray Images", "abstract": "  Du e to rapid population growth and the need to use artificial intelligence\nto make quick decisions, developing a machine learning-based disease detection\nmodel and abnormality identification system has greatly improved the level of\nmedical diagnosis Since COVID-19 has become one of the most severe diseases in\nthe world, developing an automatic COVID-19 detection framework helps medical\ndoctors in the diagnostic process of disease and provides correct and fast\nresults. In this paper, we propose a machine lear ning based framework for the\ndetection of Covid 19. The proposed model employs a Tsukamoto Neuro Fuzzy\nInference network to identify and distinguish Covid 19 disease from normal and\npneumonia cases. While the traditional training methods tune the parameters of\nthe neuro-fuzzy model by gradient-based algorithms and recursive least square\nmethod, we use an evolutionary-based optimization, the Cat swarm algorithm to\nupdate the parameters. In addition, six texture features extracted from chest\nX-ray images are give n as input to the model. Finally, the proposed model is\nconducted on the chest X-ray dataset to detect Covid 19. The simulation results\nindicate that the proposed model achieves an accuracy of 98.51%, sensitivity of\n98.35%, specificity of 98.08%, and F1 score of 98.17%.\n", "date": "2023-05-17", "categories": []}
{"id": "2303.12095", "title": "Interpretable histopathology-based prediction of disease relevant\n  features in Inflammatory Bowel Disease biopsies using weakly-supervised deep\n  learning", "abstract": "  Crohn's Disease (CD) and Ulcerative Colitis (UC) are the two main\nInflammatory Bowel Disease (IBD) types. We developed deep learning models to\nidentify histological disease features for both CD and UC using only endoscopic\nlabels. We explored fine-tuning and end-to-end training of two state-of-the-art\nself-supervised models for predicting three different endoscopic categories (i)\nCD vs UC (AUC=0.87), (ii) normal vs lesional (AUC=0.81), (iii) low vs high\ndisease severity score (AUC=0.80). We produced visual attention maps to\ninterpret what the models learned and validated them with the support of a\npathologist, where we observed a strong association between the models'\npredictions and histopathological inflammatory features of the disease.\nAdditionally, we identified several cases where the model incorrectly predicted\nnormal samples as lesional but were correct on the microscopic level when\nreviewed by the pathologist. This tendency of histological presentation to be\nmore severe than endoscopic presentation was previously published in the\nliterature. In parallel, we utilised a model trained on the Colon Nuclei\nIdentification and Counting (CoNIC) dataset to predict and explore 6 cell\npopulations. We observed correlation between areas enriched with the predicted\nimmune cells in biopsies and the pathologist's feedback on the attention maps.\nFinally, we identified several cell level features indicative of disease\nseverity in CD and UC. These models can enhance our understanding about the\npathology behind IBD and can shape our strategies for patient stratification in\nclinical trials.\n", "date": "2023-03-20", "categories": []}
{"id": "2304.14300", "title": "Learning Absorption Rates in Glucose-Insulin Dynamics from Meal\n  Covariates", "abstract": "  Traditional models of glucose-insulin dynamics rely on heuristic\nparameterizations chosen to fit observations within a laboratory setting.\nHowever, these models cannot describe glucose dynamics in daily life. One\nsource of failure is in their descriptions of glucose absorption rates after\nmeal events. A meal's macronutritional content has nuanced effects on the\nabsorption profile, which is difficult to model mechanistically. In this paper,\nwe propose to learn the effects of macronutrition content from glucose-insulin\ndata and meal covariates. Given macronutrition information and meal times, we\nuse a neural network to predict an individual's glucose absorption rate. We use\nthis neural rate function as the control function in a differential equation of\nglucose dynamics, enabling end-to-end training. On simulated data, our approach\nis able to closely approximate true absorption rates, resulting in better\nforecast than heuristic parameterizations, despite only observing glucose,\ninsulin, and macronutritional information. Our work readily generalizes to meal\nevents with higher-dimensional covariates, such as images, setting the stage\nfor glucose dynamics models that are personalized to each individual's daily\nlife.\n", "date": "2023-04-27", "categories": []}
{"id": "2302.00669", "title": "Detecting Histologic & Clinical Glioblastoma Patterns of Prognostic\n  Relevance", "abstract": "  Glioblastoma is the most common and aggressive malignant adult tumor of the\ncentral nervous system, with a grim prognosis and heterogeneous morphologic and\nmolecular profiles. Since adopting the current standard-of-care treatment 18\nyears ago, no substantial prognostic improvement has been noticed. Accurate\nprediction of patient overall survival (OS) from histopathology whole slide\nimages (WSI) integrated with clinical data using advanced computational methods\ncould optimize clinical decision-making and patient management. Here, we focus\non identifying prognostically relevant glioblastoma characteristics from H&E\nstained WSI & clinical data relating to OS. The exact approach for WSI\ncapitalizes on the comprehensive curation of apparent artifactual content and\nan interpretability mechanism via a weakly supervised attention-based\nmultiple-instance learning algorithm that further utilizes clustering to\nconstrain the search space. The automatically placed patterns of high\ndiagnostic value classify each WSI as representative of short or\nlong-survivors. Further assessment of the prognostic relevance of the\nassociated clinical patient data is performed both in isolation and in an\nintegrated manner, using XGBoost and SHapley Additive exPlanations (SHAP).\nIdentifying tumor morphological & clinical patterns associated with short and\nlong OS will enable the clinical neuropathologist to provide additional\nrelevant prognostic information to the treating team and suggest avenues of\nbiological investigation for understanding and potentially treating\nglioblastoma.\n", "date": "2023-02-01", "categories": []}
{"id": "2305.03219", "title": "All models are local: time to replace external validation with recurrent\n  local validation", "abstract": "  External validation is often recommended to ensure the generalizability of ML\nmodels. However, it neither guarantees generalizability nor equates to a\nmodel's clinical usefulness (the ultimate goal of any clinical decision-support\ntool). External validation is misaligned with current healthcare ML needs.\nFirst, patient data changes across time, geography, and facilities. These\nchanges create significant volatility in the performance of a single fixed\nmodel (especially for deep learning models, which dominate clinical ML).\nSecond, newer ML techniques, current market forces, and updated regulatory\nframeworks are enabling frequent updating and monitoring of individual deployed\nmodel instances. We submit that external validation is insufficient to\nestablish ML models' safety or utility. Proposals to fix the external\nvalidation paradigm do not go far enough. Continued reliance on it as the\nultimate test is likely to lead us astray. We propose the MLOps-inspired\nparadigm of recurring local validation as an alternative that ensures the\nvalidity of models while protecting against performance-disruptive data\nvariability. This paradigm relies on site-specific reliability tests before\nevery deployment, followed by regular and recurrent checks throughout the life\ncycle of the deployed algorithm. Initial and recurrent reliability tests\nprotect against performance-disruptive distribution shifts, and concept drifts\nthat jeopardize patient safety.\n", "date": "2023-05-05", "categories": []}
{"id": "2305.07670", "title": "Liver Infection Prediction Analysis using Machine Learning to Evaluate\n  Analytical Performance in Neural Networks by Optimization Techniques", "abstract": "  Liver infection is a common disease, which poses a great threat to human\nhealth, but there is still able to identify an optimal technique that can be\nused on large-level screening. This paper deals with ML algorithms using\ndifferent data sets and predictive analyses. Therefore, machine ML can be\nutilized in different diseases for integrating a piece of pattern for\nvisualization. This paper deals with various machine learning algorithms on\ndifferent liver illness datasets to evaluate the analytical performance using\ndifferent types of parameters and optimization techniques. The selected\nclassification algorithms analyze the difference in results and find out the\nmost excellent categorization models for liver disease. Machine learning\noptimization is the procedure of modifying hyperparameters in arrange to employ\none of the optimization approaches to minimise the cost function. To set the\nhyperparameter, include a number of Phosphotase,Direct Billirubin, Protiens,\nAlbumin and Albumin Globulin. Since it describes the difference linking the\npredictable parameter's true importance and the model's prediction, it is\ncrucial to minimise the cost function.\n", "date": "2023-05-11", "categories": []}
{"id": "2305.08139", "title": "Predicting Unplanned Readmissions in the Intensive Care Unit: A\n  Multimodality Evaluation", "abstract": "  A hospital readmission is when a patient who was discharged from the hospital\nis admitted again for the same or related care within a certain period.\nHospital readmissions are a significant problem in the healthcare domain, as\nthey lead to increased hospitalization costs, decreased patient satisfaction,\nand increased risk of adverse outcomes such as infections, medication errors,\nand even death. The problem of hospital readmissions is particularly acute in\nintensive care units (ICUs), due to the severity of the patients' conditions,\nand the substantial risk of complications. Predicting Unplanned Readmissions in\nICUs is a challenging task, as it involves analyzing different data modalities,\nsuch as static data, unstructured free text, sequences of diagnoses and\nprocedures, and multivariate time-series. Here, we investigate the\neffectiveness of each data modality separately, then alongside with others,\nusing state-of-the-art machine learning approaches in time-series analysis and\nnatural language processing. Using our evaluation process, we are able to\ndetermine the contribution of each data modality, and for the first time in the\ncontext of readmission, establish a hierarchy of their predictive value.\nAdditionally, we demonstrate the impact of Temporal Abstractions in enhancing\nthe performance of time-series approaches to readmission prediction. Due to\nconflicting definitions in the literature, we also provide a clear definition\nof the term Unplanned Readmission to enhance reproducibility and consistency of\nfuture research and to prevent any potential misunderstandings that could\nresult from diverse interpretations of the term. Our experimental results on a\nlarge benchmark clinical data set show that Discharge Notes written by\nphysicians, have better capabilities for readmission prediction than all other\nmodalities.\n", "date": "2023-05-14", "categories": []}
{"id": "2305.06416", "title": "A Method to Automate the Discharge Summary Hospital Course for Neurology\n  Patients", "abstract": "  Generation of automated clinical notes have been posited as a strategy to\nmitigate physician burnout. In particular, an automated narrative summary of a\npatient's hospital stay could supplement the hospital course section of the\ndischarge summary that inpatient physicians document in electronic health\nrecord (EHR) systems. In the current study, we developed and evaluated an\nautomated method for summarizing the hospital course section using\nencoder-decoder sequence-to-sequence transformer models. We fine tuned BERT and\nBART models and optimized for factuality through constraining beam search,\nwhich we trained and tested using EHR data from patients admitted to the\nneurology unit of an academic medical center. The approach demonstrated good\nROUGE scores with an R-2 of 13.76. In a blind evaluation, two board-certified\nphysicians rated 62% of the automated summaries as meeting the standard of\ncare, which suggests the method may be useful clinically. To our knowledge,\nthis study is among the first to demonstrate an automated method for generating\na discharge summary hospital course that approaches a quality level of what a\nphysician would write.\n", "date": "2023-05-10", "categories": []}
{"id": "2305.05722", "title": "Enhancing Clinical Predictive Modeling through Model Complexity-Driven\n  Class Proportion Tuning for Class Imbalanced Data: An Empirical Study on\n  Opioid Overdose Prediction", "abstract": "  Class imbalance problems widely exist in the medical field and heavily\ndeteriorates performance of clinical predictive models. Most techniques to\nalleviate the problem rebalance class proportions and they predominantly assume\nthe rebalanced proportions should be a function of the original data and\noblivious to the model one uses. This work challenges this prevailing\nassumption and proposes that links the optimal class proportions to the model\ncomplexity, thereby tuning the class proportions per model. Our experiments on\nthe opioid overdose prediction problem highlight the performance gain of tuning\nclass proportions. Rigorous regression analysis also confirms the advantages of\nthe theoretical framework proposed and the statistically significant\ncorrelation between the hyperparameters controlling the model complexity and\nthe optimal class proportions.\n", "date": "2023-05-09", "categories": []}
{"id": "2305.06109", "title": "XMI-ICU: Explainable Machine Learning Model for Pseudo-Dynamic\n  Prediction of Mortality in the ICU for Heart Attack Patients", "abstract": "  Heart attack remain one of the greatest contributors to mortality in the\nUnited States and globally. Patients admitted to the intensive care unit (ICU)\nwith diagnosed heart attack (myocardial infarction or MI) are at higher risk of\ndeath. In this study, we use two retrospective cohorts extracted from the eICU\nand MIMIC-IV databases, to develop a novel pseudo-dynamic machine learning\nframework for mortality prediction in the ICU with interpretability and\nclinical risk analysis. The method provides accurate prediction for ICU\npatients up to 24 hours before the event and provide time-resolved\ninterpretability results. The performance of the framework relying on extreme\ngradient boosting was evaluated on a held-out test set from eICU, and\nexternally validated on the MIMIC-IV cohort using the most important features\nidentified by time-resolved Shapley values achieving AUCs of 91.0 (balanced\naccuracy of 82.3) for 6-hour prediction of mortality respectively. We show that\nour framework successfully leverages time-series physiological measurements by\ntranslating them into stacked static prediction problems to be robustly\npredictive through time in the ICU stay and can offer clinical insight from\ntime-resolved interpretability\n", "date": "2023-05-10", "categories": []}
{"id": "2208.10240", "title": "A Multimodal Transformer: Fusing Clinical Notes with Structured EHR Data\n  for Interpretable In-Hospital Mortality Prediction", "abstract": "  Deep-learning-based clinical decision support using structured electronic\nhealth records (EHR) has been an active research area for predicting risks of\nmortality and diseases. Meanwhile, large amounts of narrative clinical notes\nprovide complementary information, but are often not integrated into predictive\nmodels. In this paper, we provide a novel multimodal transformer to fuse\nclinical notes and structured EHR data for better prediction of in-hospital\nmortality. To improve interpretability, we propose an integrated gradients (IG)\nmethod to select important words in clinical notes and discover the critical\nstructured EHR features with Shapley values. These important words and clinical\nfeatures are visualized to assist with interpretation of the prediction\noutcomes. We also investigate the significance of domain adaptive pretraining\nand task adaptive fine-tuning on the Clinical BERT, which is used to learn the\nrepresentations of clinical notes. Experiments demonstrated that our model\noutperforms other methods (AUCPR: 0.538, AUCROC: 0.877, F1:0.490).\n", "date": "2022-08-09", "categories": []}
{"id": "2305.04992", "title": "Autoencoder-based prediction of ICU clinical codes", "abstract": "  Availability of diagnostic codes in Electronic Health Records (EHRs) is\ncrucial for patient care as well as reimbursement purposes. However, entering\nthem in the EHR is tedious, and some clinical codes may be overlooked. Given an\nin-complete list of clinical codes, we investigate the performance of ML\nmethods on predicting the complete ones, and assess the added predictive value\nof including other clinical patient data in this task. We used the MIMIC-III\ndataset and frame the task of completing the clinical codes as a recommendation\nproblem. We con-sider various autoencoder approaches plus two strong baselines;\nitem co-occurrence and Singular Value Decomposition (SVD). Inputs are 1) a\nrecord's known clinical codes, 2) the codes plus variables. The\nco-occurrence-based ap-proach performed slightly better (F1 score=0.26, Mean\nAverage Precision [MAP]=0.19) than the SVD (F1=0.24, MAP=0.18). However, the\nadversarial autoencoder achieved the best performance when using the codes plus\nvariables (F1=0.32, MAP=0.25). Adversarial autoencoders performed best in terms\nof F1 and were equal to vanilla and denoising autoencoders in term of MAP.\nUsing clinical variables in addition to the incomplete codes list, improves the\npredictive performance of the models.\n", "date": "2023-05-08", "categories": []}
{"id": "2305.05001", "title": "GersteinLab at MEDIQA-Chat 2023: Clinical Note Summarization from\n  Doctor-Patient Conversations through Fine-tuning and In-context Learning", "abstract": "  This paper presents our contribution to the MEDIQA-2023 Dialogue2Note shared\ntask, encompassing both subtask A and subtask B. We approach the task as a\ndialogue summarization problem and implement two distinct pipelines: (a) a\nfine-tuning of a pre-trained dialogue summarization model and GPT-3, and (b)\nfew-shot in-context learning (ICL) using a large language model, GPT-4. Both\nmethods achieve excellent results in terms of ROUGE-1 F1, BERTScore F1\n(deberta-xlarge-mnli), and BLEURT, with scores of 0.4011, 0.7058, and 0.5421,\nrespectively. Additionally, we predict the associated section headers using\nRoBERTa and SciBERT based classification models. Our team ranked fourth among\nall teams, while each team is allowed to submit three runs as part of their\nsubmission. We also utilize expert annotations to demonstrate that the notes\ngenerated through the ICL GPT-4 are better than all other baselines. The code\nfor our submission is available.\n", "date": "2023-05-08", "categories": []}
{"id": "2305.05162", "title": "Effective Medical Code Prediction via Label Internal Alignment", "abstract": "  The clinical notes are usually typed into the system by physicians. They are\ntypically required to be marked by standard medical codes, and each code\nrepresents a diagnosis or medical treatment procedure. Annotating these notes\nis time consuming and prone to error. In this paper, we proposed a multi-view\nattention based Neural network to predict medical codes from clinical texts.\nOur method incorporates three aspects of information, the semantic context of\nthe clinical text, the relationship among the label (medical codes) space, and\nthe alignment between each pair of a clinical text and medical code. Our method\nis verified to be effective on the open source dataset. The experimental result\nshows that our method achieves better performance against the prior\nstate-of-art on multiple metrics.\n", "date": "2023-05-09", "categories": []}
{"id": "2305.05543", "title": "Walk4Me: Telehealth Community Mobility Assessment, An Automated System\n  for Early Diagnosis and Disease Progression", "abstract": "  We introduce Walk4Me, a telehealth community mobility assessment system\ndesigned to facilitate early diagnosis, severity, and progression\nidentification. Our system achieves this by 1) enabling early diagnosis, 2)\nidentifying early indicators of clinical severity, and 3) quantifying and\ntracking the progression of the disease across the ambulatory phase of the\ndisease. To accomplish this, we employ an Artificial Intelligence (AI)-based\ndetection of gait characteristics in patients and typically developing peers.\nOur system remotely and in real-time collects data from device sensors (e.g.,\nacceleration from a mobile device, etc.) using our novel Walk4Me API. Our web\napplication extracts temporal/spatial gait characteristics and raw data signal\ncharacteristics and then employs traditional machine learning and deep learning\ntechniques to identify patterns that can 1) identify patients with gait\ndisturbances associated with disease, 2) describe the degree of mobility\nlimitation, and 3) identify characteristics that change over time with disease\nprogression. We have identified several machine learning techniques that\ndifferentiate between patients and typically-developing subjects with 100%\naccuracy across the age range studied, and we have also identified\ncorresponding temporal/spatial gait characteristics associated with each group.\nOur work demonstrates the potential of utilizing the latest advances in mobile\ndevice and machine learning technology to measure clinical outcomes regardless\nof the point of care, inform early clinical diagnosis and treatment\ndecision-making, and monitor disease progression.\n", "date": "2023-05-05", "categories": []}
{"id": "2305.05648", "title": "Predicting Cardiovascular Disease Risk using Photoplethysmography and\n  Deep Learning", "abstract": "  Cardiovascular diseases (CVDs) are responsible for a large proportion of\npremature deaths in low- and middle-income countries. Early CVD detection and\nintervention is critical in these populations, yet many existing CVD risk\nscores require a physical examination or lab measurements, which can be\nchallenging in such health systems due to limited accessibility. Here we\ninvestigated the potential to use photoplethysmography (PPG), a sensing\ntechnology available on most smartphones that can potentially enable\nlarge-scale screening at low cost, for CVD risk prediction. We developed a deep\nlearning PPG-based CVD risk score (DLS) to predict the probability of having\nmajor adverse cardiovascular events (MACE: non-fatal myocardial infarction,\nstroke, and cardiovascular death) within ten years, given only age, sex,\nsmoking status and PPG as predictors. We compared the DLS with the office-based\nrefit-WHO score, which adopts the shared predictors from WHO and Globorisk\nscores (age, sex, smoking status, height, weight and systolic blood pressure)\nbut refitted on the UK Biobank (UKB) cohort. In UKB cohort, DLS's C-statistic\n(71.1%, 95% CI 69.9-72.4) was non-inferior to office-based refit-WHO score\n(70.9%, 95% CI 69.7-72.2; non-inferiority margin of 2.5%, p<0.01). The\ncalibration of the DLS was satisfactory, with a 1.8% mean absolute calibration\nerror. Adding DLS features to the office-based score increased the C-statistic\nby 1.0% (95% CI 0.6-1.4). DLS predicts ten-year MACE risk comparable with the\noffice-based refit-WHO score. It provides a proof-of-concept and suggests the\npotential of a PPG-based approach strategies for community-based primary\nprevention in resource-limited regions.\n", "date": "2023-05-09", "categories": []}
{"id": "2305.04006", "title": "Electromyography Signal Classification Using Deep Learning", "abstract": "  We have implemented a deep learning model with L2 regularization and trained\nit on Electromyography (EMG) data. The data comprises of EMG signals collected\nfrom control group, myopathy and ALS patients. Our proposed deep neural network\nconsists of eight layers; five fully connected, two batch normalization and one\ndropout layers. The data is divided into training and testing sections by\nsubsequently dividing the training data into sub-training and validation\nsections. Having implemented this model, an accuracy of 99 percent is achieved\non the test data set. The model was able to distinguishes the normal cases\n(control group) from the others at a precision of 100 percent and classify the\nmyopathy and ALS with high accuracy of 97.4 and 98.2 percents, respectively.\nThus we believe that, this highly improved classification accuracies will be\nbeneficial for their use in the clinical diagnosis of neuromuscular disorders.\n", "date": "2023-05-06", "categories": []}
{"id": "2302.12692", "title": "Language Models are Few-shot Learners for Prognostic Prediction", "abstract": "  Clinical prediction is an essential task in the healthcare industry. However,\nthe recent success of transformers, on which large language models are built,\nhas not been extended to this domain. In this research, we explore the use of\ntransformers and language models in prognostic prediction for immunotherapy\nusing real-world patients' clinical data and molecular profiles. This paper\ninvestigates the potential of transformers to improve clinical prediction\ncompared to conventional machine learning approaches and addresses the\nchallenge of few-shot learning in predicting rare disease areas. The study\nbenchmarks the efficacy of baselines and language models on prognostic\nprediction across multiple cancer types and investigates the impact of\ndifferent pretrained language models under few-shot regimes. The results\ndemonstrate significant improvements in accuracy and highlight the potential of\nNLP in clinical research to improve early detection and intervention for\ndifferent diseases.\n", "date": "2023-02-24", "categories": []}
{"id": "2305.03661", "title": "Predicting COVID-19 and pneumonia complications from admission texts", "abstract": "  In this paper we present a novel approach to risk assessment for patients\nhospitalized with pneumonia or COVID-19 based on their admission reports. We\napplied a Longformer neural network to admission reports and other textual data\navailable shortly after admission to compute risk scores for the patients. We\nused patient data of multiple European hospitals to demonstrate that our\napproach outperforms the Transformer baselines. Our experiments show that the\nproposed model generalises across institutions and diagnoses. Also, our method\nhas several other advantages described in the paper.\n", "date": "2023-05-05", "categories": []}
{"id": "2305.03715", "title": "Large Language Models in Ambulatory Devices for Home Health Diagnostics:\n  A case study of Sickle Cell Anemia Management", "abstract": "  This study investigates the potential of an ambulatory device that\nincorporates Large Language Models (LLMs) in cadence with other specialized ML\nmodels to assess anemia severity in sickle cell patients in real time. The\ndevice would rely on sensor data that measures angiogenic material levels to\nassess anemia severity, providing real-time information to patients and\nclinicians to reduce the frequency of vaso-occlusive crises because of the\nearly detection of anemia severity, allowing for timely interventions and\npotentially reducing the likelihood of serious complications. The main\nchallenges in developing such a device are the creation of a reliable\nnon-invasive tool for angiogenic level assessment, a biophysics model and the\npractical consideration of an LLM communicating with emergency personnel on\nbehalf of an incapacitated patient. A possible system is proposed, and the\nlimitations of this approach are discussed.\n", "date": "2023-05-05", "categories": []}
{"id": "2304.11794", "title": "FineEHR: Refine Clinical Note Representations to Improve Mortality\n  Prediction", "abstract": "  Monitoring the health status of patients in the Intensive Care Unit (ICU) is\na critical aspect of providing superior care and treatment. The availability of\nlarge-scale electronic health records (EHR) provides machine learning models\nwith an abundance of clinical text and vital sign data, enabling them to make\nhighly accurate predictions. Despite the emergence of advanced Natural Language\nProcessing (NLP) algorithms for clinical note analysis, the complex textual\nstructure and noise present in raw clinical data have posed significant\nchallenges. Coarse embedding approaches without domain-specific refinement have\nlimited the accuracy of these algorithms. To address this issue, we propose\nFINEEHR, a system that utilizes two representation learning techniques, namely\nmetric learning and fine-tuning, to refine clinical note embeddings, while\nleveraging the intrinsic correlations among different health statuses and note\ncategories. We evaluate the performance of FINEEHR using two metrics, namely\nArea Under the Curve (AUC) and AUC-PR, on a real-world MIMIC III dataset. Our\nexperimental results demonstrate that both refinement approaches improve\nprediction accuracy, and their combination yields the best results. Moreover,\nour proposed method outperforms prior works, with an AUC improvement of over\n10%, achieving an average AUC of 96.04% and an average AUC-PR of 96.48% across\nvarious classifiers.\n", "date": "2023-04-24", "categories": []}
{"id": "2305.02504", "title": "Learning Missing Modal Electronic Health Records with Unified\n  Multi-modal Data Embedding and Modality-Aware Attention", "abstract": "  Electronic Health Record (EHR) provides abundant information through various\nmodalities. However, learning multi-modal EHR is currently facing two major\nchallenges, namely, 1) data embedding and 2) cases with missing modality. A\nlack of shared embedding function across modalities can discard the temporal\nrelationship between different EHR modalities. On the other hand, most EHR\nstudies are limited to relying only on EHR Times-series, and therefore, missing\nmodality in EHR has not been well-explored. Therefore, in this study, we\nintroduce a Unified Multi-modal Set Embedding (UMSE) and Modality-Aware\nAttention (MAA) with Skip Bottleneck (SB). UMSE treats all EHR modalities\nwithout a separate imputation module or error-prone carry-forward, whereas MAA\nwith SB learns missing modal EHR with effective modality-aware attention. Our\nmodel outperforms other baseline models in mortality, vasopressor need, and\nintubation need prediction with the MIMIC-IV dataset.\n", "date": "2023-05-04", "categories": []}
{"id": "2305.01252", "title": "HTPS: Heterogeneous Transferring Prediction System for Healthcare\n  Datasets", "abstract": "  Medical internet of things leads to revolutionary improvements in medical\nservices, also known as smart healthcare. With the big healthcare data, data\nmining and machine learning can assist wellness management and intelligent\ndiagnosis, and achieve the P4-medicine. However, healthcare data has high\nsparsity and heterogeneity. In this paper, we propose a Heterogeneous\nTransferring Prediction System (HTPS). Feature engineering mechanism transforms\nthe dataset into sparse and dense feature matrices, and autoencoders in the\nembedding networks not only embed features but also transfer knowledge from\nheterogeneous datasets. Experimental results show that the proposed HTPS\noutperforms the benchmark systems on various prediction tasks and datasets, and\nablation studies present the effectiveness of each designed mechanism.\nExperimental results demonstrate the negative impact of heterogeneous data on\nbenchmark systems and the high transferability of the proposed HTPS.\n", "date": "2023-05-02", "categories": []}
{"id": "2305.00923", "title": "Early Detection of Alzheimer's Disease using Bottleneck Transformers", "abstract": "  Early detection of Alzheimer's Disease (AD) and its prodromal state, Mild\nCognitive Impairment (MCI), is crucial for providing suitable treatment and\npreventing the disease from progressing. It can also aid researchers and\nclinicians to identify early biomarkers and minister new treatments that have\nbeen a subject of extensive research. The application of deep learning\ntechniques on structural Magnetic Resonance Imaging (MRI) has shown promising\nresults in diagnosing the disease. In this research, we intend to introduce a\nnovel approach of using an ensemble of the self-attention-based Bottleneck\nTransformers with a sharpness aware minimizer for early detection of\nAlzheimer's Disease. The proposed approach has been tested on the widely\naccepted ADNI dataset and evaluated using accuracy, precision, recall, F1\nscore, and ROC-AUC score as the performance metrics.\n", "date": "2023-05-01", "categories": []}
{"id": "2303.10794", "title": "PheME: A deep ensemble framework for improving phenotype prediction from\n  multi-modal data", "abstract": "  Detailed phenotype information is fundamental to accurate diagnosis and risk\nestimation of diseases. As a rich source of phenotype information, electronic\nhealth records (EHRs) promise to empower diagnostic variant interpretation.\nHowever, how to accurately and efficiently extract phenotypes from the\nheterogeneous EHR data remains a challenge. In this work, we present PheME, an\nEnsemble framework using Multi-modality data of structured EHRs and\nunstructured clinical notes for accurate Phenotype prediction. Firstly, we\nemploy multiple deep neural networks to learn reliable representations from the\nsparse structured EHR data and redundant clinical notes. A multi-modal model\nthen aligns multi-modal features onto the same latent space to predict\nphenotypes. Secondly, we leverage ensemble learning to combine outputs from\nsingle-modal models and multi-modal models to improve phenotype predictions. We\nchoose seven diseases to evaluate the phenotyping performance of the proposed\nframework. Experimental results show that using multi-modal data significantly\nimproves phenotype prediction in all diseases, the proposed ensemble learning\nframework can further boost the performance.\n", "date": "2023-03-19", "categories": []}
{"id": "2304.13998", "title": "Mimic-IV-ICD: A new benchmark for eXtreme MultiLabel Classification", "abstract": "  Clinical notes are assigned ICD codes - sets of codes for diagnoses and\nprocedures. In the recent years, predictive machine learning models have been\nbuilt for automatic ICD coding. However, there is a lack of widely accepted\nbenchmarks for automated ICD coding models based on large-scale public EHR\ndata.\n  This paper proposes a public benchmark suite for ICD-10 coding using a large\nEHR dataset derived from MIMIC-IV, the most recent public EHR dataset. We\nimplement and compare several popular methods for ICD coding prediction tasks\nto standardize data preprocessing and establish a comprehensive ICD coding\nbenchmark dataset. This approach fosters reproducibility and model comparison,\naccelerating progress toward employing automated ICD coding in future studies.\nFurthermore, we create a new ICD-9 benchmark using MIMIC-IV data, providing\nmore data points and a higher number of ICD codes than MIMIC-III. Our\nopen-source code offers easy access to data processing steps, benchmark\ncreation, and experiment replication for those with MIMIC-IV access, providing\ninsights, guidance, and protocols to efficiently develop ICD coding models.\n", "date": "2023-04-27", "categories": []}
{"id": "2304.13429", "title": "GENIE-NF-AI: Identifying Neurofibromatosis Tumors using Liquid Neural\n  Network (LTC) trained on AACR GENIE Datasets", "abstract": "  In recent years, the field of medicine has been increasingly adopting\nartificial intelligence (AI) technologies to provide faster and more accurate\ndisease detection, prediction, and assessment. In this study, we propose an\ninterpretable AI approach to diagnose patients with neurofibromatosis using\nblood tests and pathogenic variables. We evaluated the proposed method using a\ndataset from the AACR GENIE project and compared its performance with modern\napproaches. Our proposed approach outperformed existing models with 99.86%\naccuracy. We also conducted NF1 and interpretable AI tests to validate our\napproach. Our work provides an explainable approach model using logistic\nregression and explanatory stimulus as well as a black-box model. The\nexplainable models help to explain the predictions of black-box models while\nthe glass-box models provide information about the best-fit features. Overall,\nour study presents an interpretable AI approach for diagnosing patients with\nneurofibromatosis and demonstrates the potential of AI in the medical field.\n", "date": "2023-04-26", "categories": []}
{"id": "2304.12447", "title": "Predicting Pulmonary Hypertension by Electrocardiograms Using Machine\n  Learning", "abstract": "  Pulmonary hypertension (PH) is a condition of high blood pressure that\naffects the arteries in the lungs and the right side of the heart (Mayo Clinic,\n2017). A mean pulmonary artery pressure greater than 25 mmHg is defined as\nPulmonary hypertension. The estimated 5-year survival rate from the time of\ndiagnosis of pulmonary hypertension is only 57% without therapy and patients\nwith right heart failure only survive for approximately 1 year without\ntreatment (Benza et al., 2012). Given the indolent nature of the disease, early\ndetection of PH remains a challenge leading to delays in therapy.\nEchocardiography is currently used as a screening tool for diagnosing PH.\nHowever, electrocardiography (ECG), a more accessible, simple to use, and\ncost-effective tool compared to echocardiography, is less studied and explored\nfor screening at-risk patients for PH. The goal of this project is to create a\nneural network model which can process an ECG signal and detect the presence of\nPH with a confidence probability. I created a dense neural network (DNN) model\nthat has an accuracy of 98% over the available training sample. For future\nsteps, the current model will be updated with a model suited for time-series\ndata. To balance the dataset with proper training samples, I will generate\nadditional data using data augmentation techniques. Through early and accurate\ndetection of conditions such as PH, we widen the spectrum of innovation in\ndetecting chronic life-threatening health conditions and reduce associated\nmortality and morbidity.\n", "date": "2023-04-24", "categories": []}
{"id": "2304.12828", "title": "A optimization framework for herbal prescription planning based on deep\n  reinforcement learning", "abstract": "  Treatment planning for chronic diseases is a critical task in medical\nartificial intelligence, particularly in traditional Chinese medicine (TCM).\nHowever, generating optimized sequential treatment strategies for patients with\nchronic diseases in different clinical encounters remains a challenging issue\nthat requires further exploration. In this study, we proposed a TCM herbal\nprescription planning framework based on deep reinforcement learning for\nchronic disease treatment (PrescDRL). PrescDRL is a sequential herbal\nprescription optimization model that focuses on long-term effectiveness rather\nthan achieving maximum reward at every step, thereby ensuring better patient\noutcomes. We constructed a high-quality benchmark dataset for sequential\ndiagnosis and treatment of diabetes and evaluated PrescDRL against this\nbenchmark. Our results showed that PrescDRL achieved a higher curative effect,\nwith the single-step reward improving by 117% and 153% compared to doctors.\nFurthermore, PrescDRL outperformed the benchmark in prescription prediction,\nwith precision improving by 40.5% and recall improving by 63%. Overall, our\nstudy demonstrates the potential of using artificial intelligence to improve\nclinical intelligent diagnosis and treatment in TCM.\n", "date": "2023-04-25", "categories": []}
{"id": "2304.10909", "title": "Automated Medical Coding on MIMIC-III and MIMIC-IV: A Critical Review\n  and Replicability Study", "abstract": "  Medical coding is the task of assigning medical codes to clinical free-text\ndocumentation. Healthcare professionals manually assign such codes to track\npatient diagnoses and treatments. Automated medical coding can considerably\nalleviate this administrative burden. In this paper, we reproduce, compare, and\nanalyze state-of-the-art automated medical coding machine learning models. We\nshow that several models underperform due to weak configurations, poorly\nsampled train-test splits, and insufficient evaluation. In previous work, the\nmacro F1 score has been calculated sub-optimally, and our correction doubles\nit. We contribute a revised model comparison using stratified sampling and\nidentical experimental setups, including hyperparameters and decision boundary\ntuning. We analyze prediction errors to validate and falsify assumptions of\nprevious works. The analysis confirms that all models struggle with rare codes,\nwhile long documents only have a negligible impact. Finally, we present the\nfirst comprehensive results on the newly released MIMIC-IV dataset using the\nreproduced models. We release our code, model parameters, and new MIMIC-III and\nMIMIC-IV training and evaluation pipelines to accommodate fair future\ncomparisons.\n", "date": "2023-04-21", "categories": []}
{"id": "2304.11080", "title": "Multimodal contrastive learning for diagnosing cardiovascular diseases\n  from electrocardiography (ECG) signals and patient metadata", "abstract": "  This work discusses the use of contrastive learning and deep learning for\ndiagnosing cardiovascular diseases from electrocardiography (ECG) signals.\nWhile the ECG signals usually contain 12 leads (channels), many healthcare\nfacilities and devices lack access to all these 12 leads. This raises the\nproblem of how to use only fewer ECG leads to produce meaningful diagnoses with\nhigh performance. We introduce a simple experiment to test whether contrastive\nlearning can be applied to this task. More specifically, we added the\nsimilarity between the embedding vectors when the 12 leads signal and the fewer\nleads ECG signal to the loss function to bring these representations closer\ntogether. Despite its simplicity, this has been shown to have improved the\nperformance of diagnosing with all lead combinations, proving the potential of\ncontrastive learning on this task.\n", "date": "2023-04-18", "categories": []}
{"id": "2207.07572", "title": "Outlier detection of vital sign trajectories from COVID-19 patients", "abstract": "  In this work, we present a novel trajectory comparison algorithm to identify\nabnormal vital sign trends, with the aim of improving recognition of\ndeteriorating health.\n  There is growing interest in continuous wearable vital sign sensors for\nmonitoring patients remotely at home. These monitors are usually coupled to an\nalerting system, which is triggered when vital sign measurements fall outside a\npredefined normal range. Trends in vital signs, such as increasing heart rate,\nare often indicative of deteriorating health, but are rarely incorporated into\nalerting systems.\n  We introduce a dynamic time warp distance-based measure to compare time\nseries trajectories. We split each multi-variable sign time series into 180\nminute, non-overlapping epochs. We then calculate the distance between all\npairs of epochs. Each epoch is characterized by its mean pairwise distance\n(average link distance) to all other epochs, with clusters forming with nearby\nepochs.\n  We demonstrate in synthetically generated data that this method can identify\nabnormal epochs and cluster epochs with similar trajectories. We then apply\nthis method to a real-world data set of vital signs from 8 patients who had\nrecently been discharged from hospital after contracting COVID-19. We show how\noutlier epochs correspond well with the abnormal vital signs and identify\npatients who were subsequently readmitted to hospital.\n", "date": "2022-07-15", "categories": []}
{"id": "2304.10000", "title": "Model Based Reinforcement Learning for Personalized Heparin Dosing", "abstract": "  A key challenge in sequential decision making is optimizing systems safely\nunder partial information. While much of the literature has focused on the\ncases of either partially known states or partially known dynamics, it is\nfurther exacerbated in cases where both states and dynamics are partially\nknown. Computing heparin doses for patients fits this paradigm since the\nconcentration of heparin in the patient cannot be measured directly and the\nrates at which patients metabolize heparin vary greatly between individuals.\nWhile many proposed solutions are model free, they require complex models and\nhave difficulty ensuring safety. However, if some of the structure of the\ndynamics is known, a model based approach can be leveraged to provide safe\npolicies. In this paper we propose such a framework to address the challenge of\noptimizing personalized heparin doses. We use a predictive model parameterized\nindividually by patient to predict future therapeutic effects. We then leverage\nthis model using a scenario generation based approach that is capable of\nensuring patient safety. We validate our models with numerical experiments by\ncomparing the predictive capabilities of our model against existing machine\nlearning techniques and demonstrating how our dosing algorithm can treat\npatients in a simulated ICU environment.\n", "date": "2023-04-19", "categories": []}
{"id": "2304.10175", "title": "Automated Dynamic Bayesian Networks for Predicting Acute Kidney Injury\n  Before Onset", "abstract": "  Several algorithms for learning the structure of dynamic Bayesian networks\n(DBNs) require an a priori ordering of variables, which influences the\ndetermined graph topology. However, it is often unclear how to determine this\norder if feature importance is unknown, especially as an exhaustive search is\nusually impractical. In this paper, we introduce Ranking Approaches for Unknown\nStructures (RAUS), an automated framework to systematically inform variable\nordering and learn networks end-to-end. RAUS leverages existing statistical\nmethods (Cramers V, chi-squared test, and information gain) to compare variable\nordering, resultant generated network topologies, and DBN performance. RAUS\nenables end-users with limited DBN expertise to implement models via command\nline interface. We evaluate RAUS on the task of predicting impending acute\nkidney injury (AKI) from inpatient clinical laboratory data. Longitudinal\nobservations from 67,460 patients were collected from our electronic health\nrecord (EHR) and Kidney Disease Improving Global Outcomes (KDIGO) criteria were\nthen applied to define AKI events. RAUS learns multiple DBNs simultaneously to\npredict a future AKI event at different time points (i.e., 24-, 48-, 72-hours\nin advance of AKI). We also compared the results of the learned AKI prediction\nmodels and variable orderings to baseline techniques (logistic regression,\nrandom forests, and extreme gradient boosting). The DBNs generated by RAUS\nachieved 73-83% area under the receiver operating characteristic curve (AUCROC)\nwithin 24-hours before AKI; and 71-79% AUCROC within 48-hours before AKI of any\nstage in a 7-day observation window. Insights from this automated framework can\nhelp efficiently implement and interpret DBNs for clinical decision support.\nThe source code for RAUS is available in GitHub at\nhttps://github.com/dgrdn08/RAUS .\n", "date": "2023-04-20", "categories": []}
{"id": "2304.09178", "title": "Alzheimers Disease Diagnosis using Machine Learning: A Review", "abstract": "  Alzheimers Disease AD is an acute neuro disease that degenerates the brain\ncells and thus leads to memory loss progressively. It is a fatal brain disease\nthat mostly affects the elderly. It steers the decline of cognitive and\nbiological functions of the brain and shrinks the brain successively, which in\nturn is known as Atrophy. For an accurate diagnosis of Alzheimers disease,\ncutting edge methods like machine learning are essential. Recently, machine\nlearning has gained a lot of attention and popularity in the medical industry.\nAs the illness progresses, those with Alzheimers have a far more difficult time\ndoing even the most basic tasks, and in the worst case, their brain completely\nstops functioning. A persons likelihood of having early-stage Alzheimers\ndisease may be determined using the ML method. In this analysis, papers on\nAlzheimers disease diagnosis based on deep learning techniques and\nreinforcement learning between 2008 and 2023 found in google scholar were\nstudied. Sixty relevant papers obtained after the search was considered for\nthis study. These papers were analysed based on the biomarkers of AD and the\nmachine-learning techniques used. The analysis shows that deep learning methods\nhave an immense ability to extract features and classify AD with good accuracy.\nThe DRL methods have not been used much in the field of image processing. The\ncomparison results of deep learning and reinforcement learning illustrate that\nthe scope of Deep Reinforcement Learning DRL in dementia detection needs to be\nexplored.\n", "date": "2023-04-17", "categories": []}
{"id": "2304.09233", "title": "Machine Learning Applications in Studying Mental Health Among Immigrants\n  and Racial and Ethnic Minorities: A Systematic Review", "abstract": "  Background: The use of machine learning (ML) in mental health (MH) research\nis increasing, especially as new, more complex data types become available to\nanalyze. By systematically examining the published literature, this review aims\nto uncover potential gaps in the current use of ML to study MH in vulnerable\npopulations of immigrants, refugees, migrants, and racial and ethnic\nminorities.\n  Methods: In this systematic review, we queried Google Scholar for ML-related\nterms, MH-related terms, and a population of a focus search term strung\ntogether with Boolean operators. Backward reference searching was also\nconducted. Included peer-reviewed studies reported using a method or\napplication of ML in an MH context and focused on the populations of interest.\nWe did not have date cutoffs. Publications were excluded if they were narrative\nor did not exclusively focus on a minority population from the respective\ncountry. Data including study context, the focus of mental healthcare, sample,\ndata type, type of ML algorithm used, and algorithm performance was extracted\nfrom each.\n  Results: Our search strategies resulted in 67,410 listed articles from Google\nScholar. Ultimately, 12 were included. All the articles were published within\nthe last 6 years, and half of them studied populations within the US. Most\nreviewed studies used supervised learning to explain or predict MH outcomes.\nSome publications used up to 16 models to determine the best predictive power.\nAlmost half of the included publications did not discuss their cross-validation\nmethod.\n  Conclusions: The included studies provide proof-of-concept for the potential\nuse of ML algorithms to address MH concerns in these special populations, few\nas they may be. Our systematic review finds that the clinical application of\nthese models for classifying and predicting MH disorders is still under\ndevelopment.\n", "date": "2023-04-18", "categories": []}
{"id": "2304.09245", "title": "Early Detection of Parkinson's Disease using Motor Symptoms and Machine\n  Learning", "abstract": "  Parkinson's disease (PD) has been found to affect 1 out of every 1000 people,\nbeing more inclined towards the population above 60 years. Leveraging\nwearable-systems to find accurate biomarkers for diagnosis has become the need\nof the hour, especially for a neurodegenerative condition like Parkinson's.\nThis work aims at focusing on early-occurring, common symptoms, such as motor\nand gait related parameters to arrive at a quantitative analysis on the\nfeasibility of an economical and a robust wearable device. A subset of the\nParkinson's Progression Markers Initiative (PPMI), PPMI Gait dataset has been\nutilised for feature-selection after a thorough analysis with various Machine\nLearning algorithms. Identified influential features has then been used to test\nreal-time data for early detection of Parkinson Syndrome, with a model accuracy\nof 91.9%\n", "date": "2023-04-18", "categories": []}
{"id": "2304.08614", "title": "Signal Processing Grand Challenge 2023 -- e-Prevention: Sleep Behavior\n  as an Indicator of Relapses in Psychotic Patients", "abstract": "  This paper presents the approach and results of USC SAIL's submission to the\nSignal Processing Grand Challenge 2023 - e-Prevention (Task 2), on detecting\nrelapses in psychotic patients. Relapse prediction has proven to be\nchallenging, primarily due to the heterogeneity of symptoms and responses to\ntreatment between individuals. We address these challenges by investigating the\nuse of sleep behavior features to estimate relapse days as outliers in an\nunsupervised machine learning setting. We extract informative features from\nhuman activity and heart rate data collected in the wild, and evaluate various\ncombinations of feature types and time resolutions. We found that short-time\nsleep behavior features outperformed their awake counterparts and larger time\nintervals. Our submission was ranked 3rd in the Task's official leaderboard,\ndemonstrating the potential of such features as an objective and non-invasive\npredictor of psychotic relapses.\n", "date": "2023-04-17", "categories": []}
{"id": "2304.07299", "title": "Supervised Machine Learning for Breast Cancer Risk Factors Analysis and\n  Survival Prediction", "abstract": "  The choice of the most effective treatment may eventually be influenced by\nbreast cancer survival prediction. To predict the chances of a patient\nsurviving, a variety of techniques were employed, such as statistical, machine\nlearning, and deep learning models. In the current study, 1904 patient records\nfrom the METABRIC dataset were utilized to predict a 5-year breast cancer\nsurvival using a machine learning approach. In this study, we compare the\noutcomes of seven classification models to evaluate how well they perform using\nthe following metrics: recall, AUC, confusion matrix, accuracy, precision,\nfalse positive rate, and true positive rate. The findings demonstrate that the\nclassifiers for Logistic Regression (LR), Support Vector Machines (SVM),\nDecision Tree (DT), Random Forest (RD), Extremely Randomized Trees (ET),\nK-Nearest Neighbor (KNN), and Adaptive Boosting (AdaBoost) can accurately\npredict the survival rate of the tested samples, which is 75,4\\%, 74,7\\%,\n71,5\\%, 75,5\\%, 70,3\\%, and 78 percent.\n", "date": "2023-04-13", "categories": []}
{"id": "2304.07787", "title": "Harnessing Digital Pathology And Causal Learning To Improve Eosinophilic\n  Esophagitis Dietary Treatment Assignment", "abstract": "  Eosinophilic esophagitis (EoE) is a chronic, food antigen-driven, allergic\ninflammatory condition of the esophagus associated with elevated esophageal\neosinophils. EoE is a top cause of chronic dysphagia after GERD. Diagnosis of\nEoE relies on counting eosinophils in histological slides, a manual and\ntime-consuming task that limits the ability to extract complex\npatient-dependent features. The treatment of EoE includes medication and food\nelimination. A personalized food elimination plan is crucial for engagement and\nefficiency, but previous attempts failed to produce significant results. In\nthis work, on the one hand, we utilize AI for inferring histological features\nfrom the entire biopsy slide, features that cannot be extracted manually. On\nthe other hand, we develop causal learning models that can process this wealth\nof data. We applied our approach to the 'Six-Food vs. One-Food Eosinophilic\nEsophagitis Diet Study', where 112 symptomatic adults aged 18-60 years with\nactive EoE were assigned to either a six-food elimination diet (6FED) or a\none-food elimination diet (1FED) for six weeks. Our results show that the\naverage treatment effect (ATE) of the 6FED treatment compared with the 1FED\ntreatment is not significant, that is, neither diet was superior to the other.\nWe examined several causal models and show that the best treatment strategy was\nobtained using T-learner with two XGBoost modules. While 1FED only and 6FED\nonly provide improvement for 35%-38% of the patients, which is not\nsignificantly different from a random treatment assignment, our causal model\nyields a significantly better improvement rate of 58.4%. This study illustrates\nthe significance of AI in enhancing treatment planning by analyzing molecular\nfeatures' distribution in histological slides through causal learning. Our\napproach can be harnessed for other conditions that rely on histology for\ndiagnosis and treatment.\n", "date": "2023-04-16", "categories": []}
{"id": "2211.09781", "title": "Monitoring machine learning (ML)-based risk prediction algorithms in the\n  presence of confounding medical interventions", "abstract": "  Performance monitoring of machine learning (ML)-based risk prediction models\nin healthcare is complicated by the issue of confounding medical interventions\n(CMI): when an algorithm predicts a patient to be at high risk for an adverse\nevent, clinicians are more likely to administer prophylactic treatment and\nalter the very target that the algorithm aims to predict. A simple approach is\nto ignore CMI and monitor only the untreated patients, whose outcomes remain\nunaltered. In general, ignoring CMI may inflate Type I error because (i)\nuntreated patients disproportionally represent those with low predicted risk\nand (ii) evolution in both the model and clinician trust in the model can\ninduce complex dependencies that violate standard assumptions. Nevertheless, we\nshow that valid inference is still possible if one monitors conditional\nperformance and if either conditional exchangeability or time-constant\nselection bias hold. Specifically, we develop a new score-based cumulative sum\n(CUSUM) monitoring procedure with dynamic control limits. Through simulations,\nwe demonstrate the benefits of combining model updating with monitoring and\ninvestigate how over-trust in a prediction model may delay detection of\nperformance deterioration. Finally, we illustrate how these monitoring methods\ncan be used to detect calibration decay of an ML-based risk calculator for\npostoperative nausea and vomiting during the COVID-19 pandemic.\n", "date": "2022-11-17", "categories": []}
{"id": "2210.16598", "title": "Self-Supervised Predictive Coding with Multimodal Fusion for Patient\n  Deterioration Prediction in Fine-grained Time Resolution", "abstract": "  Accurate time prediction of patients' critical events is crucial in urgent\nscenarios where timely decision-making is important. Though many studies have\nproposed automatic prediction methods using Electronic Health Records (EHR),\ntheir coarse-grained time resolutions limit their practical usage in urgent\nenvironments such as the emergency department (ED) and intensive care unit\n(ICU). Therefore, in this study, we propose an hourly prediction method based\non self-supervised predictive coding and multi-modal fusion for two critical\ntasks: mortality and vasopressor need prediction. Through extensive\nexperiments, we prove significant performance gains from both multi-modal\nfusion and self-supervised predictive regularization, most notably in\nfar-future prediction, which becomes especially important in practice. Our\nuni-modal/bi-modal/bi-modal self-supervision scored 0.846/0.877/0.897\n(0.824/0.855/0.886) and 0.817/0.820/0.858 (0.807/0.81/0.855) with mortality\n(far-future mortality) and with vasopressor need (far-future vasopressor need)\nprediction data in AUROC, respectively.\n", "date": "2022-10-29", "categories": []}
{"id": "2304.06384", "title": "Multi-Subset Approach to Early Sepsis Prediction", "abstract": "  Sepsis is a life-threatening organ malfunction caused by the host's inability\nto fight infection, which can lead to death without proper and immediate\ntreatment. Therefore, early diagnosis and medical treatment of sepsis in\ncritically ill populations at high risk for sepsis and sepsis-associated\nmortality are vital to providing the patient with rapid therapy. Studies show\nthat advancing sepsis detection by 6 hours leads to earlier administration of\nantibiotics, which is associated with improved mortality. However, clinical\nscores like Sequential Organ Failure Assessment (SOFA) are not applicable for\nearly prediction, while machine learning algorithms can help capture the\nprogressing pattern for early prediction. Therefore, we aim to develop a\nmachine learning algorithm that predicts sepsis onset 6 hours before it is\nsuspected clinically. Although some machine learning algorithms have been\napplied to sepsis prediction, many of them did not consider the fact that six\nhours is not a small gap. To overcome this big gap challenge, we explore a\nmulti-subset approach in which the likelihood of sepsis occurring earlier than\n6 hours is output from a previous subset and feed to the target subset as\nadditional features. Moreover, we use the hourly sampled data like vital signs\nin an observation window to derive a temporal change trend to further assist,\nwhich however is often ignored by previous studies. Our empirical study shows\nthat both the multi-subset approach to alleviating the 6-hour gap and the added\ntemporal trend features can help improve the performance of sepsis-related\nearly prediction.\n", "date": "2023-04-13", "categories": []}
{"id": "2304.04468", "title": "Toward Cohort Intelligence: A Universal Cohort Representation Learning\n  Framework for Electronic Health Record Analysis", "abstract": "  Electronic Health Records (EHR) are generated from clinical routine care\nrecording valuable information of broad patient populations, which provide\nplentiful opportunities for improving patient management and intervention\nstrategies in clinical practice. To exploit the enormous potential of EHR data,\na popular EHR data analysis paradigm in machine learning is EHR representation\nlearning, which first leverages the individual patient's EHR data to learn\ninformative representations by a backbone, and supports diverse health-care\ndownstream tasks grounded on the representations. Unfortunately, such a\nparadigm fails to access the in-depth analysis of patients' relevance, which is\ngenerally known as cohort studies in clinical practice. Specifically, patients\nin the same cohort tend to share similar characteristics, implying their\nresemblance in medical conditions such as symptoms or diseases. In this paper,\nwe propose a universal COhort Representation lEarning (CORE) framework to\naugment EHR utilization by leveraging the fine-grained cohort information among\npatients. In particular, CORE first develops an explicit patient modeling task\nbased on the prior knowledge of patients' diagnosis codes, which measures the\nlatent relevance among patients to adaptively divide the cohorts for each\npatient. Based on the constructed cohorts, CORE recodes the pre-extracted EHR\ndata representation from intra- and inter-cohort perspectives, yielding\naugmented EHR data representation learning. CORE is readily applicable to\ndiverse backbone models, serving as a universal plug-in framework to infuse\ncohort information into healthcare methods for boosted performance. We conduct\nan extensive experimental evaluation on two real-world datasets, and the\nexperimental results demonstrate the effectiveness and generalizability of\nCORE.\n", "date": "2023-04-10", "categories": []}
{"id": "2304.05986", "title": "Auditing ICU Readmission Rates in an Clinical Database: An Analysis of\n  Risk Factors and Clinical Outcomes", "abstract": "  This study presents a machine learning (ML) pipeline for clinical data\nclassification in the context of a 30-day readmission problem, along with a\nfairness audit on subgroups based on sensitive attributes. A range of ML models\nare used for classification and the fairness audit is conducted on the model\npredictions. The fairness audit uncovers disparities in equal opportunity,\npredictive parity, false positive rate parity, and false negative rate parity\ncriteria on the MIMIC III dataset based on attributes such as gender,\nethnicity, language, and insurance group. The results identify disparities in\nthe model's performance across different groups and highlights the need for\nbetter fairness and bias mitigation strategies. The study suggests the need for\ncollaborative efforts among researchers, policymakers, and practitioners to\naddress bias and fairness in artificial intelligence (AI) systems.\n", "date": "2023-04-12", "categories": []}
{"id": "2304.06015", "title": "An Improved Heart Disease Prediction Using Stacked Ensemble Method", "abstract": "  Heart disorder has just overtaken cancer as the world's biggest cause of\nmortality. Several cardiac failures, heart disease mortality, and diagnostic\ncosts can all be reduced with early identification and treatment. Medical data\nis collected in large quantities by the healthcare industry, but it is not well\nmined. The discovery of previously unknown patterns and connections in this\ninformation can help with an improved decision when it comes to forecasting\nheart disorder risk. In the proposed study, we constructed an ML-based\ndiagnostic system for heart illness forecasting, using a heart disorder\ndataset. We used data preprocessing techniques like outlier detection and\nremoval, checking and removing missing entries, feature normalization,\ncross-validation, nine classification algorithms like RF, MLP, KNN, ETC, XGB,\nSVC, ADB, DT, and GBM, and eight classifier measuring performance metrics like\nramification accuracy, precision, F1 score, specificity, ROC, sensitivity,\nlog-loss, and Matthews' correlation coefficient, as well as eight\nclassification performance evaluations. Our method can easily differentiate\nbetween people who have cardiac disease and those are normal. Receiver\noptimistic curves and also the region under the curves were determined by every\nclassifier. Most of the classifiers, pretreatment strategies, validation\nmethods, and performance assessment metrics for classification models have been\ndiscussed in this study. The performance of the proposed scheme has been\nconfirmed, utilizing all of its capabilities. In this work, the impact of\nclinical decision support systems was evaluated using a stacked ensemble\napproach that included these nine algorithms\n", "date": "2023-04-12", "categories": []}
{"id": "2303.04360", "title": "Does Synthetic Data Generation of LLMs Help Clinical Text Mining?", "abstract": "  Recent advancements in large language models (LLMs) have led to the\ndevelopment of highly potent models like OpenAI's ChatGPT. These models have\nexhibited exceptional performance in a variety of tasks, such as question\nanswering, essay composition, and code generation. However, their effectiveness\nin the healthcare sector remains uncertain. In this study, we seek to\ninvestigate the potential of ChatGPT to aid in clinical text mining by\nexamining its ability to extract structured information from unstructured\nhealthcare texts, with a focus on biological named entity recognition and\nrelation extraction. However, our preliminary results indicate that employing\nChatGPT directly for these tasks resulted in poor performance and raised\nprivacy concerns associated with uploading patients' information to the ChatGPT\nAPI. To overcome these limitations, we propose a new training paradigm that\ninvolves generating a vast quantity of high-quality synthetic data with labels\nutilizing ChatGPT and fine-tuning a local model for the downstream task. Our\nmethod has resulted in significant improvements in the performance of\ndownstream tasks, improving the F1-score from 23.37% to 63.99% for the named\nentity recognition task and from 75.86% to 83.59% for the relation extraction\ntask. Furthermore, generating data using ChatGPT can significantly reduce the\ntime and effort required for data collection and labeling, as well as mitigate\ndata privacy concerns. In summary, the proposed framework presents a promising\nsolution to enhance the applicability of LLM models to clinical text mining.\n", "date": "2023-03-08", "categories": []}
{"id": "2304.04870", "title": "DASS Good: Explainable Data Mining of Spatial Cohort Data", "abstract": "  Developing applicable clinical machine learning models is a difficult task\nwhen the data includes spatial information, for example, radiation dose\ndistributions across adjacent organs at risk. We describe the co-design of a\nmodeling system, DASS, to support the hybrid human-machine development and\nvalidation of predictive models for estimating long-term toxicities related to\nradiotherapy doses in head and neck cancer patients. Developed in collaboration\nwith domain experts in oncology and data mining, DASS incorporates\nhuman-in-the-loop visual steering, spatial data, and explainable AI to augment\ndomain knowledge with automatic data mining. We demonstrate DASS with the\ndevelopment of two practical clinical stratification models and report feedback\nfrom domain experts. Finally, we describe the design lessons learned from this\ncollaborative experience.\n", "date": "2023-04-10", "categories": []}
{"id": "2304.05352", "title": "SPOT: Sequential Predictive Modeling of Clinical Trial Outcome with\n  Meta-Learning", "abstract": "  Clinical trials are essential to drug development but time-consuming, costly,\nand prone to failure. Accurate trial outcome prediction based on historical\ntrial data promises better trial investment decisions and more trial success.\nExisting trial outcome prediction models were not designed to model the\nrelations among similar trials, capture the progression of features and designs\nof similar trials, or address the skewness of trial data which causes inferior\nperformance for less common trials.\n  To fill the gap and provide accurate trial outcome prediction, we propose\nSequential Predictive mOdeling of clinical Trial outcome (SPOT) that first\nidentifies trial topics to cluster the multi-sourced trial data into relevant\ntrial topics. It then generates trial embeddings and organizes them by topic\nand time to create clinical trial sequences. With the consideration of each\ntrial sequence as a task, it uses a meta-learning strategy to achieve a point\nwhere the model can rapidly adapt to new tasks with minimal updates. In\nparticular, the topic discovery module enables a deeper understanding of the\nunderlying structure of the data, while sequential learning captures the\nevolution of trial designs and outcomes. This results in predictions that are\nnot only more accurate but also more interpretable, taking into account the\ntemporal patterns and unique characteristics of each trial topic. We\ndemonstrate that SPOT wins over the prior methods by a significant margin on\ntrial outcome benchmark data: with a 21.5\\% lift on phase I, an 8.9\\% lift on\nphase II, and a 5.5\\% lift on phase III trials in the metric of the area under\nprecision-recall curve (PR-AUC).\n", "date": "2023-04-07", "categories": []}
{"id": "2211.07126", "title": "Discharge Summary Hospital Course Summarisation of In Patient Electronic\n  Health Record Text with Clinical Concept Guided Deep Pre-Trained Transformer\n  Models", "abstract": "  Brief Hospital Course (BHC) summaries are succinct summaries of an entire\nhospital encounter, embedded within discharge summaries, written by senior\nclinicians responsible for the overall care of a patient. Methods to\nautomatically produce summaries from inpatient documentation would be\ninvaluable in reducing clinician manual burden of summarising documents under\nhigh time-pressure to admit and discharge patients. Automatically producing\nthese summaries from the inpatient course, is a complex, multi-document\nsummarisation task, as source notes are written from various perspectives (e.g.\nnursing, doctor, radiology), during the course of the hospitalisation. We\ndemonstrate a range of methods for BHC summarisation demonstrating the\nperformance of deep learning summarisation models across extractive and\nabstractive summarisation scenarios. We also test a novel ensemble extractive\nand abstractive summarisation model that incorporates a medical concept\nontology (SNOMED) as a clinical guidance signal and shows superior performance\nin 2 real-world clinical data sets.\n", "date": "2022-11-14", "categories": []}
{"id": "2303.15354", "title": "From Single-Hospital to Multi-Centre Applications: Enhancing the\n  Generalisability of Deep Learning Models for Adverse Event Prediction in the\n  ICU", "abstract": "  Deep learning (DL) can aid doctors in detecting worsening patient states\nearly, affording them time to react and prevent bad outcomes. While DL-based\nearly warning models usually work well in the hospitals they were trained for,\nthey tend to be less reliable when applied at new hospitals. This makes it\ndifficult to deploy them at scale. Using carefully harmonised intensive care\ndata from four data sources across Europe and the US (totalling 334,812 stays),\nwe systematically assessed the reliability of DL models for three common\nadverse events: death, acute kidney injury (AKI), and sepsis. We tested whether\nusing more than one data source and/or explicitly optimising for\ngeneralisability during training improves model performance at new hospitals.\nWe found that models achieved high AUROC for mortality (0.838-0.869), AKI\n(0.823-0.866), and sepsis (0.749-0.824) at the training hospital. As expected,\nperformance dropped at new hospitals, sometimes by as much as -0.200. Using\nmore than one data source for training mitigated the performance drop, with\nmulti-source models performing roughly on par with the best single-source\nmodel. This suggests that as data from more hospitals become available for\ntraining, model robustness is likely to increase, lower-bounding robustness\nwith the performance of the most applicable data source in the training data.\nDedicated methods promoting generalisability did not noticeably improve\nperformance in our experiments.\n", "date": "2023-03-27", "categories": []}
{"id": "2304.04161", "title": "Detection of COVID19 in Chest X-Ray Images Using Transfer Learning", "abstract": "  COVID19 is a highly contagious disease infected millions of people worldwide.\nWith limited testing components, screening tools such as chest radiography can\nassist the clinicians in the diagnosis and assessing the progress of disease.\nThe performance of deep learning-based systems for diagnosis of COVID-19\ndisease in radiograph images has been encouraging. This paper investigates the\nconcept of transfer learning using two of the most well-known VGGNet\narchitectures, namely VGG-16 and VGG-19. The classifier block and\nhyperparameters are fine-tuned to adopt the models for automatic detection of\nCovid-19 in chest x-ray images. We generated two different datasets to evaluate\nthe performance of the proposed system for the identification of positive\nCovid-19 instances in a multiclass and binary classification problems. The\nexperimental outcome demonstrates the usefulness of transfer learning for\nsmall-sized datasets particularly in the field of medical imaging, not only to\nprevent over-fitting and convergence problems but also to attain optimal\nclassification performance as well.\n", "date": "2023-04-09", "categories": []}
{"id": "2304.04673", "title": "Regional Deep Atrophy: a Self-Supervised Learning Method to\n  Automatically Identify Regions Associated With Alzheimer's Disease\n  Progression From Longitudinal MRI", "abstract": "  Longitudinal assessment of brain atrophy, particularly in the hippocampus, is\na well-studied biomarker for neurodegenerative diseases, such as Alzheimer's\ndisease (AD). In clinical trials, estimation of brain progressive rates can be\napplied to track therapeutic efficacy of disease modifying treatments. However,\nmost state-of-the-art measurements calculate changes directly by segmentation\nand/or deformable registration of MRI images, and may misreport head motion or\nMRI artifacts as neurodegeneration, impacting their accuracy. In our previous\nstudy, we developed a deep learning method DeepAtrophy that uses a\nconvolutional neural network to quantify differences between longitudinal MRI\nscan pairs that are associated with time. DeepAtrophy has high accuracy in\ninferring temporal information from longitudinal MRI scans, such as temporal\norder or relative inter-scan interval. DeepAtrophy also provides an overall\natrophy score that was shown to perform well as a potential biomarker of\ndisease progression and treatment efficacy. However, DeepAtrophy is not\ninterpretable, and it is unclear what changes in the MRI contribute to\nprogression measurements. In this paper, we propose Regional Deep Atrophy\n(RDA), which combines the temporal inference approach from DeepAtrophy with a\ndeformable registration neural network and attention mechanism that highlights\nregions in the MRI image where longitudinal changes are contributing to\ntemporal inference. RDA has similar prediction accuracy as DeepAtrophy, but its\nadditional interpretability makes it more acceptable for use in clinical\nsettings, and may lead to more sensitive biomarkers for disease monitoring in\nclinical trials of early AD.\n", "date": "2023-04-10", "categories": []}
{"id": "2304.02886", "title": "Automatic ICD-10 Code Association: A Challenging Task on French Clinical\n  Texts", "abstract": "  Automatically associating ICD codes with electronic health data is a\nwell-known NLP task in medical research. NLP has evolved significantly in\nrecent years with the emergence of pre-trained language models based on\nTransformers architecture, mainly in the English language. This paper adapts\nthese models to automatically associate the ICD codes. Several neural network\narchitectures have been experimented with to address the challenges of dealing\nwith a large set of both input tokens and labels to be guessed. In this paper,\nwe propose a model that combines the latest advances in NLP and multi-label\nclassification for ICD-10 code association. Fair experiments on a Clinical\ndataset in the French language show that our approach increases the $F_1$-score\nmetric by more than 55\\% compared to state-of-the-art results.\n", "date": "2023-04-06", "categories": []}
{"id": "2304.02925", "title": "Computer-aided Diagnosis of Malaria through Transfer Learning using the\n  ResNet50 Backbone", "abstract": "  According to the World Malaria Report of 2022, 247 million cases of malaria\nand 619,000 related deaths were reported in 2021. This highlights the\npredominance of the disease, especially in the tropical and sub-tropical\nregions of Africa, parts of South-east Asia, Central and Southern America.\nMalaria is caused due to the Plasmodium parasite which is circulated through\nthe bites of the female Anopheles mosquito. Hence, the detection of the\nparasite in human blood smears could confirm malarial infestation. Since the\nmanual identification of Plasmodium is a lengthy and time-consuming task\nsubject to variability in accuracy, we propose an automated, computer-aided\ndiagnostic method to classify malarial thin smear blood cell images as\nparasitized and uninfected by using the ResNet50 Deep Neural Network. In this\npaper, we have used the pre-trained ResNet50 model on the open-access database\nprovided by the National Library of Medicine's Lister Hill National Center for\nBiomedical Communication for 150 epochs. The results obtained showed accuracy,\nprecision, and recall values of 98.75%, 99.3% and 99.5% on the\nResNet50(proposed) model. We have compared these metrics with similar models\nsuch as VGG16, Watershed Segmentation and Random Forest, which showed better\nperformance than traditional techniques as well.\n", "date": "2023-04-06", "categories": []}
{"id": "2304.01240", "title": "Identifying Mentions of Pain in Mental Health Records Text: A Natural\n  Language Processing Approach", "abstract": "  Pain is a common reason for accessing healthcare resources and is a growing\narea of research, especially in its overlap with mental health. Mental health\nelectronic health records are a good data source to study this overlap.\nHowever, much information on pain is held in the free text of these records,\nwhere mentions of pain present a unique natural language processing problem due\nto its ambiguous nature. This project uses data from an anonymised mental\nhealth electronic health records database. The data are used to train a machine\nlearning based classification algorithm to classify sentences as discussing\npatient pain or not. This will facilitate the extraction of relevant pain\ninformation from large databases, and the use of such outputs for further\nstudies on pain and mental health. 1,985 documents were manually\ntriple-annotated for creation of gold standard training data, which was used to\ntrain three commonly used classification algorithms. The best performing model\nachieved an F1-score of 0.98 (95% CI 0.98-0.99).\n", "date": "2023-04-03", "categories": []}
{"id": "2304.02191", "title": "Building predictive models of healthcare costs with open healthcare data", "abstract": "  Due to rapidly rising healthcare costs worldwide, there is significant\ninterest in controlling them. An important aspect concerns price transparency,\nas preliminary efforts have demonstrated that patients will shop for lower\ncosts, driving efficiency. This requires the data to be made available, and\nmodels that can predict healthcare costs for a wide range of patient\ndemographics and conditions. We present an approach to this problem by\ndeveloping a predictive model using machine-learning techniques. We analyzed\nde-identified patient data from New York State SPARCS (statewide planning and\nresearch cooperative system), consisting of 2.3 million records in 2016. We\nbuilt models to predict costs from patient diagnoses and demographics. We\ninvestigated two model classes consisting of sparse regression and decision\ntrees. We obtained the best performance by using a decision tree with depth 10.\nWe obtained an R-square value of 0.76 which is better than the values reported\nin the literature for similar problems.\n", "date": "2023-04-05", "categories": []}
{"id": "2304.01214", "title": "Parkinsons Disease Detection via Resting-State Electroencephalography\n  Using Signal Processing and Machine Learning Techniques", "abstract": "  Parkinsons Disease (PD) is a neurodegenerative disorder resulting in motor\ndeficits due to advancing degeneration of dopaminergic neurons. PD patients\nreport experiencing tremor, rigidity, visual impairment, bradykinesia, and\nseveral cognitive deficits. Although Electroencephalography (EEG) indicates\nabnormalities in PD patients, one major challenge is the lack of a consistent,\naccurate, and systemic biomarker for PD in order to closely monitor the disease\nwith therapeutic treatments and medication. In this study, we collected\nElectroencephalographic data from 15 PD patients and 16 Healthy Controls (HC).\nWe first preprocessed every EEG signal using several techniques and extracted\nrelevant features using many feature extraction algorithms. Afterwards, we\napplied several machine learning algorithms to classify PD versus HC. We found\nthe most significant metrics to be achieved by the Random Forest ensemble\nlearning approach, with an accuracy, precision, recall, F1 score, and AUC of\n97.5%, 100%, 95%, 0.967, and 0.975, respectively. The results of this study\nshow promise for exposing PD abnormalities using EEG during clinical diagnosis,\nand automating this process using signal processing techniques and ML\nalgorithms to evaluate the difference between healthy individuals and PD\npatients.\n", "date": "2023-03-29", "categories": []}
{"id": "2304.01220", "title": "Evaluating the impact of an explainable machine learning system on the\n  interobserver agreement in chest radiograph interpretation", "abstract": "  We conducted a prospective study to measure the clinical impact of an\nexplainable machine learning system on interobserver agreement in chest\nradiograph interpretation. The AI system, which we call as it VinDr-CXR when\nused as a diagnosis-supporting tool, significantly improved the agreement\nbetween six radiologists with an increase of 1.5% in mean Fleiss' Kappa. In\naddition, we also observed that, after the radiologists consulted AI's\nsuggestions, the agreement between each radiologist and the system was\nremarkably increased by 3.3% in mean Cohen's Kappa. This work has been accepted\nfor publication in IEEE Access and this paper is our short version submitted to\nthe Midwest Machine Learning Symposium (MMLS 2023), Chicago, IL, USA.\n", "date": "2023-04-01", "categories": []}
{"id": "2304.00111", "title": "Identifying Symptoms of Delirium from Clinical Narratives Using Natural\n  Language Processing", "abstract": "  Delirium is an acute decline or fluctuation in attention, awareness, or other\ncognitive function that can lead to serious adverse outcomes. Despite the\nsevere outcomes, delirium is frequently unrecognized and uncoded in patients'\nelectronic health records (EHRs) due to its transient and diverse nature.\nNatural language processing (NLP), a key technology that extracts medical\nconcepts from clinical narratives, has shown great potential in studies of\ndelirium outcomes and symptoms. To assist in the diagnosis and phenotyping of\ndelirium, we formed an expert panel to categorize diverse delirium symptoms,\ncomposed annotation guidelines, created a delirium corpus with diverse delirium\nsymptoms, and developed NLP methods to extract delirium symptoms from clinical\nnotes. We compared 5 state-of-the-art transformer models including 2 models\n(BERT and RoBERTa) from the general domain and 3 models (BERT_MIMIC,\nRoBERTa_MIMIC, and GatorTron) from the clinical domain. GatorTron achieved the\nbest strict and lenient F1 scores of 0.8055 and 0.8759, respectively. We\nconducted an error analysis to identify challenges in annotating delirium\nsymptoms and developing NLP systems. To the best of our knowledge, this is the\nfirst large language model-based delirium symptom extraction system. Our study\nlays the foundation for the future development of computable phenotypes and\ndiagnosis methods for delirium.\n", "date": "2023-03-31", "categories": []}
{"id": "2304.00115", "title": "Extracting Thyroid Nodules Characteristics from Ultrasound Reports Using\n  Transformer-based Natural Language Processing Methods", "abstract": "  The ultrasound characteristics of thyroid nodules guide the evaluation of\nthyroid cancer in patients with thyroid nodules. However, the characteristics\nof thyroid nodules are often documented in clinical narratives such as\nultrasound reports. Previous studies have examined natural language processing\n(NLP) methods in extracting a limited number of characteristics (<9) using\nrule-based NLP systems. In this study, a multidisciplinary team of NLP experts\nand thyroid specialists, identified thyroid nodule characteristics that are\nimportant for clinical care, composed annotation guidelines, developed a\ncorpus, and compared 5 state-of-the-art transformer-based NLP methods,\nincluding BERT, RoBERTa, LongFormer, DeBERTa, and GatorTron, for extraction of\nthyroid nodule characteristics from ultrasound reports. Our GatorTron model, a\ntransformer-based large language model trained using over 90 billion words of\ntext, achieved the best strict and lenient F1-score of 0.8851 and 0.9495 for\nthe extraction of a total number of 16 thyroid nodule characteristics, and\n0.9321 for linking characteristics to nodules, outperforming other clinical\ntransformer models. To the best of our knowledge, this is the first study to\nsystematically categorize and apply transformer-based NLP models to extract a\nlarge number of clinical relevant thyroid nodule characteristics from\nultrasound reports. This study lays ground for assessing the documentation\nquality of thyroid ultrasound reports and examining outcomes of patients with\nthyroid nodules using electronic health records.\n", "date": "2023-03-31", "categories": []}
{"id": "2304.00249", "title": "From Conception to Deployment: Intelligent Stroke Prediction Framework\n  using Machine Learning and Performance Evaluation", "abstract": "  Stroke is the second leading cause of death worldwide. Machine learning\nclassification algorithms have been widely adopted for stroke prediction.\nHowever, these algorithms were evaluated using different datasets and\nevaluation metrics. Moreover, there is no comprehensive framework for stroke\ndata analytics. This paper proposes an intelligent stroke prediction framework\nbased on a critical examination of machine learning prediction algorithms in\nthe literature. The five most used machine learning algorithms for stroke\nprediction are evaluated using a unified setup for objective comparison.\nComparative analysis and numerical results reveal that the Random Forest\nalgorithm is best suited for stroke prediction.\n", "date": "2023-04-01", "categories": []}
{"id": "2304.00311", "title": "Medical Pathologies Prediction : Systematic Review and Proposed Approach", "abstract": "  The healthcare sector is an important pillar of every community, numerous\nresearch studies have been carried out in this context to optimize medical\nprocesses and improve care quality and facilitate patient management. In this\narticle we have analyzed and examined different works concerning the\nexploitation of the most recent technologies such as big data, artificial\nintelligence, machine learning, and deep learning for the improvement of health\ncare, which enabled us to propose our general approach concentrating on the\ncollection, preprocessing and clustering of medical data to facilitate access,\nafter analysis, to the patients and health professionals to predict the most\nfrequent pathologies with better precision within a notable timeframe.\n  keywords: Healthcare, big data, artificial intelligence, automatic language\nprocessing, data mining, predictive models.\n", "date": "2023-04-01", "categories": []}
{"id": "2304.00774", "title": "MGMT promoter methylation status prediction using MRI scans? An\n  extensive experimental evaluation of deep learning models", "abstract": "  The number of studies on deep learning for medical diagnosis is expanding,\nand these systems are often claimed to outperform clinicians. However, only a\nfew systems have shown medical efficacy. From this perspective, we examine a\nwide range of deep learning algorithms for the assessment of glioblastoma - a\ncommon brain tumor in older adults that is lethal. Surgery, chemotherapy, and\nradiation are the standard treatments for glioblastoma patients. The\nmethylation status of the MGMT promoter, a specific genetic sequence found in\nthe tumor, affects chemotherapy's effectiveness. MGMT promoter methylation\nimproves chemotherapy response and survival in several cancers. MGMT promoter\nmethylation is determined by a tumor tissue biopsy, which is then genetically\ntested. This lengthy and invasive procedure increases the risk of infection and\nother complications. Thus, researchers have used deep learning models to\nexamine the tumor from brain MRI scans to determine the MGMT promoter's\nmethylation state. We employ deep learning models and one of the largest public\nMRI datasets of 585 participants to predict the methylation status of the MGMT\npromoter in glioblastoma tumors using MRI scans. We test these models using\nGrad-CAM, occlusion sensitivity, feature visualizations, and training loss\nlandscapes. Our results show no correlation between these two, indicating that\nexternal cohort data should be used to verify these models' performance to\nassure the accuracy and reliability of deep learning systems in cancer\ndiagnosis.\n", "date": "2023-04-03", "categories": []}
{"id": "2303.17616", "title": "Patterns Detection in Glucose Time Series by Domain Transformations and\n  Deep Learning", "abstract": "  People with diabetes have to manage their blood glucose level to keep it\nwithin an appropriate range. Predicting whether future glucose values will be\noutside the healthy threshold is of vital importance in order to take\ncorrective actions to avoid potential health damage. In this paper we describe\nour research with the aim of predicting the future behavior of blood glucose\nlevels, so that hypoglycemic events may be anticipated. The approach of this\nwork is the application of transformation functions on glucose time series, and\ntheir use in convolutional neural networks. We have tested our proposed method\nusing real data from 4 different diabetes patients with promising results.\n", "date": "2023-03-30", "categories": []}
{"id": "2211.07717", "title": "Deep Temporal Modelling of Clinical Depression through Social Media Text", "abstract": "  We describe the development of a model to detect user-level clinical\ndepression based on a user's temporal social media posts. Our model uses a\nDepression Symptoms Detection (DSD) classifier, which is trained on the largest\nexisting samples of clinician annotated tweets for clinical depression\nsymptoms. We subsequently use our DSD model to extract clinically relevant\nfeatures, e.g., depression scores and their consequent temporal patterns, as\nwell as user posting activity patterns, e.g., quantifying their ``no activity''\nor ``silence.'' Furthermore, to evaluate the efficacy of these extracted\nfeatures, we create three kinds of datasets including a test dataset, from two\nexisting well-known benchmark datasets for user-level depression detection. We\nthen provide accuracy measures based on single features, baseline features and\nfeature ablation tests, at several different levels of temporal granularity.\nThe relevant data distributions and clinical depression detection related\nsettings can be exploited to draw a complete picture of the impact of different\nfeatures across our created datasets. Finally, we show that, in general, only\nsemantic oriented representation models perform well. However, clinical\nfeatures may enhance overall performance provided that the training and testing\ndistribution is similar, and there is more data in a user's timeline. The\nconsequence is that the predictive capability of depression scores increase\nsignificantly while used in a more sensitive clinical depression detection\nsettings.\n", "date": "2022-10-28", "categories": []}
{"id": "2212.09281", "title": "Boosting Automatic COVID-19 Detection Performance with Self-Supervised\n  Learning and Batch Knowledge Ensembling", "abstract": "  Problem: Detecting COVID-19 from chest X-Ray (CXR) images has become one of\nthe fastest and easiest methods for detecting COVID-19. However, the existing\nmethods usually use supervised transfer learning from natural images as a\npretraining process. These methods do not consider the unique features of\nCOVID-19 and the similar features between COVID-19 and other pneumonia. Aim: In\nthis paper, we want to design a novel high-accuracy COVID-19 detection method\nthat uses CXR images, which can consider the unique features of COVID-19 and\nthe similar features between COVID-19 and other pneumonia. Methods: Our method\nconsists of two phases. One is self-supervised learning-based pertaining; the\nother is batch knowledge ensembling-based fine-tuning. Self-supervised\nlearning-based pretraining can learn distinguished representations from CXR\nimages without manually annotated labels. On the other hand, batch knowledge\nensembling-based fine-tuning can utilize category knowledge of images in a\nbatch according to their visual feature similarities to improve detection\nperformance. Unlike our previous implementation, we introduce batch knowledge\nensembling into the fine-tuning phase, reducing the memory used in\nself-supervised learning and improving COVID-19 detection accuracy. Results: On\ntwo public COVID-19 CXR datasets, namely, a large dataset and an unbalanced\ndataset, our method exhibited promising COVID-19 detection performance. Our\nmethod maintains high detection accuracy even when annotated CXR training\nimages are reduced significantly (e.g., using only 10% of the original\ndataset). In addition, our method is insensitive to changes in hyperparameters.\n", "date": "2022-12-19", "categories": []}
{"id": "2303.15202", "title": "Towards Outcome-Driven Patient Subgroups: A Machine Learning Analysis\n  Across Six Depression Treatment Studies", "abstract": "  Major depressive disorder (MDD) is a heterogeneous condition; multiple\nunderlying neurobiological substrates could be associated with treatment\nresponse variability. Understanding the sources of this variability and\npredicting outcomes has been elusive. Machine learning has shown promise in\npredicting treatment response in MDD, but one limitation has been the lack of\nclinical interpretability of machine learning models. We analyzed data from six\nclinical trials of pharmacological treatment for depression (total n = 5438)\nusing the Differential Prototypes Neural Network (DPNN), a neural network model\nthat derives patient prototypes which can be used to derive treatment-relevant\npatient clusters while learning to generate probabilities for differential\ntreatment response. A model classifying remission and outputting individual\nremission probabilities for five first-line monotherapies and three combination\ntreatments was trained using clinical and demographic data. Model validity and\nclinical utility were measured based on area under the curve (AUC) and expected\nimprovement in sample remission rate with model-guided treatment, respectively.\nPost-hoc analyses yielded clusters (subgroups) based on patient prototypes\nlearned during training. Prototypes were evaluated for interpretability by\nassessing differences in feature distributions and treatment-specific outcomes.\nA 3-prototype model achieved an AUC of 0.66 and an expected absolute\nimprovement in population remission rate compared to the sample remission rate.\nWe identified three treatment-relevant patient clusters which were clinically\ninterpretable. It is possible to produce novel treatment-relevant patient\nprofiles using machine learning models; doing so may improve precision medicine\nfor depression. Note: This model is not currently the subject of any active\nclinical trials and is not intended for clinical use.\n", "date": "2023-03-24", "categories": []}
{"id": "2303.16417", "title": "Problems and shortcuts in deep learning for screening mammography", "abstract": "  This work reveals undiscovered challenges in the performance and\ngeneralizability of deep learning models. We (1) identify spurious shortcuts\nand evaluation issues that can inflate performance and (2) propose training and\nanalysis methods to address them.\n  We trained an AI model to classify cancer on a retrospective dataset of\n120,112 US exams (3,467 cancers) acquired from 2008 to 2017 and 16,693 UK exams\n(5,655 cancers) acquired from 2011 to 2015.\n  We evaluated on a screening mammography test set of 11,593 US exams (102\ncancers; 7,594 women; age 57.1 \\pm 11.0) and 1,880 UK exams (590 cancers; 1,745\nwomen; age 63.3 \\pm 7.2). A model trained on images of only view markers (no\nbreast) achieved a 0.691 AUC. The original model trained on both datasets\nachieved a 0.945 AUC on the combined US+UK dataset but paradoxically only 0.838\nand 0.892 on the US and UK datasets, respectively. Sampling cancers equally\nfrom both datasets during training mitigated this shortcut. A similar AUC\nparadox (0.903) occurred when evaluating diagnostic exams vs screening exams\n(0.862 vs 0.861, respectively). Removing diagnostic exams during training\nalleviated this bias. Finally, the model did not exhibit the AUC paradox over\nscanner models but still exhibited a bias toward Selenia Dimension (SD) over\nHologic Selenia (HS) exams. Analysis showed that this AUC paradox occurred when\na dataset attribute had values with a higher cancer prevalence (dataset bias)\nand the model consequently assigned a higher probability to these attribute\nvalues (model bias). Stratification and balancing cancer prevalence can\nmitigate shortcuts during evaluation.\n  Dataset and model bias can introduce shortcuts and the AUC paradox,\npotentially pervasive issues within the healthcare AI space. Our methods can\nverify and mitigate shortcuts while providing a clear understanding of\nperformance.\n", "date": "2023-03-29", "categories": []}
{"id": "2303.16757", "title": "How can Deep Learning Retrieve the Write-Missing Additional Diagnosis\n  from Chinese Electronic Medical Record For DRG", "abstract": "  The purpose of write-missing diagnosis detection is to find diseases that\nhave been clearly diagnosed from medical records but are missed in the\ndischarge diagnosis. Unlike the definition of missed diagnosis, the\nwrite-missing diagnosis is clearly manifested in the medical record without\nfurther reasoning. The write-missing diagnosis is a common problem, often\ncaused by physician negligence. The write-missing diagnosis will result in an\nincomplete diagnosis of medical records. While under DRG grouping, the\nwrite-missing diagnoses will miss important additional diagnoses (CC, MCC),\nthus affecting the correct rate of DRG enrollment.\n  Under the circumstance that countries generally start to adopt DRG enrollment\nand payment, the problem of write-missing diagnosis is a common and serious\nproblem. The current manual-based method is expensive due to the complex\ncontent of the full medical record. We think this problem is suitable to be\nsolved as natural language processing. But to the best of our knowledge, no\nresearchers have conducted research on this problem based on natural language\nprocessing methods.\n  We propose a framework for solving the problem of write-missing diagnosis,\nwhich mainly includes three modules: disease recall module, disease context\nlogic judgment module, and disease relationship comparison module. Through this\nframework, we verify that the problem of write-missing diagnosis can be solved\nwell, and the results are interpretable. At the same time, we propose advanced\nsolutions for the disease context logic judgment module and disease\nrelationship comparison module, which have obvious advantages compared with the\nmainstream methods of the same type of problems. Finally, we verified the value\nof our proposed framework under DRG medical insurance payment in a tertiary\nhospital.\n", "date": "2023-03-28", "categories": []}
{"id": "2303.12888", "title": "A dynamic risk score for early prediction of cardiogenic shock using\n  machine learning", "abstract": "  Myocardial infarction and heart failure are major cardiovascular diseases\nthat affect millions of people in the US. The morbidity and mortality are\nhighest among patients who develop cardiogenic shock. Early recognition of\ncardiogenic shock is critical. Prompt implementation of treatment measures can\nprevent the deleterious spiral of ischemia, low blood pressure, and reduced\ncardiac output due to cardiogenic shock. However, early identification of\ncardiogenic shock has been challenging due to human providers' inability to\nprocess the enormous amount of data in the cardiac intensive care unit (ICU)\nand lack of an effective risk stratification tool. We developed a deep\nlearning-based risk stratification tool, called CShock, for patients admitted\ninto the cardiac ICU with acute decompensated heart failure and/or myocardial\ninfarction to predict onset of cardiogenic shock. To develop and validate\nCShock, we annotated cardiac ICU datasets with physician adjudicated outcomes.\nCShock achieved an area under the receiver operator characteristic curve\n(AUROC) of 0.820, which substantially outperformed CardShock (AUROC 0.519), a\nwell-established risk score for cardiogenic shock prognosis. CShock was\nexternally validated in an independent patient cohort and achieved an AUROC of\n0.800, demonstrating its generalizability in other cardiac ICUs.\n", "date": "2023-03-22", "categories": []}
{"id": "2303.15656", "title": "Predicting Adverse Neonatal Outcomes for Preterm Neonates with\n  Multi-Task Learning", "abstract": "  Diagnosis of adverse neonatal outcomes is crucial for preterm survival since\nit enables doctors to provide timely treatment. Machine learning (ML)\nalgorithms have been demonstrated to be effective in predicting adverse\nneonatal outcomes. However, most previous ML-based methods have only focused on\npredicting a single outcome, ignoring the potential correlations between\ndifferent outcomes, and potentially leading to suboptimal results and\noverfitting issues. In this work, we first analyze the correlations between\nthree adverse neonatal outcomes and then formulate the diagnosis of multiple\nneonatal outcomes as a multi-task learning (MTL) problem. We then propose an\nMTL framework to jointly predict multiple adverse neonatal outcomes. In\nparticular, the MTL framework contains shared hidden layers and multiple\ntask-specific branches. Extensive experiments have been conducted using\nElectronic Health Records (EHRs) from 121 preterm neonates. Empirical results\ndemonstrate the effectiveness of the MTL framework. Furthermore, the feature\nimportance is analyzed for each neonatal outcome, providing insights into model\ninterpretability.\n", "date": "2023-03-28", "categories": []}
{"id": "2303.12961", "title": "The Shaky Foundations of Clinical Foundation Models: A Survey of Large\n  Language Models and Foundation Models for EMRs", "abstract": "  The successes of foundation models such as ChatGPT and AlphaFold have spurred\nsignificant interest in building similar models for electronic medical records\n(EMRs) to improve patient care and hospital operations. However, recent hype\nhas obscured critical gaps in our understanding of these models' capabilities.\nWe review over 80 foundation models trained on non-imaging EMR data (i.e.\nclinical text and/or structured data) and create a taxonomy delineating their\narchitectures, training data, and potential use cases. We find that most models\nare trained on small, narrowly-scoped clinical datasets (e.g. MIMIC-III) or\nbroad, public biomedical corpora (e.g. PubMed) and are evaluated on tasks that\ndo not provide meaningful insights on their usefulness to health systems. In\nlight of these findings, we propose an improved evaluation framework for\nmeasuring the benefits of clinical foundation models that is more closely\ngrounded to metrics that matter in healthcare.\n", "date": "2023-03-22", "categories": []}
{"id": "2303.14920", "title": "Adapting Pretrained Language Models for Solving Tabular Prediction\n  Problems in the Electronic Health Record", "abstract": "  We propose an approach for adapting the DeBERTa model for electronic health\nrecord (EHR) tasks using domain adaptation. We pretrain a small DeBERTa model\non a dataset consisting of MIMIC-III discharge summaries, clinical notes,\nradiology reports, and PubMed abstracts. We compare this model's performance\nwith a DeBERTa model pre-trained on clinical texts from our institutional EHR\n(MeDeBERTa) and an XGBoost model. We evaluate performance on three benchmark\ntasks for emergency department outcomes using the MIMIC-IV-ED dataset. We\npreprocess the data to convert it into text format and generate four versions\nof the original datasets to compare data processing and data inclusion. The\nresults show that our proposed approach outperforms the alternative models on\ntwo of three tasks (p<0.001) and matches performance on the third task, with\nthe use of descriptive columns improving performance over the original column\nnames.\n", "date": "2023-03-27", "categories": []}
{"id": "2303.13790", "title": "Towards Fair Patient-Trial Matching via Patient-Criterion Level Fairness\n  Constraint", "abstract": "  Clinical trials are indispensable in developing new treatments, but they face\nobstacles in patient recruitment and retention, hindering the enrollment of\nnecessary participants. To tackle these challenges, deep learning frameworks\nhave been created to match patients to trials. These frameworks calculate the\nsimilarity between patients and clinical trial eligibility criteria,\nconsidering the discrepancy between inclusion and exclusion criteria. Recent\nstudies have shown that these frameworks outperform earlier approaches.\nHowever, deep learning models may raise fairness issues in patient-trial\nmatching when certain sensitive groups of individuals are underrepresented in\nclinical trials, leading to incomplete or inaccurate data and potential harm.\nTo tackle the issue of fairness, this work proposes a fair patient-trial\nmatching framework by generating a patient-criterion level fairness constraint.\nThe proposed framework considers the inconsistency between the embedding of\ninclusion and exclusion criteria among patients of different sensitive groups.\nThe experimental results on real-world patient-trial and patient-criterion\nmatching tasks demonstrate that the proposed framework can successfully\nalleviate the predictions that tend to be biased.\n", "date": "2023-03-24", "categories": []}
{"id": "2303.13314", "title": "Leveraging Foundation Models for Clinical Text Analysis", "abstract": "  Infectious diseases are a significant public health concern globally, and\nextracting relevant information from scientific literature can facilitate the\ndevelopment of effective prevention and treatment strategies. However, the\nlarge amount of clinical data available presents a challenge for information\nextraction. To address this challenge, this study proposes a natural language\nprocessing (NLP) framework that uses a pre-trained transformer model fine-tuned\non task-specific data to extract key information related to infectious diseases\nfrom free-text clinical data. The proposed framework includes three components:\na data layer for preparing datasets from clinical texts, a foundation model\nlayer for entity extraction, and an assessment layer for performance analysis.\nThe results of the evaluation indicate that the proposed method outperforms\nstandard methods, and leveraging prior knowledge through the pre-trained\ntransformer model makes it useful for investigating other infectious diseases\nin the future.\n", "date": "2023-03-20", "categories": []}
{"id": "2303.12707", "title": "Comparison of Probabilistic Deep Learning Methods for Autism Detection", "abstract": "  Autism Spectrum Disorder (ASD) is one neuro developmental disorder that is\nnow widespread in the world. ASD persists throughout the life of an individual,\nimpacting the way they behave and communicate, resulting to notable deficits\nconsisting of social life retardation, repeated behavioural traits and a\nrestriction in their interests. Early detection of the disorder helps in the\nonset treatment and helps one to lead a normal life. There are clinical\napproaches used in detection of autism, relying on behavioural data and in\nworst cases, neuroimaging. Quantitative methods involving machine learning have\nbeen studied and developed to overcome issues with clinical approaches. These\nquantitative methods rely on machine learning, with some complex methods based\non deep learning developed to accelerate detection and diagnosis of ASD. These\nliterature is aimed at exploring most state-of-the-art probabilistic methods in\nuse today, characterizing them with the type of dataset they're most applied\non, their accuracy according to their novel research and how well they are\nsuited in ASD classification. The findings will purposely serve as a benchmark\nin selection of the model to use when performing ASD detection.\n", "date": "2023-03-09", "categories": []}
{"id": "2303.11429", "title": "Machine learning-based detection of cardiovascular disease using ECG\n  signals: performance vs. complexity", "abstract": "  Cardiovascular disease remains a significant problem in modern society. Among\nnon-invasive techniques, the electrocardiogram (ECG) is one of the most\nreliable methods for detecting abnormalities in cardiac activities. However,\nECG interpretation requires expert knowledge and it is time-consuming.\nDeveloping a novel method to detect the disease early could prevent death and\ncomplication. The paper presents novel various approaches for classifying\ncardiac diseases from ECG recordings. The first approach suggests the Poincare\nrepresentation of ECG signal and deep-learning-based image classifiers\n(ResNet50 and DenseNet121 were learned over Poincare diagrams), which showed\ndecent performance in predicting AF (atrial fibrillation) but not other types\nof arrhythmia. XGBoost, a gradient-boosting model, showed an acceptable\nperformance in long-term data but had a long inference time due to\nhighly-consuming calculation within the pre-processing phase. Finally, the 1D\nconvolutional model, specifically the 1D ResNet, showed the best results in\nboth studied CinC 2017 and CinC 2020 datasets, reaching the F1 score of 85% and\n71%, respectively, and that was superior to the first-ranking solution of each\nchallenge. The paper also investigated efficiency metrics such as power\nconsumption and equivalent CO2 emissions, with one-dimensional models like 1D\nCNN and 1D ResNet being the most energy efficient. Model interpretation\nanalysis showed that the DenseNet detected AF using heart rate variability\nwhile the 1DResNet assessed AF pattern in raw ECG signals.\n", "date": "2023-03-10", "categories": []}
{"id": "2303.11563", "title": "Dynamic Healthcare Embeddings for Improving Patient Care", "abstract": "  As hospitals move towards automating and integrating their computing systems,\nmore fine-grained hospital operations data are becoming available. These data\ninclude hospital architectural drawings, logs of interactions between patients\nand healthcare professionals, prescription data, procedures data, and data on\npatient admission, discharge, and transfers. This has opened up many\nfascinating avenues for healthcare-related prediction tasks for improving\npatient care. However, in order to leverage off-the-shelf machine learning\nsoftware for these tasks, one needs to learn structured representations of\nentities involved from heterogeneous, dynamic data streams. Here, we propose\nDECENT, an auto-encoding heterogeneous co-evolving dynamic neural network, for\nlearning heterogeneous dynamic embeddings of patients, doctors, rooms, and\nmedications from diverse data streams. These embeddings capture similarities\namong doctors, rooms, patients, and medications based on static attributes and\ndynamic interactions. DECENT enables several applications in healthcare\nprediction, such as predicting mortality risk and case severity of patients,\nadverse events (e.g., transfer back into an intensive care unit), and future\nhealthcare-associated infections. The results of using the learned patient\nembeddings in predictive modeling show that DECENT has a gain of up to 48.1% on\nthe mortality risk prediction task, 12.6% on the case severity prediction task,\n6.4% on the medical intensive care unit transfer task, and 3.8% on the\nClostridioides difficile (C.diff) Infection (CDI) prediction task over the\nstate-of-the-art baselines. In addition, case studies on the learned doctor,\nmedication, and room embeddings show that our approach learns meaningful and\ninterpretable embeddings.\n", "date": "2023-03-21", "categories": []}
{"id": "2303.11759", "title": "Simulating Malaria Detection in Laboratories using Deep Learning", "abstract": "  Malaria is usually diagnosed by a microbiologist by examining a small sample\nof blood smear. Reducing mortality from malaria infection is possible if it is\ndiagnosed early and followed with appropriate treatment. While the WHO has set\naudacious goals of reducing malaria incidence and mortality rates by 90% in\n2030 and eliminating malaria in 35 countries by that time, it still remains a\ndifficult challenge. Computer-assisted diagnostics are on the rise these days\nas they can be used effectively as a primary test in the absence of or\nproviding assistance to a physician or pathologist. The purpose of this paper\nis to describe an approach to detecting, localizing and counting parasitic\ncells in blood sample images towards easing the burden on healthcare workers.\n", "date": "2023-03-21", "categories": []}
{"id": "2211.05409", "title": "Radiomics-enhanced Deep Multi-task Learning for Outcome Prediction in\n  Head and Neck Cancer", "abstract": "  Outcome prediction is crucial for head and neck cancer patients as it can\nprovide prognostic information for early treatment planning. Radiomics methods\nhave been widely used for outcome prediction from medical images. However,\nthese methods are limited by their reliance on intractable manual segmentation\nof tumor regions. Recently, deep learning methods have been proposed to perform\nend-to-end outcome prediction so as to remove the reliance on manual\nsegmentation. Unfortunately, without segmentation masks, these methods will\ntake the whole image as input, such that makes them difficult to focus on tumor\nregions and potentially unable to fully leverage the prognostic information\nwithin the tumor regions. In this study, we propose a radiomics-enhanced deep\nmulti-task framework for outcome prediction from PET/CT images, in the context\nof HEad and neCK TumOR segmentation and outcome prediction challenge (HECKTOR\n2022). In our framework, our novelty is to incorporate radiomics as an\nenhancement to our recently proposed Deep Multi-task Survival model (DeepMTS).\nThe DeepMTS jointly learns to predict the survival risk scores of patients and\nthe segmentation masks of tumor regions. Radiomics features are extracted from\nthe predicted tumor regions and combined with the predicted survival risk\nscores for final outcome prediction, through which the prognostic information\nin tumor regions can be further leveraged. Our method achieved a C-index of\n0.681 on the testing set, placing the 2nd on the leaderboard with only 0.00068\nlower in C-index than the 1st place.\n", "date": "2022-11-10", "categories": []}
{"id": "2303.09440", "title": "Enhanced detection of the presence and severity of COVID-19 from CT\n  scans using lung segmentation", "abstract": "  Improving automated analysis of medical imaging will provide clinicians more\noptions in providing care for patients. The 2023 AI-enabled Medical Image\nAnalysis Workshop and Covid-19 Diagnosis Competition (AI-MIA-COV19D) provides\nan opportunity to test and refine machine learning methods for detecting the\npresence and severity of COVID-19 in patients from CT scans. This paper\npresents version 2 of Cov3d, a deep learning model submitted in the 2022\ncompetition. The model has been improved through a preprocessing step which\nsegments the lungs in the CT scan and crops the input to this region. It\nresults in a validation macro F1 score for predicting the presence of COVID-19\nin the CT scans at 93.2% which is significantly above the baseline of 74\\%. It\ngives a macro F1 score for predicting the severity of COVID-19 on the\nvalidation set for task 2 as 72.8% which is above the baseline of 38%.\n", "date": "2023-03-16", "categories": []}
{"id": "2303.10401", "title": "Smart ROI Detection for Alzheimer's disease prediction using explainable\n  AI", "abstract": "  Purpose Predicting the progression of MCI to Alzheimer's disease is an\nimportant step in reducing the progression of the disease. Therefore, many\nmethods have been introduced for this task based on deep learning. Among these\napproaches, the methods based on ROIs are in a good position in terms of\naccuracy and complexity. In these techniques, some specific parts of the brain\nare extracted as ROI manually for all of the patients. Extracting ROI manually\nis time-consuming and its results depend on human expertness and precision.\nMethod To overcome these limitations, we propose a novel smart method for\ndetecting ROIs automatically based on Explainable AI using Grad-Cam and a 3DCNN\nmodel that extracts ROIs per patient. After extracting the ROIs automatically,\nAlzheimer's disease is predicted using extracted ROI-based 3D CNN. Results We\nimplement our method on 176 MCI patients of the famous ADNI dataset and obtain\nremarkable results compared to the state-of-the-art methods. The accuracy\nacquired using 5-fold cross-validation is 98.6 and the AUC is 1. We also\ncompare the results of the ROI-based method with the whole brain-based method.\nThe results show that the performance is impressively increased. Conclusion The\nexperimental results show that the proposed smart ROI extraction, which\nextracts the ROIs automatically, performs well for Alzheimer's disease\nprediction. The proposed method can also be used for Alzheimer's disease\nclassification and diagnosis.\n", "date": "2023-03-18", "categories": []}
{"id": "2303.10607", "title": "Automatic pain recognition from Blood Volume Pulse (BVP) signal using\n  machine learning techniques", "abstract": "  Physiological responses to pain have received increasing attention among\nresearchers for developing an automated pain recognition sensing system. Though\nless explored, Blood Volume Pulse (BVP) is one of the candidate physiological\nmeasures that could help objective pain assessment. In this study, we applied\nmachine learning techniques on BVP signals to device a non-invasive modality\nfor pain sensing. Thirty-two healthy subjects participated in this study.\nFirst, we investigated a novel set of time-domain, frequency-domain and\nnonlinear dynamics features that could potentially be sensitive to pain. These\ninclude 24 features from BVP signals and 20 additional features from Inter-beat\nIntervals (IBIs) derived from the same BVP signals. Utilizing these features,\nwe built machine learning models for detecting the presence of pain and its\nintensity. We explored different machine learning models, including Logistic\nRegression, Random Forest, Support Vector Machines, Adaptive Boosting\n(AdaBoost) and Extreme Gradient Boosting (XGBoost). Among them, we found that\nthe XGBoost offered the best model performance for both pain classification and\npain intensity estimation tasks. The ROC-AUC of the XGBoost model to detect low\npain, medium pain and high pain with no pain as the baseline were 80.06 %,\n85.81 %, and 90.05 % respectively. Moreover, the XGboost classifier\ndistinguished medium pain from high pain with ROC-AUC of 91%. For the\nmulti-class classification among three pain levels, the XGBoost offered the\nbest performance with an average F1-score of 80.03%. Our results suggest that\nBVP signal together with machine learning algorithms is a promising\nphysiological measurement for automated pain assessment. This work will have a\nnational impact on accurate pain assessment, effective pain management,\nreducing drug-seeking behavior among patients, and addressing national opioid\ncrisis.\n", "date": "2023-03-19", "categories": []}
{"id": "2303.10730", "title": "Pretrained Vision Models for Predicting High-Risk Breast Cancer Stage", "abstract": "  Cancer is increasingly a global health issue. Seconding cardiovascular\ndiseases, cancers are the second biggest cause of death in the world with\nmillions of people succumbing to the disease every year. According to the World\nHealth Organization (WHO) report, by the end of 2020, more than 7.8 million\nwomen have been diagnosed with breast cancer, making it the world's most\nprevalent cancer. In this paper, using the Nightingale Open Science dataset of\ndigital pathology (breast biopsy) images, we leverage the capabilities of\npre-trained computer vision models for the breast cancer stage prediction task.\nWhile individual models achieve decent performances, we find out that the\npredictions of an ensemble model are more efficient, and offer a winning\nsolution\\footnote{https://www.nightingalescience.org/updates/hbc1-results}. We\nalso provide analyses of the results and explore pathways for better\ninterpretability and generalization. Our code is open-source at\n\\url{https://github.com/bonaventuredossou/nightingale_winning_solution}\n", "date": "2023-03-19", "categories": []}
{"id": "2303.10789", "title": "A hybrid CNN-RNN approach for survival analysis in a Lung Cancer\n  Screening study", "abstract": "  In this study, we present a hybrid CNN-RNN approach to investigate long-term\nsurvival of subjects in a lung cancer screening study. Subjects who died of\ncardiovascular and respiratory causes were identified whereby the CNN model was\nused to capture imaging features in the CT scans and the RNN model was used to\ninvestigate time series and thus global information. The models were trained on\nsubjects who underwent cardiovascular and respiratory deaths and a control\ncohort matched to participant age, gender, and smoking history. The combined\nmodel can achieve an AUC of 0.76 which outperforms humans at cardiovascular\nmortality prediction. The corresponding F1 and Matthews Correlation Coefficient\nare 0.63 and 0.42 respectively. The generalisability of the model is further\nvalidated on an 'external' cohort. The same models were applied to survival\nanalysis with the Cox Proportional Hazard model. It was demonstrated that\nincorporating the follow-up history can lead to improvement in survival\nprediction. The Cox neural network can achieve an IPCW C-index of 0.75 on the\ninternal dataset and 0.69 on an external dataset. Delineating imaging features\nassociated with long-term survival can help focus preventative interventions\nappropriately, particularly for under-recognised pathologies thereby\npotentially reducing patient morbidity.\n", "date": "2023-03-19", "categories": []}
{"id": "2303.11042", "title": "Hospitalization Length of Stay Prediction using Patient Event Sequences", "abstract": "  Predicting patients hospital length of stay (LOS) is essential for improving\nresource allocation and supporting decision-making in healthcare organizations.\nThis paper proposes a novel approach for predicting LOS by modeling patient\ninformation as sequences of events. Specifically, we present a\ntransformer-based model, termed Medic-BERT (M-BERT), for LOS prediction using\nthe unique features describing patients medical event sequences. We performed\nempirical experiments on a cohort of more than 45k emergency care patients from\na large Danish hospital. Experimental results show that M-BERT can achieve high\naccuracy on a variety of LOS problems and outperforms traditional\nnonsequence-based machine learning approaches.\n", "date": "2023-03-20", "categories": []}
{"id": "2303.11130", "title": "Deep learning automated quantification of lung disease in pulmonary\n  hypertension on CT pulmonary angiography: A preliminary clinical study with\n  external validation", "abstract": "  Purpose: Lung disease assessment in precapillary pulmonary hypertension (PH)\nis essential for appropriate patient management. This study aims to develop an\nartificial intelligence (AI) deep learning model for lung texture\nclassification in CT Pulmonary Angiography (CTPA), and evaluate its correlation\nwith clinical assessment methods.\n  Materials and Methods: In this retrospective study with external validation,\n122 patients with pre-capillary PH were used to train (n=83), validate (n=17)\nand test (n=10 internal test, n=12 external test) a patch based DenseNet-121\nclassification model. \"Normal\", \"Ground glass\", \"Ground glass with\nreticulation\", \"Honeycombing\", and \"Emphysema\" were classified as per the\nFleishner Society glossary of terms. Ground truth classes were segmented by two\nradiologists with patches extracted from the labelled regions. Proportion of\nlung volume for each texture was calculated by classifying patches throughout\nthe entire lung volume to generate a coarse texture classification mapping\nthroughout the lung parenchyma. AI output was assessed against diffusing\ncapacity of carbon monoxide (DLCO) and specialist radiologist reported disease\nseverity.\n  Results: Micro-average AUCs for the validation, internal test, and external\ntest were 0.92, 0.95, and 0.94, respectively. The model had consistent\nperformance across parenchymal textures, demonstrated strong correlation with\ndiffusing capacity of carbon monoxide (DLCO), and showed good correspondence\nwith disease severity reported by specialist radiologists.\n  Conclusion: The classification model demonstrates excellent performance on\nexternal validation. The clinical utility of its output has been demonstrated.\nThis objective, repeatable measure of disease severity can aid in patient\nmanagement in adjunct to radiological reporting.\n", "date": "2023-03-20", "categories": []}
{"id": "2303.02075", "title": "Adaptive Interventions for Global Health: A Case Study of Malaria", "abstract": "  Malaria can be prevented, diagnosed, and treated; however, every year, there\nare more than 200 million cases and 200.000 preventable deaths. Malaria remains\na pressing public health concern in low- and middle-income countries,\nespecially in sub-Saharan Africa. We describe how by means of mobile health\napplications, machine-learning-based adaptive interventions can strengthen\nmalaria surveillance and treatment adherence, increase testing, measure\nprovider skills and quality of care, improve public health by supporting\nfront-line workers and patients (e.g., by capacity building and encouraging\nbehavioral changes, like using bed nets), reduce test stockouts in pharmacies\nand clinics and informing public health for policy intervention.\n", "date": "2023-03-03", "categories": []}
{"id": "2303.09913", "title": "Short: Basal-Adjust: Trend Prediction Alerts and Adjusted Basal Rates\n  for Hyperglycemia Prevention", "abstract": "  Significant advancements in type 1 diabetes treatment have been made in the\ndevelopment of state-of-the-art Artificial Pancreas Systems (APS). However,\nlapses currently exist in the timely treatment of unsafe blood glucose (BG)\nlevels, especially in the case of rebound hyperglycemia. We propose a machine\nlearning (ML) method for predictive BG scenario categorization that outputs\nmessages alerting the patient to upcoming BG trends to allow for earlier,\neducated treatment. In addition to standard notifications of predicted\nhypoglycemia and hyperglycemia, we introduce BG scenario-specific alert\nmessages and the preliminary steps toward precise basal suggestions for the\nprevention of rebound hyperglycemia. Experimental evaluation on the DCLP3\nclinical dataset achieves >98% accuracy and >79% precision for predicting\nrebound high events for patient alerts.\n", "date": "2023-03-16", "categories": []}
{"id": "2303.08273", "title": "Towards a Deep Learning Pain-Level Detection Deployment at UAE for\n  Patient-Centric-Pain Management and Diagnosis Support: Framework and\n  Performance Evaluation", "abstract": "  The outbreak of the COVID-19 pandemic revealed the criticality of timely\nintervention in a situation exacerbated by a shortage in medical staff and\nequipment. Pain-level screening is the initial step toward identifying the\nseverity of patient conditions. Automatic recognition of state and feelings\nhelp in identifying patient symptoms to take immediate adequate action and\nproviding a patient-centric medical plan tailored to a patient's state. In this\npaper, we propose a framework for pain-level detection for deployment in the\nUnited Arab Emirates and assess its performance using the most used approaches\nin the literature. Our results show that a deployment of a pain-level deep\nlearning detection framework is promising in identifying the pain level\naccurately.\n", "date": "2023-03-14", "categories": []}
{"id": "2303.08448", "title": "A Cross-institutional Evaluation on Breast Cancer Phenotyping NLP\n  Algorithms on Electronic Health Records", "abstract": "  Objective: The generalizability of clinical large language models is usually\nignored during the model development process. This study evaluated the\ngeneralizability of BERT-based clinical NLP models across different clinical\nsettings through a breast cancer phenotype extraction task.\n  Materials and Methods: Two clinical corpora of breast cancer patients were\ncollected from the electronic health records from the University of Minnesota\nand the Mayo Clinic, and annotated following the same guideline. We developed\nthree types of NLP models (i.e., conditional random field, bi-directional long\nshort-term memory and CancerBERT) to extract cancer phenotypes from clinical\ntexts. The models were evaluated for their generalizability on different test\nsets with different learning strategies (model transfer vs. locally trained).\nThe entity coverage score was assessed with their association with the model\nperformances.\n  Results: We manually annotated 200 and 161 clinical documents at UMN and MC,\nrespectively. The corpora of the two institutes were found to have higher\nsimilarity between the target entities than the overall corpora. The CancerBERT\nmodels obtained the best performances among the independent test sets from two\nclinical institutes and the permutation test set. The CancerBERT model\ndeveloped in one institute and further fine-tuned in another institute achieved\nreasonable performance compared to the model developed on local data (micro-F1:\n0.925 vs 0.932).\n  Conclusions: The results indicate the CancerBERT model has the best learning\nability and generalizability among the three types of clinical NLP models. The\ngeneralizability of the models was found to be correlated with the similarity\nof the target entities between the corpora.\n", "date": "2023-03-15", "categories": []}
{"id": "2303.07125", "title": "Don't PANIC: Prototypical Additive Neural Network for Interpretable\n  Classification of Alzheimer's Disease", "abstract": "  Alzheimer's disease (AD) has a complex and multifactorial etiology, which\nrequires integrating information about neuroanatomy, genetics, and\ncerebrospinal fluid biomarkers for accurate diagnosis. Hence, recent deep\nlearning approaches combined image and tabular information to improve\ndiagnostic performance. However, the black-box nature of such neural networks\nis still a barrier for clinical applications, in which understanding the\ndecision of a heterogeneous model is integral. We propose PANIC, a prototypical\nadditive neural network for interpretable AD classification that integrates 3D\nimage and tabular data. It is interpretable by design and, thus, avoids the\nneed for post-hoc explanations that try to approximate the decision of a\nnetwork. Our results demonstrate that PANIC achieves state-of-the-art\nperformance in AD classification, while directly providing local and global\nexplanations. Finally, we show that PANIC extracts biologically meaningful\nsignatures of AD, and satisfies a set of desirable desiderata for trustworthy\nmachine learning. Our implementation is available at\nhttps://github.com/ai-med/PANIC .\n", "date": "2023-03-13", "categories": []}
{"id": "2303.07537", "title": "Fractional dynamics foster deep learning of COPD stage prediction", "abstract": "  Chronic obstructive pulmonary disease (COPD) is one of the leading causes of\ndeath worldwide. Current COPD diagnosis (i.e., spirometry) could be unreliable\nbecause the test depends on an adequate effort from the tester and testee.\nMoreover, the early diagnosis of COPD is challenging. We address COPD detection\nby constructing two novel physiological signals datasets (4432 records from 54\npatients in the WestRo COPD dataset and 13824 medical records from 534 patients\nin the WestRo Porti COPD dataset). The authors demonstrate their complex\ncoupled fractal dynamical characteristics and perform a fractional-order\ndynamics deep learning analysis to diagnose COPD. The authors found that the\nfractional-order dynamical modeling can extract distinguishing signatures from\nthe physiological signals across patients with all COPD stages from stage 0\n(healthy) to stage 4 (very severe). They use the fractional signatures to\ndevelop and train a deep neural network that predicts COPD stages based on the\ninput features (such as thorax breathing effort, respiratory rate, or oxygen\nsaturation). The authors show that the fractional dynamic deep learning model\n(FDDLM) achieves a COPD prediction accuracy of 98.66% and can serve as a robust\nalternative to spirometry. The FDDLM also has high accuracy when validated on a\ndataset with different physiological signals.\n", "date": "2023-03-13", "categories": []}
{"id": "2303.07847", "title": "Transfer Learning for Real-time Deployment of a Screening Tool for\n  Depression Detection Using Actigraphy", "abstract": "  Automated depression screening and diagnosis is a highly relevant problem\ntoday. There are a number of limitations of the traditional depression\ndetection methods, namely, high dependence on clinicians and biased\nself-reporting. In recent years, research has suggested strong potential in\nmachine learning (ML) based methods that make use of the user's passive data\ncollected via wearable devices. However, ML is data hungry. Especially in the\nhealthcare domain primary data collection is challenging. In this work, we\npresent an approach based on transfer learning, from a model trained on a\nsecondary dataset, for the real time deployment of the depression screening\ntool based on the actigraphy data of users. This approach enables machine\nlearning modelling even with limited primary data samples. A modified version\nof leave one out cross validation approach performed on the primary set\nresulted in mean accuracy of 0.96, where in each iteration one subject's data\nfrom the primary set was set aside for testing.\n", "date": "2023-03-14", "categories": []}
{"id": "2209.11173", "title": "U-Sleep's resilience to AASM guidelines", "abstract": "  AASM guidelines are the result of decades of efforts aiming at standardizing\nsleep scoring procedure, with the final goal of sharing a worldwide common\nmethodology. The guidelines cover several aspects from the technical/digital\nspecifications,e.g., recommended EEG derivations, to detailed sleep scoring\nrules accordingly to age. Automated sleep scoring systems have always largely\nexploited the standards as fundamental guidelines. In this context, deep\nlearning has demonstrated better performance compared to classical machine\nlearning. Our present work shows that a deep learning based sleep scoring\nalgorithm may not need to fully exploit the clinical knowledge or to strictly\nadhere to the AASM guidelines. Specifically, we demonstrate that U-Sleep, a\nstate-of-the-art sleep scoring algorithm, can be strong enough to solve the\nscoring task even using clinically non-recommended or non-conventional\nderivations, and with no need to exploit information about the chronological\nage of the subjects. We finally strengthen a well-known finding that using data\nfrom multiple data centers always results in a better performing model compared\nwith training on a single cohort. Indeed, we show that this latter statement is\nstill valid even by increasing the size and the heterogeneity of the single\ndata cohort. In all our experiments we used 28528 polysomnography studies from\n13 different clinical studies.\n", "date": "2022-09-19", "categories": []}
{"id": "2303.06253", "title": "Predicting risk of delirium from ambient noise and light information in\n  the ICU", "abstract": "  Existing Intensive Care Unit (ICU) delirium prediction models do not consider\nenvironmental factors despite strong evidence of their influence on delirium.\nThis study reports the first deep-learning based delirium prediction model for\nICU patients using only ambient noise and light information. Ambient light and\nnoise intensities were measured from ICU rooms of 102 patients from May 2021 to\nSeptember 2022 using Thunderboard, ActiGraph sensors and an iPod with\nAudioTools application. These measurements were divided into daytime (0700 to\n1859) and nighttime (1900 to 0659). Deep learning models were trained using\nthis data to predict the incidence of delirium during ICU stay or within 4 days\nof discharge. Finally, outcome scores were analyzed to evaluate the importance\nand directionality of every feature. Daytime noise levels were significantly\nhigher than nighttime noise levels. When using only noise features or a\ncombination of noise and light features 1-D convolutional neural networks (CNN)\nachieved the strongest performance: AUC=0.77, 0.74; Sensitivity=0.60, 0.56;\nSpecificity=0.74, 0.74; Precision=0.46, 0.40 respectively. Using only light\nfeatures, Long Short-Term Memory (LSTM) networks performed best: AUC=0.80,\nSensitivity=0.60, Specificity=0.77, Precision=0.37. Maximum nighttime and\nminimum daytime noise levels were the strongest positive and negative\npredictors of delirium respectively. Nighttime light level was a stronger\npredictor of delirium than daytime light level. Total influence of light\nfeatures outweighed that of noise features on the second and fourth day of ICU\nstay. This study shows that ambient light and noise intensities are strong\npredictors of long-term delirium incidence in the ICU. It reveals that daytime\nand nighttime environmental factors might influence delirium differently and\nthat the importance of light and noise levels vary over the course of an ICU\nstay.\n", "date": "2023-03-11", "categories": []}
{"id": "2303.06269", "title": "DEPLOYR: A technical framework for deploying custom real-time machine\n  learning models into the electronic medical record", "abstract": "  Machine learning (ML) applications in healthcare are extensively researched,\nbut successful translations to the bedside are scant. Healthcare institutions\nare establishing frameworks to govern and promote the implementation of\naccurate, actionable and reliable models that integrate with clinical workflow.\nSuch governance frameworks require an accompanying technical framework to\ndeploy models in a resource efficient manner. Here we present DEPLOYR, a\ntechnical framework for enabling real-time deployment and monitoring of\nresearcher created clinical ML models into a widely used electronic medical\nrecord (EMR) system. We discuss core functionality and design decisions,\nincluding mechanisms to trigger inference based on actions within EMR software,\nmodules that collect real-time data to make inferences, mechanisms that\nclose-the-loop by displaying inferences back to end-users within their\nworkflow, monitoring modules that track performance of deployed models over\ntime, silent deployment capabilities, and mechanisms to prospectively evaluate\na deployed model's impact. We demonstrate the use of DEPLOYR by silently\ndeploying and prospectively evaluating twelve ML models triggered by clinician\nbutton-clicks in Stanford Health Care's production instance of Epic. Our study\nhighlights the need and feasibility for such silent deployment, because\nprospectively measured performance varies from retrospective estimates. By\ndescribing DEPLOYR, we aim to inform ML deployment best practices and help\nbridge the model implementation gap.\n", "date": "2023-03-11", "categories": []}
{"id": "2303.06376", "title": "Assessing gender fairness in EEG-based machine learning detection of\n  Parkinson's disease: A multi-center study", "abstract": "  As the number of automatic tools based on machine learning (ML) and\nresting-state electroencephalography (rs-EEG) for Parkinson's disease (PD)\ndetection keeps growing, the assessment of possible exacerbation of health\ndisparities by means of fairness and bias analysis becomes more relevant.\nProtected attributes, such as gender, play an important role in PD diagnosis\ndevelopment. However, analysis of sub-group populations stemming from different\ngenders is seldom taken into consideration in ML models' development or the\nperformance assessment for PD detection. In this work, we perform a systematic\nanalysis of the detection ability for gender sub-groups in a multi-center\nsetting of a previously developed ML algorithm based on power spectral density\n(PSD) features of rs-EEG. We find significant differences in the PD detection\nability for males and females at testing time (80.5% vs. 63.7% accuracy) and\nsignificantly higher activity for a set of parietal and frontal EEG channels\nand frequency sub-bands for PD and non-PD males that might explain the\ndifferences in the PD detection ability for the gender sub-groups.\n", "date": "2023-03-11", "categories": []}
{"id": "2303.06868", "title": "Deep Learning-based Eye-Tracking Analysis for Diagnosis of Alzheimer's\n  Disease Using 3D Comprehensive Visual Stimuli", "abstract": "  Alzheimer's Disease (AD) causes a continuous decline in memory, thinking, and\njudgment. Traditional diagnoses are usually based on clinical experience, which\nis limited by some realistic factors. In this paper, we focus on exploiting\ndeep learning techniques to diagnose AD based on eye-tracking behaviors. Visual\nattention, as typical eye-tracking behavior, is of great clinical value to\ndetect cognitive abnormalities in AD patients. To better analyze the\ndifferences in visual attention between AD patients and normals, we first\nconduct a 3D comprehensive visual task on a non-invasive eye-tracking system to\ncollect visual attention heatmaps. We then propose a multi-layered comparison\nconvolution neural network (MC-CNN) to distinguish the visual attention\ndifferences between AD patients and normals. In MC-CNN, the multi-layered\nrepresentations of heatmaps are obtained by hierarchical convolution to better\nencode eye-movement behaviors, which are further integrated into a distance\nvector to benefit the comprehensive visual task. Extensive experimental results\non the collected dataset demonstrate that MC-CNN achieves consistent validity\nin classifying AD patients and normals with eye-tracking data.\n", "date": "2023-03-13", "categories": []}
{"id": "2303.07305", "title": "Transformer Models for Acute Brain Dysfunction Prediction", "abstract": "  Acute brain dysfunctions (ABD), which include coma and delirium, are\nprevalent in the ICU, especially among older patients. The current approach in\nmanual assessment of ABD by care providers may be sporadic and subjective.\nHence, there exists a need for a data-driven robust system automating the\nassessment and prediction of ABD. In this work, we develop a machine learning\nsystem for real-time prediction of ADB using Electronic Health Record (HER)\ndata. Our data processing pipeline enables integration of static and temporal\ndata, and extraction of features relevant to ABD. We train several\nstate-of-the-art transformer models and baseline machine learning models\nincluding CatBoost and XGB on the data that was collected from patients\nadmitted to the ICU at UF Shands Hospital. We demonstrate the efficacy of our\nsystem for tasks related to acute brain dysfunction including binary\nclassification of brain acuity and multi-class classification (i.e., coma,\ndelirium, death, or normal), achieving a mean AUROC of 0.953 on our Long-former\nimplementation. Our system can then be deployed for real-time prediction of ADB\nin ICUs to reduce the number of incidents caused by ABD. Moreover, the\nreal-time system has the potential to reduce costs, duration of patients stays\nin the ICU, and mortality among those afflicted.\n", "date": "2023-03-13", "categories": []}
{"id": "2303.05752", "title": "Deep Learning for Predicting Metastasis on Melanoma WSIs", "abstract": "  Northern Europe has the second highest mortality rate of melanoma globally.\nIn 2020, the mortality rate of melanoma rose to 1.9 per 100 000 habitants.\nMelanoma prognosis is based on a pathologist's subjective visual analysis of\nthe patient's tumor. This methodology is heavily time-consuming, and the\nprognosis variability among experts is notable, drastically jeopardizing its\nreproducibility. Thus, the need for faster and more reproducible methods\narises. Machine learning has paved its way into digital pathology, but so far,\nmost contributions are on localization, segmentation, and diagnostics, with\nlittle emphasis on prognostics. This paper presents a convolutional neural\nnetwork (CNN) method based on VGG16 to predict melanoma prognosis as the\npresence of metastasis within five years. Patches are extracted from regions of\ninterest from Whole Slide Images (WSIs) at different magnification levels used\nin model training and validation. Results infer that utilizing WSI patches at\n20x magnification level has the best performance, with an F1 score of 0.7667\nand an AUC of 0.81.\n", "date": "2023-03-10", "categories": []}
{"id": "2303.05972", "title": "Classifying the evolution of COVID-19 severity on patients with combined\n  dynamic Bayesian networks and neural networks", "abstract": "  When we face patients arriving to a hospital suffering from the effects of\nsome illness, one of the main problems we can encounter is evaluating whether\nor not said patients are going to require intensive care in the near future.\nThis intensive care requires allotting valuable and scarce resources, and\nknowing beforehand the severity of a patients illness can improve both its\ntreatment and the organization of resources. We illustrate this issue in a\ndataset consistent of Spanish COVID-19 patients from the sixth epidemic wave\nwhere we label patients as critical when they either had to enter the intensive\ncare unit or passed away. We then combine the use of dynamic Bayesian networks,\nto forecast the vital signs and the blood analysis results of patients over the\nnext 40 hours, and neural networks, to evaluate the severity of a patients\ndisease in that interval of time. Our empirical results show that the\ntransposition of the current state of a patient to future values with the DBN\nfor its subsequent use in classification obtains better the accuracy and g-mean\nscore than a direct application with a classifier.\n", "date": "2023-03-10", "categories": []}
{"id": "2303.06052", "title": "Analysis and Evaluation of Explainable Artificial Intelligence on\n  Suicide Risk Assessment", "abstract": "  This study investigates the effectiveness of Explainable Artificial\nIntelligence (XAI) techniques in predicting suicide risks and identifying the\ndominant causes for such behaviours. Data augmentation techniques and ML models\nare utilized to predict the associated risk. Furthermore, SHapley Additive\nexPlanations (SHAP) and correlation analysis are used to rank the importance of\nvariables in predictions. Experimental results indicate that Decision Tree\n(DT), Random Forest (RF) and eXtreme Gradient Boosting (XGBoost) models achieve\nthe best results while DT has the best performance with an accuracy of 95:23%\nand an Area Under Curve (AUC) of 0.95. As per SHAP results, anger problems,\ndepression, and social isolation are the leading variables in predicting the\nrisk of suicide, and patients with good incomes, respected occupations, and\nuniversity education have the least risk. Results demonstrate the effectiveness\nof machine learning and XAI framework for suicide risk prediction, and they can\nassist psychiatrists in understanding complex human behaviours and can also\nassist in reliable clinical decision-making.\n", "date": "2023-03-09", "categories": []}
{"id": "2303.05504", "title": "Computable Phenotypes to Characterize Changing Patient Brain Dysfunction\n  in the Intensive Care Unit", "abstract": "  In the United States, more than 5 million patients are admitted annually to\nICUs, with ICU mortality of 10%-29% and costs over $82 billion. Acute brain\ndysfunction status, delirium, is often underdiagnosed or undervalued. This\nstudy's objective was to develop automated computable phenotypes for acute\nbrain dysfunction states and describe transitions among brain dysfunction\nstates to illustrate the clinical trajectories of ICU patients. We created two\nsingle-center, longitudinal EHR datasets for 48,817 adult patients admitted to\nan ICU at UFH Gainesville (GNV) and Jacksonville (JAX). We developed algorithms\nto quantify acute brain dysfunction status including coma, delirium, normal, or\ndeath at 12-hour intervals of each ICU admission and to identify acute brain\ndysfunction phenotypes using continuous acute brain dysfunction status and\nk-means clustering approach. There were 49,770 admissions for 37,835 patients\nin UFH GNV dataset and 18,472 admissions for 10,982 patients in UFH JAX\ndataset. In total, 18% of patients had coma as the worst brain dysfunction\nstatus; every 12 hours, around 4%-7% would transit to delirium, 22%-25% would\nrecover, 3%-4% would expire, and 67%-68% would remain in a coma in the ICU.\nAdditionally, 7% of patients had delirium as the worst brain dysfunction\nstatus; around 6%-7% would transit to coma, 40%-42% would be no delirium, 1%\nwould expire, and 51%-52% would remain delirium in the ICU. There were three\nphenotypes: persistent coma/delirium, persistently normal, and transition from\ncoma/delirium to normal almost exclusively in first 48 hours after ICU\nadmission. We developed phenotyping scoring algorithms that determined acute\nbrain dysfunction status every 12 hours while admitted to the ICU. This\napproach may be useful in developing prognostic and decision-support tools to\naid patients and clinicians in decision-making on resource use and escalation\nof care.\n", "date": "2023-03-09", "categories": []}
{"id": "2302.11231", "title": "Drugs Resistance Analysis from Scarce Health Records via Multi-task\n  Graph Representation", "abstract": "  Clinicians prescribe antibiotics by looking at the patient's health record\nwith an experienced eye. However, the therapy might be rendered futile if the\npatient has drug resistance. Determining drug resistance requires\ntime-consuming laboratory-level testing while applying clinicians' heuristics\nin an automated way is difficult due to the categorical or binary medical\nevents that constitute health records. In this paper, we propose a novel\nframework for rapid clinical intervention by viewing health records as graphs\nwhose nodes are mapped from medical events and edges as correspondence between\nevents in given a time window. A novel graph-based model is then proposed to\nextract informative features and yield automated drug resistance analysis from\nthose high-dimensional and scarce graphs. The proposed method integrates\nmulti-task learning into a common feature extracting graph encoder for\nsimultaneous analyses of multiple drugs as well as stabilizing learning. On a\nmassive dataset comprising over 110,000 patients with urinary tract infections,\nwe verify the proposed method is capable of attaining superior performance on\nthe drug resistance prediction problem. Furthermore, automated drug\nrecommendations resemblant to laboratory-level testing can also be made based\non the model resistance analysis.\n", "date": "2023-02-22", "categories": []}
{"id": "2302.12856", "title": "Machine Learning based prediction of Glucose Levels in Type 1 Diabetes\n  Patients with the use of Continuous Glucose Monitoring Data", "abstract": "  A task of vital clinical importance, within Diabetes management, is the\nprevention of hypo/hyperglycemic events. Increasingly adopted Continuous\nGlucose Monitoring (CGM) devices offer detailed, non-intrusive and real time\ninsights into a patient's blood glucose concentrations. Leveraging advanced\nMachine Learning (ML) Models as methods of prediction of future glucose levels,\ngives rise to substantial quality of life improvements, as well as providing a\nvital tool for monitoring diabetes.\n  A regression based prediction approach is implemented recursively, with a\nseries of Machine Learning Models: Linear Regression, Hidden Markov Model,\nLong-Short Term Memory Network. By exploiting a patient's past 11 hours of\nblood glucose (BG) concentration measurements, a prediction of the 60 minutes\nis made. Results will be assessed using performance metrics including: Root\nMean Squared Error (RMSE), normalised energy of the second-order differences\n(ESOD) and F1 score.\n  Research of past and current approaches, as well as available dataset, led to\nthe establishment of an optimal training methodology for the CITY dataset,\nwhich may be leveraged by future model development. Performance was aligned\nwith similar state-of-art ML models, with LSTM having RMSE of 28.55, however no\nsignificant advantage was observed over classical Auto-regressive AR models.\nCompelling insights into LSTM prediction behaviour could increase public and\nlegislative trust and understanding, progressing the certification of ML models\nin Artificial Pancreas Systems (APS).\n", "date": "2023-02-24", "categories": []}
{"id": "2209.04526", "title": "Gluformer: Transformer-Based Personalized Glucose Forecasting with\n  Uncertainty Quantification", "abstract": "  Deep learning models achieve state-of-the art results in predicting blood\nglucose trajectories, with a wide range of architectures being proposed.\nHowever, the adaptation of such models in clinical practice is slow, largely\ndue to the lack of uncertainty quantification of provided predictions. In this\nwork, we propose to model the future glucose trajectory conditioned on the past\nas an infinite mixture of basis distributions (i.e., Gaussian, Laplace, etc.).\nThis change allows us to learn the uncertainty and predict more accurately in\nthe cases when the trajectory has a heterogeneous or multi-modal distribution.\nTo estimate the parameters of the predictive distribution, we utilize the\nTransformer architecture. We empirically demonstrate the superiority of our\nmethod over existing state-of-the-art techniques both in terms of accuracy and\nuncertainty on the synthetic and benchmark glucose data sets.\n", "date": "2022-09-09", "categories": []}
{"id": "2303.03511", "title": "Applying Artificial Intelligence to Clinical Decision Support in Mental\n  Health: What Have We Learned?", "abstract": "  Clinical decision support systems (CDSS) augmented with artificial\nintelligence (AI) models are emerging as potentially valuable tools in\nhealthcare. Despite their promise, the development and implementation of these\nsystems typically encounter several barriers, hindering the potential for\nwidespread adoption. Here we present a case study of a recently developed\nAI-CDSS, Aifred Health, aimed at supporting the selection and management of\ntreatment in major depressive disorder. We consider both the principles\nespoused during development and testing of this AI-CDSS, as well as the\npractical solutions developed to facilitate implementation. We also propose\nrecommendations to consider throughout the building, validation, training, and\nimplementation process of an AI-CDSS. These recommendations include:\nidentifying the key problem, selecting the type of machine learning approach\nbased on this problem, determining the type of data required, determining the\nformat required for a CDSS to provide clinical utility, gathering physician and\npatient feedback, and validating the tool across multiple settings. Finally, we\nexplore the potential benefits of widespread adoption of these systems, while\nbalancing these against implementation challenges such as ensuring systems do\nnot disrupt the clinical workflow, and designing systems in a manner that\nengenders trust on the part of end users.\n", "date": "2023-03-06", "categories": []}
{"id": "2303.03577", "title": "A Review of and Roadmap for Data Science and Machine Learning for the\n  Neuropsychiatric Phenotype of Autism", "abstract": "  Autism Spectrum Disorder (autism) is a neurodevelopmental delay which affects\nat least 1 in 44 children. Like many neurological disorder phenotypes, the\ndiagnostic features are observable, can be tracked over time, and can be\nmanaged or even eliminated through proper therapy and treatments. Yet, there\nare major bottlenecks in the diagnostic, therapeutic, and longitudinal tracking\npipelines for autism and related delays, creating an opportunity for novel data\nscience solutions to augment and transform existing workflows and provide\naccess to services for more affected families. Several prior efforts conducted\nby a multitude of research labs have spawned great progress towards improved\ndigital diagnostics and digital therapies for children with autism. We review\nthe literature of digital health methods for autism behavior quantification\nusing data science. We describe both case-control studies and classification\nsystems for digital phenotyping. We then discuss digital diagnostics and\ntherapeutics which integrate machine learning models of autism-related\nbehaviors, including the factors which must be addressed for translational use.\nFinally, we describe ongoing challenges and potent opportunities for the field\nof autism data science. Given the heterogeneous nature of autism and the\ncomplexities of the relevant behaviors, this review contains insights which are\nrelevant to neurological behavior analysis and digital psychiatry more broadly.\n", "date": "2023-03-07", "categories": []}
{"id": "2303.02405", "title": "Decision Support System for Chronic Diseases Based on Drug-Drug\n  Interactions", "abstract": "  Many patients with chronic diseases resort to multiple medications to relieve\nvarious symptoms, which raises concerns about the safety of multiple medication\nuse, as severe drug-drug antagonism can lead to serious adverse effects or even\ndeath. This paper presents a Decision Support System, called DSSDDI, based on\ndrug-drug interactions to support doctors prescribing decisions. DSSDDI\ncontains three modules, Drug-Drug Interaction (DDI) module, Medical Decision\n(MD) module and Medical Support (MS) module. The DDI module learns safer and\nmore effective drug representations from the drug-drug interactions. To capture\nthe potential causal relationship between DDI and medication use, the MD module\nconsiders the representations of patients and drugs as context, DDI and\npatients' similarity as treatment, and medication use as outcome to construct\ncounterfactual links for the representation learning. Furthermore, the MS\nmodule provides drug candidates to doctors with explanations. Experiments on\nthe chronic data collected from the Hong Kong Chronic Disease Study Project and\na public diagnostic data MIMIC-III demonstrate that DSSDDI can be a reliable\nreference for doctors in terms of safety and efficiency of clinical diagnosis,\nwith significant improvements compared to baseline methods.\n", "date": "2023-03-04", "categories": []}
{"id": "2303.02570", "title": "Time Associated Meta Learning for Clinical Prediction", "abstract": "  Rich Electronic Health Records (EHR), have created opportunities to improve\nclinical processes using machine learning methods. Prediction of the same\npatient events at different time horizons can have very different applications\nand interpretations; however, limited number of events in each potential time\nwindow hurts the effectiveness of conventional machine learning algorithms. We\npropose a novel time associated meta learning (TAML) method to make effective\npredictions at multiple future time points. We view time-associated disease\nprediction as classification tasks at multiple time points. Such\nclosely-related classification tasks are an excellent candidate for model-based\nmeta learning. To address the sparsity problem after task splitting, TAML\nemploys a temporal information sharing strategy to augment the number of\npositive samples and include the prediction of related phenotypes or events in\nthe meta-training phase. We demonstrate the effectiveness of TAML on multiple\nclinical datasets, where it consistently outperforms a range of strong\nbaselines. We also develop a MetaEHR package for implementing both\ntime-associated and time-independent few-shot prediction on EHR data.\n", "date": "2023-03-05", "categories": []}
{"id": "2303.01949", "title": "Artificial Intelligence for Dementia Research Methods Optimization", "abstract": "  Introduction: Machine learning (ML) has been extremely successful in\nidentifying key features from high-dimensional datasets and executing\ncomplicated tasks with human expert levels of accuracy or greater. Methods: We\nsummarize and critically evaluate current applications of ML in dementia\nresearch and highlight directions for future research. Results: We present an\noverview of ML algorithms most frequently used in dementia research and\nhighlight future opportunities for the use of ML in clinical practice,\nexperimental medicine, and clinical trials. We discuss issues of\nreproducibility, replicability and interpretability and how these impact the\nclinical applicability of dementia research. Finally, we give examples of how\nstate-of-the-art methods, such as transfer learning, multi-task learning, and\nreinforcement learning, may be applied to overcome these issues and aid the\ntranslation of research to clinical practice in the future. Discussion:\nML-based models hold great promise to advance our understanding of the\nunderlying causes and pathological mechanisms of dementia.\n", "date": "2023-03-02", "categories": []}
{"id": "2212.08570", "title": "Audio-based AI classifiers show no evidence of improved COVID-19\n  screening over simple symptoms checkers", "abstract": "  Recent work has reported that AI classifiers trained on audio recordings can\naccurately predict severe acute respiratory syndrome coronavirus 2 (SARSCoV2)\ninfection status. Here, we undertake a large scale study of audio-based deep\nlearning classifiers, as part of the UK governments pandemic response. We\ncollect and analyse a dataset of audio recordings from 67,842 individuals with\nlinked metadata, including reverse transcription polymerase chain reaction\n(PCR) test outcomes, of whom 23,514 tested positive for SARS CoV 2. Subjects\nwere recruited via the UK governments National Health Service Test-and-Trace\nprogramme and the REal-time Assessment of Community Transmission (REACT)\nrandomised surveillance survey. In an unadjusted analysis of our dataset AI\nclassifiers predict SARS-CoV-2 infection status with high accuracy (Receiver\nOperating Characteristic Area Under the Curve (ROCAUC) 0.846 [0.838, 0.854])\nconsistent with the findings of previous studies. However, after matching on\nmeasured confounders, such as age, gender, and self reported symptoms, our\nclassifiers performance is much weaker (ROC-AUC 0.619 [0.594, 0.644]). Upon\nquantifying the utility of audio based classifiers in practical settings, we\nfind them to be outperformed by simple predictive scores based on user reported\nsymptoms.\n", "date": "2022-12-15", "categories": []}
{"id": "2303.01389", "title": "Machine Learning-Based Detection of Parkinson's Disease From\n  Resting-State EEG: A Multi-Center Study", "abstract": "  Resting-state EEG (rs-EEG) has been demonstrated to aid in Parkinson's\ndisease (PD) diagnosis. In particular, the power spectral density (PSD) of\nlow-frequency bands ({\\delta} and {\\theta}) and high-frequency bands ({\\alpha}\nand \\b{eta}) has been shown to be significantly different in patients with PD\nas compared to subjects without PD (non-PD). However, rs-EEG feature extraction\nand the interpretation thereof can be time-intensive and prone to examiner\nvariability. Machine learning (ML) has the potential to automatize the analysis\nof rs-EEG recordings and provides a supportive tool for clinicians to ease\ntheir workload. In this work, we use rs-EEG recordings of 84 PD and 85 non-PD\nsubjects pooled from four datasets obtained at different centers. We propose an\nend-to-end pipeline consisting of preprocessing, extraction of PSD features\nfrom clinically validated frequency bands, and feature selection before\nevaluating the classification ability of the features via ML algorithms to\nstratify between PD and non-PD subjects. Further, we evaluate the effect of\nfeature harmonization, given the multi-center nature of the datasets. Our\nvalidation results show, on average, an improvement in PD detection ability\n(69.6% vs. 75.5% accuracy) by logistic regression when harmonizing the features\nand performing univariate feature selection (k = 202 features). Our final\nresults show an average global accuracy of 72.2% with balanced accuracy results\nfor all the centers included in the study: 60.6%, 68.7%, 77.7%, and 82.2%,\nrespectively.\n", "date": "2023-03-02", "categories": []}
{"id": "2212.08571", "title": "Statistical Design and Analysis for Robust Machine Learning: A Case\n  Study from COVID-19", "abstract": "  Since early in the coronavirus disease 2019 (COVID-19) pandemic, there has\nbeen interest in using artificial intelligence methods to predict COVID-19\ninfection status based on vocal audio signals, for example cough recordings.\nHowever, existing studies have limitations in terms of data collection and of\nthe assessment of the performances of the proposed predictive models. This\npaper rigorously assesses state-of-the-art machine learning techniques used to\npredict COVID-19 infection status based on vocal audio signals, using a dataset\ncollected by the UK Health Security Agency. This dataset includes acoustic\nrecordings and extensive study participant meta-data. We provide guidelines on\ntesting the performance of methods to classify COVID-19 infection status based\non acoustic features and we discuss how these can be extended more generally to\nthe development and assessment of predictive methods based on public health\ndatasets.\n", "date": "2022-12-15", "categories": []}
{"id": "2302.13631", "title": "Curriculum Based Multi-Task Learning for Parkinson's Disease Detection", "abstract": "  There is great interest in developing radiological classifiers for diagnosis,\nstaging, and predictive modeling in progressive diseases such as Parkinson's\ndisease (PD), a neurodegenerative disease that is difficult to detect in its\nearly stages. Here we leverage severity-based meta-data on the stages of\ndisease to define a curriculum for training a deep convolutional neural network\n(CNN). Typically, deep learning networks are trained by randomly selecting\nsamples in each mini-batch. By contrast, curriculum learning is a training\nstrategy that aims to boost classifier performance by starting with examples\nthat are easier to classify. Here we define a curriculum to progressively\nincrease the difficulty of the training data corresponding to the Hoehn and\nYahr (H&Y) staging system for PD (total N=1,012; 653 PD patients, 359 controls;\nage range: 20.0-84.9 years). Even with our multi-task setting using pre-trained\nCNNs and transfer learning, PD classification based on T1-weighted (T1-w) MRI\nwas challenging (ROC AUC: 0.59-0.65), but curriculum training boosted\nperformance (by 3.9%) compared to our baseline model. Future work with\nmultimodal imaging may further boost performance.\n", "date": "2023-02-27", "categories": []}
{"id": "2302.12666", "title": "Modelling Temporal Document Sequences for Clinical ICD Coding", "abstract": "  Past studies on the ICD coding problem focus on predicting clinical codes\nprimarily based on the discharge summary. This covers only a small fraction of\nthe notes generated during each hospital stay and leaves potential for\nimproving performance by analysing all the available clinical notes. We propose\na hierarchical transformer architecture that uses text across the entire\nsequence of clinical notes in each hospital stay for ICD coding, and\nincorporates embeddings for text metadata such as their position, time, and\ntype of note. While using all clinical notes increases the quantity of data\nsubstantially, superconvergence can be used to reduce training costs. We\nevaluate the model on the MIMIC-III dataset. Our model exceeds the prior\nstate-of-the-art when using only discharge summaries as input, and achieves\nfurther performance improvements when all clinical notes are used as input.\n", "date": "2023-02-24", "categories": []}
{"id": "2302.08605", "title": "Using Explainable AI to Cross-Validate Socio-economic Disparities Among\n  Covid-19 Patient Mortality", "abstract": "  This paper applies eXplainable Artificial Intelligence (XAI) methods to\ninvestigate the socioeconomic disparities in COVID patient mortality. An\nExtreme Gradient Boosting (XGBoost) prediction model is built based on a\nde-identified Austin area hospital dataset to predict the mortality of COVID-19\npatients. We apply two XAI methods, Shapley Additive exPlanations (SHAP) and\nLocally Interpretable Model Agnostic Explanations (LIME), to compare the global\nand local interpretation of feature importance. This paper demonstrates the\nadvantages of using XAI which shows the feature importance and decisive\ncapability. Furthermore, we use the XAI methods to cross-validate their\ninterpretations for individual patients. The XAI models reveal that Medicare\nfinancial class, older age, and gender have high impact on the mortality\nprediction. We find that LIME local interpretation does not show significant\ndifferences in feature importance comparing to SHAP, which suggests pattern\nconfirmation. This paper demonstrates the importance of XAI methods in\ncross-validation of feature attributions.\n", "date": "2023-02-16", "categories": []}
{"id": "2302.12075", "title": "Explorative analysis of human disease-symptoms relations using the\n  Convolutional Neural Network", "abstract": "  In the field of health-care and bio-medical research, understanding the\nrelationship between the symptoms of diseases is crucial for early diagnosis\nand determining hidden relationships between diseases. The study aimed to\nunderstand the extent of symptom types in disease prediction tasks. In this\nresearch, we analyze a pre-generated symptom-based human disease dataset and\ndemonstrate the degree of predictability for each disease based on the\nConvolutional Neural Network and the Support Vector Machine. Ambiguity of\ndisease is studied using the K-Means and the Principal Component Analysis. Our\nresults indicate that machine learning can potentially diagnose diseases with\nthe 98-100% accuracy in the early stage, taking the characteristics of symptoms\ninto account. Our result highlights that types of unusual symptoms are a good\nproxy for disease early identification accurately. We also highlight that\nunusual symptoms increase the accuracy of the disease prediction task.\n", "date": "2023-02-23", "categories": []}
{"id": "2212.07724", "title": "Attention-based Multiple Instance Learning for Survival Prediction on\n  Lung Cancer Tissue Microarrays", "abstract": "  Attention-based multiple instance learning (AMIL) algorithms have proven to\nbe successful in utilizing gigapixel whole-slide images (WSIs) for a variety of\ndifferent computational pathology tasks such as outcome prediction and cancer\nsubtyping problems. We extended an AMIL approach to the task of survival\nprediction by utilizing the classical Cox partial likelihood as a loss\nfunction, converting the AMIL model into a nonlinear proportional hazards\nmodel. We applied the model to tissue microarray (TMA) slides of 330 lung\ncancer patients. The results show that AMIL approaches can handle very small\namounts of tissue from a TMA and reach similar C-index performance compared to\nestablished survival prediction methods trained with highly discriminative\nclinical factors such as age, cancer grade, and cancer stage\n", "date": "2022-12-15", "categories": []}
{"id": "2302.10901", "title": "Machine Learning Techniques for Predicting the Short-Term Outcome of\n  Resective Surgery in Lesional-Drug Resistance Epilepsy", "abstract": "  In this study, we developed and tested machine learning models to predict\nepilepsy surgical outcome using noninvasive clinical and demographic data from\npatients. Methods: Seven dif-ferent categorization algorithms were used to\nanalyze the data. The techniques are also evaluated using the Leave-One-Out\nmethod. For precise evaluation of the results, the parameters accuracy,\nprecision, recall and, F1-score are calculated. Results: Our findings revealed\nthat a machine learning-based presurgical model of patients' clinical features\nmay accurately predict the outcome of epilepsy surgery in patients with\ndrug-resistant lesional epilepsy. The support vector machine (SVM) with the\nlinear kernel yielded 76.1% in terms of accuracy could predict results in 96.7%\nof temporal lobe epilepsy (TLE) patients and 79.5% of extratemporal lobe\nepilepsy (ETLE) cases using ten clinical features. Significance: To predict the\noutcome of epilepsy surgery, this study recommends the use of a machine\nlearning strategy based on supervised classification and se-lection of feature\nsubsets data mining. Progress in the development of machine learning-based\nprediction models offers optimism for personalised medicine access.\n", "date": "2023-02-10", "categories": []}
{"id": "2302.10180", "title": "Diagnosis of Covid-19 Via Patient Breath Data Using Artificial\n  Intelligence", "abstract": "  Using machine learning algorithms for the rapid diagnosis and detection of\nthe COVID-19 pandemic and isolating the patients from crowded environments are\nvery important to controlling the epidemic. This study aims to develop a\npoint-of-care testing (POCT) system that can detect COVID-19 by detecting\nvolatile organic compounds (VOCs) in a patient's exhaled breath using the\nGradient Boosted Trees Learner Algorithm. 294 breath samples were collected\nfrom 142 patients at Istanbul Medipol Mega Hospital between December 2020 and\nMarch 2021. 84 cases out of 142 resulted in negatives, and 58 cases resulted in\npositives. All these breath samples have been converted into numeric values\nthrough five air sensors. 10% of the data have been used for the validation of\nthe model, while 75% of the test data have been used for training an AI model\nto predict the coronavirus presence. 25% have been used for testing. The SMOTE\noversampling method was used to increase the training set size and reduce the\nimbalance of negative and positive classes in training and test data. Different\nmachine learning algorithms have also been tried to develop the e-nose model.\nThe test results have suggested that the Gradient Boosting algorithm created\nthe best model. The Gradient Boosting model provides 95% recall when predicting\nCOVID-19 positive patients and 96% accuracy when predicting COVID-19 negative\npatients.\n", "date": "2023-01-24", "categories": []}
{"id": "2302.10277", "title": "A Comparative Analysis of CNN-Based Pretrained Models for the Detection\n  and Prediction of Monkeypox", "abstract": "  Monkeypox is a rare disease that raised concern among medical specialists\nfollowing the convi-19 pandemic. It's concerning since monkeypox is difficult\nto diagnose early on because of symptoms that are similar to chickenpox and\nmeasles. Furthermore, because this is a rare condition, there is a knowledge\ngap among healthcare professionals. As a result, there is an urgent need for a\nnovel technique to combat and anticipate the disease in the early phases of\nindividual virus infection. Multiple CNN-based pre-trained models, including\nVGG-16, VGG-19, Restnet50, Inception-V3, Densnet, Xception, MobileNetV2,\nAlexnet, Lenet, and majority Voting, were employed in classification in this\nstudy. For this study, multiple data sets were combined, such as monkeypox vs\nchickenpox, monkeypox versus measles, monkeypox versus normal, and monkeypox\nversus all diseases. Majority voting performed 97% in monkeypox vs chickenpox,\nXception achieved 79% in monkeypox against measles, MobileNetV2 scored 96% in\nmonkeypox vs normal, and Lenet performed 80% in monkeypox versus all.\n", "date": "2023-01-20", "categories": []}
{"id": "2302.10301", "title": "Artificial Intelligence System for Detection and Screening of Cardiac\n  Abnormalities using Electrocardiogram Images", "abstract": "  The artificial intelligence (AI) system has achieved expert-level performance\nin electrocardiogram (ECG) signal analysis. However, in underdeveloped\ncountries or regions where the healthcare information system is imperfect, only\npaper ECGs can be provided. Analysis of real-world ECG images (photos or scans\nof paper ECGs) remains challenging due to complex environments or interference.\nIn this study, we present an AI system developed to detect and screen cardiac\nabnormalities (CAs) from real-world ECG images. The system was evaluated on a\nlarge dataset of 52,357 patients from multiple regions and populations across\nthe world. On the detection task, the AI system obtained area under the\nreceiver operating curve (AUC) of 0.996 (hold-out test), 0.994 (external test\n1), 0.984 (external test 2), and 0.979 (external test 3), respectively.\nMeanwhile, the detection results of AI system showed a strong correlation with\nthe diagnosis of cardiologists (cardiologist 1 (R=0.794, p<1e-3), cardiologist\n2 (R=0.812, p<1e-3)). On the screening task, the AI system achieved AUCs of\n0.894 (hold-out test) and 0.850 (external test). The screening performance of\nthe AI system was better than that of the cardiologists (AI system (0.846) vs.\ncardiologist 1 (0.520) vs. cardiologist 2 (0.480)). Our study demonstrates the\nfeasibility of an accurate, objective, easy-to-use, fast, and low-cost AI\nsystem for CA detection and screening. The system has the potential to be used\nby healthcare professionals, caregivers, and general users to assess CAs based\non real-world ECG images.\n", "date": "2023-02-10", "categories": []}
{"id": "2302.09488", "title": "A Picture May Be Worth a Thousand Lives: An Interpretable Artificial\n  Intelligence Strategy for Predictions of Suicide Risk from Social Media\n  Images", "abstract": "  The promising research on Artificial Intelligence usages in suicide\nprevention has principal gaps, including black box methodologies, inadequate\noutcome measures, and scarce research on non-verbal inputs, such as social\nmedia images (despite their popularity today, in our digital era). This study\naddresses these gaps and combines theory-driven and bottom-up strategies to\nconstruct a hybrid and interpretable prediction model of valid suicide risk\nfrom images. The lead hypothesis was that images contain valuable information\nabout emotions and interpersonal relationships, two central concepts in\nsuicide-related treatments and theories. The dataset included 177,220 images by\n841 Facebook users who completed a gold-standard suicide scale. The images were\nrepresented with CLIP, a state-of-the-art algorithm, which was utilized,\nunconventionally, to extract predefined features that served as inputs to a\nsimple logistic-regression prediction model (in contrast to complex neural\nnetworks). The features addressed basic and theory-driven visual elements using\neveryday language (e.g., bright photo, photo of sad people). The results of the\nhybrid model (that integrated theory-driven and bottom-up methods) indicated\nhigh prediction performance that surpassed common bottom-up algorithms, thus\nproviding a first proof that images (alone) can be leveraged to predict\nvalidated suicide risk. Corresponding with the lead hypothesis, at-risk users\nhad images with increased negative emotions and decreased belonginess. The\nresults are discussed in the context of non-verbal warning signs of suicide.\nNotably, the study illustrates the advantages of hybrid models in such\ncomplicated tasks and provides simple and flexible prediction strategies that\ncould be utilized to develop real-life monitoring tools of suicide.\n", "date": "2023-02-19", "categories": []}
{"id": "2302.09659", "title": "Interpret Your Care: Predicting the Evolution of Symptoms for Cancer\n  Patients", "abstract": "  Cancer treatment is an arduous process for patients and causes many\nside-effects during and post-treatment. The treatment can affect almost all\nbody systems and result in pain, fatigue, sleep disturbances, cognitive\nimpairments, etc. These conditions are often under-diagnosed or under-treated.\nIn this paper, we use patient data to predict the evolution of their symptoms\nsuch that treatment-related impairments can be prevented or effects\nmeaningfully ameliorated. The focus of this study is on predicting the pain and\ntiredness level of a patient post their diagnosis. We implement an\ninterpretable decision tree based model called LightGBM on real-world patient\ndata consisting of 20163 patients. There exists a class imbalance problem in\nthe dataset which we resolve using the oversampling technique of SMOTE. Our\nempirical results show that the value of the previous level of a symptom is a\nkey indicator for prediction and the weighted average deviation in prediction\nof pain level is 3.52 and of tiredness level is 2.27.\n", "date": "2023-02-19", "categories": []}
{"id": "2302.09691", "title": "Forecasting Pressure Of Ventilator Using A Hybrid Deep Learning Model\n  Built With Bi-LSTM and Bi-GRU To Simulate Ventilation", "abstract": "  A ventilator simulation system can make mechanical ventilation easier and\nmore effective. As a result, predicting a patient's ventilator pressure is\nessential when designing a simulation ventilator. We suggested a hybrid deep\nlearning-based approach to forecast required ventilator pressure for patients.\nThis system is made up of Bi-LSTM and Bi-GRU networks. The SELU activation\nfunction was used in our proposed model. MAE and MSE were used to examine the\naccuracy of the proposed model so that our proposed methodology can be applied\nto real-world problems. The model performed well against test data and created\nfar too few losses. Major parts of our research were data collection, data\nanalysis, data cleaning, building hybrid Bi-LSTM and Bi-GRU model, training the\nmodel, model evaluation, and result analysis. We compared the results of our\nresearch with some contemporary works, and our proposed model performed better\nthan those models.\n", "date": "2023-02-19", "categories": []}
{"id": "2302.08802", "title": "Risk Classification of Brain Metastases via Radiomics, Delta-Radiomics\n  and Machine Learning", "abstract": "  Stereotactic radiotherapy (SRT) is one of the most important treatment for\npatients with brain metastases (BM). Conventionally, following SRT patients are\nmonitored by serial imaging and receive salvage treatments in case of\nsignificant tumor growth. We hypothesized that using radiomics and machine\nlearning (ML), metastases at high risk for subsequent progression could be\nidentified during follow-up prior to the onset of significant tumor growth,\nenabling personalized follow-up intervals and early selection for salvage\ntreatment. All experiments are performed on a dataset from clinical routine of\nthe Radiation Oncology department of the University Hospital Erlangen (UKER).\nThe classification is realized via the maximum-relevance minimal-redundancy\n(MRMR) technique and support vector machines (SVM). The pipeline leads to a\nclassification with a mean area under the curve (AUC) score of 0.83 in internal\ncross-validation and allows a division of the cohort into two subcohorts that\ndiffer significantly in their median time to progression (low-risk metastasis\n(LRM): 17.3 months, high-risk metastasis (HRM): 9.6 months, p < 0.01). The\nclassification performance is especially enhanced by the analysis of medical\nimages from different points in time (AUC 0.53 -> AUC 0.74). The results\nindicate that risk stratification of BM based on radiomics and machine learning\nduring post-SRT follow-up is possible with good accuracy and should be further\npursued to personalize and improve post-SRT follow-up.\n", "date": "2023-02-17", "categories": []}
{"id": "2302.09004", "title": "CovidExpert: A Triplet Siamese Neural Network framework for the\n  detection of COVID-19", "abstract": "  Patients with the COVID-19 infection may have pneumonia-like symptoms as well\nas respiratory problems which may harm the lungs. From medical images,\ncoronavirus illness may be accurately identified and predicted using a variety\nof machine learning methods. Most of the published machine learning methods may\nneed extensive hyperparameter adjustment and are unsuitable for small datasets.\nBy leveraging the data in a comparatively small dataset, few-shot learning\nalgorithms aim to reduce the requirement of large datasets. This inspired us to\ndevelop a few-shot learning model for early detection of COVID-19 to reduce the\npost-effect of this dangerous disease. The proposed architecture combines\nfew-shot learning with an ensemble of pre-trained convolutional neural networks\nto extract feature vectors from CT scan images for similarity learning. The\nproposed Triplet Siamese Network as the few-shot learning model classified CT\nscan images into Normal, COVID-19, and Community-Acquired Pneumonia. The\nsuggested model achieved an overall accuracy of 98.719%, a specificity of\n99.36%, a sensitivity of 98.72%, and a ROC score of 99.9% with only 200 CT\nscans per category for training data.\n", "date": "2023-02-17", "categories": []}
{"id": "2302.08091", "title": "Do We Still Need Clinical Language Models?", "abstract": "  Although recent advances in scaling large language models (LLMs) have\nresulted in improvements on many NLP tasks, it remains unclear whether these\nmodels trained primarily with general web text are the right tool in highly\nspecialized, safety critical domains such as clinical text. Recent results have\nsuggested that LLMs encode a surprising amount of medical knowledge. This\nraises an important question regarding the utility of smaller domain-specific\nlanguage models. With the success of general-domain LLMs, is there still a need\nfor specialized clinical models? To investigate this question, we conduct an\nextensive empirical analysis of 12 language models, ranging from 220M to 175B\nparameters, measuring their performance on 3 different clinical tasks that test\ntheir ability to parse and reason over electronic health records. As part of\nour experiments, we train T5-Base and T5-Large models from scratch on clinical\nnotes from MIMIC III and IV to directly investigate the efficiency of clinical\ntokens. We show that relatively small specialized clinical models substantially\noutperform all in-context learning approaches, even when finetuned on limited\nannotated data. Further, we find that pretraining on clinical tokens allows for\nsmaller, more parameter-efficient models that either match or outperform much\nlarger language models trained on general text. We release the code and the\nmodels used under the PhysioNet Credentialed Health Data license and data use\nagreement.\n", "date": "2023-02-16", "categories": []}
{"id": "2302.08427", "title": "Learning to diagnose cirrhosis from radiological and histological labels\n  with joint self and weakly-supervised pretraining strategies", "abstract": "  Identifying cirrhosis is key to correctly assess the health of the liver.\nHowever, the gold standard diagnosis of the cirrhosis needs a medical\nintervention to obtain the histological confirmation, e.g. the METAVIR score,\nas the radiological presentation can be equivocal. In this work, we propose to\nleverage transfer learning from large datasets annotated by radiologists, which\nwe consider as a weak annotation, to predict the histological score available\non a small annex dataset. To this end, we propose to compare different\npretraining methods, namely weakly-supervised and self-supervised ones, to\nimprove the prediction of the cirrhosis. Finally, we introduce a loss function\ncombining both supervised and self-supervised frameworks for pretraining. This\nmethod outperforms the baseline classification of the METAVIR score, reaching\nan AUC of 0.84 and a balanced accuracy of 0.75, compared to 0.77 and 0.72 for a\nbaseline classifier.\n", "date": "2023-02-16", "categories": []}
{"id": "2301.08834", "title": "ManyDG: Many-domain Generalization for Healthcare Applications", "abstract": "  The vast amount of health data has been continuously collected for each\npatient, providing opportunities to support diverse healthcare predictive tasks\nsuch as seizure detection and hospitalization prediction. Existing models are\nmostly trained on other patients data and evaluated on new patients. Many of\nthem might suffer from poor generalizability. One key reason can be overfitting\ndue to the unique information related to patient identities and their data\ncollection environments, referred to as patient covariates in the paper. These\npatient covariates usually do not contribute to predicting the targets but are\noften difficult to remove. As a result, they can bias the model training\nprocess and impede generalization. In healthcare applications, most existing\ndomain generalization methods assume a small number of domains. In this paper,\nconsidering the diversity of patient covariates, we propose a new setting by\ntreating each patient as a separate domain (leading to many domains). We\ndevelop a new domain generalization method ManyDG, that can scale to such\nmany-domain problems. Our method identifies the patient domain covariates by\nmutual reconstruction and removes them via an orthogonal projection step.\nExtensive experiments show that ManyDG can boost the generalization performance\non multiple real-world healthcare tasks (e.g., 3.7% Jaccard improvements on\nMIMIC drug recommendation) and support realistic but challenging settings such\nas insufficient data and continuous learning.\n", "date": "2023-01-21", "categories": []}
{"id": "2302.07648", "title": "Atrial Fibrillation Detection Using RR-Intervals for Application in\n  Photoplethysmographs", "abstract": "  Atrial Fibrillation is a common form of irregular heart rhythm that can be\nvery dangerous. Our primary goal is to analyze Atrial Fibrillation data within\nECGs to develop a model based only on RR-Intervals, or the length between\nheart-beats, to create a real time classification model for Atrial Fibrillation\nto be implemented in common heart-rate monitors on the market today.\nPhysionet's MIT-BIH Atrial Fibrillation Database\n\\cite{goldberger2000physiobank} and 2017 Challenge Database\n\\cite{clifford2017af} were used to identify patterns of Atrial Fibrillation and\ntest classification models on. These two datasets are very different. The\nMIT-BIH database contains long samples taken with a medical grade device, which\nis not useful for simulating a consumer device, but is useful for Atrial\nFibrillation pattern detection. The 2017 Challenge database includes short\n($<60sec$) samples taken with a portable device and reveals many of the\nchallenges of Atrial Fibrillation classification in a real-time device. We\ndeveloped multiple SVM models with three sets of extracted features as\npredictor variables which gave us moderately high accuracies with low\ncomputational intensity. With robust filtering techniques already applied in\nmany Photoplethysmograph-based consumer heart-rate monitors, this method can be\nused to develop a reliable real time model for Atrial Fibrillation detection in\nconsumer-grade heart-rate monitors.\n", "date": "2023-02-13", "categories": []}
{"id": "2209.09610", "title": "Generalisability of fetal ultrasound deep learning models to\n  low-resource imaging settings in five African countries", "abstract": "  Most artificial intelligence (AI) research have concentrated in high-income\ncountries, where imaging data, IT infrastructures and clinical expertise are\nplentiful. However, slower progress has been made in limited-resource\nenvironments where medical imaging is needed. For example, in Sub-Saharan\nAfrica the rate of perinatal mortality is very high due to limited access to\nantenatal screening. In these countries, AI models could be implemented to help\nclinicians acquire fetal ultrasound planes for diagnosis of fetal\nabnormalities. So far, deep learning models have been proposed to identify\nstandard fetal planes, but there is no evidence of their ability to generalise\nin centres with limited access to high-end ultrasound equipment and data. This\nwork investigates different strategies to reduce the domain-shift effect for a\nfetal plane classification model trained on a high-resource clinical centre and\ntransferred to a new low-resource centre. To that end, a classifier trained\nwith 1,792 patients from Spain is first evaluated on a new centre in Denmark in\noptimal conditions with 1,008 patients and is later optimised to reach the same\nperformance in five African centres (Egypt, Algeria, Uganda, Ghana and Malawi)\nwith 25 patients each. The results show that a transfer learning approach can\nbe a solution to integrate small-size African samples with existing large-scale\ndatabases in developed countries. In particular, the model can be re-aligned\nand optimised to boost the performance on African populations by increasing the\nrecall to $0.92\\pm0.04$ and at the same time maintaining a high precision\nacross centres. This framework shows promise for building new AI models\ngeneralisable across clinical centres with limited data acquired in challenging\nand heterogeneous conditions and calls for further research to develop new\nsolutions for usability of AI in countries with less resources.\n", "date": "2022-09-20", "categories": []}
{"id": "2302.03822", "title": "Clinical BioBERT Hyperparameter Optimization using Genetic Algorithm", "abstract": "  Clinical factors account only for a small portion, about 10-30%, of the\ncontrollable factors that affect an individual's health outcomes. The remaining\nfactors include where a person was born and raised, where he/she pursued their\neducation, what their work and family environment is like, etc. These factors\nare collectively referred to as Social Determinants of Health (SDoH). The\nmajority of SDoH data is recorded in unstructured clinical notes by physicians\nand practitioners. Recording SDoH data in a structured manner (in an EHR) could\ngreatly benefit from a dedicated ontology of SDoH terms. Our research focuses\non extracting sentences from clinical notes, making use of such an SDoH\nontology (called SOHO) to provide appropriate concepts. We utilize recent\nadvancements in Deep Learning to optimize the hyperparameters of a Clinical\nBioBERT model for SDoH text. A genetic algorithm-based hyperparameter tuning\nregimen was implemented to identify optimal parameter settings. To implement a\ncomplete classifier, we pipelined Clinical BioBERT with two subsequent linear\nlayers and two dropout layers. The output predicts whether a text fragment\ndescribes an SDoH issue of the patient. We compared the AdamW, Adafactor, and\nLAMB optimizers. In our experiments, AdamW outperformed the others in terms of\naccuracy.\n", "date": "2023-02-08", "categories": []}
{"id": "2302.05752", "title": "Informing clinical assessment by contextualizing post-hoc explanations\n  of risk prediction models in type-2 diabetes", "abstract": "  Medical experts may use Artificial Intelligence (AI) systems with greater\ntrust if these are supported by contextual explanations that let the\npractitioner connect system inferences to their context of use. However, their\nimportance in improving model usage and understanding has not been extensively\nstudied. Hence, we consider a comorbidity risk prediction scenario and focus on\ncontexts regarding the patients clinical state, AI predictions about their risk\nof complications, and algorithmic explanations supporting the predictions. We\nexplore how relevant information for such dimensions can be extracted from\nMedical guidelines to answer typical questions from clinical practitioners. We\nidentify this as a question answering (QA) task and employ several\nstate-of-the-art LLMs to present contexts around risk prediction model\ninferences and evaluate their acceptability. Finally, we study the benefits of\ncontextual explanations by building an end-to-end AI pipeline including data\ncohorting, AI risk modeling, post-hoc model explanations, and prototyped a\nvisual dashboard to present the combined insights from different context\ndimensions and data sources, while predicting and identifying the drivers of\nrisk of Chronic Kidney Disease - a common type-2 diabetes comorbidity. All of\nthese steps were performed in engagement with medical experts, including a\nfinal evaluation of the dashboard results by an expert medical panel. We show\nthat LLMs, in particular BERT and SciBERT, can be readily deployed to extract\nsome relevant explanations to support clinical usage. To understand the\nvalue-add of the contextual explanations, the expert panel evaluated these\nregarding actionable insights in the relevant clinical setting. Overall, our\npaper is one of the first end-to-end analyses identifying the feasibility and\nbenefits of contextual explanations in a real-world clinical use case.\n", "date": "2023-02-11", "categories": []}
{"id": "2302.05179", "title": "AIOSA: An approach to the automatic identification of obstructive sleep\n  apnea events based on deep learning", "abstract": "  Obstructive Sleep Apnea Syndrome (OSAS) is the most common sleep-related\nbreathing disorder. It is caused by an increased upper airway resistance during\nsleep, which determines episodes of partial or complete interruption of\nairflow. The detection and treatment of OSAS is particularly important in\nstroke patients, because the presence of severe OSAS is associated with higher\nmortality, worse neurological deficits, worse functional outcome after\nrehabilitation, and a higher likelihood of uncontrolled hypertension. The gold\nstandard test for diagnosing OSAS is polysomnography (PSG). Unfortunately,\nperforming a PSG in an electrically hostile environment, like a stroke unit, on\nneurologically impaired patients is a difficult task; also, the number of\nstrokes per day outnumbers the availability of polysomnographs and dedicated\nhealthcare professionals. Thus, a simple and automated recognition system to\nidentify OSAS among acute stroke patients, relying on routinely recorded vital\nsigns, is desirable. The majority of the work done so far focuses on data\nrecorded in ideal conditions and highly selected patients, and thus it is\nhardly exploitable in real-life settings, where it would be of actual use. In\nthis paper, we propose a convolutional deep learning architecture able to\nreduce the temporal resolution of raw waveform data, like physiological\nsignals, extracting key features that can be used for further processing. We\nexploit models based on such an architecture to detect OSAS events in stroke\nunit recordings obtained from the monitoring of unselected patients. Unlike\nexisting approaches, annotations are performed at one-second granularity,\nallowing physicians to better interpret the model outcome. Results are\nconsidered to be satisfactory by the domain experts. Moreover, based on a\nwidely-used benchmark, we show that the proposed approach outperforms current\nstate-of-the-art solutions.\n", "date": "2023-02-10", "categories": []}
{"id": "2301.07107", "title": "Mortality Prediction with Adaptive Feature Importance Recalibration for\n  Peritoneal Dialysis Patients: a deep-learning-based study on a real-world\n  longitudinal follow-up dataset", "abstract": "  Objective: Peritoneal Dialysis (PD) is one of the most widely used\nlife-supporting therapies for patients with End-Stage Renal Disease (ESRD).\nPredicting mortality risk and identifying modifiable risk factors based on the\nElectronic Medical Records (EMR) collected along with the follow-up visits are\nof great importance for personalized medicine and early intervention. Here, our\nobjective is to develop a deep learning model for a real-time, individualized,\nand interpretable mortality prediction model - AICare. Method and Materials:\nOur proposed model consists of a multi-channel feature extraction module and an\nadaptive feature importance recalibration module. AICare explicitly identifies\nthe key features that strongly indicate the outcome prediction for each patient\nto build the health status embedding individually. This study has collected\n13,091 clinical follow-up visits and demographic data of 656 PD patients. To\nverify the application universality, this study has also collected 4,789 visits\nof 1,363 hemodialysis dialysis (HD) as an additional experiment dataset to test\nthe prediction performance, which will be discussed in the Appendix. Results:\n1) Experiment results show that AICare achieves 81.6%/74.3% AUROC and\n47.2%/32.5% AUPRC for the 1-year mortality prediction task on PD/HD dataset\nrespectively, which outperforms the state-of-the-art comparative deep learning\nmodels. 2) This study first provides a comprehensive elucidation of the\nrelationship between the causes of mortality in patients with PD and clinical\nfeatures based on an end-to-end deep learning model. 3) This study first\nreveals the pattern of variation in the importance of each feature in the\nmortality prediction based on built-in interpretability. 4) We develop a\npractical AI-Doctor interaction system to visualize the trajectory of patients'\nhealth status and risk indicators.\n", "date": "2023-01-17", "categories": []}
{"id": "2302.03137", "title": "Predicting Development of Chronic Obstructive Pulmonary Disease and its\n  Risk Factor Analysis", "abstract": "  Chronic Obstructive Pulmonary Disease (COPD) is an irreversible airway\nobstruction with a high societal burden. Although smoking is known to be the\nbiggest risk factor, additional components need to be considered. In this\nstudy, we aim to identify COPD risk factors by applying machine learning models\nthat integrate sociodemographic, clinical, and genetic data to predict COPD\ndevelopment.\n", "date": "2023-02-06", "categories": []}
{"id": "2302.03355", "title": "AMFPMC -- An improved method of detecting multiple types of drug-drug\n  interactions using only known drug-drug interactions", "abstract": "  Adverse drug interactions are largely preventable causes of medical\naccidents, which frequently result in physician and emergency room encounters.\nThe detection of drug interactions in a lab, prior to a drug's use in medical\npractice, is essential, however it is costly and time-consuming. Machine\nlearning techniques can provide an efficient and accurate means of predicting\npossible drug-drug interactions and combat the growing problem of adverse drug\ninteractions. Most existing models for predicting interactions rely on the\nchemical properties of drugs. While such models can be accurate, the required\nproperties are not always available.\n", "date": "2023-02-07", "categories": []}
{"id": "2302.02406", "title": "Pre-screening breast cancer with machine learning and deep learning", "abstract": "  We suggest that deep learning can be used for pre-screening cancer by\nanalyzing demographic and anthropometric information of patients, as well as\nbiological markers obtained from routine blood samples and relative risks\nobtained from meta-analysis and international databases. We applied feature\nselection algorithms to a database of 116 women, including 52 healthy women and\n64 women diagnosed with breast cancer, to identify the best pre-screening\npredictors of cancer. We utilized the best predictors to perform k-fold Monte\nCarlo cross-validation experiments that compare deep learning against\ntraditional machine learning algorithms. Our results indicate that a deep\nlearning model with an input-layer architecture that is fine-tuned using\nfeature selection can effectively distinguish between patients with and without\ncancer. Additionally, compared to machine learning, deep learning has the\nlowest uncertainty in its predictions. These findings suggest that deep\nlearning algorithms applied to cancer pre-screening offer a radiation-free,\nnon-invasive, and affordable complement to screening methods based on imagery.\nThe implementation of deep learning algorithms in cancer pre-screening offer\nopportunities to identify individuals who may require imaging-based screening,\ncan encourage self-examination, and decrease the psychological externalities\nassociated with false positives in cancer screening. The integration of deep\nlearning algorithms for both screening and pre-screening will ultimately lead\nto earlier detection of malignancy, reducing the healthcare and societal burden\nassociated to cancer treatment.\n", "date": "2023-02-05", "categories": []}
{"id": "2302.02986", "title": "Fitness Dependent Optimizer with Neural Networks for COVID-19 patients", "abstract": "  The Coronavirus, known as COVID-19, which appeared in 2019 in China, has\nsignificantly affected global health and become a huge burden on health\ninstitutions all over the world. These effects are continuing today. One\nstrategy for limiting the virus's transmission is to have an early diagnosis of\nsuspected cases and take appropriate measures before the disease spreads\nfurther. This work aims to diagnose and show the probability of getting\ninfected by the disease according to textual clinical data. In this work, we\nused five machine learning techniques (GWO_MLP, GWO_CMLP, MGWO_MLP, FDO_MLP,\nFDO_CMLP) all of which aim to classify Covid-19 patients into two categories\n(Positive and Negative). Experiments showed promising results for all used\nmodels. The applied methods showed very similar performance, typically in terms\nof accuracy. However, in each tested dataset, FDO_MLP and FDO_CMLP produced the\nbest results with 100% accuracy. The other models' results varied from one\nexperiment to the other. It is concluded that the models on which the FDO\nalgorithm was used as a learning algorithm had the possibility of obtaining\nhigher accuracy. However, it is found that FDO has the longest runtime compared\nto the other algorithms. The link to the covid 19 models is found here:\nhttps://github.com/Tarik4Rashid4/covid19models\n", "date": "2023-01-06", "categories": []}
{"id": "2302.01536", "title": "Using natural language processing and structured medical data to\n  phenotype patients hospitalized due to COVID-19", "abstract": "  To identify patients who are hospitalized because of COVID-19 as opposed to\nthose who were admitted for other indications, we compared the performance of\ndifferent computable phenotype definitions for COVID-19 hospitalizations that\nuse different types of data from the electronic health records (EHR), including\nstructured EHR data elements, provider notes, or a combination of both data\ntypes. And conduct a retrospective data analysis utilizing chart review-based\nvalidation. Participants are 586 hospitalized individuals who tested positive\nfor SARS-CoV-2 during January 2022. We used natural language processing to\nincorporate data from provider notes and LASSO regression and Random Forests to\nfit classification algorithms that incorporated structured EHR data elements,\nprovider notes, or a combination of structured data and provider notes.\nResults: Based on a chart review, 38% of 586 patients were determined to be\nhospitalized for reasons other than COVID-19 despite having tested positive for\nSARS-CoV-2. A classification algorithm that used provider notes had\nsignificantly better discrimination than one that used structured EHR data\nelements (AUROC: 0.894 vs 0.841, p < 0.001), and performed similarly to a model\nthat combined provider notes with structured data elements (AUROC: 0.894 vs\n0.893). Assessments of hospital outcome metrics significantly differed based on\nwhether the population included all hospitalized patients who tested positive\nfor SARS-CoV-2 versus those who were determined to have been hospitalized due\nto COVID-19. This work demonstrates the utility of natural language processing\napproaches to derive information related to patient hospitalizations in cases\nwhere there may be multiple conditions that could serve as the primary\nindication for hospitalization.\n", "date": "2023-02-03", "categories": []}
{"id": "2302.00794", "title": "Using Machine Learning to Develop Smart Reflex Testing Protocols", "abstract": "  Objective: Reflex testing protocols allow clinical laboratories to perform\nsecond line diagnostic tests on existing specimens based on the results of\ninitially ordered tests. Reflex testing can support optimal clinical laboratory\ntest ordering and diagnosis. In current clinical practice, reflex testing\ntypically relies on simple \"if-then\" rules; however, this limits their scope\nsince most test ordering decisions involve more complexity than a simple rule\nwill allow. Here, using the analyte ferritin as an example, we propose an\nalternative machine learning-based approach to \"smart\" reflex testing with a\nwider scope and greater impact than traditional rule-based approaches. Methods:\nUsing patient data, we developed a machine learning model to predict whether a\npatient getting CBC testing will also have ferritin testing ordered, consider\napplications of this model to \"smart\" reflex testing, and evaluate the model by\ncomparing its performance to possible rule-based approaches. Results: Our\nunderlying machine learning models performed moderately well in predicting\nferritin test ordering and demonstrated greater suitability to reflex testing\nthan rule-based approaches. Using chart review, we demonstrate that our model\nmay improve ferritin test ordering. Finally, as a secondary goal, we\ndemonstrate that ferritin test results are missing not at random (MNAR), a\nfinding with implications for unbiased imputation of missing test results.\nConclusions: Machine learning may provide a foundation for new types of reflex\ntesting with enhanced benefits for clinical diagnosis and laboratory\nutilization management.\n", "date": "2023-02-01", "categories": []}
{"id": "2302.00953", "title": "Deep-Learning Tool for Early Identifying Non-Traumatic Intracranial\n  Hemorrhage Etiology based on CT Scan", "abstract": "  Background: To develop an artificial intelligence system that can accurately\nidentify acute non-traumatic intracranial hemorrhage (ICH) etiology based on\nnon-contrast CT (NCCT) scans and investigate whether clinicians can benefit\nfrom it in a diagnostic setting. Materials and Methods: The deep learning model\nwas developed with 1868 eligible NCCT scans with non-traumatic ICH collected\nbetween January 2011 and April 2018. We tested the model on two independent\ndatasets (TT200 and SD 98) collected after April 2018. The model's diagnostic\nperformance was compared with clinicians's performance. We further designed a\nsimulated study to compare the clinicians's performance with and without the\ndeep learning system augmentation. Results: The proposed deep learning system\nachieved area under the receiver operating curve of 0.986 (95% CI 0.967-1.000)\non aneurysms, 0.952 (0.917-0.987) on hypertensive hemorrhage, 0.950\n(0.860-1.000) on arteriovenous malformation (AVM), 0.749 (0.586-0.912) on\nMoyamoya disease (MMD), 0.837 (0.704-0.969) on cavernous malformation (CM), and\n0.839 (0.722-0.959) on other causes in TT200 dataset. Given a 90% specificity\nlevel, the sensitivities of our model were 97.1% and 90.9% for aneurysm and AVM\ndiagnosis, respectively. The model also shows an impressive generalizability in\nan independent dataset SD98. The clinicians achieve significant improvements in\nthe sensitivity, specificity, and accuracy of diagnoses of certain hemorrhage\netiologies with proposed system augmentation. Conclusions: The proposed deep\nlearning algorithms can be an effective tool for early identification of\nhemorrhage etiologies based on NCCT scans. It may also provide more information\nfor clinicians for triage and further imaging examination selection.\n", "date": "2023-02-02", "categories": []}
{"id": "2302.01025", "title": "Semantic Coherence Markers for the Early Diagnosis of the Alzheimer\n  Disease", "abstract": "  In this work we explore how language models can be employed to analyze\nlanguage and discriminate between mentally impaired and healthy subjects\nthrough the perplexity metric. Perplexity was originally conceived as an\ninformation-theoretic measure to assess how much a given language model is\nsuited to predict a text sequence or, equivalently, how much a word sequence\nfits into a specific language model. We carried out an extensive\nexperimentation with the publicly available data, and employed language models\nas diverse as N-grams, from 2-grams to 5-grams, and GPT-2, a transformer-based\nlanguage model. We investigated whether perplexity scores may be used to\ndiscriminate between the transcripts of healthy subjects and subjects suffering\nfrom Alzheimer Disease (AD). Our best performing models achieved full accuracy\nand F-score (1.00 in both precision/specificity and recall/sensitivity) in\ncategorizing subjects from both the AD class and control subjects. These\nresults suggest that perplexity can be a valuable analytical metrics with\npotential application to supporting early diagnosis of symptoms of mental\ndisorders.\n", "date": "2023-02-02", "categories": []}
{"id": "2302.00188", "title": "Deep Learning Approach to Predict Hemorrhage in Moyamoya Disease", "abstract": "  Objective: Reliable tools to predict moyamoya disease (MMD) patients at risk\nfor hemorrhage could have significant value. The aim of this paper is to\ndevelop three machine learning classification algorithms to predict hemorrhage\nin moyamoya disease. Methods: Clinical data of consecutive MMD patients who\nwere admitted to our hospital between 2009 and 2015 were reviewed.\nDemographics, clinical, radiographic data were analyzed to develop artificial\nneural network (ANN), support vector machine (SVM), and random forest models.\nResults: We extracted 33 parameters, including 11 demographic and 22\nradiographic features as input for model development. Of all compared\nclassification results, ANN achieved the highest overall accuracy of 75.7% (95%\nCI, 68.6%-82.8%), followed by SVM with 69.2% (95% CI, 56.9%-81.5%) and random\nforest with 70.0% (95% CI, 57.0%-83.0%). Conclusions: The proposed ANN\nframework can be a potential effective tool to predict the possibility of\nhemorrhage among adult MMD patients based on clinical information and\nradiographic features.\n", "date": "2023-02-01", "categories": []}
{"id": "2302.00612", "title": "Clinical Decision Transformer: Intended Treatment Recommendation through\n  Goal Prompting", "abstract": "  With recent achievements in tasks requiring context awareness, foundation\nmodels have been adopted to treat large-scale data from electronic health\nrecord (EHR) systems. However, previous clinical recommender systems based on\nfoundation models have a limited purpose of imitating clinicians' behavior and\ndo not directly consider a problem of missing values. In this paper, we propose\nClinical Decision Transformer (CDT), a recommender system that generates a\nsequence of medications to reach a desired range of clinical states given as\ngoal prompts. For this, we conducted goal-conditioned sequencing, which\ngenerated a subsequence of treatment history with prepended future goal state,\nand trained the CDT to model sequential medications required to reach that goal\nstate. For contextual embedding over intra-admission and inter-admissions, we\nadopted a GPT-based architecture with an admission-wise attention mask and\ncolumn embedding. In an experiment, we extracted a diabetes dataset from an EHR\nsystem, which contained treatment histories of 4788 patients. We observed that\nthe CDT achieved the intended treatment effect according to goal prompt ranges\n(e.g., NormalA1c, LowerA1c, and HigherA1c), contrary to the case with behavior\ncloning. To the best of our knowledge, this is the first study to explore\nclinical recommendations from the perspective of goal prompting. See\nhttps://clinical-decision-transformer.github.io for code and additional\ninformation.\n", "date": "2023-02-01", "categories": []}
{"id": "2301.06916", "title": "Automated speech- and text-based classification of neuropsychiatric\n  conditions in a multidiagnostic setting", "abstract": "  Speech patterns have been identified as potential diagnostic markers for\nneuropsychiatric conditions. However, most studies only compare a single\nclinical group to healthy controls, whereas clinical practice often requires\ndifferentiating between multiple potential diagnoses (multiclass settings). To\naddress this, we assembled a dataset of repeated recordings from 420\nparticipants (67 with major depressive disorder, 106 with schizophrenia and 46\nwith autism, as well as matched controls), and tested the performance of a\nrange of conventional machine learning models and advanced Transformer models\non both binary and multiclass classification, based on voice and text features.\n  While binary models performed comparably to previous research (F1 scores\nbetween 0.54-0.75 for autism spectrum disorder, ASD; 0.67-0.92 for major\ndepressive disorder, MDD; and 0.71-0.83 for schizophrenia); when\ndifferentiating between multiple diagnostic groups performance decreased\nmarkedly (F1 scores between 0.35-0.44 for ASD, 0.57-0.75 for MDD, 0.15-0.66 for\nschizophrenia, and 0.38-0.52 macro F1). Combining voice and text-based models\nyielded increased performance, suggesting that they capture complementary\ndiagnostic information.\n  Our results indicate that models trained on binary classification may learn\nto rely on markers of generic differences between clinical and non-clinical\npopulations, or markers of clinical features that overlap across conditions,\nrather than identifying markers specific to individual conditions. We provide\nrecommendations for future research in the field, suggesting increased focus on\ndeveloping larger transdiagnostic datasets that include more fine-grained\nclinical features, and that can support the development of models that better\ncapture the complexity of neuropsychiatric conditions and naturalistic\ndiagnostic assessment.\n", "date": "2023-01-13", "categories": []}
{"id": "2208.12814", "title": "Interpretable (not just posthoc-explainable) medical claims modeling for\n  discharge placement to prevent avoidable all-cause readmissions or death", "abstract": "  We developed an inherently interpretable multilevel Bayesian framework for\nrepresenting variation in regression coefficients that mimics the piecewise\nlinearity of ReLU-activated deep neural networks. We used the framework to\nformulate a survival model for using medical claims to predict hospital\nreadmission and death that focuses on discharge placement, adjusting for\nconfounding in estimating causal local average treatment effects. We trained\nthe model on a 5% sample of Medicare beneficiaries from 2008 and 2011, based on\ntheir 2009--2011 inpatient episodes, and then tested the model on 2012\nepisodes. The model scored an AUROC of approximately 0.76 on predicting\nall-cause readmissions -- defined using official Centers for Medicare and\nMedicaid Services (CMS) methodology -- or death within 30-days of discharge,\nbeing competitive against XGBoost and a Bayesian deep neural network,\ndemonstrating that one need-not sacrifice interpretability for accuracy.\nCrucially, as a regression model, we provide what blackboxes cannot -- the\nexact gold-standard global interpretation of the model, identifying relative\nrisk factors and quantifying the effect of discharge placement. We also show\nthat the posthoc explainer SHAP fails to provide accurate explanations.\n", "date": "2022-08-28", "categories": []}
{"id": "2301.11315", "title": "Evaluate underdiagnosis and overdiagnosis bias of deep learning model on\n  primary open-angle glaucoma diagnosis in under-served patient populations", "abstract": "  In the United States, primary open-angle glaucoma (POAG) is the leading cause\nof blindness, especially among African American and Hispanic individuals. Deep\nlearning has been widely used to detect POAG using fundus images as its\nperformance is comparable to or even surpasses diagnosis by clinicians.\nHowever, human bias in clinical diagnosis may be reflected and amplified in the\nwidely-used deep learning models, thus impacting their performance. Biases may\ncause (1) underdiagnosis, increasing the risks of delayed or inadequate\ntreatment, and (2) overdiagnosis, which may increase individuals' stress, fear,\nwell-being, and unnecessary/costly treatment. In this study, we examined the\nunderdiagnosis and overdiagnosis when applying deep learning in POAG detection\nbased on the Ocular Hypertension Treatment Study (OHTS) from 22 centers across\n16 states in the United States. Our results show that the widely-used deep\nlearning model can underdiagnose or overdiagnose underserved populations. The\nmost underdiagnosed group is female younger (< 60 yrs) group, and the most\noverdiagnosed group is Black older (>=60 yrs) group. Biased diagnosis through\ntraditional deep learning methods may delay disease detection, treatment and\ncreate burdens among under-served populations, thereby, raising ethical\nconcerns about using deep learning models in ophthalmology clinics.\n", "date": "2023-01-26", "categories": []}
{"id": "2301.12260", "title": "TemporAI: Facilitating Machine Learning Innovation in Time Domain Tasks\n  for Medicine", "abstract": "  TemporAI is an open source Python software library for machine learning (ML)\ntasks involving data with a time component, focused on medicine and healthcare\nuse cases. It supports data in time series, static, and eventmodalities and\nprovides an interface for prediction, causal inference, and time-to-event\nanalysis, as well as common preprocessing utilities and model interpretability\nmethods. The library aims to facilitate innovation in the medical ML space by\noffering a standardized temporal setting toolkit for model development,\nprototyping and benchmarking, bridging the gaps in the ML research, healthcare\nprofessional, medical/pharmacological industry, and data science communities.\nTemporAI is available on GitHub (https://github.com/vanderschaarlab/temporai)\nand we welcome community engagement through use, feedback, and code\ncontributions.\n", "date": "2023-01-28", "categories": []}
{"id": "2301.12289", "title": "Predicting Visit Cost of Obstructive Sleep Apnea using Electronic\n  Healthcare Records with Transformer", "abstract": "  Background: Obstructive sleep apnea (OSA) is growing increasingly prevalent\nin many countries as obesity rises. Sufficient, effective treatment of OSA\nentails high social and financial costs for healthcare. Objective: For\ntreatment purposes, predicting OSA patients' visit expenses for the coming year\nis crucial. Reliable estimates enable healthcare decision-makers to perform\ncareful fiscal management and budget well for effective distribution of\nresources to hospitals. The challenges created by scarcity of high-quality\npatient data are exacerbated by the fact that just a third of those data from\nOSA patients can be used to train analytics models: only OSA patients with more\nthan 365 days of follow-up are relevant for predicting a year's expenditures.\nMethods and procedures: The authors propose a method applying two Transformer\nmodels, one for augmenting the input via data from shorter visit histories and\nthe other predicting the costs by considering both the material thus enriched\nand cases with more than a year's follow-up. Results: The two-model solution\npermits putting the limited body of OSA patient data to productive use.\nRelative to a single-Transformer solution using only a third of the\nhigh-quality patient data, the solution with two models improved the prediction\nperformance's $R^{2}$ from 88.8% to 97.5%. Even using baseline models with the\nmodel-augmented data improved the $R^{2}$ considerably, from 61.6% to 81.9%.\nConclusion: The proposed method makes prediction with the most of the available\nhigh-quality data by carefully exploiting details, which are not directly\nrelevant for answering the question of the next year's likely expenditure.\n", "date": "2023-01-28", "categories": []}
{"id": "2301.11608", "title": "A Multi-View Joint Learning Framework for Embedding Clinical Codes and\n  Text Using Graph Neural Networks", "abstract": "  Learning to represent free text is a core task in many clinical machine\nlearning (ML) applications, as clinical text contains observations and plans\nnot otherwise available for inference. State-of-the-art methods use large\nlanguage models developed with immense computational resources and training\ndata; however, applying these models is challenging because of the highly\nvarying syntax and vocabulary in clinical free text. Structured information\nsuch as International Classification of Disease (ICD) codes often succinctly\nabstracts the most important facts of a clinical encounter and yields good\nperformance, but is often not as available as clinical text in real-world\nscenarios. We propose a \\textbf{multi-view learning framework} that jointly\nlearns from codes and text to combine the availability and forward-looking\nnature of text and better performance of ICD codes. The learned text embeddings\ncan be used as inputs to predictive algorithms independent of the ICD codes\nduring inference. Our approach uses a Graph Neural Network (GNN) to process ICD\ncodes, and Bi-LSTM to process text. We apply Deep Canonical Correlation\nAnalysis (DCCA) to enforce the two views to learn a similar representation of\neach patient. In experiments using planned surgical procedure text, our model\noutperforms BERT models fine-tuned to clinical data, and in experiments using\ndiverse text in MIMIC-III, our model is competitive to a fine-tuned BERT at a\ntiny fraction of its computational effort.\n", "date": "2023-01-27", "categories": []}
{"id": "2301.10951", "title": "Cross Modal Global Local Representation Learning from Radiology Reports\n  and X-Ray Chest Images", "abstract": "  Deep learning models can be applied successfully in real-work problems;\nhowever, training most of these models requires massive data. Recent methods\nuse language and vision, but unfortunately, they rely on datasets that are not\nusually publicly available. Here we pave the way for further research in the\nmultimodal language-vision domain for radiology. In this paper, we train a\nrepresentation learning method that uses local and global representations of\nthe language and vision through an attention mechanism and based on the\npublicly available Indiana University Radiology Report (IU-RR) dataset.\nFurthermore, we use the learned representations to diagnose five lung\npathologies: atelectasis, cardiomegaly, edema, pleural effusion, and\nconsolidation. Finally, we use both supervised and zero-shot classifications to\nextensively analyze the performance of the representation learning on the IU-RR\ndataset. Average Area Under the Curve (AUC) is used to evaluate the accuracy of\nthe classifiers for classifying the five lung pathologies. The average AUC for\nclassifying the five lung pathologies on the IU-RR test set ranged from 0.85 to\n0.87 using the different training datasets, namely CheXpert and CheXphoto.\nThese results compare favorably to other studies using UI-RR. Extensive\nexperiments confirm consistent results for classifying lung pathologies using\nthe multimodal global local representations of language and vision information.\n", "date": "2023-01-26", "categories": []}
{"id": "2301.10450", "title": "HealthEdge: A Machine Learning-Based Smart Healthcare Framework for\n  Prediction of Type 2 Diabetes in an Integrated IoT, Edge, and Cloud Computing\n  System", "abstract": "  Diabetes Mellitus has no permanent cure to date and is one of the leading\ncauses of death globally. The alarming increase in diabetes calls for the need\nto take precautionary measures to avoid/predict the occurrence of diabetes.\nThis paper proposes HealthEdge, a machine learning-based smart healthcare\nframework for type 2 diabetes prediction in an integrated IoT-edge-cloud\ncomputing system. Numerical experiments and comparative analysis were carried\nout between the two most used machine learning algorithms in the literature,\nRandom Forest (RF) and Logistic Regression (LR), using two real-life diabetes\ndatasets. The results show that RF predicts diabetes with 6% more accuracy on\naverage compared to LR.\n", "date": "2023-01-25", "categories": []}
{"id": "2212.08072", "title": "Foresight -- Generative Pretrained Transformer (GPT) for Modelling of\n  Patient Timelines using EHRs", "abstract": "  Background: Electronic Health Records hold detailed longitudinal information\nabout each patient's health status and general clinical history, a large\nportion of which is stored within the unstructured text. Existing approaches\nfocus mostly on structured data and a subset of single-domain outcomes. We\nexplore how temporal modelling of patients from free text and structured data,\nusing deep generative transformers can be used to forecast a wide range of\nfuture disorders, substances, procedures or findings. Methods: We present\nForesight, a novel transformer-based pipeline that uses named entity\nrecognition and linking tools to convert document text into structured, coded\nconcepts, followed by providing probabilistic forecasts for future medical\nevents such as disorders, substances, procedures and findings. We processed the\nentire free-text portion from three different hospital datasets totalling\n811336 patients covering both physical and mental health. Findings: On tests in\ntwo UK hospitals (King's College Hospital, South London and Maudsley) and the\nUS MIMIC-III dataset precision@10 0.68, 0.76 and 0.88 was achieved for\nforecasting the next disorder in a patient timeline, while precision@10 of\n0.80, 0.81 and 0.91 was achieved for forecasting the next biomedical concept.\nForesight was also validated on 34 synthetic patient timelines by five\nclinicians and achieved relevancy of 97% for the top forecasted candidate\ndisorder. As a generative model, it can forecast follow-on biomedical concepts\nfor as many steps as required. Interpretation: Foresight is a general-purpose\nmodel for biomedical concept modelling that can be used for real-world risk\nforecasting, virtual trials and clinical research to study the progression of\ndisorders, simulate interventions and counterfactuals, and educational\npurposes.\n", "date": "2022-12-13", "categories": []}
{"id": "2301.09815", "title": "Mixed Effects Random Forests for Personalised Predictions of Clinical\n  Depression Severity", "abstract": "  This work demonstrates how mixed effects random forests enable accurate\npredictions of depression severity using multimodal physiological and digital\nactivity data collected from an 8-week study involving 31 patients with major\ndepressive disorder. We show that mixed effects random forests outperform\nstandard random forests and personal average baselines when predicting clinical\nHamilton Depression Rating Scale scores (HDRS_17). Compared to the latter\nbaseline, accuracy is significantly improved for each patient by an average of\n0.199-0.276 in terms of mean absolute error (p<0.05). This is noteworthy as\nthese simple baselines frequently outperform machine learning methods in mental\nhealth prediction tasks. We suggest that this improved performance results from\nthe ability of the mixed effects random forest to personalise model parameters\nto individuals in the dataset. However, we find that these improvements pertain\nexclusively to scenarios where labelled patient data are available to the model\nat training time. Investigating methods that improve accuracy when generalising\nto new patients is left as important future work.\n", "date": "2023-01-24", "categories": []}
{"id": "2301.10168", "title": "CovidRhythm: A Deep Learning Model for Passive Prediction of Covid-19\n  using Biobehavioral Rhythms Derived from Wearable Physiological Data", "abstract": "  To investigate whether a deep learning model can detect Covid-19 from\ndisruptions in the human body's physiological (heart rate) and rest-activity\nrhythms (rhythmic dysregulation) caused by the SARS-CoV-2 virus. We propose\nCovidRhythm, a novel Gated Recurrent Unit (GRU) Network with Multi-Head\nSelf-Attention (MHSA) that combines sensor and rhythmic features extracted from\nheart rate and activity (steps) data gathered passively using consumer-grade\nsmart wearable to predict Covid-19. A total of 39 features were extracted\n(standard deviation, mean, min/max/avg length of sedentary and active bouts)\nfrom wearable sensor data. Biobehavioral rhythms were modeled using nine\nparameters (mesor, amplitude, acrophase, and intra-daily variability). These\nfeatures were then input to CovidRhythm for predicting Covid-19 in the\nincubation phase (one day before biological symptoms manifest). A combination\nof sensor and biobehavioral rhythm features achieved the highest AUC-ROC of\n0.79 [Sensitivity = 0.69, Specificity=0.89, F$_{0.1}$ = 0.76], outperforming\nprior approaches in discriminating Covid-positive patients from healthy\ncontrols using 24 hours of historical wearable physiological. Rhythmic features\nwere the most predictive of Covid-19 infection when utilized either alone or in\nconjunction with sensor features. Sensor features predicted healthy subjects\nbest. Circadian rest-activity rhythms that combine 24h activity and sleep\ninformation were the most disrupted. CovidRhythm demonstrates that\nbiobehavioral rhythms derived from consumer-grade wearable data can facilitate\ntimely Covid-19 detection. To the best of our knowledge, our work is the first\nto detect Covid-19 using deep learning and biobehavioral rhythms features\nderived from consumer-grade wearable data.\n", "date": "2023-01-12", "categories": []}
{"id": "2301.10174", "title": "Analysis of Arrhythmia Classification on ECG Dataset", "abstract": "  The heart is one of the most vital organs in the human body. It supplies\nblood and nutrients in other parts of the body. Therefore, maintaining a\nhealthy heart is essential. As a heart disorder, arrhythmia is a condition in\nwhich the heart's pumping mechanism becomes aberrant. The Electrocardiogram is\nused to analyze the arrhythmia problem from the ECG signals because of its\nfewer difficulties and cheapness. The heart peaks shown in the ECG graph are\nused to detect heart diseases, and the R peak is used to analyze arrhythmia\ndisease. Arrhythmia is grouped into two groups - Tachycardia and Bradycardia\nfor detection. In this paper, we discussed many different techniques such as\nDeep CNNs, LSTM, SVM, NN classifier, Wavelet, TQWT, etc., that have been used\nfor detecting arrhythmia using various datasets throughout the previous decade.\nThis work shows the analysis of some arrhythmia classification on the ECG\ndataset. Here, Data preprocessing, feature extraction, classification processes\nwere applied on most research work and achieved better performance for\nclassifying ECG signals to detect arrhythmia. Automatic arrhythmia detection\ncan help cardiologists make the right decisions immediately to save human life.\nIn addition, this research presents various previous research limitations with\nsome challenges in detecting arrhythmia that will help in future research.\n", "date": "2023-01-10", "categories": []}
{"id": "2301.10181", "title": "Interpretable Tsetlin Machine-based Premature Ventricular Contraction\n  Identification", "abstract": "  Neural network-based models have found wide use in automatic long-term\nelectrocardiogram (ECG) analysis. However, such black box models are inadequate\nfor analysing physiological signals where credibility and interpretability are\ncrucial. Indeed, how to make ECG analysis transparent is still an open problem.\nIn this study, we develop a Tsetlin machine (TM) based architecture for\npremature ventricular contraction (PVC) identification by analysing long-term\nECG signals. The architecture is transparent by describing patterns directly\nwith logical AND rules. To validate the accuracy of our approach, we compare\nthe TM performance with those of convolutional neural networks (CNNs). Our\nnumerical results demonstrate that TM provides comparable performance with CNNs\non the MIT-BIH database. To validate interpretability, we provide explanatory\ndiagrams that show how TM makes the PVC identification from confirming and\ninvalidating patterns. We argue that these are compatible with medical\nknowledge so that they can be readily understood and verified by a medical\ndoctor. Accordingly, we believe this study paves the way for machine learning\n(ML) for ECG analysis in clinical practice.\n", "date": "2023-01-20", "categories": []}
{"id": "2301.08828", "title": "AI enabled RPM for Mental Health Facility", "abstract": "  Mental healthcare is one of the prominent parts of the healthcare industry\nwith alarming concerns related to patients depression, stress leading to\nself-harm and threat to fellow patients and medical staff. To provide a\ntherapeutic environment for both patients and staff, aggressive or agitated\npatients need to be monitored remotely and track their vital signs and physical\nactivities continuously. Remote patient monitoring (RPM) using non-invasive\ntechnology could enable contactless monitoring of acutely ill patients in a\nmental health facility. Enabling the RPM system with AI unlocks a predictive\nenvironment in which future vital signs of the patients can be forecasted. This\npaper discusses an AI-enabled RPM system framework with a non-invasive digital\ntechnology RFID using its in-built NCS mechanism to retrieve vital signs and\nphysical actions of patients. Based on the retrieved time series data, future\nvital signs of patients for the upcoming 3 hours and classify their physical\nactions into 10 labelled physical activities. This framework assists to avoid\nany unforeseen clinical disasters and take precautionary measures with medical\nintervention at right time. A case study of a middle-aged PTSD patient treated\nwith the AI-enabled RPM system is demonstrated in this study.\n", "date": "2023-01-20", "categories": []}
{"id": "2301.08479", "title": "Pneumonia Detection in Chest X-Ray Images : Handling Class Imbalance", "abstract": "  People all over the globe are affected by pneumonia but deaths due to it are\nhighest in Sub-Saharan Asia and South Asia. In recent years, the overall\nincidence and mortality rate of pneumonia regardless of the utilization of\neffective vaccines and compelling antibiotics has escalated. Thus, pneumonia\nremains a disease that needs spry prevention and treatment. The widespread\nprevalence of pneumonia has caused the research community to come up with a\nframework that helps detect, diagnose and analyze diseases accurately and\npromptly. One of the major hurdles faced by the Artificial Intelligence (AI)\nresearch community is the lack of publicly available datasets for chest\ndiseases, including pneumonia . Secondly, few of the available datasets are\nhighly imbalanced (normal examples are over sampled, while samples with ailment\nare in severe minority) making the problem even more challenging. In this\narticle we present a novel framework for the detection of pneumonia. The\nnovelty of the proposed methodology lies in the tackling of class imbalance\nproblem. The Generative Adversarial Network (GAN), specifically a combination\nof Deep Convolutional Generative Adversarial Network (DCGAN) and Wasserstein\nGAN gradient penalty (WGAN-GP) was applied on the minority class ``Pneumonia''\nfor augmentation, whereas Random Under-Sampling (RUS) was done on the majority\nclass ``No Findings'' to deal with the imbalance problem. The ChestX-Ray8\ndataset, one of the biggest datasets, is used to validate the performance of\nthe proposed framework. The learning phase is completed using transfer learning\non state-of-the-art deep learning models i.e. ResNet-50, Xception, and VGG-16.\nResults obtained exceed state-of-the-art.\n", "date": "2023-01-20", "categories": []}
{"id": "2301.08534", "title": "Prodromal Diagnosis of Lewy Body Diseases Based on the Assessment of\n  Graphomotor and Handwriting Difficulties", "abstract": "  To this date, studies focusing on the prodromal diagnosis of Lewy body\ndiseases (LBDs) based on quantitative analysis of graphomotor and handwriting\ndifficulties are missing. In this work, we enrolled 18 subjects diagnosed with\npossible or probable mild cognitive impairment with Lewy bodies (MCI-LB), 7\nsubjects having more than 50% probability of developing Parkinson's disease\n(PD), 21 subjects with both possible/probable MCI-LB and probability of PD >\n50%, and 37 age- and gender-matched healthy controls (HC). Each participant\nperformed three tasks: Archimedean spiral drawing (to quantify graphomotor\ndifficulties), sentence writing task (to quantify handwriting difficulties),\nand pentagon copying test (to quantify cognitive decline). Next, we\nparameterized the acquired data by various temporal, kinematic, dynamic,\nspatial, and task-specific features. And finally, we trained classification\nmodels for each task separately as well as a model for their combination to\nestimate the predictive power of the features for the identification of LBDs.\nUsing this approach we were able to identify prodromal LBDs with 74% accuracy\nand showed the promising potential of computerized objective and non-invasive\ndiagnosis of LBDs based on the assessment of graphomotor and handwriting\ndifficulties.\n", "date": "2023-01-20", "categories": []}
{"id": "2301.04844", "title": "SACDNet: Towards Early Type 2 Diabetes Prediction with Uncertainty for\n  Electronic Health Records", "abstract": "  Type 2 diabetes mellitus (T2DM) is one of the most common diseases and a\nleading cause of death. The problem of early diagnosis of T2DM is challenging\nand necessary to prevent serious complications. This study proposes a novel\nneural network architecture for early T2DM prediction using multi-headed\nself-attention and dense layers to extract features from historic diagnoses,\npatient vitals, and demographics. The proposed technique is called the\nSelf-Attention for Comorbid Disease Net (SACDNet), achieving an accuracy of\n89.3% and an F1-Score of 89.1%, having a 1.6% increased accuracy and 1.3%\nincreased f1-score compared to the baseline techniques. Monte Carlo (MC)\nDropout is applied to the SACDNet to get a bayesian approximation. A T2DM\nprediction framework based on the MC Dropout SACDNet is proposed to quantize\nthe uncertainty associated with the predictions. A T2DM prediction dataset is\nalso built as part of this study which is based on real-world routine\nElectronic Health Record (EHR) data comprising 4,124 diabetic and 181,767\nnon-diabetic examples, collected from 295 different EHR systems running in\ndifferent parts of the United States of America. This dataset is further used\nto evaluate 7 different machine learning and 3 deep learning-based models.\nFinally, a detailed analysis of the fairness of every technique against\ndifferent patient demographic groups is performed to validate the unbiased\ngeneralization of the techniques and the diversity of the data.\n", "date": "2023-01-12", "categories": []}
{"id": "2301.07710", "title": "Fully Elman Neural Network: A Novel Deep Recurrent Neural Network\n  Optimized by an Improved Harris Hawks Algorithm for Classification of\n  Pulmonary Arterial Wedge Pressure", "abstract": "  Heart failure (HF) is one of the most prevalent life-threatening\ncardiovascular diseases in which 6.5 million people are suffering in the USA\nand more than 23 million worldwide. Mechanical circulatory support of HF\npatients can be achieved by implanting a left ventricular assist device (LVAD)\ninto HF patients as a bridge to transplant, recovery or destination therapy and\ncan be controlled by measurement of normal and abnormal pulmonary arterial\nwedge pressure (PAWP). While there are no commercial long-term implantable\npressure sensors to measure PAWP, real-time non-invasive estimation of abnormal\nand normal PAWP becomes vital. In this work, first an improved Harris Hawks\noptimizer algorithm called HHO+ is presented and tested on 24 unimodal and\nmultimodal benchmark functions. Second, a novel fully Elman neural network\n(FENN) is proposed to improve the classification performance. Finally, four\nnovel 18-layer deep learning methods of convolutional neural networks (CNNs)\nwith multi-layer perceptron (CNN-MLP), CNN with Elman neural networks\n(CNN-ENN), CNN with fully Elman neural networks (CNN-FENN), and CNN with fully\nElman neural networks optimized by HHO+ algorithm (CNN-FENN-HHO+) for\nclassification of abnormal and normal PAWP using estimated HVAD pump flow were\ndeveloped and compared. The estimated pump flow was derived by a non-invasive\nmethod embedded into the commercial HVAD controller. The proposed methods are\nevaluated on an imbalanced clinical dataset using 5-fold cross-validation. The\nproposed CNN-FENN-HHO+ method outperforms the proposed CNN-MLP, CNN-ENN and\nCNN-FENN methods and improved the classification performance metrics across\n5-fold cross-validation. The proposed methods can reduce the likelihood of\nhazardous events like pulmonary congestion and ventricular suction for HF\npatients and notify identified abnormal cases to the hospital, clinician and\ncardiologist.\n", "date": "2023-01-16", "categories": []}
{"id": "2301.08019", "title": "Identification, explanation and clinical evaluation of hospital patient\n  subtypes", "abstract": "  We present a pipeline in which unsupervised machine learning techniques are\nused to automatically identify subtypes of hospital patients admitted between\n2017 and 2021 in a large UK teaching hospital. With the use of state-of-the-art\nexplainability techniques, the identified subtypes are interpreted and assigned\nclinical meaning. In parallel, clinicians assessed intra-cluster similarities\nand inter-cluster differences of the identified patient subtypes within the\ncontext of their clinical knowledge. By confronting the outputs of both\nautomatic and clinician-based explanations, we aim to highlight the mutual\nbenefit of combining machine learning techniques with clinical expertise.\n", "date": "2023-01-19", "categories": []}
{"id": "2301.07496", "title": "Machine learning techniques for the Schizophrenia diagnosis: A\n  comprehensive review and future research directions", "abstract": "  Schizophrenia (SCZ) is a brain disorder where different people experience\ndifferent symptoms, such as hallucination, delusion, flat-talk, disorganized\nthinking, etc. In the long term, this can cause severe effects and diminish\nlife expectancy by more than ten years. Therefore, early and accurate diagnosis\nof SCZ is prevalent, and modalities like structural magnetic resonance imaging\n(sMRI), functional MRI (fMRI), diffusion tensor imaging (DTI), and\nelectroencephalogram (EEG) assist in witnessing the brain abnormalities of the\npatients. Moreover, for accurate diagnosis of SCZ, researchers have used\nmachine learning (ML) algorithms for the past decade to distinguish the brain\npatterns of healthy and SCZ brains using MRI and fMRI images. This paper seeks\nto acquaint SCZ researchers with ML and to discuss its recent applications to\nthe field of SCZ study. This paper comprehensively reviews state-of-the-art\ntechniques such as ML classifiers, artificial neural network (ANN), deep\nlearning (DL) models, methodological fundamentals, and applications with\nprevious studies. The motivation of this paper is to benefit from finding the\nresearch gaps that may lead to the development of a new model for accurate SCZ\ndiagnosis. The paper concludes with the research finding, followed by the\nfuture scope that directly contributes to new research directions.\n", "date": "2023-01-16", "categories": []}
{"id": "2301.06570", "title": "Cross-institution text mining to uncover clinical associations: a case\n  study relating social factors and code status in intensive care medicine", "abstract": "  Objective: Text mining of clinical notes embedded in electronic medical\nrecords is increasingly used to extract patient characteristics otherwise not\nor only partly available, to assess their association with relevant health\noutcomes. As manual data labeling needed to develop text mining models is\nresource intensive, we investigated whether off-the-shelf text mining models\ndeveloped at external institutions, together with limited within-institution\nlabeled data, could be used to reliably extract study variables to conduct\nassociation studies.\n  Materials and Methods: We developed multiple text mining models on different\ncombinations of within-institution and external-institution data to extract\nsocial factors from discharge reports of intensive care patients. Subsequently,\nwe assessed the associations between social factors and having a\ndo-not-resuscitate/intubate code. Results: Important differences were found\nbetween associations based on manually labeled data compared to text-mined\nsocial factors in three out of five cases. Adopting external-institution text\nmining models using manually labeled within-institution data resulted in models\nwith higher F1-scores, but not in meaningfully different associations.\n  Discussion: While text mining facilitated scaling analyses to larger samples\nleading to discovering a larger number of associations, the estimates may be\nunreliable. Confirmation is needed with better text mining models, ideally on a\nlarger manually labeled dataset.\n  Conclusion: The currently used text mining models were not sufficiently\naccurate to be used reliably in an association study. Model adaptation using\nwithin-institution data did not improve the estimates. Further research is\nneeded to set conditions for reliable use of text mining in medical research.\n", "date": "2023-01-16", "categories": []}
{"id": "2301.06908", "title": "MAFUS: a Framework to predict mortality risk in MAFLD subjects", "abstract": "  Metabolic (dysfunction) associated fatty liver disease (MAFLD) establishes\nnew criteria for diagnosing fatty liver disease independent of alcohol\nconsumption and concurrent viral hepatitis infection. However, the long-term\noutcome of MAFLD subjects is sparse. Few articles are focused on mortality in\nMAFLD subjects, and none investigate how to predict a fatal outcome. In this\npaper, we propose an artificial intelligence-based framework named MAFUS that\nphysicians can use for predicting mortality in MAFLD subjects. The framework\nuses data from various anthropometric and biochemical sources based on Machine\nLearning (ML) algorithms. The framework has been tested on a state-of-the-art\ndataset on which five ML algorithms are trained. Support Vector Machines\nresulted in being the best model. Furthermore, an Explainable Artificial\nIntelligence (XAI) analysis has been performed to understand the SVM diagnostic\nreasoning and the contribution of each feature to the prediction. The MAFUS\nframework is easy to apply, and the required parameters are readily available\nin the dataset.\n", "date": "2023-01-17", "categories": []}
{"id": "2301.07051", "title": "ActSafe: Predicting Violations of Medical Temporal Constraints for\n  Medication Adherence", "abstract": "  Prescription medications often impose temporal constraints on regular health\nbehaviors (RHBs) of patients, e.g., eating before taking medication. Violations\nof such medical temporal constraints (MTCs) can result in adverse effects.\nDetecting and predicting such violations before they occur can help alert the\npatient. We formulate the problem of modeling MTCs and develop a\nproof-of-concept solution, ActSafe, to predict violations of MTCs well ahead of\ntime. ActSafe utilizes a context-free grammar based approach for extracting and\nmapping MTCs from patient education materials. It also addresses the challenges\nof accurately predicting RHBs central to MTCs (e.g., medication intake). Our\nnovel behavior prediction model, HERBERT , utilizes a basis vectorization of\ntime series that is generalizable across temporal scale and duration of\nbehaviors, explicitly capturing the dependency between temporally collocated\nbehaviors. Based on evaluation using a real-world RHB dataset collected from 28\npatients in uncontrolled environments, HERBERT outperforms baseline models with\nan average of 51% reduction in root mean square error. Based on an evaluation\ninvolving patients with chronic conditions, ActSafe can predict MTC violations\na day ahead of time with an average F1 score of 0.86.\n", "date": "2023-01-17", "categories": []}
{"id": "2212.10320", "title": "Construction of extra-large scale screening tools for risks of severe\n  mental illnesses using real world healthcare data", "abstract": "  Importance: The prevalence of severe mental illnesses (SMIs) in the United\nStates is approximately 3% of the whole population. The ability to conduct risk\nscreening of SMIs at large scale could inform early prevention and treatment.\n  Objective: A scalable machine learning based tool was developed to conduct\npopulation-level risk screening for SMIs, including schizophrenia,\nschizoaffective disorders, psychosis, and bipolar disorders,using 1) healthcare\ninsurance claims and 2) electronic health records (EHRs).\n  Design, setting and participants: Data from beneficiaries from a nationwide\ncommercial healthcare insurer with 77.4 million members and data from patients\nfrom EHRs from eight academic hospitals based in the U.S. were used. First, the\npredictive models were constructed and tested using data in case-control\ncohorts from insurance claims or EHR data. Second, performance of the\npredictive models across data sources were analyzed. Third, as an illustrative\napplication, the models were further trained to predict risks of SMIs among\n18-year old young adults and individuals with substance associated conditions.\n  Main outcomes and measures: Machine learning-based predictive models for SMIs\nin the general population were built based on insurance claims and EHR.\n", "date": "2022-12-20", "categories": []}
{"id": "2208.05433", "title": "Detecting COVID-19 from digitized ECG printouts using 1D convolutional\n  neural networks", "abstract": "  The COVID-19 pandemic has exposed the vulnerability of healthcare services\nworldwide, raising the need to develop novel tools to provide rapid and\ncost-effective screening and diagnosis. Clinical reports indicated that\nCOVID-19 infection may cause cardiac injury, and electrocardiograms (ECG) may\nserve as a diagnostic biomarker for COVID-19. This study aims to utilize ECG\nsignals to detect COVID-19 automatically. We propose a novel method to extract\nECG signals from ECG paper records, which are then fed into a one-dimensional\nconvolution neural network (1D-CNN) to learn and diagnose the disease. To\nevaluate the quality of digitized signals, R peaks in the paper-based ECG\nimages are labeled. Afterward, RR intervals calculated from each image are\ncompared to RR intervals of the corresponding digitized signal. Experiments on\nthe COVID-19 ECG images dataset demonstrate that the proposed digitization\nmethod is able to capture correctly the original signals, with a mean absolute\nerror of 28.11 ms. Our proposed 1D-CNN model, which is trained on the digitized\nECG signals, allows identifying individuals with COVID-19 and other subjects\naccurately, with classification accuracies of 98.42%, 95.63%, and 98.50% for\nclassifying COVID-19 vs. Normal, COVID-19 vs. Abnormal Heartbeats, and COVID-19\nvs. other classes, respectively. Furthermore, the proposed method also achieves\na high-level of performance for the multi-classification task. Our findings\nindicate that a deep learning system trained on digitized ECG signals can serve\nas a potential tool for diagnosing COVID-19.\n", "date": "2022-08-10", "categories": []}
{"id": "2209.04794", "title": "Learning to diagnose common thorax diseases on chest radiographs from\n  radiology reports in Vietnamese", "abstract": "  We propose a data collecting and annotation pipeline that extracts\ninformation from Vietnamese radiology reports to provide accurate labels for\nchest X-ray (CXR) images. This can benefit Vietnamese radiologists and\nclinicians by annotating data that closely match their endemic diagnosis\ncategories which may vary from country to country. To assess the efficacy of\nthe proposed labeling technique, we built a CXR dataset containing 9,752\nstudies and evaluated our pipeline using a subset of this dataset. With an\nF1-score of at least 0.9923, the evaluation demonstrates that our labeling tool\nperforms precisely and consistently across all classes. After building the\ndataset, we train deep learning models that leverage knowledge transferred from\nlarge public CXR datasets. We employ a variety of loss functions to overcome\nthe curse of imbalanced multi-label datasets and conduct experiments with\nvarious model architectures to select the one that delivers the best\nperformance. Our best model (CheXpert-pretrained EfficientNet-B2) yields an\nF1-score of 0.6989 (95% CI 0.6740, 0.7240), AUC of 0.7912, sensitivity of\n0.7064 and specificity of 0.8760 for the abnormal diagnosis in general.\nFinally, we demonstrate that our coarse classification (based on five specific\nlocations of abnormalities) yields comparable results to fine classification\n(twelve pathologies) on the benchmark CheXpert dataset for general anomaly\ndetection while delivering better performance in terms of the average\nperformance of all classes.\n", "date": "2022-09-11", "categories": []}
{"id": "2301.03093", "title": "Prognosis and Treatment Prediction of Type-2 Diabetes Using Deep Neural\n  Network and Machine Learning Classifiers", "abstract": "  Type 2 Diabetes is a fast-growing, chronic metabolic disorder due to\nimbalanced insulin activity.The motion of this research is a comparative study\nof seven machine learning classifiers and an artificial neural network method\nto prognosticate the detection and treatment of diabetes with high accuracy,in\norder to identify and treat diabetes patients at an early age.Our training and\ntest dataset is an accumulation of 9483 diabetes patients information.The\ntraining dataset is large enough to negate overfitting and provide for highly\naccurate test performance.We use performance measures such as accuracy and\nprecision to find out the best algorithm deep ANN which outperforms with 95.14%\naccuracy among all other tested machine learning classifiers.We hope our\nhigh-performing model can be used by hospitals to predict diabetes and drive\nresearch into more accurate prediction models.\n", "date": "2023-01-08", "categories": []}
{"id": "2301.03531", "title": "Leveraging Contextual Relatedness to Identify Suicide Documentation in\n  Clinical Notes through Zero Shot Learning", "abstract": "  Identifying suicidality including suicidal ideation, attempts, and risk\nfactors in electronic health record data in clinical notes is difficult. A\nmajor difficulty is the lack of training samples given the small number of true\npositive instances among the increasingly large number of patients being\nscreened. This paper describes a novel methodology that identifies suicidality\nin clinical notes by addressing this data sparsity issue through zero-shot\nlearning. U.S. Veterans Affairs clinical notes served as data. The training\ndataset label was determined using diagnostic codes of suicide attempt and\nself-harm. A base string associated with the target label of suicidality was\nused to provide auxiliary information by narrowing the positive training cases\nto those containing the base string. A deep neural network was trained by\nmapping the training documents contents to a semantic space. For comparison, we\ntrained another deep neural network using the identical training dataset labels\nand bag-of-words features. The zero shot learning model outperformed the\nbaseline model in terms of AUC, sensitivity, specificity, and positive\npredictive value at multiple probability thresholds. In applying a 0.90\nprobability threshold, the methodology identified notes not associated with a\nrelevant ICD 10 CM code that documented suicidality, with 94 percent accuracy.\nThis new method can effectively identify suicidality without requiring manual\nannotation.\n", "date": "2023-01-09", "categories": []}
{"id": "2301.02383", "title": "Deep Biological Pathway Informed Pathology-Genomic Multimodal Survival\n  Prediction", "abstract": "  The integration of multi-modal data, such as pathological images and genomic\ndata, is essential for understanding cancer heterogeneity and complexity for\npersonalized treatments, as well as for enhancing survival predictions. Despite\nthe progress made in integrating pathology and genomic data, most existing\nmethods cannot mine the complex inter-modality relations thoroughly.\nAdditionally, identifying explainable features from these models that govern\npreclinical discovery and clinical prediction is crucial for cancer diagnosis,\nprognosis, and therapeutic response studies. We propose PONET- a novel\nbiological pathway-informed pathology-genomic deep model that integrates\npathological images and genomic data not only to improve survival prediction\nbut also to identify genes and pathways that cause different survival rates in\npatients. Empirical results on six of The Cancer Genome Atlas (TCGA) datasets\nshow that our proposed method achieves superior predictive performance and\nreveals meaningful biological interpretations. The proposed method establishes\ninsight into how to train biologically informed deep networks on multimodal\nbiomedical data which will have general applicability for understanding\ndiseases and predicting response and resistance to treatment.\n", "date": "2023-01-06", "categories": []}
{"id": "2301.01469", "title": "Machine Learning-based Signal Quality Assessment for Cardiac Volume\n  Monitoring in Electrical Impedance Tomography", "abstract": "  Owing to recent advances in thoracic electrical impedance tomography, a\npatient's hemodynamic function can be noninvasively and continuously estimated\nin real-time by surveilling a cardiac volume signal associated with stroke\nvolume and cardiac output. In clinical applications, however, a cardiac volume\nsignal is often of low quality, mainly because of the patient's deliberate\nmovements or inevitable motions during clinical interventions. This study aims\nto develop a signal quality indexing method that assesses the influence of\nmotion artifacts on transient cardiac volume signals. The assessment is\nperformed on each cardiac cycle to take advantage of the periodicity and\nregularity in cardiac volume changes. Time intervals are identified using the\nsynchronized electrocardiography system. We apply divergent machine-learning\nmethods, which can be sorted into discriminative-model and manifold-learning\napproaches. The use of machine-learning could be suitable for our real-time\nmonitoring application that requires fast inference and automation as well as\nhigh accuracy. In the clinical environment, the proposed method can be utilized\nto provide immediate warnings so that clinicians can minimize confusion\nregarding patients' conditions, reduce clinical resource utilization, and\nimprove the confidence level of the monitoring system. Numerous experiments\nusing actual EIT data validate the capability of cardiac volume signals\ndegraded by motion artifacts to be accurately and automatically assessed in\nreal-time by machine learning. The best model achieved an accuracy of 0.95,\npositive and negative predictive values of 0.96 and 0.86, sensitivity of 0.98,\nspecificity of 0.77, and AUC of 0.96.\n", "date": "2023-01-04", "categories": []}
{"id": "2301.01596", "title": "Hospital transfer risk prediction for COVID-19 patients from a\n  medicalized hotel based on Diffusion GraphSAGE", "abstract": "  The global COVID-19 pandemic has caused more than six million deaths\nworldwide. Medicalized hotels were established in Taiwan as quarantine\nfacilities for COVID-19 patients with no or mild symptoms. Due to limited\nmedical care available at these hotels, it is of paramount importance to\nidentify patients at risk of clinical deterioration. This study aimed to\ndevelop and evaluate a graph-based deep learning approach for progressive\nhospital transfer risk prediction in a medicalized hotel setting. Vital sign\nmeasurements were obtained for 632 patients and daily patient similarity graphs\nwere constructed. Inductive graph convolutional network models were trained on\ntop of the temporally integrated graphs to predict hospital transfer risk. The\nproposed models achieved AUC scores above 0.83 for hospital transfer risk\nprediction based on the measurements of past 1, 2, and 3 days, outperforming\nbaseline machine learning methods. A post-hoc analysis on the constructed\ndiffusion-based graph using Local Clustering Coefficient discovered a high-risk\ncluster with significantly older mean age, higher body temperature, lower SpO2,\nand shorter length of stay. Further time-to-hospital-transfer survival analysis\nalso revealed a significant decrease in survival probability in the discovered\nhigh-risk cluster. The obtained results demonstrated promising predictability\nand interpretability of the proposed graph-based approach. This technique may\nhelp preemptively detect high-risk patients at community-based medical\nfacilities similar to a medicalized hotel.\n", "date": "2022-12-31", "categories": []}
{"id": "2301.01679", "title": "COVID-Net USPro: An Open-Source Explainable Few-Shot Deep Prototypical\n  Network to Monitor and Detect COVID-19 Infection from Point-of-Care\n  Ultrasound Images", "abstract": "  As the Coronavirus Disease 2019 (COVID-19) continues to impact many aspects\nof life and the global healthcare systems, the adoption of rapid and effective\nscreening methods to prevent further spread of the virus and lessen the burden\non healthcare providers is a necessity. As a cheap and widely accessible\nmedical image modality, point-of-care ultrasound (POCUS) imaging allows\nradiologists to identify symptoms and assess severity through visual inspection\nof the chest ultrasound images. Combined with the recent advancements in\ncomputer science, applications of deep learning techniques in medical image\nanalysis have shown promising results, demonstrating that artificial\nintelligence-based solutions can accelerate the diagnosis of COVID-19 and lower\nthe burden on healthcare professionals. However, the lack of a huge amount of\nwell-annotated data poses a challenge in building effective deep neural\nnetworks in the case of novel diseases and pandemics. Motivated by this, we\npresent COVID-Net USPro, an explainable few-shot deep prototypical network,\nthat monitors and detects COVID-19 positive cases with high precision and\nrecall from minimal ultrasound images. COVID-Net USPro achieves 99.65% overall\naccuracy, 99.7% recall and 99.67% precision for COVID-19 positive cases when\ntrained with only 5 shots. The analytic pipeline and results were verified by\nour contributing clinician with extensive experience in POCUS interpretation,\nensuring that the network makes decisions based on actual patterns.\n", "date": "2023-01-04", "categories": []}
{"id": "2210.13535", "title": "Human-centered XAI for Burn Depth Characterization", "abstract": "  Approximately 1.25 million people in the United States are treated each year\nfor burn injuries. Precise burn injury classification is an important aspect of\nthe medical AI field. In this work, we propose an explainable human-in-the-loop\nframework for improving burn ultrasound classification models. Our framework\nleverages an explanation system based on the LIME classification explainer to\ncorroborate and integrate a burn expert's knowledge -- suggesting new features\nand ensuring the validity of the model. Using this framework, we discover that\nB-mode ultrasound classifiers can be enhanced by supplying textural features.\nMore specifically, we confirm that texture features based on the Gray Level\nCo-occurance Matrix (GLCM) of ultrasound frames can increase the accuracy of\ntransfer learned burn depth classifiers. We test our hypothesis on real data\nfrom porcine subjects. We show improvements in the accuracy of burn depth\nclassification -- from ~88% to ~94% -- once modified according to our\nframework.\n", "date": "2022-10-24", "categories": []}
{"id": "2301.00254", "title": "Depression Diagnosis and Analysis via Multimodal Multi-order Factor\n  Fusion", "abstract": "  Depression is a leading cause of death worldwide, and the diagnosis of\ndepression is nontrivial. Multimodal learning is a popular solution for\nautomatic diagnosis of depression, and the existing works suffer two main\ndrawbacks: 1) the high-order interactions between different modalities can not\nbe well exploited; and 2) interpretability of the models are weak. To remedy\nthese drawbacks, we propose a multimodal multi-order factor fusion (MMFF)\nmethod. Our method can well exploit the high-order interactions between\ndifferent modalities by extracting and assembling modality factors under the\nguide of a shared latent proxy. We conduct extensive experiments on two recent\nand popular datasets, E-DAIC-WOZ and CMDC, and the results show that our method\nachieve significantly better performance compared with other existing\napproaches. Besides, by analyzing the process of factor assembly, our model can\nintuitively show the contribution of each factor. This helps us understand the\nfusion mechanism.\n", "date": "2022-12-31", "categories": []}
{"id": "2212.14797", "title": "Easing Automatic Neurorehabilitation via Classification and Smoothness\n  Analysis", "abstract": "  Assessing the quality of movements for post-stroke patients during the\nrehabilitation phase is vital given that there is no standard stroke\nrehabilitation plan for all the patients. In fact, it depends basically on the\npatient's functional independence and its progress along the rehabilitation\nsessions. To tackle this challenge and make neurorehabilitation more agile, we\npropose an automatic assessment pipeline that starts by recognizing patients'\nmovements by means of a shallow deep learning architecture, then measuring the\nmovement quality using jerk measure and related measures. A particularity of\nthis work is that the dataset used is clinically relevant, since it represents\nmovements inspired from Fugl-Meyer a well common upper-limb clinical stroke\nassessment scale for stroke patients. We show that it is possible to detect the\ncontrast between healthy and patients movements in terms of smoothness, besides\nachieving conclusions about the patients' progress during the rehabilitation\nsessions that correspond to the clinicians' findings about each case.\n", "date": "2022-12-09", "categories": []}
{"id": "2212.13929", "title": "Evaluating Generalizability of Deep Learning Models Using\n  Indian-COVID-19 CT Dataset", "abstract": "  Computer tomography (CT) have been routinely used for the diagnosis of lung\ndiseases and recently, during the pandemic, for detecting the infectivity and\nseverity of COVID-19 disease. One of the major concerns in using ma-chine\nlearning (ML) approaches for automatic processing of CT scan images in clinical\nsetting is that these methods are trained on limited and biased sub-sets of\npublicly available COVID-19 data. This has raised concerns regarding the\ngeneralizability of these models on external datasets, not seen by the model\nduring training. To address some of these issues, in this work CT scan images\nfrom confirmed COVID-19 data obtained from one of the largest public\nrepositories, COVIDx CT 2A were used for training and internal vali-dation of\nmachine learning models. For the external validation we generated\nIndian-COVID-19 CT dataset, an open-source repository containing 3D CT volumes\nand 12096 chest CT images from 288 COVID-19 patients from In-dia. Comparative\nperformance evaluation of four state-of-the-art machine learning models, viz.,\na lightweight convolutional neural network (CNN), and three other CNN based\ndeep learning (DL) models such as VGG-16, ResNet-50 and Inception-v3 in\nclassifying CT images into three classes, viz., normal, non-covid pneumonia,\nand COVID-19 is carried out on these two datasets. Our analysis showed that the\nperformance of all the models is comparable on the hold-out COVIDx CT 2A test\nset with 90% - 99% accuracies (96% for CNN), while on the external\nIndian-COVID-19 CT dataset a drop in the performance is observed for all the\nmodels (8% - 19%). The traditional ma-chine learning model, CNN performed the\nbest on the external dataset (accu-racy 88%) in comparison to the deep learning\nmodels, indicating that a light-weight CNN is better generalizable on unseen\ndata. The data and code are made available at https://github.com/aleesuss/c19.\n", "date": "2022-12-28", "categories": []}
{"id": "2212.13032", "title": "Diagnosis of COVID-19 based on Chest Radiography", "abstract": "  The Coronavirus disease 2019 (COVID-19) was first identified in Wuhan, China,\nin early December 2019 and now becoming a pandemic. When COVID-19 patients\nundergo radiography examination, radiologists can observe the present of\nradiographic abnormalities from their chest X-ray (CXR) images. In this study,\na deep convolutional neural network (CNN) model was proposed to aid\nradiologists in diagnosing COVID-19 patients. First, this work conducted a\ncomparative study on the performance of modified VGG-16, ResNet-50 and\nDenseNet-121 to classify CXR images into normal, COVID-19 and viral pneumonia.\nThen, the impact of image augmentation on the classification results was\nevaluated. The publicly available COVID-19 Radiography Database was used\nthroughout this study. After comparison, ResNet-50 achieved the highest\naccuracy with 95.88%. Next, after training ResNet-50 with rotation,\ntranslation, horizontal flip, intensity shift and zoom augmented dataset, the\naccuracy dropped to 80.95%. Furthermore, an ablation study on the effect of\nimage augmentation on the classification results found that the combinations of\nrotation and intensity shift augmentation methods obtained an accuracy higher\nthan baseline, which is 96.14%. Finally, ResNet-50 with rotation and intensity\nshift augmentations performed the best and was proposed as the final\nclassification model in this work. These findings demonstrated that the\nproposed classification model can provide a promising result for COVID-19\ndiagnosis.\n", "date": "2022-12-26", "categories": []}
{"id": "2212.13048", "title": "GWO-FI: A novel machine learning framework by combining Gray Wolf\n  Optimizer and Frequent Itemsets to diagnose and investigate effective factors\n  on In-Hospital Mortality and Length of Stay among Kermanshahian\n  Cardiovascular Disease patients", "abstract": "  Investigation and analysis of patient outcomes, including in-hospital\nmortality and length of stay, are crucial for assisting clinicians in\ndetermining a patient's result at the outset of their hospitalization and for\nassisting hospitals in allocating their resources. This paper proposes an\napproach based on combining the well-known gray wolf algorithm with frequent\nitems extracted by association rule mining algorithms. First, original features\nare combined with the discriminative extracted frequent items. The best subset\nof these features is then chosen, and the parameters of the used classification\nalgorithms are also adjusted, using the gray wolf algorithm. This framework was\nevaluated using a real dataset made up of 2816 patients from the Imam Ali\nKermanshah Hospital in Iran. The study's findings indicate that low Ejection\nFraction, old age, high CPK values, and high Creatinine levels are the main\ncontributors to patients' mortality. Several significant and interesting rules\nrelated to mortality in hospitals and length of stay have also been extracted\nand presented. Additionally, the accuracy, sensitivity, specificity, and auroc\nof the proposed framework for the diagnosis of mortality in the hospital using\nthe SVM classifier were 0.9961, 0.9477, 0.9992, and 0.9734, respectively.\nAccording to the framework's findings, adding frequent items as features\nconsiderably improves classification accuracy.\n", "date": "2022-12-26", "categories": []}
{"id": "2212.09860", "title": "Predicting Ejection Fraction from Chest X-rays Using Computer Vision for\n  Diagnosing Heart Failure", "abstract": "  Heart failure remains a major public health challenge with growing costs.\nEjection fraction (EF) is a key metric for the diagnosis and management of\nheart failure however estimation of EF using echocardiography remains expensive\nfor the healthcare system and subject to intra/inter operator variability.\nWhile chest x-rays (CXR) are quick, inexpensive, and require less expertise,\nthey do not provide sufficient information to the human eye to estimate EF.\nThis work explores the efficacy of computer vision techniques to predict\nreduced EF solely from CXRs. We studied a dataset of 3488 CXRs from the MIMIC\nCXR-jpg (MCR) dataset. Our work establishes benchmarks using multiple\nstate-of-the-art convolutional neural network architectures. The subsequent\nanalysis shows increasing model sizes from 8M to 23M parameters improved\nclassification performance without overfitting the dataset. We further show how\ndata augmentation techniques such as CXR rotation and random cropping further\nimproves model performance another ~5%. Finally, we conduct an error analysis\nusing saliency maps and Grad-CAMs to better understand the failure modes of\nconvolutional models on this task.\n", "date": "2022-12-19", "categories": []}
{"id": "2212.12067", "title": "Enhancing the prediction of disease outcomes using electronic health\n  records and pretrained deep learning models", "abstract": "  Question: Can an encoder-decoder architecture pretrained on a large dataset\nof longitudinal electronic health records improves patient outcome predictions?\nFindings: In this prognostic study of 6.8 million patients, our denoising\nsequence-to-sequence prediction model of multiple outcomes outperformed\nstate-of-the-art models scuh pretrained BERT on a broad range of patient\noutcomes, including intentional self-harm and pancreatic cancer. Meaning: Deep\nbidirectional and autoregressive representation improves patient outcome\nprediction.\n", "date": "2022-12-22", "categories": []}
{"id": "2212.12114", "title": "Predicting Survival of Tongue Cancer Patients by Machine Learning Models", "abstract": "  Tongue cancer is a common oral cavity malignancy that originates in the mouth\nand throat. Much effort has been invested in improving its diagnosis,\ntreatment, and management. Surgical removal, chemotherapy, and radiation\ntherapy remain the major treatment for tongue cancer. The survival of patients\ndetermines the treatment effect. Previous studies have identified certain\nsurvival and risk factors based on descriptive statistics, ignoring the\ncomplex, nonlinear relationship among clinical and demographic variables. In\nthis study, we utilize five cutting-edge machine learning models and clinical\ndata to predict the survival of tongue cancer patients after treatment.\nFive-fold cross-validation, bootstrap analysis, and permutation feature\nimportance are applied to estimate and interpret model performance. The\nprognostic factors identified by our method are consistent with previous\nclinical studies. Our method is accurate, interpretable, and thus useable as\nadditional evidence in tongue cancer treatment and management.\n", "date": "2022-12-23", "categories": []}
{"id": "2212.12303", "title": "Introduction to Machine Learning for Physicians: A Survival Guide for\n  Data Deluge", "abstract": "  Many modern research fields increasingly rely on collecting and analysing\nmassive, often unstructured, and unwieldy datasets. Consequently, there is\ngrowing interest in machine learning and artificial intelligence applications\nthat can harness this `data deluge'. This broad nontechnical overview provides\na gentle introduction to machine learning with a specific focus on medical and\nbiological applications. We explain the common types of machine learning\nalgorithms and typical tasks that can be solved, illustrating the basics with\nconcrete examples from healthcare. Lastly, we provide an outlook on open\nchallenges, limitations, and potential impacts of machine-learning-powered\nmedicine.\n", "date": "2022-12-23", "categories": []}
{"id": "2212.11879", "title": "A Study of Left Before Treatment Complete Emergency Department Patients:\n  An Optimized Explanatory Machine Learning Framework", "abstract": "  The issue of left before treatment complete (LBTC) patients is common in\nemergency departments (EDs). This issue represents a medico-legal risk and may\ncause a revenue loss. Thus, understanding the factors that cause patients to\nleave before treatment is complete is vital to mitigate and potentially\neliminate these adverse effects. This paper proposes a framework for studying\nthe factors that affect LBTC outcomes in EDs. The framework integrates machine\nlearning, metaheuristic optimization, and model interpretation techniques.\nMetaheuristic optimization is used for hyperparameter optimization--one of the\nmain challenges of machine learning model development. Three metaheuristic\noptimization algorithms are employed for optimizing the parameters of extreme\ngradient boosting (XGB), which are simulated annealing (SA), adaptive simulated\nannealing (ASA), and adaptive tabu simulated annealing (ATSA). The optimized\nXGB models are used to predict the LBTC outcomes for the patients under\ntreatment in ED. The designed algorithms are trained and tested using four data\ngroups resulting from the feature selection phase. The model with the best\npredictive performance is interpreted using SHaply Additive exPlanations (SHAP)\nmethod. The findings show that ATSA-XGB outperformed other mode configurations\nwith an accuracy, area under the curve (AUC), sensitivity, specificity, and\nF1-score of 86.61%, 87.50%, 85.71%, 87.51%, and 86.60%, respectively. The\ndegree and the direction of effects of each feature were determined and\nexplained using the SHAP method.\n", "date": "2022-12-22", "categories": []}
{"id": "2208.04718", "title": "Improving COVID-19 CT Classification of CNNs by Learning\n  Parameter-Efficient Representation", "abstract": "  COVID-19 pandemic continues to spread rapidly over the world and causes a\ntremendous crisis in global human health and the economy. Its early detection\nand diagnosis are crucial for controlling the further spread. Many deep\nlearning-based methods have been proposed to assist clinicians in automatic\nCOVID-19 diagnosis based on computed tomography imaging. However, challenges\nstill remain, including low data diversity in existing datasets, and\nunsatisfied detection resulting from insufficient accuracy and sensitivity of\ndeep learning models. To enhance the data diversity, we design augmentation\ntechniques of incremental levels and apply them to the largest open-access\nbenchmark dataset, COVIDx CT-2A. Meanwhile, similarity regularization (SR)\nderived from contrastive learning is proposed in this study to enable CNNs to\nlearn more parameter-efficient representations, thus improving the accuracy and\nsensitivity of CNNs. The results on seven commonly used CNNs demonstrate that\nCNN performance can be improved stably through applying the designed\naugmentation and SR techniques. In particular, DenseNet121 with SR achieves an\naverage test accuracy of 99.44% in three trials for three-category\nclassification, including normal, non-COVID-19 pneumonia, and COVID-19\npneumonia. And the achieved precision, sensitivity, and specificity for the\nCOVID-19 pneumonia category are 98.40%, 99.59%, and 99.50%, respectively. These\nstatistics suggest that our method has surpassed the existing state-of-the-art\nmethods on the COVIDx CT-2A dataset.\n", "date": "2022-08-09", "categories": []}
{"id": "2212.08975", "title": "Clinical Deterioration Prediction in Brazilian Hospitals Based on\n  Artificial Neural Networks and Tree Decision Models", "abstract": "  Early recognition of clinical deterioration (CD) has vital importance in\npatients' survival from exacerbation or death. Electronic health records (EHRs)\ndata have been widely employed in Early Warning Scores (EWS) to measure CD risk\nin hospitalized patients. Recently, EHRs data have been utilized in Machine\nLearning (ML) models to predict mortality and CD. The ML models have shown\nsuperior performance in CD prediction compared to EWS. Since EHRs data are\nstructured and tabular, conventional ML models are generally applied to them,\nand less effort is put into evaluating the artificial neural network's\nperformance on EHRs data. Thus, in this article, an extremely boosted neural\nnetwork (XBNet) is used to predict CD, and its performance is compared to\neXtreme Gradient Boosting (XGBoost) and random forest (RF) models. For this\npurpose, 103,105 samples from thirteen Brazilian hospitals are used to generate\nthe models. Moreover, the principal component analysis (PCA) is employed to\nverify whether it can improve the adopted models' performance. The performance\nof ML models and Modified Early Warning Score (MEWS), an EWS candidate, are\nevaluated in CD prediction regarding the accuracy, precision, recall, F1-score,\nand geometric mean (G-mean) metrics in a 10-fold cross-validation approach.\nAccording to the experiments, the XGBoost model obtained the best results in\npredicting CD among Brazilian hospitals' data.\n", "date": "2022-12-17", "categories": []}
{"id": "2212.09276", "title": "COVID-19 Detection Based on Self-Supervised Transfer Learning Using\n  Chest X-Ray Images", "abstract": "  Purpose: Considering several patients screened due to COVID-19 pandemic,\ncomputer-aided detection has strong potential in assisting clinical workflow\nefficiency and reducing the incidence of infections among radiologists and\nhealthcare providers. Since many confirmed COVID-19 cases present radiological\nfindings of pneumonia, radiologic examinations can be useful for fast\ndetection. Therefore, chest radiography can be used to fast screen COVID-19\nduring the patient triage, thereby determining the priority of patient's care\nto help saturated medical facilities in a pandemic situation. Methods: In this\npaper, we propose a new learning scheme called self-supervised transfer\nlearning for detecting COVID-19 from chest X-ray (CXR) images. We compared six\nself-supervised learning (SSL) methods (Cross, BYOL, SimSiam, SimCLR,\nPIRL-jigsaw, and PIRL-rotation) with the proposed method. Additionally, we\ncompared six pretrained DCNNs (ResNet18, ResNet50, ResNet101, CheXNet,\nDenseNet201, and InceptionV3) with the proposed method. We provide quantitative\nevaluation on the largest open COVID-19 CXR dataset and qualitative results for\nvisual inspection. Results: Our method achieved a harmonic mean (HM) score of\n0.985, AUC of 0.999, and four-class accuracy of 0.953. We also used the\nvisualization technique Grad-CAM++ to generate visual explanations of different\nclasses of CXR images with the proposed method to increase the\ninterpretability. Conclusions: Our method shows that the knowledge learned from\nnatural images using transfer learning is beneficial for SSL of the CXR images\nand boosts the performance of representation learning for COVID-19 detection.\nOur method promises to reduce the incidence of infections among radiologists\nand healthcare providers.\n", "date": "2022-12-19", "categories": []}
{"id": "2212.09606", "title": "Discrimination, calibration, and point estimate accuracy of\n  GRU-D-Weibull architecture for real-time individualized endpoint prediction", "abstract": "  Real-time individual endpoint prediction has always been a challenging task\nbut of great clinic utility for both patients and healthcare providers. With\n6,879 chronic kidney disease stage 4 (CKD4) patients as a use case, we explored\nthe feasibility and performance of gated recurrent units with decay that models\nWeibull probability density function (GRU-D-Weibull) as a semi-parametric\nlongitudinal model for real-time individual endpoint prediction. GRU-D-Weibull\nhas a maximum C-index of 0.77 at 4.3 years of follow-up, compared to 0.68\nachieved by competing models. The L1-loss of GRU-D-Weibull is ~66% of XGB(AFT),\n~60% of MTLR, and ~30% of AFT model at CKD4 index date. The average absolute\nL1-loss of GRU-D-Weibull is around one year, with a minimum of 40% Parkes\nserious error after index date. GRU-D-Weibull is not calibrated and\nsignificantly underestimates true survival probability. Feature importance\ntests indicate blood pressure becomes increasingly important during follow-up,\nwhile eGFR and blood albumin are less important. Most continuous features have\nnon-linear/parabola impact on predicted survival time, and the results are\ngenerally consistent with existing knowledge. GRU-D-Weibull as a\nsemi-parametric temporal model shows advantages in built-in parameterization of\nmissing, native support for asynchronously arrived measurement, capability of\noutput both probability and point estimates at arbitrary time point for\narbitrary prediction horizon, improved discrimination and point estimate\naccuracy after incorporating newly arrived data. Further research on its\nperformance with more comprehensive input features, in-process or post-process\ncalibration are warranted to benefit CKD4 or alike terminally-ill patients.\n", "date": "2022-12-19", "categories": []}
{"id": "2212.08111", "title": "Routine Outcome Monitoring in Psychotherapy Treatment using\n  Sentiment-Topic Modelling Approach", "abstract": "  Despite the importance of emphasizing the right psychotherapy treatment for\nan individual patient, assessing the outcome of the therapy session is equally\ncrucial. Evidence showed that continuous monitoring patient's progress can\nsignificantly improve the therapy outcomes to an expected change. By monitoring\nthe outcome, the patient's progress can be tracked closely to help clinicians\nidentify patients who are not progressing in the treatment. These monitoring\ncan help the clinician to consider any necessary actions for the patient's\ntreatment as early as possible, e.g., recommend different types of treatment,\nor adjust the style of approach. Currently, the evaluation system is based on\nthe clinical-rated and self-report questionnaires that measure patients'\nprogress pre- and post-treatment. While outcome monitoring tends to improve the\ntherapy outcomes, however, there are many challenges in the current method,\ne.g. time and financial burden for administering questionnaires, scoring and\nanalysing the results. Therefore, a computational method for measuring and\nmonitoring patient progress over the course of treatment is needed, in order to\nenhance the likelihood of positive treatment outcome. Moreover, this\ncomputational method could potentially lead to an inexpensive monitoring tool\nto evaluate patients' progress in clinical care that could be administered by a\nwider range of health-care professionals.\n", "date": "2022-12-08", "categories": []}
{"id": "2211.15476", "title": "Meta-analysis of individualized treatment rules via sign-coherency", "abstract": "  Medical treatments tailored to a patient's baseline characteristics hold the\npotential of improving patient outcomes while reducing negative side effects.\nLearning individualized treatment rules (ITRs) often requires aggregation of\nmultiple datasets(sites); however, current ITR methodology does not take\nbetween-site heterogeneity into account, which can hurt model generalizability\nwhen deploying back to each site. To address this problem, we develop a method\nfor individual-level meta-analysis of ITRs, which jointly learns site-specific\nITRs while borrowing information about feature sign-coherency via a\nscientifically-motivated directionality principle. We also develop an adaptive\nprocedure for model tuning, using information criteria tailored to the ITR\nlearning problem. We study the proposed methods through numerical experiments\nto understand their performance under different levels of between-site\nheterogeneity and apply the methodology to estimate ITRs in a large\nmulti-center database of electronic health records. This work extends several\npopular methodologies for estimating ITRs (A-learning, weighted learning) to\nthe multiple-sites setting.\n", "date": "2022-11-28", "categories": []}
{"id": "2212.06267", "title": "Mortality Prediction Models with Clinical Notes Using Sparse Attention\n  at the Word and Sentence Levels", "abstract": "  Intensive Care in-hospital mortality prediction has various clinical\napplications. Neural prediction models, especially when capitalising on\nclinical notes, have been put forward as improvement on currently existing\nmodels. However, to be acceptable these models should be performant and\ntransparent. This work studies different attention mechanisms for clinical\nneural prediction models in terms of their discrimination and calibration.\nSpecifically, we investigate sparse attention as an alternative to dense\nattention weights in the task of in-hospital mortality prediction from clinical\nnotes. We evaluate the attention mechanisms based on: i) local self-attention\nover words in a sentence, and ii) global self-attention with a transformer\narchitecture across sentences. We demonstrate that the sparse mechanism\napproach outperforms the dense one for the local self-attention in terms of\npredictive performance with a publicly available dataset, and puts higher\nattention to prespecified relevant directive words. The performance at the\nsentence level, however, deteriorates as sentences including the influential\ndirective words tend to be dropped all together.\n", "date": "2022-12-12", "categories": []}
{"id": "2212.06364", "title": "ALRt: An Active Learning Framework for Irregularly Sampled Temporal Data", "abstract": "  Sepsis is a deadly condition affecting many patients in the hospital. Recent\nstudies have shown that patients diagnosed with sepsis have significant\nmortality and morbidity, resulting from the body's dysfunctional host response\nto infection. Clinicians often rely on the use of Sequential Organ Failure\nAssessment (SOFA), Systemic Inflammatory Response Syndrome (SIRS), and the\nModified Early Warning Score (MEWS) to identify early signs of clinical\ndeterioration requiring further work-up and treatment. However, many of these\ntools are manually computed and were not designed for automated computation.\nThere have been different methods used for developing sepsis onset models, but\nmany of these models must be trained on a sufficient number of patient\nobservations in order to form accurate sepsis predictions. Additionally, the\naccurate annotation of patients with sepsis is a major ongoing challenge. In\nthis paper, we propose the use of Active Learning Recurrent Neural Networks\n(ALRts) for short temporal horizons to improve the prediction of irregularly\nsampled temporal events such as sepsis. We show that an active learning RNN\nmodel trained on limited data can form robust sepsis predictions comparable to\nmodels using the entire training dataset.\n", "date": "2022-12-13", "categories": []}
{"id": "2212.06809", "title": "Real-Time Artificial Intelligence Assistance for Safe Laparoscopic\n  Cholecystectomy: Early-Stage Clinical Evaluation", "abstract": "  Artificial intelligence is set to be deployed in operating rooms to improve\nsurgical care. This early-stage clinical evaluation shows the feasibility of\nconcurrently attaining real-time, high-quality predictions from several deep\nneural networks for endoscopic video analysis deployed for assistance during\nthree laparoscopic cholecystectomies.\n", "date": "2022-12-13", "categories": []}
{"id": "2208.11223", "title": "POPDx: An Automated Framework for Patient Phenotyping across 392,246\n  Individuals in the UK Biobank Study", "abstract": "  Objective For the UK Biobank standardized phenotype codes are associated with\npatients who have been hospitalized but are missing for many patients who have\nbeen treated exclusively in an outpatient setting. We describe a method for\nphenotype recognition that imputes phenotype codes for all UK Biobank\nparticipants. Materials and Methods POPDx (Population-based Objective\nPhenotyping by Deep Extrapolation) is a bilinear machine learning framework for\nsimultaneously estimating the probabilities of 1,538 phenotype codes. We\nextracted phenotypic and health-related information of 392,246 individuals from\nthe UK Biobank for POPDx development and evaluation. A total of 12,803 ICD-10\ndiagnosis codes of the patients were converted to 1,538 Phecodes as gold\nstandard labels. The POPDx framework was evaluated and compared to other\navailable methods on automated multi-phenotype recognition. Results POPDx can\npredict phenotypes that are rare or even unobserved in training. We demonstrate\nsubstantial improvement of automated multi-phenotype recognition across 22\ndisease categories, and its application in identifying key epidemiological\nfeatures associated with each phenotype. Conclusions POPDx helps provide\nwell-defined cohorts for downstream studies. It is a general purpose method\nthat can be applied to other biobanks with diverse but incomplete data.\n", "date": "2022-08-23", "categories": []}
{"id": "2212.06040", "title": "Semantic Decomposition Improves Learning of Large Language Models on EHR\n  Data", "abstract": "  Electronic health records (EHR) are widely believed to hold a profusion of\nactionable insights, encrypted in an irregular, semi-structured format, amidst\na loud noise background. To simplify learning patterns of health and disease,\nmedical codes in EHR can be decomposed into semantic units connected by\nhierarchical graphs. Building on earlier synergy between Bidirectional Encoder\nRepresentations from Transformers (BERT) and Graph Attention Networks (GAT), we\npresent H-BERT, which ingests complete graph tree expansions of hierarchical\nmedical codes as opposed to only ingesting the leaves and pushes patient-level\nlabels down to each visit. This methodology significantly improves prediction\nof patient membership in over 500 medical diagnosis classes as measured by\naggregated AUC and APS, and creates distinct representations of patients in\nclosely related but clinically distinct phenotypes.\n", "date": "2022-11-14", "categories": []}
{"id": "2212.06042", "title": "AD-BERT: Using Pre-trained contextualized embeddings to Predict the\n  Progression from Mild Cognitive Impairment to Alzheimer's Disease", "abstract": "  Objective: We develop a deep learning framework based on the pre-trained\nBidirectional Encoder Representations from Transformers (BERT) model using\nunstructured clinical notes from electronic health records (EHRs) to predict\nthe risk of disease progression from Mild Cognitive Impairment (MCI) to\nAlzheimer's Disease (AD). Materials and Methods: We identified 3657 patients\ndiagnosed with MCI together with their progress notes from Northwestern\nMedicine Enterprise Data Warehouse (NMEDW) between 2000-2020. The progress\nnotes no later than the first MCI diagnosis were used for the prediction. We\nfirst preprocessed the notes by deidentification, cleaning and splitting, and\nthen pretrained a BERT model for AD (AD-BERT) based on the publicly available\nBio+Clinical BERT on the preprocessed notes. The embeddings of all the sections\nof a patient's notes processed by AD-BERT were combined by MaxPooling to\ncompute the probability of MCI-to-AD progression. For replication, we conducted\na similar set of experiments on 2563 MCI patients identified at Weill Cornell\nMedicine (WCM) during the same timeframe. Results: Compared with the 7 baseline\nmodels, the AD-BERT model achieved the best performance on both datasets, with\nArea Under receiver operating characteristic Curve (AUC) of 0.8170 and F1 score\nof 0.4178 on NMEDW dataset and AUC of 0.8830 and F1 score of 0.6836 on WCM\ndataset. Conclusion: We developed a deep learning framework using BERT models\nwhich provide an effective solution for prediction of MCI-to-AD progression\nusing clinical note analysis.\n", "date": "2022-11-07", "categories": []}
{"id": "2212.04984", "title": "Transformer-based normative modelling for anomaly detection of early\n  schizophrenia", "abstract": "  Despite the impact of psychiatric disorders on clinical health, early-stage\ndiagnosis remains a challenge. Machine learning studies have shown that\nclassifiers tend to be overly narrow in the diagnosis prediction task. The\noverlap between conditions leads to high heterogeneity among participants that\nis not adequately captured by classification models. To address this issue,\nnormative approaches have surged as an alternative method. By using a\ngenerative model to learn the distribution of healthy brain data patterns, we\ncan identify the presence of pathologies as deviations or outliers from the\ndistribution learned by the model. In particular, deep generative models showed\ngreat results as normative models to identify neurological lesions in the\nbrain. However, unlike most neurological lesions, psychiatric disorders present\nsubtle changes widespread in several brain regions, making these alterations\nchallenging to identify. In this work, we evaluate the performance of\ntransformer-based normative models to detect subtle brain changes expressed in\nadolescents and young adults. We trained our model on 3D MRI scans of\nneurotypical individuals (N=1,765). Then, we obtained the likelihood of\nneurotypical controls and psychiatric patients with early-stage schizophrenia\nfrom an independent dataset (N=93) from the Human Connectome Project. Using the\npredicted likelihood of the scans as a proxy for a normative score, we obtained\nan AUROC of 0.82 when assessing the difference between controls and individuals\nwith early-stage schizophrenia. Our approach surpassed recent normative methods\nbased on brain age and Gaussian Process, showing the promising use of deep\ngenerative models to help in individualised analyses.\n", "date": "2022-12-08", "categories": []}
{"id": "2212.02346", "title": "Accu-Help: A Machine Learning based Smart Healthcare Framework for\n  Accurate Detection of Obsessive Compulsive Disorder", "abstract": "  In recent years the importance of Smart Healthcare cannot be overstated. The\ncurrent work proposed to expand the state-of-art of smart healthcare in\nintegrating solutions for Obsessive Compulsive Disorder (OCD). Identification\nof OCD from oxidative stress biomarkers (OSBs) using machine learning is an\nimportant development in the study of OCD. However, this process involves the\ncollection of OCD class labels from hospitals, collection of corresponding OSBs\nfrom biochemical laboratories, integrated and labeled dataset creation, use of\nsuitable machine learning algorithm for designing OCD prediction model, and\nmaking these prediction models available for different biochemical laboratories\nfor OCD prediction for unlabeled OSBs. Further, from time to time, with\nsignificant growth in the volume of the dataset with labeled samples,\nredesigning the prediction model is required for further use. The whole process\nrequires distributed data collection, data integration, coordination between\nthe hospital and biochemical laboratory, dynamic machine learning OCD\nprediction mode design using a suitable machine learning algorithm, and making\nthe machine learning model available for the biochemical laboratories. Keeping\nall these things in mind, Accu-Help a fully automated, smart, and accurate OCD\ndetection conceptual model is proposed to help the biochemical laboratories for\nefficient detection of OCD from OSBs. OSBs are classified into three classes:\nHealthy Individual (HI), OCD Affected Individual (OAI), and Genetically\nAffected Individual (GAI). The main component of this proposed framework is the\nmachine learning OCD prediction model design. In this Accu-Help, a neural\nnetwork-based approach is presented with an OCD prediction accuracy of 86\npercent.\n", "date": "2022-12-05", "categories": []}
{"id": "2209.08601", "title": "Comparative study of machine learning and deep learning methods on ASD\n  classification", "abstract": "  The autism dataset is studied to identify the differences between autistic\nand healthy groups. For this, the resting-state Functional Magnetic Resonance\nImaging (rs-fMRI) data of the two groups are analyzed, and networks of\nconnections between brain regions were created. Several classification\nframeworks are developed to distinguish the connectivity patterns between the\ngroups. The best models for statistical inference and precision were compared,\nand the tradeoff between precision and model interpretability was analyzed.\nFinally, the classification accuracy measures were reported to justify the\nperformance of our framework. Our best model can classify autistic and healthy\npatients on the multisite ABIDE I data with 71% accuracy.\n", "date": "2022-09-18", "categories": []}
{"id": "2212.01027", "title": "Progress and Challenges for the Application of Machine Learning for\n  Neglected Tropical Diseases", "abstract": "  Neglected tropical diseases (NTDs) continue to affect the livelihood of\nindividuals in countries in the Southeast Asia and Western Pacific region.\nThese diseases have been long existing and have caused devastating health\nproblems and economic decline to people in low- and middle-income (developing)\ncountries. An estimated 1.7 billion of the world's population suffer one or\nmore NTDs annually, this puts approximately one in five individuals at risk for\nNTDs. In addition to health and social impact, NTDs inflict significant\nfinancial burden to patients, close relatives, and are responsible for billions\nof dollars lost in revenue from reduced labor productivity in developing\ncountries alone. There is an urgent need to better improve the control and\neradication or elimination efforts towards NTDs. This can be achieved by\nutilizing machine learning tools to better the surveillance, prediction and\ndetection program, and combat NTDs through the discovery of new therapeutics\nagainst these pathogens. This review surveys the current application of machine\nlearning tools for NTDs and the challenges to elevate the state-of-the-art of\nNTDs surveillance, management, and treatment.\n", "date": "2022-12-02", "categories": []}
{"id": "2211.14559", "title": "Boosting COVID-19 Severity Detection with Infection-aware Contrastive\n  Mixup Classification", "abstract": "  This paper presents our solution for the 2nd COVID-19 Severity Detection\nCompetition. This task aims to distinguish the Mild, Moderate, Severe, and\nCritical grades in COVID-19 chest CT images. In our approach, we devise a novel\ninfection-aware 3D Contrastive Mixup Classification network for severity\ngrading. Specifcally, we train two segmentation networks to first extract the\nlung region and then the inner lesion region. The lesion segmentation mask\nserves as complementary information for the original CT slices. To relieve the\nissue of imbalanced data distribution, we further improve the advanced\nContrastive Mixup Classification network by weighted cross-entropy loss. On the\nCOVID-19 severity detection leaderboard, our approach won the first place with\na Macro F1 Score of 51.76%. It significantly outperforms the baseline method by\nover 11.46%.\n", "date": "2022-11-26", "categories": []}
{"id": "2212.00414", "title": "A Comprehensive Study on Machine Learning Methods to Increase the\n  Prediction Accuracy of Classifiers and Reduce the Number of Medical Tests\n  Required to Diagnose Alzheimer'S Disease", "abstract": "  Alzheimer's patients gradually lose their ability to think, behave, and\ninteract with others. Medical history, laboratory tests, daily activities, and\npersonality changes can all be used to diagnose the disorder. A series of\ntime-consuming and expensive tests are used to diagnose the illness. The most\neffective way to identify Alzheimer's disease is using a Random-forest\nclassifier in this study, along with various other Machine Learning techniques.\nThe main goal of this study is to fine-tune the classifier to detect illness\nwith fewer tests while maintaining a reasonable disease discovery accuracy. We\nsuccessfully identified the condition in almost 94% of cases using four of the\nthirty frequently utilized indicators.\n", "date": "2022-12-01", "categories": []}
{"id": "2212.00557", "title": "Deep Kernel Learning for Mortality Prediction in the Face of Temporal\n  Shift", "abstract": "  Neural models, with their ability to provide novel representations, have\nshown promising results in prediction tasks in healthcare. However, patient\ndemographics, medical technology, and quality of care change over time. This\noften leads to drop in the performance of neural models for prospective\npatients, especially in terms of their calibration. The deep kernel learning\n(DKL) framework may be robust to such changes as it combines neural models with\nGaussian processes, which are aware of prediction uncertainty. Our hypothesis\nis that out-of-distribution test points will result in probabilities closer to\nthe global mean and hence prevent overconfident predictions. This in turn, we\nhypothesise, will result in better calibration on prospective data.\n  This paper investigates DKL's behaviour when facing a temporal shift, which\nwas naturally introduced when an information system that feeds a cohort\ndatabase was changed. We compare DKL's performance to that of a neural baseline\nbased on recurrent neural networks. We show that DKL indeed produced superior\ncalibrated predictions. We also confirm that the DKL's predictions were indeed\nless sharp. In addition, DKL's discrimination ability was even improved: its\nAUC was 0.746 (+- 0.014 std), compared to 0.739 (+- 0.028 std) for the\nbaseline. The paper demonstrated the importance of including uncertainty in\nneural computing, especially for their prospective use.\n", "date": "2022-12-01", "categories": []}
{"id": "2208.08268", "title": "Prediction of Oral Food Challenge Outcomes via Ensemble Learning", "abstract": "  Oral Food Challenges (OFCs) are essential to accurately diagnosing food\nallergy due to the limitations of existing clinical testing. However, some\npatients are hesitant to undergo OFCs, while those willing suffer from limited\naccess to allergists in rural/community healthcare settings. Despite its\nsuccess in predicting patient outcomes in other clinical settings, few\napplications of machine learning to food allergy have been developed. Thus, in\nthis study, we seek to leverage machine learning methodologies for OFC outcome\nprediction. Retrospective data was gathered from 1,112 patients who\ncollectively underwent a total of 1,284 OFCs, and consisted of clinical factors\nincluding serum-specific Immunoglobulin E (IgE), total IgE, skin prick tests\n(SPTs), comorbidities, sex, and age. Using these features, multiple machine\nlearning models were constructed to predict OFC outcomes for three common\nallergens: peanut, egg, and milk. The best performing model for each allergen\nwas an ensemble of random forest (egg) or Learning Using Concave and Convex\nKernels (LUCCK) (peanut, milk) models, which achieved an Area under the Curve\n(AUC) of 0.91, 0.96, and 0.94, in predicting OFC outcomes for peanut, egg, and\nmilk, respectively. Moreover, all such models had sensitivity and specificity\nvalues 89%. Model interpretation via SHapley Additive exPlanations (SHAP)\nindicates that specific IgE, along with wheal and flare values from SPTs, are\nhighly predictive of OFC outcomes. The results of this analysis suggest that\nensemble learning has the potential to predict OFC outcomes and reveal relevant\nclinical factors for further study.\n", "date": "2022-08-17", "categories": []}
{"id": "2211.16398", "title": "Self-Supervised Mental Disorder Classifiers via Time Reversal", "abstract": "  Data scarcity is a notable problem, especially in the medical domain, due to\npatient data laws. Therefore, efficient Pre-Training techniques could help in\ncombating this problem. In this paper, we demonstrate that a model trained on\nthe time direction of functional neuro-imaging data could help in any\ndownstream task, for example, classifying diseases from healthy controls in\nfMRI data. We train a Deep Neural Network on Independent components derived\nfrom fMRI data using the Independent component analysis (ICA) technique. It\nlearns time direction in the ICA-based data. This pre-trained model is further\ntrained to classify brain disorders in different datasets. Through various\nexperiments, we have shown that learning time direction helps a model learn\nsome causal relation in fMRI data that helps in faster convergence, and\nconsequently, the model generalizes well in downstream classification tasks\neven with fewer data records.\n", "date": "2022-11-29", "categories": []}
{"id": "2211.17121", "title": "sEHR-CE: Language modelling of structured EHR data for efficient and\n  generalizable patient cohort expansion", "abstract": "  Electronic health records (EHR) offer unprecedented opportunities for\nin-depth clinical phenotyping and prediction of clinical outcomes. Combining\nmultiple data sources is crucial to generate a complete picture of disease\nprevalence, incidence and trajectories. The standard approach to combining\nclinical data involves collating clinical terms across different terminology\nsystems using curated maps, which are often inaccurate and/or incomplete. Here,\nwe propose sEHR-CE, a novel framework based on transformers to enable\nintegrated phenotyping and analyses of heterogeneous clinical datasets without\nrelying on these mappings. We unify clinical terminologies using textual\ndescriptors of concepts, and represent individuals' EHR as sections of text. We\nthen fine-tune pre-trained language models to predict disease phenotypes more\naccurately than non-text and single terminology approaches. We validate our\napproach using primary and secondary care data from the UK Biobank, a\nlarge-scale research study. Finally, we illustrate in a type 2 diabetes use\ncase how sEHR-CE identifies individuals without diagnosis that share clinical\ncharacteristics with patients.\n", "date": "2022-11-30", "categories": []}
{"id": "2210.12342", "title": "Detection of Risk Predictors of COVID-19 Mortality with Classifier\n  Machine Learning Models Operated with Routine Laboratory Biomarkers", "abstract": "  Early evaluation of patients who require special care and who have high\ndeath-expectancy in COVID-19, and the effective determination of relevant\nbiomarkers on large sample-groups are important to reduce mortality. This study\naimed to reveal the routine blood-value predictors of COVID-19 mortality and to\ndetermine the lethal-risk levels of these predictors during the disease\nprocess. The dataset of the study consists of 38 routine blood-values of 2597\npatients who died (n = 233) and those who recovered (n = 2364) from COVID-19 in\nAugust-December, 2021. In this study, the histogram-based gradient-boosting\n(HGB) model was the most successful machine-learning classifier in detecting\nliving and deceased COVID-19 patients (with squared F1 metrics F1^2 = 1). The\nmost efficient binary combinations with procalcitonin were obtained with\nD-dimer, ESR, D-Bil and ferritin. The HGB model operated with these feature\npairs correctly detected almost all of the patients who survived and those who\ndied (precision > 0.98, recall > 0.98, F1^2 > 0.98). Furthermore, in the HGB\nmodel operated with a single feature, the most efficient features were\nprocalcitonin (F1^2 = 0.96) and ferritin (F1^2 = 0.91). In addition, according\nto the two-threshold approach, ferritin values between 376.2 mkg/L and 396.0\nmkg/L (F1^2 = 0.91) and pro-calcitonin values between 0.2 mkg/L and 5.2 mkg/L\n(F1^2 = 0.95) were found to be fatal risk levels for COVID-19. Considering all\nthe results, we suggest that many features combined with these features,\nespecially procalcitonin and ferritin, operated with the HGB model, can be used\nto achieve very successful results in the classification of those who live, and\nthose who die from COVID-19. Moreover, we strongly recommend that clinicians\nconsider the critical levels we have found for procalcitonin and ferritin\nproperties, to reduce the lethality of the COVID-19 disease.\n", "date": "2022-10-22", "categories": []}
{"id": "2211.14929", "title": "Multi-Label Chest X-Ray Classification via Deep Learning", "abstract": "  In this era of pandemic, the future of healthcare industry has never been\nmore exciting. Artificial intelligence and machine learning (AI & ML) present\nopportunities to develop solutions that cater for very specific needs within\nthe industry. Deep learning in healthcare had become incredibly powerful for\nsupporting clinics and in transforming patient care in general. Deep learning\nis increasingly being applied for the detection of clinically important\nfeatures in the images beyond what can be perceived by the naked human eye.\nChest X-ray images are one of the most common clinical method for diagnosing a\nnumber of diseases such as pneumonia, lung cancer and many other abnormalities\nlike lesions and fractures. Proper diagnosis of a disease from X-ray images is\noften challenging task for even expert radiologists and there is a growing need\nfor computerized support systems due to the large amount of information encoded\nin X-Ray images. The goal of this paper is to develop a lightweight solution to\ndetect 14 different chest conditions from an X ray image. Given an X-ray image\nas input, our classifier outputs a label vector indicating which of 14 disease\nclasses does the image fall into. Along with the image features, we are also\ngoing to use non-image features available in the data such as X-ray view type,\nage, gender etc. The original study conducted Stanford ML Group is our base\nline. Original study focuses on predicting 5 diseases. Our aim is to improve\nupon previous work, expand prediction to 14 diseases and provide insight for\nfuture chest radiography research.\n", "date": "2022-11-27", "categories": []}
{"id": "2211.15234", "title": "Shoupa: An AI System for Early Diagnosis of Parkinson's Disease", "abstract": "  Parkinson's Disease (PD) is a progressive nervous system disorder that has\naffected more than 5.8 million people, especially the elderly. Due to the\ncomplexity of its symptoms and its similarity to other neurological disorders,\nearly detection requires neurologists or PD specialists to be involved, which\nis not accessible to most old people. Therefore, we integrate smart mobile\ndevices with AI technologies. In this paper, we introduce the framework of our\ndeveloped PD early detection system which combines different tasks evaluating\nboth motor and non-motor symptoms. With the developed model, we help users\ndetect PD punctually in non-clinical settings and figure out their most severe\nsymptoms. The results are expected to be further used for PD rehabilitation\nguidance and detection of other neurological disorders.\n", "date": "2022-11-28", "categories": []}
{"id": "2211.15459", "title": "Classification of Human Monkeypox Disease Using Deep Learning Models and\n  Attention Mechanisms", "abstract": "  As the world is still trying to rebuild from the destruction caused by the\nwidespread reach of the COVID-19 virus, and the recent alarming surge of human\nmonkeypox disease outbreaks in numerous countries threatens to become a new\nglobal pandemic too. Human monkeypox disease syndromes are quite similar to\nchickenpox, and measles classic symptoms, with very intricate differences such\nas skin blisters, which come in diverse forms. Various deep-learning methods\nhave shown promising performances in the image-based diagnosis of COVID-19,\ntumor cell, and skin disease classification tasks. In this paper, we try to\nintegrate deep transfer-learning-based methods, along with a convolutional\nblock attention module (CBAM), to focus on the relevant portion of the feature\nmaps to conduct an image-based classification of human monkeypox disease. We\nimplement five deep-learning models, VGG19, Xception, DenseNet121,\nEfficientNetB3, and MobileNetV2, along with integrated channel and spatial\nattention mechanisms, and perform a comparative analysis among them. An\narchitecture consisting of Xception-CBAM-Dense layers performed better than the\nother models at classifying human monkeypox and other diseases with a\nvalidation accuracy of 83.89%.\n", "date": "2022-11-21", "categories": []}
{"id": "2211.11749", "title": "Towards Automatic Prediction of Outcome in Treatment of Cerebral\n  Aneurysms", "abstract": "  Intrasaccular flow disruptors treat cerebral aneurysms by diverting the blood\nflow from the aneurysm sac. Residual flow into the sac after the intervention\nis a failure that could be due to the use of an undersized device, or to\nvascular anatomy and clinical condition of the patient. We report a machine\nlearning model based on over 100 clinical and imaging features that predict the\noutcome of wide-neck bifurcation aneurysm treatment with an intravascular\nembolization device. We combine clinical features with a diverse set of common\nand novel imaging measurements within a random forest model. We also develop\nneural network segmentation algorithms in 2D and 3D to contour the sac in\nangiographic images and automatically calculate the imaging features. These\ndeliver 90% overlap with manual contouring in 2D and 83% in 3D. Our predictive\nmodel classifies complete vs. partial occlusion outcomes with an accuracy of\n75.31%, and weighted F1-score of 0.74.\n", "date": "2022-11-18", "categories": []}
{"id": "2211.11944", "title": "COVID-Net Assistant: A Deep Learning-Driven Virtual Assistant for\n  COVID-19 Symptom Prediction and Recommendation", "abstract": "  As the COVID-19 pandemic continues to put a significant burden on healthcare\nsystems worldwide, there has been growing interest in finding inexpensive\nsymptom pre-screening and recommendation methods to assist in efficiently using\navailable medical resources such as PCR tests. In this study, we introduce the\ndesign of COVID-Net Assistant, an efficient virtual assistant designed to\nprovide symptom prediction and recommendations for COVID-19 by analyzing users'\ncough recordings through deep convolutional neural networks. We explore a\nvariety of highly customized, lightweight convolutional neural network\narchitectures generated via machine-driven design exploration (which we refer\nto as COVID-Net Assistant neural networks) on the Covid19-Cough benchmark\ndataset. The Covid19-Cough dataset comprises 682 cough recordings from a\nCOVID-19 positive cohort and 642 from a COVID-19 negative cohort. Among the 682\ncough recordings labeled positive, 382 recordings were verified by PCR test.\nOur experimental results show promising, with the COVID-Net Assistant neural\nnetworks demonstrating robust predictive performance, achieving AUC scores of\nover 0.93, with the best score over 0.95 while being fast and efficient in\ninference. The COVID-Net Assistant models are made available in an open source\nmanner through the COVID-Net open initiative and, while not a production-ready\nsolution, we hope their availability acts as a good resource for clinical\nscientists, machine learning researchers, as well as citizen scientists to\ndevelop innovative solutions.\n", "date": "2022-11-22", "categories": []}
{"id": "2211.12422", "title": "PiRL: Participant-Invariant Representation Learning for Healthcare", "abstract": "  Due to individual heterogeneity, performance gaps are observed between\ngeneric (one-size-fits-all) models and person-specific models in data-driven\nhealth applications. However, in real-world applications, generic models are\nusually more favorable due to new-user-adaptation issues and system\ncomplexities, etc. To improve the performance of the generic model, we propose\na representation learning framework that learns participant-invariant\nrepresentations, named PiRL. The proposed framework utilizes maximum mean\ndiscrepancy (MMD) loss and domain-adversarial training to encourage the model\nto learn participant-invariant representations. Further, a triplet loss, which\nconstrains the model for inter-class alignment of the representations, is\nutilized to optimize the learned representations for downstream health\napplications. We evaluated our frameworks on two public datasets related to\nphysical and mental health, for detecting sleep apnea and stress, respectively.\nAs preliminary results, we found the proposed approach shows around a 5%\nincrease in accuracy compared to the baseline.\n", "date": "2022-11-21", "categories": []}
{"id": "2211.10828", "title": "Instability in clinical risk stratification models using deep learning", "abstract": "  While it has been well known in the ML community that deep learning models\nsuffer from instability, the consequences for healthcare deployments are under\ncharacterised. We study the stability of different model architectures trained\non electronic health records, using a set of outpatient prediction tasks as a\ncase study. We show that repeated training runs of the same deep learning model\non the same training data can result in significantly different outcomes at a\npatient level even though global performance metrics remain stable. We propose\ntwo stability metrics for measuring the effect of randomness of model training,\nas well as mitigation strategies for improving model stability.\n", "date": "2022-11-20", "categories": []}
{"id": "2211.09856", "title": "Machine Learning-Assisted Recurrence Prediction for Early-Stage\n  Non-Small-Cell Lung Cancer Patients", "abstract": "  Background: Stratifying cancer patients according to risk of relapse can\npersonalize their care. In this work, we provide an answer to the following\nresearch question: How to utilize machine learning to estimate probability of\nrelapse in early-stage non-small-cell lung cancer patients?\n  Methods: For predicting relapse in 1,387 early-stage (I-II), non-small-cell\nlung cancer (NSCLC) patients from the Spanish Lung Cancer Group data (65.7\naverage age, 24.8% females, 75.2% males) we train tabular and graph machine\nlearning models. We generate automatic explanations for the predictions of such\nmodels. For models trained on tabular data, we adopt SHAP local explanations to\ngauge how each patient feature contributes to the predicted outcome. We explain\ngraph machine learning predictions with an example-based method that highlights\ninfluential past patients. Results: Machine learning models trained on tabular\ndata exhibit a 76% accuracy for the Random Forest model at predicting relapse\nevaluated with a 10-fold cross-validation (model was trained 10 times with\ndifferent independent sets of patients in test, train and validation sets, the\nreported metrics are averaged over these 10 test sets). Graph machine learning\nreaches 68% accuracy over a 200-patient, held-out test set, calibrated on a\nheld-out set of 100 patients. Conclusions: Our results show that machine\nlearning models trained on tabular and graph data can enable objective,\npersonalised and reproducible prediction of relapse and therefore, disease\noutcome in patients with early-stage NSCLC. With further prospective and\nmultisite validation, and additional radiological and molecular data, this\nprognostic model could potentially serve as a predictive decision support tool\nfor deciding the use of adjuvant treatments in early-stage lung cancer.\nKeywords: Non-Small-Cell Lung Cancer, Tumor Recurrence Prediction, Machine\nLearning\n", "date": "2022-11-17", "categories": []}
{"id": "2211.07814", "title": "Extending the Neural Additive Model for Survival Analysis with EHR Data", "abstract": "  With increasing interest in applying machine learning to develop healthcare\nsolutions, there is a desire to create interpretable deep learning models for\nsurvival analysis. In this paper, we extend the Neural Additive Model (NAM) by\nincorporating pairwise feature interaction networks and equip these models with\nloss functions that fit both proportional and non-proportional extensions of\nthe Cox model. We show that within this extended framework, we can construct\nnon-proportional hazard models, which we call TimeNAM, that significantly\nimprove performance over the standard NAM model architecture on benchmark\nsurvival datasets. We apply these model architectures to data from the\nElectronic Health Record (EHR) database of Seoul National University Hospital\nGangnam Center (SNUHGC) to build an interpretable neural network survival model\nfor gastric cancer prediction. We demonstrate that on both benchmark survival\nanalysis datasets, as well as on our gastric cancer dataset, our model\narchitectures yield performance that matches, or surpasses, the current\nstate-of-the-art black-box methods.\n", "date": "2022-11-15", "categories": []}
{"id": "2211.08429", "title": "An Automatic ICD Coding Network Using Partition-Based Label Attention", "abstract": "  International Classification of Diseases (ICD) is a global medical\nclassification system which provides unique codes for diagnoses and procedures\nappropriate to a patient's clinical record. However, manual coding by human\ncoders is expensive and error-prone. Automatic ICD coding has the potential to\nsolve this problem. With the advancement of deep learning technologies, many\ndeep learning-based methods for automatic ICD coding are being developed. In\nparticular, a label attention mechanism is effective for multi-label\nclassification, i.e., the ICD coding. It effectively obtains the label-specific\nrepresentations from the input clinical records. However, because the existing\nlabel attention mechanism finds key tokens in the entire text at once, the\nimportant information dispersed in each paragraph may be omitted from the\nattention map. To overcome this, we propose a novel neural network architecture\ncomposed of two parts of encoders and two kinds of label attention layers. The\ninput text is segmentally encoded in the former encoder and integrated by the\nfollower. Then, the conventional and partition-based label attention mechanisms\nextract important global and local feature representations. Our classifier\neffectively integrates them to enhance the ICD coding performance. We verified\nthe proposed method using the MIMIC-III, a benchmark dataset of the ICD coding.\nOur results show that our network improves the ICD coding performance based on\nthe partition-based mechanism.\n", "date": "2022-11-15", "categories": []}
{"id": "2211.08685", "title": "Automated Analysis of Drawing Process for Detecting Prodromal and\n  Clinical Dementia", "abstract": "  Early diagnosis of dementia, particularly in the prodromal stage (i.e., mild\ncognitive impairment, or MCI), has become a research and clinical priority but\nremains challenging. Automated analysis of the drawing process has been studied\nas a promising means for screening prodromal and clinical dementia, providing\nmultifaceted information encompassing features, such as drawing speed, pen\nposture, writing pressure, and pauses. We examined the feasibility of using\nthese features not only for detecting prodromal and clinical dementia but also\nfor predicting the severity of cognitive impairments assessed using Mini-Mental\nState Examination (MMSE) as well as the severity of neuropathological changes\nassessed by medial temporal lobe (MTL) atrophy. We collected drawing data with\na digitizing tablet and pen from 145 older adults of cognitively normal (CN),\nMCI, and dementia. The nested cross-validation results indicate that the\ncombination of drawing features could be used to classify CN, MCI, and dementia\nwith an AUC of 0.909 and 75.1% accuracy (CN vs. MCI: 82.4% accuracy; CN vs.\ndementia: 92.2% accuracy; MCI vs. dementia: 80.3% accuracy) and predict MMSE\nscores with an $R^2$ of 0.491 and severity of MTL atrophy with an $R^2$ of\n0.293. Our findings suggest that automated analysis of the drawing process can\nprovide information about cognitive impairments and neuropathological changes\ndue to dementia, which can help identify prodromal and clinical dementia as a\ndigital biomarker.\n", "date": "2022-11-16", "categories": []}
{"id": "2211.08742", "title": "Auditing Algorithmic Fairness in Machine Learning for Health with\n  Severity-Based LOGAN", "abstract": "  Auditing machine learning-based (ML) healthcare tools for bias is critical to\npreventing patient harm, especially in communities that disproportionately face\nhealth inequities. General frameworks are becoming increasingly available to\nmeasure ML fairness gaps between groups. However, ML for health (ML4H) auditing\nprinciples call for a contextual, patient-centered approach to model\nassessment. Therefore, ML auditing tools must be (1) better aligned with ML4H\nauditing principles and (2) able to illuminate and characterize communities\nvulnerable to the most harm. To address this gap, we propose supplementing ML4H\nauditing frameworks with SLOGAN (patient Severity-based LOcal Group biAs\ndetectioN), an automatic tool for capturing local biases in a clinical\nprediction task. SLOGAN adapts an existing tool, LOGAN (LOcal Group biAs\ndetectioN), by contextualizing group bias detection in patient illness severity\nand past medical history. We investigate and compare SLOGAN's bias detection\ncapabilities to LOGAN and other clustering techniques across patient subgroups\nin the MIMIC-III dataset. On average, SLOGAN identifies larger fairness\ndisparities in over 75% of patient groups than LOGAN while maintaining\nclustering quality. Furthermore, in a diabetes case study, health disparity\nliterature corroborates the characterizations of the most biased clusters\nidentified by SLOGAN. Our results contribute to the broader discussion of how\nmachine learning biases may perpetuate existing healthcare disparities.\n", "date": "2022-11-16", "categories": []}
{"id": "2211.09068", "title": "Ischemic Stroke Lesion Prediction using imbalanced Temporal Deep\n  Gaussian Process (iTDGP)", "abstract": "  As one of the leading causes of mortality and disability worldwide, Acute\nIschemic Stroke (AIS) occurs when the blood supply to the brain is suddenly\ninterrupted because of a blocked artery. Within seconds of AIS onset, the brain\ncells surrounding the blocked artery die, which leads to the progression of the\nlesion. The automated and precise prediction of the existing lesion plays a\nvital role in the AIS treatment planning and prevention of further injuries.\nThe current standard AIS assessment method, which thresholds the 3D measurement\nmaps extracted from Computed Tomography Perfusion (CTP) images, is not accurate\nenough. Due to this fact, in this article, we propose the imbalanced Temporal\nDeep Gaussian Process (iTDGP), a probabilistic model that can improve AIS\nlesions prediction by using baseline CTP time series. Our proposed model can\neffectively extract temporal information from the CTP time series and map it to\nthe class labels of the brain's voxels. In addition, by using batch training\nand voxel-level analysis iTDGP can learn from a few patients and it is robust\nagainst imbalanced classes. Moreover, our model incorporates a post-processor\ncapable of improving prediction accuracy using spatial information. Our\ncomprehensive experiments, on the ISLES 2018 and the University of Alberta\nHospital (UAH) datasets, show that iTDGP performs better than state-of-the-art\nAIS lesion predictors, obtaining the (cross-validation) Dice score of 71.42%\nand 65.37% with a significant p<0.05, respectively.\n", "date": "2022-11-16", "categories": []}
{"id": "2211.02943", "title": "Predicting Treatment Adherence of Tuberculosis Patients at Scale", "abstract": "  Tuberculosis (TB), an infectious bacterial disease, is a significant cause of\ndeath, especially in low-income countries, with an estimated ten million new\ncases reported globally in $2020$. While TB is treatable, non-adherence to the\nmedication regimen is a significant cause of morbidity and mortality. Thus,\nproactively identifying patients at risk of dropping off their medication\nregimen enables corrective measures to mitigate adverse outcomes. Using a proxy\nmeasure of extreme non-adherence and a dataset of nearly $700,000$ patients\nfrom four states in India, we formulate and solve the machine learning (ML)\nproblem of early prediction of non-adherence based on a custom rank-based\nmetric. We train ML models and evaluate against baselines, achieving a $\\sim\n100\\%$ lift over rule-based baselines and $\\sim 214\\%$ over a random\nclassifier, taking into account country-wide large-scale future deployment. We\ndeal with various issues in the process, including data quality,\nhigh-cardinality categorical data, low target prevalence, distribution shift,\nvariation across cohorts, algorithmic fairness, and the need for robustness and\nexplainability. Our findings indicate that risk stratification of non-adherent\npatients is a viable, deployable-at-scale ML solution. As the official AI\npartner of India's Central TB Division, we are working on multiple city and\nstate-level pilots with the goal of pan-India deployment.\n", "date": "2022-11-05", "categories": []}
{"id": "2211.05633", "title": "Transfer learning and Local interpretable model agnostic based visual\n  approach in Monkeypox Disease Detection and Classification: A Deep Learning\n  insights", "abstract": "  The recent development of Monkeypox disease among various nations poses a\nglobal pandemic threat when the world is still fighting Coronavirus\nDisease-2019 (COVID-19). At its dawn, the slow and steady transmission of\nMonkeypox disease among individuals needs to be addressed seriously. Over the\nyears, Deep learning (DL) based disease prediction has demonstrated true\npotential by providing early, cheap, and affordable diagnosis facilities.\nConsidering this opportunity, we have conducted two studies where we modified\nand tested six distinct deep learning models-VGG16, InceptionResNetV2,\nResNet50, ResNet101, MobileNetV2, and VGG19-using transfer learning approaches.\nOur preliminary computational results show that the proposed modified\nInceptionResNetV2 and MobileNetV2 models perform best by achieving an accuracy\nranging from 93% to 99%. Our findings are reinforced by recent academic work\nthat demonstrates improved performance in constructing multiple disease\ndiagnosis models using transfer learning approaches. Lastly, we further explain\nour model prediction using Local Interpretable Model-Agnostic Explanations\n(LIME), which play an essential role in identifying important features that\ncharacterize the onset of Monkeypox disease.\n", "date": "2022-11-01", "categories": []}
{"id": "2211.07549", "title": "Phenotype Detection in Real World Data via Online MixEHR Algorithm", "abstract": "  Understanding patterns of diagnoses, medications, procedures, and laboratory\ntests from electronic health records (EHRs) and health insurer claims is\nimportant for understanding disease risk and for efficient clinical\ndevelopment, which often require rules-based curation in collaboration with\nclinicians. We extended an unsupervised phenotyping algorithm, mixEHR, to an\nonline version allowing us to use it on order of magnitude larger datasets\nincluding a large, US-based claims dataset and a rich regional EHR dataset. In\naddition to recapitulating previously observed disease groups, we discovered\nclinically meaningful disease subtypes and comorbidities. This work scaled up\nan effective unsupervised learning method, reinforced existing clinical\nknowledge, and is a promising approach for efficient collaboration with\nclinicians.\n", "date": "2022-11-14", "categories": []}
{"id": "2211.07652", "title": "Machine Learning Performance Analysis to Predict Stroke Based on\n  Imbalanced Medical Dataset", "abstract": "  Cerebral stroke, the second most substantial cause of death universally, has\nbeen a primary public health concern over the last few years. With the help of\nmachine learning techniques, early detection of various stroke alerts is\naccessible, which can efficiently prevent or diminish the stroke. Medical\ndataset, however, are frequently unbalanced in their class label, with a\ntendency to poorly predict minority classes. In this paper, the potential risk\nfactors for stroke are investigated. Moreover, four distinctive approaches are\napplied to improve the classification of the minority class in the imbalanced\nstroke dataset, which are the ensemble weight voting classifier, the Synthetic\nMinority Over-sampling Technique (SMOTE), Principal Component Analysis with\nK-Means Clustering (PCA-Kmeans), Focal Loss with the Deep Neural Network (DNN)\nand compare their performance. Through the analysis results, SMOTE and\nPCA-Kmeans with DNN-Focal Loss work best for the limited size of a large severe\nimbalanced dataset,which is 2-4 times outperform Kaggle work.\n", "date": "2022-11-14", "categories": []}
{"id": "2211.07713", "title": "How Long Is Enough? Exploring the Optimal Intervals of Long-Range\n  Clinical Note Language Modeling", "abstract": "  Large pre-trained language models (LMs) have been widely adopted in\nbiomedical and clinical domains, introducing many powerful LMs such as bio-lm\nand BioELECTRA. However, the applicability of these methods to real clinical\nuse cases is hindered, due to the limitation of pre-trained LMs in processing\nlong textual data with thousands of words, which is a common length for a\nclinical note. In this work, we explore long-range adaptation from such LMs\nwith Longformer, allowing the LMs to capture longer clinical notes context. We\nconduct experiments on three n2c2 challenges datasets and a longitudinal\nclinical dataset from Hong Kong Hospital Authority electronic health record\n(EHR) system to show the effectiveness and generalizability of this concept,\nachieving 10\\% F1-score improvement. Based on our experiments, we conclude that\ncapturing a longer clinical note interval is beneficial to the model\nperformance, but there are different cut-off intervals to achieve the optimal\nperformance for different target variables. Our code is available at\nhttps://github.com/HLTCHKUST/long-biomedical-model.\n", "date": "2022-10-25", "categories": []}
{"id": "2211.07867", "title": "Machine Learning Methods Applied to Cortico-Cortical Evoked Potentials\n  Aid in Localizing Seizure Onset Zones", "abstract": "  Epilepsy affects millions of people, reducing quality of life and increasing\nrisk of premature death. One-third of epilepsy cases are drug-resistant and\nrequire surgery for treatment, which necessitates localizing the seizure onset\nzone (SOZ) in the brain. Attempts have been made to use cortico-cortical evoked\npotentials (CCEPs) to improve SOZ localization but none have been successful\nenough for clinical adoption. Here, we compare the performance of ten machine\nlearning classifiers in localizing SOZ from CCEP data. This preliminary study\nvalidates a novel application of machine learning, and the results establish\nour approach as a promising line of research that warrants further\ninvestigation. This work also serves to facilitate discussion and collaboration\nwith fellow machine learning and/or epilepsy researchers.\n", "date": "2022-11-15", "categories": []}
{"id": "2211.08077", "title": "EDEN : An Event DEtection Network for the annotation of Breast Cancer\n  recurrences in administrative claims data", "abstract": "  While the emergence of large administrative claims data provides\nopportunities for research, their use remains limited by the lack of clinical\nannotations relevant to disease outcomes, such as recurrence in breast cancer\n(BC). Several challenges arise from the annotation of such endpoints in\nadministrative claims, including the need to infer both the occurrence and the\ndate of the recurrence, the right-censoring of data, or the importance of time\nintervals between medical visits. Deep learning approaches have been\nsuccessfully used to label temporal medical sequences, but no method is\ncurrently able to handle simultaneously right-censoring and visit temporality\nto detect survival events in medical sequences. We propose EDEN (Event\nDEtection Network), a time-aware Long-Short-Term-Memory network for survival\nanalyses, and its custom loss function. Our method outperforms several\nstate-of-the-art approaches on real-world BC datasets. EDEN constitutes a\npowerful tool to annotate disease recurrence from administrative claims, thus\npaving the way for the massive use of such data in BC research.\n", "date": "2022-11-15", "categories": []}
{"id": "2211.06045", "title": "Integrated Convolutional and Recurrent Neural Networks for Health Risk\n  Prediction using Patient Journey Data with Many Missing Values", "abstract": "  Predicting the health risks of patients using Electronic Health Records (EHR)\nhas attracted considerable attention in recent years, especially with the\ndevelopment of deep learning techniques. Health risk refers to the probability\nof the occurrence of a specific health outcome for a specific patient. The\npredicted risks can be used to support decision-making by healthcare\nprofessionals. EHRs are structured patient journey data. Each patient journey\ncontains a chronological set of clinical events, and within each clinical\nevent, there is a set of clinical/medical activities. Due to variations of\npatient conditions and treatment needs, EHR patient journey data has an\ninherently high degree of missingness that contains important information\naffecting relationships among variables, including time. Existing deep\nlearning-based models generate imputed values for missing values when learning\nthe relationships. However, imputed data in EHR patient journey data may\ndistort the clinical meaning of the original EHR patient journey data,\nresulting in classification bias. This paper proposes a novel end-to-end\napproach to modeling EHR patient journey data with Integrated Convolutional and\nRecurrent Neural Networks. Our model can capture both long- and short-term\ntemporal patterns within each patient journey and effectively handle the high\ndegree of missingness in EHR data without any imputation data generation.\nExtensive experimental results using the proposed model on two real-world\ndatasets demonstrate robust performance as well as superior prediction accuracy\ncompared to existing state-of-the-art imputation-based prediction methods.\n", "date": "2022-11-11", "categories": []}
{"id": "2211.06570", "title": "End-to-End Machine Learning Framework for Facial AU Detection in\n  Intensive Care Units", "abstract": "  Pain is a common occurrence among patients admitted to Intensive Care Units.\nPain assessment in ICU patients still remains a challenge for clinicians and\nICU staff, specifically in cases of non-verbal sedated, mechanically\nventilated, and intubated patients. Current manual observation-based pain\nassessment tools are limited by the frequency of pain observations administered\nand are subjective to the observer. Facial behavior is a major component in\nobservation-based tools. Furthermore, previous literature shows the feasibility\nof painful facial expression detection using facial action units (AUs).\nHowever, these approaches are limited to controlled or semi-controlled\nenvironments and have never been validated in clinical settings. In this study,\nwe present our Pain-ICU dataset, the largest dataset available targeting facial\nbehavior analysis in the dynamic ICU environment. Our dataset comprises 76,388\npatient facial image frames annotated with AUs obtained from 49 adult patients\nadmitted to ICUs at the University of Florida Health Shands hospital. In this\nwork, we evaluated two vision transformer models, namely ViT and SWIN, for AU\ndetection on our Pain-ICU dataset and also external datasets. We developed a\ncompletely end-to-end AU detection pipeline with the objective of performing\nreal-time AU detection in the ICU. The SWIN transformer Base variant achieved\n0.88 F1-score and 0.85 accuracy on the held-out test partition of the Pain-ICU\ndataset.\n", "date": "2022-11-12", "categories": []}
{"id": "2211.06637", "title": "Modular Clinical Decision Support Networks (MoDN) -- Updatable,\n  Interpretable, and Portable Predictions for Evolving Clinical Environments", "abstract": "  Data-driven Clinical Decision Support Systems (CDSS) have the potential to\nimprove and standardise care with personalised probabilistic guidance. However,\nthe size of data required necessitates collaborative learning from analogous\nCDSS's, which are often unsharable or imperfectly interoperable (IIO), meaning\ntheir feature sets are not perfectly overlapping. We propose Modular Clinical\nDecision Support Networks (MoDN) which allow flexible, privacy-preserving\nlearning across IIO datasets, while providing interpretable, continuous\npredictive feedback to the clinician.\n  MoDN is a novel decision tree composed of feature-specific neural network\nmodules. It creates dynamic personalised representations of patients, and can\nmake multiple predictions of diagnoses, updatable at each step of a\nconsultation. The modular design allows it to compartmentalise training updates\nto specific features and collaboratively learn between IIO datasets without\nsharing any data.\n", "date": "2022-11-12", "categories": []}
{"id": "2211.06778", "title": "Textual Data Augmentation for Patient Outcomes Prediction", "abstract": "  Deep learning models have demonstrated superior performance in various\nhealthcare applications. However, the major limitation of these deep models is\nusually the lack of high-quality training data due to the private and sensitive\nnature of this field. In this study, we propose a novel textual data\naugmentation method to generate artificial clinical notes in patients'\nElectronic Health Records (EHRs) that can be used as additional training data\nfor patient outcomes prediction. Essentially, we fine-tune the generative\nlanguage model GPT-2 to synthesize labeled text with the original training\ndata. More specifically, We propose a teacher-student framework where we first\npre-train a teacher model on the original data, and then train a student model\non the GPT-augmented data under the guidance of the teacher. We evaluate our\nmethod on the most common patient outcome, i.e., the 30-day readmission rate.\nThe experimental results show that deep models can improve their predictive\nperformance with the augmented data, indicating the effectiveness of the\nproposed architecture.\n", "date": "2022-11-13", "categories": []}
{"id": "2211.06925", "title": "Early Diagnosis of Chronic Obstructive Pulmonary Disease from Chest\n  X-Rays using Transfer Learning and Fusion Strategies", "abstract": "  Chronic obstructive pulmonary disease (COPD) is one of the most common\nchronic illnesses in the world and the third leading cause of mortality\nworldwide. It is often underdiagnosed or not diagnosed until later in the\ndisease course. Spirometry tests are the gold standard for diagnosing COPD but\ncan be difficult to obtain, especially in resource-poor countries. Chest X-rays\n(CXRs), however, are readily available and may serve as a screening tool to\nidentify patients with COPD who should undergo further testing. Currently, no\nresearch applies deep learning (DL) algorithms that use large multi-site and\nmulti-modal data to detect COPD patients and evaluate fairness across\ndemographic groups. We use three CXR datasets in our study, CheXpert to\npre-train models, MIMIC-CXR to develop, and Emory-CXR to validate our models.\nThe CXRs from patients in the early stage of COPD and not on mechanical\nventilation are selected for model training and validation. We visualize the\nGrad-CAM heatmaps of the true positive cases on the base model for both\nMIMIC-CXR and Emory-CXR test datasets. We further propose two fusion schemes,\n(1) model-level fusion, including bagging and stacking methods using MIMIC-CXR,\nand (2) data-level fusion, including multi-site data using MIMIC-CXR and\nEmory-CXR, and multi-modal using MIMIC-CXRs and MIMIC-IV EHR, to improve the\noverall model performance. Fairness analysis is performed to evaluate if the\nfusion schemes have a discrepancy in the performance among different\ndemographic groups. The results demonstrate that DL models can detect COPD\nusing CXRs, which can facilitate early screening, especially in low-resource\nregions where CXRs are more accessible than spirometry. The multi-site data\nfusion scheme could improve the model generalizability on the Emory-CXR test\ndata. Further studies on using CXR or other modalities to predict COPD ought to\nbe in future work.\n", "date": "2022-11-13", "categories": []}
{"id": "2211.07165", "title": "Model Evaluation in Medical Datasets Over Time", "abstract": "  Machine learning models deployed in healthcare systems face data drawn from\ncontinually evolving environments. However, researchers proposing such models\ntypically evaluate them in a time-agnostic manner, with train and test splits\nsampling patients throughout the entire study period. We introduce the\nEvaluation on Medical Datasets Over Time (EMDOT) framework and Python package,\nwhich evaluates the performance of a model class over time. Across five medical\ndatasets and a variety of models, we compare two training strategies: (1) using\nall historical data, and (2) using a window of the most recent data. We note\nchanges in performance over time, and identify possible explanations for these\nshocks.\n", "date": "2022-11-14", "categories": []}
{"id": "2211.07430", "title": "The Far Side of Failure: Investigating the Impact of Speech Recognition\n  Errors on Subsequent Dementia Classification", "abstract": "  Linguistic anomalies detectable in spontaneous speech have shown promise for\nvarious clinical applications including screening for dementia and other forms\nof cognitive impairment. The feasibility of deploying automated tools that can\nclassify language samples obtained from speech in large-scale clinical settings\ndepends on the ability to capture and automatically transcribe the speech for\nsubsequent analysis. However, the impressive performance of self-supervised\nlearning (SSL) automatic speech recognition (ASR) models with curated speech\ndata is not apparent with challenging speech samples from clinical settings.\nOne of the key questions for successfully applying ASR models for clinical\napplications is whether imperfect transcripts they generate provide sufficient\ninformation for downstream tasks to operate at an acceptable level of accuracy.\nIn this study, we examine the relationship between the errors produced by\nseveral deep learning ASR systems and their impact on the downstream task of\ndementia classification. One of our key findings is that, paradoxically, ASR\nsystems with relatively high error rates can produce transcripts that result in\nbetter downstream classification accuracy than classification based on verbatim\ntranscripts.\n", "date": "2022-11-11", "categories": []}
{"id": "2211.02636", "title": "Towards Alzheimer's Disease Progression Assessment: A Review of Machine\n  Learning Methods", "abstract": "  Alzheimer's Disease (AD), as the most devastating neurodegenerative disease\nworldwide, has reached nearly 10 million new cases annually. Current technology\nprovides unprecedented opportunities to study the progression and etiology of\nthis disease with the advanced in imaging techniques. With the recent emergence\nof a society driven by big data and machine learning (ML), researchers have\nexerted considerable effort to summarize recent advances in ML-based AD\ndiagnosis. Here, we outline some of the most prevalent and recent ML models for\nassessing the progression of AD and provide insights on the challenges,\nopportunities, and future directions that could be advantageous to future\nresearch in AD using ML.\n", "date": "2022-11-01", "categories": []}
{"id": "2211.05786", "title": "Data-Driven Disease Progression Modelling", "abstract": "  Intense debate in the Neurology community before 2010 culminated in\nhypothetical models of Alzheimer's disease progression: a pathophysiological\ncascade of biomarkers, each dynamic for only a segment of the full disease\ntimeline. Inspired by this, data-driven disease progression modelling emerged\nfrom the computer science community with the aim to reconstruct\nneurodegenerative disease timelines using data from large cohorts of patients,\nhealthy controls, and prodromal/at-risk individuals. This chapter describes\nselected highlights from the field, with a focus on utility for understanding\nand forecasting of disease progression.\n", "date": "2022-11-01", "categories": []}
{"id": "2211.06034", "title": "Does Deep Learning REALLY Outperform Non-deep Machine Learning for\n  Clinical Prediction on Physiological Time Series?", "abstract": "  Machine learning has been widely used in healthcare applications to\napproximate complex models, for clinical diagnosis, prognosis, and treatment.\nAs deep learning has the outstanding ability to extract information from time\nseries, its true capabilities on sparse, irregularly sampled, multivariate, and\nimbalanced physiological data are not yet fully explored. In this paper, we\nsystematically examine the performance of machine learning models for the\nclinical prediction task based on the EHR, especially physiological time\nseries. We choose Physionet 2019 challenge public dataset to predict Sepsis\noutcomes in ICU units. Ten baseline machine learning models are compared,\nincluding 3 deep learning methods and 7 non-deep learning methods, commonly\nused in the clinical prediction domain. Nine evaluation metrics with specific\nclinical implications are used to assess the performance of models. Besides, we\nsub-sample training dataset sizes and use learning curve fit to investigate the\nimpact of the training dataset size on the performance of the machine learning\nmodels. We also propose the general pre-processing method for the physiology\ntime-series data and use Dice Loss to deal with the dataset imbalanced problem.\nThe results show that deep learning indeed outperforms non-deep learning, but\nwith certain conditions: firstly, evaluating with some particular evaluation\nmetrics (AUROC, AUPRC, Sensitivity, and FNR), but not others; secondly, the\ntraining dataset size is large enough (with an estimation of a magnitude of\nthousands).\n", "date": "2022-11-11", "categories": []}
{"id": "2211.06212", "title": "From Competition to Collaboration: Making Toy Datasets on Kaggle\n  Clinically Useful for Chest X-Ray Diagnosis Using Federated Learning", "abstract": "  Chest X-ray (CXR) datasets hosted on Kaggle, though useful from a data\nscience competition standpoint, have limited utility in clinical use because of\ntheir narrow focus on diagnosing one specific disease. In real-world clinical\nuse, multiple diseases need to be considered since they can co-exist in the\nsame patient. In this work, we demonstrate how federated learning (FL) can be\nused to make these toy CXR datasets from Kaggle clinically useful.\nSpecifically, we train a single FL classification model (`global`) using two\nseparate CXR datasets -- one annotated for presence of pneumonia and the other\nfor presence of pneumothorax (two common and life-threatening conditions) --\ncapable of diagnosing both. We compare the performance of the global FL model\nwith models trained separately on both datasets (`baseline`) for two different\nmodel architectures. On a standard, naive 3-layer CNN architecture, the global\nFL model achieved AUROC of 0.84 and 0.81 for pneumonia and pneumothorax,\nrespectively, compared to 0.85 and 0.82, respectively, for both baseline models\n(p>0.05). Similarly, on a pretrained DenseNet121 architecture, the global FL\nmodel achieved AUROC of 0.88 and 0.91 for pneumonia and pneumothorax,\nrespectively, compared to 0.89 and 0.91, respectively, for both baseline models\n(p>0.05). Our results suggest that FL can be used to create global `meta`\nmodels to make toy datasets from Kaggle clinically useful, a step forward\ntowards bridging the gap from bench to bedside.\n", "date": "2022-11-11", "categories": []}
{"id": "2211.05597", "title": "Perfectly predicting ICU length of stay: too good to be true", "abstract": "  A paper of Alsinglawi et al was recently accepted and published in Scientific\nReports. In this paper, the authors aim to predict length of stay (LOS),\ndiscretized into either long (> 7 days) or short stays (< 7 days), of lung\ncancer patients in an ICU department using various machine learning techniques.\nThe authors claim to achieve perfect results with an Area Under the Receiver\nOperating Characteristic curve (AUROC) of 100% with a Random Forest (RF)\nclassifier with ADASYN class balancing over sampling technique, which if\naccurate could have significant implications for hospital management. However,\nwe have identified several methodological flaws within the manuscript which\ncause the results to be overly optimistic and would have serious consequences\nif used in a clinical practice. Moreover, the reporting of the methodology is\nunclear and many important details are missing from the manuscript, which makes\nreproduction extremely difficult. We highlight the effect these oversights have\nhad on the result and provide a more believable result of 88.91% AUROC when\nthese oversights are corrected.\n", "date": "2022-11-10", "categories": []}
{"id": "2211.04850", "title": "Final infarct prediction in acute ischemic stroke", "abstract": "  This article focuses on the control center of each human body: the brain. We\nwill point out the pivotal role of the cerebral vasculature and how its complex\nmechanisms may vary between subjects. We then emphasize a specific acute\npathological state, i.e., acute ischemic stroke, and show how medical imaging\nand its analysis can be used to define the treatment. We show how the\ncore-penumbra concept is used in practice using mismatch criteria and how\nmachine learning can be used to make predictions of the final infarct, either\nvia deconvolution or convolutional neural networks.\n", "date": "2022-11-09", "categories": []}
{"id": "2211.04234", "title": "Learning Spatio-Temporal Model of Disease Progression with NeuralODEs\n  from Longitudinal Volumetric Data", "abstract": "  Robust forecasting of the future anatomical changes inflicted by an ongoing\ndisease is an extremely challenging task that is out of grasp even for\nexperienced healthcare professionals. Such a capability, however, is of great\nimportance since it can improve patient management by providing information on\nthe speed of disease progression already at the admission stage, or it can\nenrich the clinical trials with fast progressors and avoid the need for control\narms by the means of digital twins. In this work, we develop a deep learning\nmethod that models the evolution of age-related disease by processing a single\nmedical scan and providing a segmentation of the target anatomy at a requested\nfuture point in time. Our method represents a time-invariant physical process\nand solves a large-scale problem of modeling temporal pixel-level changes\nutilizing NeuralODEs. In addition, we demonstrate the approaches to incorporate\nthe prior domain-specific constraints into our method and define temporal Dice\nloss for learning temporal objectives. To evaluate the applicability of our\napproach across different age-related diseases and imaging modalities, we\ndeveloped and tested the proposed method on the datasets with 967 retinal OCT\nvolumes of 100 patients with Geographic Atrophy, and 2823 brain MRI volumes of\n633 patients with Alzheimer's Disease. For Geographic Atrophy, the proposed\nmethod outperformed the related baseline models in the atrophy growth\nprediction. For Alzheimer's Disease, the proposed method demonstrated\nremarkable performance in predicting the brain ventricle changes induced by the\ndisease, achieving the state-of-the-art result on TADPOLE challenge.\n", "date": "2022-11-08", "categories": []}
{"id": "2211.00644", "title": "minoHealth.ai: A Clinical Evaluation Of Deep Learning Systems For the\n  Diagnosis of Pleural Effusion and Cardiomegaly In Ghana, Vietnam and the\n  United States of America", "abstract": "  A rapid and accurate diagnosis of cardiomegaly and pleural effusion is of the\nutmost importance to reduce mortality and medical costs. Artificial\nIntelligence has shown promise in diagnosing medical conditions. With this\nstudy, we seek to evaluate how well Artificial Intelligence (AI) systems,\ndeveloped my minoHealth AI Labs, will perform at diagnosing cardiomegaly and\npleural effusion, using chest x-rays from Ghana, Vietnam and the USA, and how\nwell AI systems will perform when compared with radiologists working in Ghana.\nThe evaluation dataset used in this study contained 100 images randomly\nselected from three datasets. The Deep Learning models were further tested on a\nlarger Ghanaian dataset containing five hundred and sixty one (561) samples.\nTwo AI systems were then evaluated on the evaluation dataset, whilst we also\ngave the same chest x-ray images within the evaluation dataset to 4\nradiologists, with 5 - 20 years experience, to diagnose independently. For\ncardiomegaly, minoHealth-ai systems scored Area under the Receiver operating\ncharacteristic Curve (AUC-ROC) of 0.9 and 0.97 while the AUC-ROC of individual\nradiologists ranged from 0.77 to 0.87. For pleural effusion, the minoHealth-ai\nsystems scored 0.97 and 0.91 whereas individual radiologists scored between\n0.75 and 0.86. On both conditions, the best performing AI model outperforms the\nbest performing radiologist by about 10%. We also evaluate the specificity,\nsensitivity, negative predictive value (NPV), and positive predictive value\n(PPV) between the minoHealth-ai systems and radiologists.\n", "date": "2022-10-31", "categories": []}
{"id": "2211.02679", "title": "Automatic Seizure Prediction using CNN and LSTM", "abstract": "  The electroencephalogram (EEG) is one of the most precious technologies to\nunderstand the happenings inside our brain and further understand our body's\nhappenings. Automatic prediction of oncoming seizures using the EEG signals\nhelps the doctors and clinical experts and reduces their workload. This paper\nproposes an end-to-end deep learning algorithm to fully automate seizure\nprediction's laborious task without any heavy pre-processing on the EEG data or\nfeature engineering. The proposed deep learning network is a blend of signal\nprocessing and deep learning pipeline, which automates the seizure prediction\nframework using the EEG signals. This proposed model was evaluated on an open\nEEG dataset, CHB-MIT. The network achieved an average sensitivity of\n97.746\\text{\\%} and a false positive rate (FPR) of 0.2373 per hour.\n", "date": "2022-10-28", "categories": []}
{"id": "2211.03280", "title": "Multimodal Learning for Non-small Cell Lung Cancer Prognosis", "abstract": "  This paper focuses on the task of survival time analysis for lung cancer.\nAlthough much progress has been made in this problem in recent years, the\nperformance of existing methods is still far from satisfactory. Traditional and\nsome deep learning-based survival time analyses for lung cancer are mostly\nbased on textual clinical information such as staging, age, histology, etc.\nUnlike existing methods that predicting on the single modality, we observe that\na human clinician usually takes multimodal data such as text clinical data and\nvisual scans to estimate survival time. Motivated by this, in this work, we\ncontribute a smart cross-modality network for survival analysis network named\nLite-ProSENet that simulates a human's manner of decision making. Extensive\nexperiments were conducted using data from 422 NSCLC patients from The Cancer\nImaging Archive (TCIA). The results show that our Lite-ProSENet outperforms\nfavorably again all comparison methods and achieves the new state of the art\nwith the 89.3% on concordance. The code will be made publicly available.\n", "date": "2022-11-07", "categories": []}
{"id": "2210.16850", "title": "Medical Codes Prediction from Clinical Notes: From Human Coders to\n  Machines", "abstract": "  Prediction of medical codes from clinical notes is a practical and essential\nneed for every healthcare delivery organization within current medical systems.\nAutomating annotation will save significant time and excessive effort that\nhuman coders spend today. However, the biggest challenge is directly\nidentifying appropriate medical codes from several thousands of\nhigh-dimensional codes from unstructured free-text clinical notes. This complex\nmedical codes prediction problem from clinical notes has received substantial\ninterest in the NLP community, and several recent studies have shown the\nstate-of-the-art code prediction results of full-fledged deep learning-based\nmethods. This progress raises the fundamental question of how far automated\nmachine learning systems are from human coders' working performance, as well as\nthe important question of how well current explainability methods apply to\nadvanced neural network models such as transformers. This is to predict correct\ncodes and present references in clinical notes that support code prediction, as\nthis level of explainability and accuracy of the prediction outcomes is\ncritical to gaining trust from professional medical coders.\n", "date": "2022-10-30", "categories": []}
{"id": "2210.17292", "title": "M$^3$Care: Learning with Missing Modalities in Multimodal Healthcare\n  Data", "abstract": "  Multimodal electronic health record (EHR) data are widely used in clinical\napplications. Conventional methods usually assume that each sample (patient) is\nassociated with the unified observed modalities, and all modalities are\navailable for each sample. However, missing modality caused by various clinical\nand social reasons is a common issue in real-world clinical scenarios. Existing\nmethods mostly rely on solving a generative model that learns a mapping from\nthe latent space to the original input space, which is an unstable ill-posed\ninverse problem. To relieve the underdetermined system, we propose a model\nsolving a direct problem, dubbed learning with Missing Modalities in Multimodal\nhealthcare data (M3Care). M3Care is an end-to-end model compensating the\nmissing information of the patients with missing modalities to perform clinical\nanalysis. Instead of generating raw missing data, M3Care imputes the\ntask-related information of the missing modalities in the latent space by the\nauxiliary information from each patient's similar neighbors, measured by a\ntask-guided modality-adaptive similarity metric, and thence conducts the\nclinical tasks. The task-guided modality-adaptive similarity metric utilizes\nthe uncensored modalities of the patient and the other patients who also have\nthe same uncensored modalities to find similar patients. Experiments on\nreal-world datasets show that M3Care outperforms the state-of-the-art\nbaselines. Moreover, the findings discovered by M3Care are consistent with\nexperts and medical knowledge, demonstrating the capability and the potential\nof providing useful insights and explanations.\n", "date": "2022-10-28", "categories": []}
{"id": "2208.06917", "title": "MTCSNN: Multi-task Clinical Siamese Neural Network for Diabetic\n  Retinopathy Severity Prediction", "abstract": "  Diabetic Retinopathy (DR) has become one of the leading causes of vision\nimpairment in working-aged people and is a severe problem worldwide. However,\nmost of the works ignored the ordinal information of labels. In this project,\nwe propose a novel design MTCSNN, a Multi-task Clinical Siamese Neural Network\nfor Diabetic Retinopathy severity prediction task. The novelty of this project\nis to utilize the ordinal information among labels and add a new regression\ntask, which can help the model learn more discriminative feature embedding for\nfine-grained classification tasks. We perform comprehensive experiments over\nthe RetinaMNIST, comparing MTCSNN with other models like ResNet-18, 34, 50. Our\nresults indicate that MTCSNN outperforms the benchmark models in terms of AUC\nand accuracy on the test dataset.\n", "date": "2022-08-14", "categories": []}
{"id": "2210.15901", "title": "Mitigating Health Disparities in EHR via Deconfounder", "abstract": "  Health disparities, or inequalities between different patient demographics,\nare becoming crucial in medical decision-making, especially in Electronic\nHealth Record (EHR) predictive modeling. To ensure the fairness of sensitive\nattributes, conventional studies mainly adopt calibration or re-weighting\nmethods to balance the performance on among different demographic groups.\nHowever, we argue that these methods have some limitations. First, these\nmethods usually mean a trade-off between the model's performance and fairness.\nSecond, many methods completely attribute unfairness to the data collection\nprocess, which lacks substantial evidence. In this paper, we provide an\nempirical study to discover the possibility of using deconfounder to address\nthe disparity issue in healthcare. Our study can be summarized in two parts.\nThe first part is a pilot study demonstrating the exacerbation of disparity\nwhen unobserved confounders exist. The second part proposed a novel framework,\nParity Medical Deconfounder (PriMeD), to deal with the disparity issue in\nhealthcare datasets. Inspired by the deconfounder theory, PriMeD adopts a\nConditional Variational Autoencoder (CVAE) to learn latent factors (substitute\nconfounders) for observational data, and extensive experiments are provided to\nshow its effectiveness.\n", "date": "2022-10-28", "categories": []}
{"id": "2210.16074", "title": "Improving Chest X-Ray Classification by RNN-based Patient Monitoring", "abstract": "  Chest X-Ray imaging is one of the most common radiological tools for\ndetection of various pathologies related to the chest area and lung function.\nIn a clinical setting, automated assessment of chest radiographs has the\npotential of assisting physicians in their decision making process and optimize\nclinical workflows, for example by prioritizing emergency patients.\n  Most work analyzing the potential of machine learning models to classify\nchest X-ray images focuses on vision methods processing and predicting\npathologies for one image at a time. However, many patients undergo such a\nprocedure multiple times during course of a treatment or during a single\nhospital stay. The patient history, that is previous images and especially the\ncorresponding diagnosis contain useful information that can aid a\nclassification system in its prediction.\n  In this study, we analyze how information about diagnosis can improve\nCNN-based image classification models by constructing a novel dataset from the\nwell studied CheXpert dataset of chest X-rays. We show that a model trained on\nadditional patient history information outperforms a model trained without the\ninformation by a significant margin.\n  We provide code to replicate the dataset creation and model training.\n", "date": "2022-10-28", "categories": []}
{"id": "2208.14923", "title": "Few-Shot Learning for Clinical Natural Language Processing Using Siamese\n  Neural Networks", "abstract": "  Clinical Natural Language Processing (NLP) has become an emerging technology\nin healthcare that leverages a large amount of free-text data in electronic\nhealth records (EHRs) to improve patient care, support clinical decisions, and\nfacilitate clinical and translational science research. Recently, deep learning\nhas achieved state-of-the-art performance in many clinical NLP tasks. However,\ntraining deep learning models usually requires large annotated datasets, which\nare normally not publicly available and can be time-consuming to build in\nclinical domains. Working with smaller annotated datasets is typical in\nclinical NLP and therefore, ensuring that deep learning models perform well is\ncrucial for the models to be used in real-world applications. A widely adopted\napproach is fine-tuning existing Pre-trained Language Models (PLMs), but these\nattempts fall short when the training dataset contains only a few annotated\nsamples. Few-Shot Learning (FSL) has recently been investigated to tackle this\nproblem. Siamese Neural Network (SNN) has been widely utilized as an FSL\napproach in computer vision, but has not been studied well in NLP. Furthermore,\nthe literature on its applications in clinical domains is scarce. In this\npaper, we propose two SNN-based FSL approaches for clinical NLP, including\nPre-Trained SNN (PT-SNN) and SNN with Second-Order Embeddings (SOE-SNN). We\nevaluated the proposed approaches on two clinical tasks, namely clinical text\nclassification and clinical named entity recognition. We tested three few-shot\nsettings including 4-shot, 8-shot, and 16-shot learning. Both clinical NLP\ntasks were benchmarked using three PLMs, including BERT,BioBERT, and\nBioClinicalBERT. The experimental results verified the effectiveness of the\nproposed SNN-based FSL approaches in both NLP tasks.\n", "date": "2022-08-31", "categories": []}
{"id": "2210.14906", "title": "An Intelligent Decision Support Ensemble Voting Model for Coronary\n  Artery Disease Prediction in Smart Healthcare Monitoring Environments", "abstract": "  Coronary artery disease (CAD) is one of the most common cardiac diseases\nworldwide and causes disability and economic burden. It is the world's leading\nand most serious cause of mortality, with approximately 80% of deaths reported\nin low- and middle-income countries. The preferred and most precise diagnostic\ntool for CAD is angiography, but it is invasive, expensive, and technically\ndemanding. However, the research community is increasingly interested in the\ncomputer-aided diagnosis of CAD via the utilization of machine learning (ML)\nmethods. The purpose of this work is to present an e-diagnosis tool based on ML\nalgorithms that can be used in a smart healthcare monitoring system. We applied\nthe most accurate machine learning methods that have shown superior results in\nthe literature to different medical datasets such as RandomForest, XGboost,\nMLP, J48, AdaBoost, NaiveBayes, LogitBoost, KNN. Every single classifier can be\nefficient on a different dataset. Thus, an ensemble model using majority voting\nwas designed to take advantage of the well-performed single classifiers,\nEnsemble learning aims to combine the forecasts of multiple individual\nclassifiers to achieve higher performance than individual classifiers in terms\nof precision, specificity, sensitivity, and accuracy; furthermore, we have\nbenchmarked our proposed model with the most efficient and well-known ensemble\nmodels, such as Bagging, Stacking methods based on the cross-validation\ntechnique, The experimental results confirm that the ensemble majority voting\napproach based on the top 3 classifiers: MultilayerPerceptron, RandomForest,\nand AdaBoost, achieves the highest accuracy of 88,12% and outperforms all other\nclassifiers. This study demonstrates that the majority voting ensemble approach\nproposed above is the most accurate machine learning classification approach\nfor the prediction and detection of coronary artery disease.\n", "date": "2022-10-25", "categories": []}
{"id": "2210.14909", "title": "Automated Diagnosis of Cardiovascular Diseases from Cardiac Magnetic\n  Resonance Imaging Using Deep Learning Models: A Review", "abstract": "  In recent years, cardiovascular diseases (CVDs) have become one of the\nleading causes of mortality globally. CVDs appear with minor symptoms and\nprogressively get worse. The majority of people experience symptoms such as\nexhaustion, shortness of breath, ankle swelling, fluid retention, and other\nsymptoms when starting CVD. Coronary artery disease (CAD), arrhythmia,\ncardiomyopathy, congenital heart defect (CHD), mitral regurgitation, and angina\nare the most common CVDs. Clinical methods such as blood tests,\nelectrocardiography (ECG) signals, and medical imaging are the most effective\nmethods used for the detection of CVDs. Among the diagnostic methods, cardiac\nmagnetic resonance imaging (CMR) is increasingly used to diagnose, monitor the\ndisease, plan treatment and predict CVDs. Coupled with all the advantages of\nCMR data, CVDs diagnosis is challenging for physicians due to many slices of\ndata, low contrast, etc. To address these issues, deep learning (DL) techniques\nhave been employed to the diagnosis of CVDs using CMR data, and much research\nis currently being conducted in this field. This review provides an overview of\nthe studies performed in CVDs detection using CMR images and DL techniques. The\nintroduction section examined CVDs types, diagnostic methods, and the most\nimportant medical imaging techniques. In the following, investigations to\ndetect CVDs using CMR images and the most significant DL methods are presented.\nAnother section discussed the challenges in diagnosing CVDs from CMR data.\nNext, the discussion section discusses the results of this review, and future\nwork in CVDs diagnosis from CMR images and DL techniques are outlined. The most\nimportant findings of this study are presented in the conclusion section.\n", "date": "2022-10-26", "categories": []}
{"id": "2210.15603", "title": "Working Alliance Transformer for Psychotherapy Dialogue Classification", "abstract": "  As a predictive measure of the treatment outcome in psychotherapy, the\nworking alliance measures the agreement of the patient and the therapist in\nterms of their bond, task and goal. Long been a clinical quantity estimated by\nthe patients' and therapists' self-evaluative reports, we believe that the\nworking alliance can be better characterized using natural language processing\ntechnique directly in the dialogue transcribed in each therapy session. In this\nwork, we propose the Working Alliance Transformer (WAT), a Transformer-based\nclassification model that has a psychological state encoder which infers the\nworking alliance scores by projecting the embedding of the dialogues turns onto\nthe embedding space of the clinical inventory for working alliance. We evaluate\nour method in a real-world dataset with over 950 therapy sessions with anxiety,\ndepression, schizophrenia and suicidal patients and demonstrate an empirical\nadvantage of using information about the therapeutic states in this sequence\nclassification task of psychotherapy dialogues.\n", "date": "2022-10-27", "categories": []}
{"id": "2207.06921", "title": "Automatic Sleep Scoring from Large-scale Multi-channel Pediatric EEG", "abstract": "  Sleep is particularly important to the health of infants, children, and\nadolescents, and sleep scoring is the first step to accurate diagnosis and\ntreatment of potentially life-threatening conditions. But pediatric sleep is\nseverely under-researched compared to adult sleep in the context of machine\nlearning for health, and sleep scoring algorithms developed for adults usually\nperform poorly on infants. Here, we present the first automated sleep scoring\nresults on a recent large-scale pediatric sleep study dataset that was\ncollected during standard clinical care. We develop a transformer-based model\nthat learns to classify five sleep stages from millions of multi-channel\nelectroencephalogram (EEG) sleep epochs with 78% overall accuracy. Further, we\nconduct an in-depth analysis of the model performance based on patient\ndemographics and EEG channels. The results point to the growing need for\nmachine learning research on pediatric sleep.\n", "date": "2022-06-30", "categories": []}
{"id": "2210.09403", "title": "A Transfer Learning Based Approach for Classification of COVID-19 and\n  Pneumonia in CT Scan Imaging", "abstract": "  The world is still overwhelmed by the spread of the COVID-19 virus. With over\n250 Million infected cases as of November 2021 and affecting 219 countries and\nterritories, the world remains in the pandemic period. Detecting COVID-19 using\nthe deep learning method on CT scan images can play a vital role in assisting\nmedical professionals and decision authorities in controlling the spread of the\ndisease and providing essential support for patients. The convolution neural\nnetwork is widely used in the field of large-scale image recognition. The\ncurrent method of RT-PCR to diagnose COVID-19 is time-consuming and universally\nlimited. This research aims to propose a deep learning-based approach to\nclassify COVID-19 pneumonia patients, bacterial pneumonia, viral pneumonia, and\nhealthy (normal cases). This paper used deep transfer learning to classify the\ndata via Inception-ResNet-V2 neural network architecture. The proposed model\nhas been intentionally simplified to reduce the implementation cost so that it\ncan be easily implemented and used in different geographical areas, especially\nrural and developing regions.\n", "date": "2022-10-17", "categories": []}
{"id": "2210.13891", "title": "Predicting Survival Outcomes in the Presence of Unlabeled Data", "abstract": "  Many clinical studies require the follow-up of patients over time. This is\nchallenging: apart from frequently observed drop-out, there are often also\norganizational and financial challenges, which can lead to reduced data\ncollection and, in turn, can complicate subsequent analyses. In contrast, there\nis often plenty of baseline data available of patients with similar\ncharacteristics and background information, e.g., from patients that fall\noutside the study time window. In this article, we investigate whether we can\nbenefit from the inclusion of such unlabeled data instances to predict accurate\nsurvival times. In other words, we introduce a third level of supervision in\nthe context of survival analysis, apart from fully observed and censored\ninstances, we also include unlabeled instances. We propose three approaches to\ndeal with this novel setting and provide an empirical comparison over fifteen\nreal-life clinical and gene expression survival datasets. Our results\ndemonstrate that all approaches are able to increase the predictive performance\nover independent test data. We also show that integrating the partial\nsupervision provided by censored data in a semi-supervised wrapper approach\ngenerally provides the best results, often achieving high improvements,\ncompared to not using unlabeled data.\n", "date": "2022-10-25", "categories": []}
{"id": "2208.01320", "title": "Compound Density Networks for Risk Prediction using Electronic Health\n  Records", "abstract": "  Electronic Health Records (EHRs) exhibit a high amount of missing data due to\nvariations of patient conditions and treatment needs. Imputation of missing\nvalues has been considered an effective approach to deal with this challenge.\nExisting work separates imputation method and prediction model as two\nindependent parts of an EHR-based machine learning system. We propose an\nintegrated end-to-end approach by utilizing a Compound Density Network (CDNet)\nthat allows the imputation method and prediction model to be tuned together\nwithin a single framework. CDNet consists of a Gated recurrent unit (GRU), a\nMixture Density Network (MDN), and a Regularized Attention Network (RAN). The\nGRU is used as a latent variable model to model EHR data. The MDN is designed\nto sample latent variables generated by GRU. The RAN serves as a regularizer\nfor less reliable imputed values. The architecture of CDNet enables GRU and MDN\nto iteratively leverage the output of each other to impute missing values,\nleading to a more accurate and robust prediction. We validate CDNet on the\nmortality prediction task on the MIMIC-III dataset. Our model outperforms\nstate-of-the-art models by significant margins. We also empirically show that\nregularizing imputed values is a key factor for superior prediction\nperformance. Analysis of prediction uncertainty shows that our model can\ncapture both aleatoric and epistemic uncertainties, which offers model users a\nbetter understanding of the model results.\n", "date": "2022-08-02", "categories": []}
{"id": "2210.08016", "title": "Prediction of drug effectiveness in rheumatoid arthritis patients based\n  on machine learning algorithms", "abstract": "  Rheumatoid arthritis (RA) is an autoimmune condition caused when patients'\nimmune system mistakenly targets their own tissue. Machine learning (ML) has\nthe potential to identify patterns in patient electronic health records (EHR)\nto forecast the best clinical treatment to improve patient outcomes. This study\nintroduced a Drug Response Prediction (DRP) framework with two main goals: 1)\ndesign a data processing pipeline to extract information from tabular clinical\ndata, and then preprocess it for functional use, and 2) predict RA patient's\nresponses to drugs and evaluate classification models' performance. We propose\na novel two-stage ML framework based on European Alliance of Associations for\nRheumatology (EULAR) criteria cutoffs to model drug effectiveness. Our model\nStacked-Ensemble DRP was developed and cross-validated using data from 425 RA\npatients. The evaluation used a subset of 124 patients (30%) from the same data\nsource. In the evaluation of the test set, two-stage DRP leads to improved\nclassification accuracy over other end-to-end classification models for binary\nclassification. Our proposed method provides a complete pipeline to predict\ndisease activity scores and identify the group that does not respond well to\nanti-TNF treatments, thus showing promise in supporting clinical decisions\nbased on EHR information.\n", "date": "2022-10-14", "categories": []}
{"id": "2210.12220", "title": "Considerations for Visualizing Uncertainty in Clinical Machine Learning\n  Models", "abstract": "  Clinician-facing predictive models are increasingly present in the healthcare\nsetting. Regardless of their success with respect to performance metrics, all\nmodels have uncertainty. We investigate how to visually communicate uncertainty\nin this setting in an actionable, trustworthy way. To this end, we conduct a\nqualitative study with cardiac critical care clinicians. Our results reveal\nthat clinician trust may be impacted most not by the degree of uncertainty, but\nrather by how transparent the visualization of what the sources of uncertainty\nare. Our results show a clear connection between feature interpretability and\nclinical actionability.\n", "date": "2022-10-21", "categories": []}
{"id": "2209.03522", "title": "Machine Learning Sensors for Diagnosis of COVID-19 Disease Using Routine\n  Blood Values for Internet of Things Application", "abstract": "  Healthcare digitalization requires effective applications of human sensors,\nwhen various parameters of the human body are instantly monitored in everyday\nlife due to the Internet of Things (IoT). In particular, machine learning (ML)\nsensors for the prompt diagnosis of COVID-19 are an important option for IoT\napplication in healthcare and ambient assisted living (AAL). Determining a\nCOVID-19 infected status with various diagnostic tests and imaging results is\ncostly and time-consuming. This study provides a fast, reliable and\ncost-effective alternative tool for the diagnosis of COVID-19 based on the\nroutine blood values (RBVs) measured at admission. The dataset of the study\nconsists of a total of 5296 patients with the same number of negative and\npositive COVID-19 test results and 51 routine blood values. In this study, 13\npopular classifier machine learning models and the LogNNet neural network model\nwere exanimated. The most successful classifier model in terms of time and\naccuracy in the detection of the disease was the histogram-based gradient\nboosting (HGB) (accuracy: 100%, time: 6.39 sec). The HGB classifier identified\nthe 11 most important features (LDL, cholesterol, HDL-C, MCHC, triglyceride,\namylase, UA, LDH, CK-MB, ALP and MCH) to detect the disease with 100% accuracy.\nIn addition, the importance of single, double and triple combinations of these\nfeatures in the diagnosis of the disease was discussed. We propose to use these\n11 features and their binary combinations as important biomarkers for ML\nsensors in the diagnosis of the disease, supporting edge computing on Arduino\nand cloud IoT service.\n", "date": "2022-09-08", "categories": []}
{"id": "2210.11366", "title": "Deep conditional transformation models for survival analysis", "abstract": "  An every increasing number of clinical trials features a time-to-event\noutcome and records non-tabular patient data, such as magnetic resonance\nimaging or text data in the form of electronic health records. Recently,\nseveral neural-network based solutions have been proposed, some of which are\nbinary classifiers. Parametric, distribution-free approaches which make full\nuse of survival time and censoring status have not received much attention. We\npresent deep conditional transformation models (DCTMs) for survival outcomes as\na unifying approach to parametric and semiparametric survival analysis. DCTMs\nallow the specification of non-linear and non-proportional hazards for both\ntabular and non-tabular data and extend to all types of censoring and\ntruncation. On real and semi-synthetic data, we show that DCTMs compete with\nstate-of-the-art DL approaches to survival analysis.\n", "date": "2022-10-20", "categories": []}
{"id": "2210.11408", "title": "Hierarchical Deep Learning with Generative Adversarial Network for\n  Automatic Cardiac Diagnosis from ECG Signals", "abstract": "  Cardiac disease is the leading cause of death in the US. Accurate heart\ndisease detection is of critical importance for timely medical treatment to\nsave patients' lives. Routine use of electrocardiogram (ECG) is the most common\nmethod for physicians to assess the electrical activities of the heart and\ndetect possible abnormal cardiac conditions. Fully utilizing the ECG data for\nreliable heart disease detection depends on developing effective analytical\nmodels. In this paper, we propose a two-level hierarchical deep learning\nframework with Generative Adversarial Network (GAN) for automatic diagnosis of\nECG signals. The first-level model is composed of a Memory-Augmented Deep\nauto-Encoder with GAN (MadeGAN), which aims to differentiate abnormal signals\nfrom normal ECGs for anomaly detection. The second-level learning aims at\nrobust multi-class classification for different arrhythmias identification,\nwhich is achieved by integrating the transfer learning technique to transfer\nknowledge from the first-level learning with the multi-branching architecture\nto handle the data-lacking and imbalanced data issue. We evaluate the\nperformance of the proposed framework using real-world medical data from the\nMIT-BIH arrhythmia database. Experimental results show that our proposed model\noutperforms existing methods that are commonly used in current practice.\n", "date": "2022-10-19", "categories": []}
{"id": "2210.08500", "title": "This Patient Looks Like That Patient: Prototypical Networks for\n  Interpretable Diagnosis Prediction from Clinical Text", "abstract": "  The use of deep neural models for diagnosis prediction from clinical text has\nshown promising results. However, in clinical practice such models must not\nonly be accurate, but provide doctors with interpretable and helpful results.\nWe introduce ProtoPatient, a novel method based on prototypical networks and\nlabel-wise attention with both of these abilities. ProtoPatient makes\npredictions based on parts of the text that are similar to prototypical\npatients - providing justifications that doctors understand. We evaluate the\nmodel on two publicly available clinical datasets and show that it outperforms\nexisting baselines. Quantitative and qualitative evaluations with medical\ndoctors further demonstrate that the model provides valuable explanations for\nclinical decision support.\n", "date": "2022-10-16", "categories": []}
{"id": "2210.05671", "title": "iMedBot: A Web-based Intelligent Agent for Healthcare Related Prediction\n  and Deep Learning", "abstract": "  Background: Breast cancer is a multifactorial disease, genetic and\nenvironmental factors will affect its incidence probability. Breast cancer\nmetastasis is one of the main cause of breast cancer related deaths reported by\nthe American Cancer Society (ACS). Method: the iMedBot is a web application\nthat we developed using the python Flask web framework and deployed on Amazon\nWeb Services. It contains a frontend and a backend. The backend is supported by\na python program we developed using the python Keras and scikit-learn packages,\nwhich can be used to learn deep feedforward neural network (DFNN) models.\nResult: the iMedBot can provide two main services: 1. it can predict 5-, 10-,\nor 15-year breast cancer metastasis based on a set of clinical information\nprovided by a user. The prediction is done by using a set of DFNN models that\nwere pretrained, and 2. It can train DFNN models for a user using user-provided\ndataset. The model trained will be evaluated using AUC and both the AUC value\nand the AUC ROC curve will be provided. Conclusion: The iMedBot web application\nprovides a user-friendly interface for user-agent interaction in conducting\npersonalized prediction and model training. It is an initial attempt to convert\nresults of deep learning research into an online tool that may stir further\nresearch interests in this direction. Keywords: Deep learning, Breast Cancer,\nWeb application, Model training.\n", "date": "2022-10-07", "categories": []}
{"id": "2210.06291", "title": "ECG for high-throughput screening of multiple diseases: Proof-of-concept\n  using multi-diagnosis deep learning from population-based datasets", "abstract": "  Electrocardiogram (ECG) abnormalities are linked to cardiovascular diseases,\nbut may also occur in other non-cardiovascular conditions such as mental,\nneurological, metabolic and infectious conditions. However, most of the recent\nsuccess of deep learning (DL) based diagnostic predictions in selected patient\ncohorts have been limited to a small set of cardiac diseases. In this study, we\nuse a population-based dataset of >250,000 patients with >1000 medical\nconditions and >2 million ECGs to identify a wide range of diseases that could\nbe accurately diagnosed from the patient's first in-hospital ECG. Our DL models\nuncovered 128 diseases and 68 disease categories with strong discriminative\nperformance.\n", "date": "2022-10-06", "categories": []}
{"id": "2210.06293", "title": "Two-stream Network for ECG Signal Classification", "abstract": "  Electrocardiogram (ECG), a technique for medical monitoring of cardiac\nactivity, is an important method for identifying cardiovascular disease.\nHowever, analyzing the increasing quantity of ECG data consumes a lot of\nmedical resources. This paper explores an effective algorithm for automatic\nclassifications of multi-classes of heartbeat types based on ECG. Most neural\nnetwork based methods target the individual heartbeats, ignoring the secrets\nembedded in the temporal sequence. And the ECG signal has temporal variation\nand unique individual characteristics, which means that the same type of ECG\nsignal varies among patients under different physical conditions. A two-stream\narchitecture is used in this paper and presents an enhanced version of ECG\nrecognition based on this. The architecture achieves classification of holistic\nECG signal and individual heartbeat and incorporates identified and temporal\nstream networks. Identified networks are used to extract features of individual\nheartbeats, while temporal networks aim to extract temporal correlations\nbetween heartbeats. Results on the MIT-BIH Arrhythmia Database demonstrate that\nthe proposed algorithm performs an accuracy of 99.38\\%. In addition, the\nproposed algorithm reaches an 88.07\\% positive accuracy on massive data in real\nlife, showing that the proposed algorithm can efficiently categorize different\nclasses of heartbeat with high diagnostic performance.\n", "date": "2022-10-05", "categories": []}
{"id": "2210.04555", "title": "Everything is Varied: The Surprising Impact of Individual Variation on\n  ML Robustness in Medicine", "abstract": "  In medical settings, Individual Variation (IV) refers to variation that is\ndue not to population differences or errors, but rather to within-subject\nvariation, that is the intrinsic and characteristic patterns of variation\npertaining to a given instance or the measurement process. While taking into\naccount IV has been deemed critical for proper analysis of medical data, this\nsource of uncertainty and its impact on robustness have so far been neglected\nin Machine Learning (ML). To fill this gap, we look at how IV affects ML\nperformance and generalization and how its impact can be mitigated.\nSpecifically, we provide a methodological contribution to formalize the problem\nof IV in the statistical learning framework and, through an experiment based on\none of the largest real-world laboratory medicine datasets for the problem of\nCOVID-19 diagnosis, we show that: 1) common state-of-the-art ML models are\nseverely impacted by the presence of IV in data; and 2) advanced learning\nstrategies, based on data augmentation and data imprecisiation, and proper\nstudy designs can be effective at improving robustness to IV. Our findings\ndemonstrate the critical relevance of correctly accounting for IV to enable\nsafe deployment of ML in clinical settings.\n", "date": "2022-10-10", "categories": []}
{"id": "2210.05023", "title": "Using Deep Learning to Improve Early Diagnosis of Pneumonia in\n  Underdeveloped Countries", "abstract": "  As advancements in technology and medicine are being made, many countries are\nstill unable to access quality medical care due to cost and lack of qualified\nmedical personnel. This discrepancy in healthcare has caused many preventable\ndeaths, either due to lack of detection or lack of care. One of the most\nprevalent diseases in the world is pneumonia, an infection of the lungs that\nkilled 2.56 million people worldwide in 2017. In this same year, the United\nStates recorded a pneumonia death rate of 15.88 people per 100000 in\npopulation, while much of Sub-Saharan Africa, such as Chad and Guinea,\nexperienced death rates of over 150 people per 100000. In sub-Saharan Africa,\nthere is an extreme shortage of doctors and nurses, estimated to be around 2.4\nmillion. The hypothesis being tested is that a deep learning model can receive\ninput in the form of an x-ray and produce a diagnosis with the equivalent\naccuracy of a physician, compared to a prediagnosed image. The model used in\nthis project is a modified convolutional neural network. The model was trained\non a set of 2000 x-ray images that have predetermined normal and abnormal lung\nfindings, and then tested on a set of 400 images that contains evenly split\nimages of pneumonia and healthy lungs. For each computer-run test, data was\ncollected on a base measurement of accuracy, as well as more specific metrics\nsuch as specificity and sensitivity. Results show that the algorithm tested was\nable to accurately identify abnormal lung findings an average of 82.5% of the\ntime. The model achieved a maximum specificity of 98.5% and a maximum\nsensitivity of 90% separately, and the highest simultaneous values of these two\nmetrics was a sensitivity of 90% and a specificity of 78.5%. This research can\nbe further improved by testing other deep learning models as well as machine\nlearning models to improve the metric scores and chance of correct diagnoses.\n", "date": "2022-10-10", "categories": []}
{"id": "2209.04445", "title": "Privacy-Preserving Deep Learning Model for Covid-19 Disease Detection", "abstract": "  Recent studies demonstrated that X-ray radiography showed higher accuracy\nthan Polymerase Chain Reaction (PCR) testing for COVID-19 detection. Therefore,\napplying deep learning models to X-rays and radiography images increases the\nspeed and accuracy of determining COVID-19 cases. However, due to Health\nInsurance Portability and Accountability (HIPAA) compliance, the hospitals were\nunwilling to share patient data due to privacy concerns. To maintain privacy,\nwe propose differential private deep learning models to secure the patients'\nprivate information. The dataset from the Kaggle website is used to evaluate\nthe designed model for COVID-19 detection. The EfficientNet model version was\nselected according to its highest test accuracy. The injection of differential\nprivacy constraints into the best-obtained model was made to evaluate\nperformance. The accuracy is noted by varying the trainable layers, privacy\nloss, and limiting information from each sample. We obtained 84\\% accuracy with\na privacy loss of 10 during the fine-tuning process.\n", "date": "2022-09-07", "categories": []}
{"id": "2210.03332", "title": "Explainable AI based Glaucoma Detection using Transfer Learning and LIME", "abstract": "  Glaucoma is the second driving reason for partial or complete blindness among\nall the visual deficiencies which mainly occurs because of excessive pressure\nin the eye due to anxiety or depression which damages the optic nerve and\ncreates complications in vision. Traditional glaucoma screening is a\ntime-consuming process that necessitates the medical professionals' constant\nattention, and even so time to time due to the time constrains and pressure\nthey fail to classify correctly that leads to wrong treatment. Numerous efforts\nhave been made to automate the entire glaucoma classification procedure\nhowever, these existing models in general have a black box characteristics that\nprevents users from understanding the key reasons behind the prediction and\nthus medical practitioners generally can not rely on these system. In this\narticle after comparing with various pre-trained models, we propose a transfer\nlearning model that is able to classify Glaucoma with 94.71\\% accuracy. In\naddition, we have utilized Local Interpretable Model-Agnostic\nExplanations(LIME) that introduces explainability in our system. This\nimprovement enables medical professionals obtain important and comprehensive\ninformation that aid them in making judgments. It also lessen the opacity and\nfragility of the traditional deep learning models.\n", "date": "2022-10-07", "categories": []}
{"id": "2207.10970", "title": "Opportunistic hip fracture risk prediction in Men from X-ray: Findings\n  from the Osteoporosis in Men (MrOS) Study", "abstract": "  Osteoporosis is a common disease that increases fracture risk. Hip fractures,\nespecially in elderly people, lead to increased morbidity, decreased quality of\nlife and increased mortality. Being a silent disease before fracture,\nosteoporosis often remains undiagnosed and untreated. Areal bone mineral\ndensity (aBMD) assessed by dual-energy X-ray absorptiometry (DXA) is the\ngold-standard method for osteoporosis diagnosis and hence also for future\nfracture prediction (prognostic). However, the required special equipment is\nnot broadly available everywhere, in particular not to patients in developing\ncountries. We propose a deep learning classification model (FORM) that can\ndirectly predict hip fracture risk from either plain radiographs (X-ray) or 2D\nprojection images of computed tomography (CT) data. Our method is fully\nautomated and therefore well suited for opportunistic screening settings,\nidentifying high risk patients in a broader population without additional\nscreening. FORM was trained and evaluated on X-rays and CT projections from the\nOsteoporosis in Men (MrOS) study. 3108 X-rays (89 incident hip fractures) or\n2150 CTs (80 incident hip fractures) with a 80/20 split were used. We show that\nFORM can correctly predict the 10-year hip fracture risk with a validation AUC\nof 81.44 +- 3.11% / 81.04 +- 5.54% (mean +- STD) including additional\ninformation like age, BMI, fall history and health background across a 5-fold\ncross validation on the X-ray and CT cohort, respectively. Our approach\nsignificantly (p < 0.01) outperforms previous methods like Cox\nProportional-Hazards Model and \\frax with 70.19 +- 6.58 and 74.72 +- 7.21\nrespectively on the X-ray cohort. Our model outperform on both cohorts hip aBMD\nbased predictions. We are confident that FORM can contribute on improving\nosteoporosis diagnosis at an early stage.\n", "date": "2022-07-22", "categories": []}
{"id": "2210.02189", "title": "A Generalizable Artificial Intelligence Model for COVID-19\n  Classification Task Using Chest X-ray Radiographs: Evaluated Over Four\n  Clinical Datasets with 15,097 Patients", "abstract": "  Purpose: To answer the long-standing question of whether a model trained from\na single clinical site can be generalized to external sites.\n  Materials and Methods: 17,537 chest x-ray radiographs (CXRs) from 3,264\nCOVID-19-positive patients and 4,802 COVID-19-negative patients were collected\nfrom a single site for AI model development. The generalizability of the\ntrained model was retrospectively evaluated using four different real-world\nclinical datasets with a total of 26,633 CXRs from 15,097 patients (3,277\nCOVID-19-positive patients). The area under the receiver operating\ncharacteristic curve (AUC) was used to assess diagnostic performance.\n  Results: The AI model trained using a single-source clinical dataset achieved\nan AUC of 0.82 (95% CI: 0.80, 0.84) when applied to the internal temporal test\nset. When applied to datasets from two external clinical sites, an AUC of 0.81\n(95% CI: 0.80, 0.82) and 0.82 (95% CI: 0.80, 0.84) were achieved. An AUC of\n0.79 (95% CI: 0.77, 0.81) was achieved when applied to a multi-institutional\nCOVID-19 dataset collected by the Medical Imaging and Data Resource Center\n(MIDRC). A power-law dependence, N^(k )(k is empirically found to be -0.21 to\n-0.25), indicates a relatively weak performance dependence on the training data\nsizes.\n  Conclusion: COVID-19 classification AI model trained using well-curated data\nfrom a single clinical site is generalizable to external clinical sites without\na significant drop in performance.\n", "date": "2022-10-04", "categories": []}
{"id": "2210.00589", "title": "Uncertainty estimations methods for a deep learning model to aid in\n  clinical decision-making -- a clinician's perspective", "abstract": "  Prediction uncertainty estimation has clinical significance as it can\npotentially quantify prediction reliability. Clinicians may trust 'blackbox'\nmodels more if robust reliability information is available, which may lead to\nmore models being adopted into clinical practice. There are several deep\nlearning-inspired uncertainty estimation techniques, but few are implemented on\nmedical datasets -- fewer on single institutional datasets/models. We sought to\ncompare dropout variational inference (DO), test-time augmentation (TTA),\nconformal predictions, and single deterministic methods for estimating\nuncertainty using our model trained to predict feeding tube placement for 271\nhead and neck cancer patients treated with radiation. We compared the area\nunder the curve (AUC), sensitivity, specificity, positive predictive value\n(PPV), and negative predictive value (NPV) trends for each method at various\ncutoffs that sought to stratify patients into 'certain' and 'uncertain'\ncohorts. These cutoffs were obtained by calculating the percentile\n\"uncertainty\" within the validation cohort and applied to the testing cohort.\nBroadly, the AUC, sensitivity, and NPV increased as the predictions were more\n'certain' -- i.e., lower uncertainty estimates. However, when a majority vote\n(implementing 2/3 criteria: DO, TTA, conformal predictions) or a stricter\napproach (3/3 criteria) were used, AUC, sensitivity, and NPV improved without a\nnotable loss in specificity or PPV. Especially for smaller, single\ninstitutional datasets, it may be important to evaluate multiple estimations\ntechniques before incorporating a model into clinical practice.\n", "date": "2022-10-02", "categories": []}
{"id": "2209.14377", "title": "Clinical Language Understanding Evaluation (CLUE)", "abstract": "  Clinical language processing has received a lot of attention in recent years,\nresulting in new models or methods for disease phenotyping, mortality\nprediction, and other tasks. Unfortunately, many of these approaches are tested\nunder different experimental settings (e.g., data sources, training and testing\nsplits, metrics, evaluation criteria, etc.) making it difficult to compare\napproaches and determine state-of-the-art. To address these issues and\nfacilitate reproducibility and comparison, we present the Clinical Language\nUnderstanding Evaluation (CLUE) benchmark with a set of four clinical language\nunderstanding tasks, standard training, development, validation and testing\nsets derived from MIMIC data, as well as a software toolkit. It is our hope\nthat these data will enable direct comparison between approaches, improve\nreproducibility, and reduce the barrier-to-entry for developing novel models or\nmethods for these clinical language understanding tasks.\n", "date": "2022-09-28", "categories": []}
{"id": "2209.14907", "title": "Patients' Severity States Classification based on Electronic Health\n  Record (EHR) Data using Multiple Machine Learning and Deep Learning\n  Approaches", "abstract": "  This research presents an examination of categorizing the severity states of\npatients based on their electronic health records during a certain time range\nusing multiple machine learning and deep learning approaches. The suggested\nmethod uses an EHR dataset collected from an open-source platform to categorize\nseverity. Some tools were used in this research, such as openRefine was used to\npre-process, RapidMiner was used for implementing three algorithms (Fast Large\nMargin, Generalized Linear Model, Multi-layer Feed-forward Neural Network) and\nTableau was used to visualize the data, for implementation of algorithms we\nused Google Colab. Here we implemented several supervised and unsupervised\nalgorithms along with semi-supervised and deep learning algorithms. The\nexperimental results reveal that hyperparameter-tuned Random Forest\noutperformed all the other supervised machine learning algorithms with 76%\naccuracy as well as Generalized Linear algorithm achieved the highest precision\nscore 78%, whereas the hyperparameter-tuned Hierarchical Clustering with 86%\nprecision score and Gaussian Mixture Model with 61% accuracy outperformed other\nunsupervised approaches. Dimensionality Reduction improved results a lot for\nmost unsupervised techniques. For implementing Deep Learning we employed a\nfeed-forward neural network (multi-layer) and the Fast Large Margin approach\nfor semi-supervised learning. The Fast Large Margin performed really well with\na recall score of 84% and an F1 score of 78%. Finally, the Multi-layer\nFeed-forward Neural Network performed admirably with 75% accuracy, 75%\nprecision, 87% recall, 81% F1 score.\n", "date": "2022-09-29", "categories": []}
{"id": "2209.13799", "title": "Analysis and prediction of heart stroke from ejection fraction and serum\n  creatinine using LSTM deep learning approach", "abstract": "  The combination of big data and deep learning is a world-shattering\ntechnology that can greatly impact any objective if used properly. With the\navailability of a large volume of health care datasets and progressions in deep\nlearning techniques, systems are now well equipped to predict the future trend\nof any health problems. From the literature survey, we found the SVM was used\nto predict the heart failure rate without relating objective factors. Utilizing\nthe intensity of important historical information in electronic health records\n(EHR), we have built a smart and predictive model utilizing long short-term\nmemory (LSTM) and predict the future trend of heart failure based on that\nhealth record. Hence the fundamental commitment of this work is to predict the\nfailure of the heart using an LSTM based on the patient's electronic medicinal\ninformation. We have analyzed a dataset containing the medical records of 299\nheart failure patients collected at the Faisalabad Institute of Cardiology and\nthe Allied Hospital in Faisalabad (Punjab, Pakistan). The patients consisted of\n105 women and 194 men and their ages ranged from 40 and 95 years old. The\ndataset contains 13 features, which report clinical, body, and lifestyle\ninformation responsible for heart failure. We have found an increasing trend in\nour analysis which will contribute to advancing the knowledge in the field of\nheart stroke prediction.\n", "date": "2022-09-28", "categories": []}
{"id": "2209.13399", "title": "CCTCOVID: COVID-19 Detection from Chest X-Ray Images Using Compact\n  Convolutional Transformers", "abstract": "  COVID-19 is a novel virus that attacks the upper respiratory tract and the\nlungs. Its person-to-person transmissibility is considerably rapid and this has\ncaused serious problems in approximately every facet of individuals lives.\nWhile some infected individuals may remain completely asymptomatic, others have\nbeen frequently witnessed to have mild to severe symptoms. In addition to this,\nthousands of death cases around the globe indicated that detecting COVID-19 is\nan urgent demand in the communities. Practically, this is prominently done with\nthe help of screening medical images such as Computed Tomography (CT) and X-ray\nimages. However, the cumbersome clinical procedures and a large number of daily\ncases have imposed great challenges on medical practitioners. Deep\nLearning-based approaches have demonstrated a profound potential in a wide\nrange of medical tasks. As a result, we introduce a transformer-based method\nfor automatically detecting COVID-19 from X-ray images using Compact\nConvolutional Transformers (CCT). Our extensive experiments prove the efficacy\nof the proposed method with an accuracy of 98% which outperforms the previous\nworks.\n", "date": "2022-09-27", "categories": []}
{"id": "2206.08826", "title": "Multimodal Attention-based Deep Learning for Alzheimer's Disease\n  Diagnosis", "abstract": "  Alzheimer's Disease (AD) is the most common neurodegenerative disorder with\none of the most complex pathogeneses, making effective and clinically\nactionable decision support difficult. The objective of this study was to\ndevelop a novel multimodal deep learning framework to aid medical professionals\nin AD diagnosis. We present a Multimodal Alzheimer's Disease Diagnosis\nframework (MADDi) to accurately detect the presence of AD and mild cognitive\nimpairment (MCI) from imaging, genetic, and clinical data. MADDi is novel in\nthat we use cross-modal attention, which captures interactions between\nmodalities - a method not previously explored in this domain. We perform\nmulti-class classification, a challenging task considering the strong\nsimilarities between MCI and AD. We compare with previous state-of-the-art\nmodels, evaluate the importance of attention, and examine the contribution of\neach modality to the model's performance. MADDi classifies MCI, AD, and\ncontrols with 96.88% accuracy on a held-out test set. When examining the\ncontribution of different attention schemes, we found that the combination of\ncross-modal attention with self-attention performed the best, and no attention\nlayers in the model performed the worst, with a 7.9% difference in F1-Scores.\nOur experiments underlined the importance of structured clinical data to help\nmachine learning models contextualize and interpret the remaining modalities.\nExtensive ablation studies showed that any multimodal mixture of input features\nwithout access to structured clinical information suffered marked performance\nlosses. This study demonstrates the merit of combining multiple input\nmodalities via cross-modal attention to deliver highly accurate AD diagnostic\ndecision support.\n", "date": "2022-06-17", "categories": []}
{"id": "2209.12546", "title": "Myopia prediction for adolescents via time-aware deep learning", "abstract": "  Background: Quantitative prediction of the adolescents' spherical equivalent\nbased on their variable-length historical vision records.\n  Methods: From October 2019 to March 2022, we examined binocular uncorrected\nvisual acuity, axial length, corneal curvature, and axial of 75,172 eyes from\n37,586 adolescents aged 6-20 years in Chengdu, China. 80\\% samples consist of\nthe training set and the remaining 20\\% form the testing set. Time-Aware Long\nShort-Term Memory was used to quantitatively predict the adolescents' spherical\nequivalent within two and a half years.\n  Result: The mean absolute prediction error on the testing set was 0.273-0.257\nfor spherical equivalent, ranging from 0.189-0.160 to 0.596-0.473 if we\nconsider different lengths of historical records and different prediction\ndurations.\n  Conclusions: Time-Aware Long Short-Term Memory was applied to captured the\ntemporal features in irregularly sampled time series, which is more in line\nwith the characteristics of real data and thus has higher applicability, and\nhelps to identify the progression of myopia earlier. The overall error 0.273 is\nmuch smaller than the criterion for clinically acceptable prediction, say 0.75.\n", "date": "2022-09-26", "categories": []}
{"id": "2209.10750", "title": "Common human diseases prediction using machine learning based on survey\n  data", "abstract": "  In this era, the moment has arrived to move away from disease as the primary\nemphasis of medical treatment. Although impressive, the multiple techniques\nthat have been developed to detect the diseases. In this time, there are some\ntypes of diseases COVID-19, normal flue, migraine, lung disease, heart disease,\nkidney disease, diabetics, stomach disease, gastric, bone disease, autism are\nthe very common diseases. In this analysis, we analyze disease symptoms and\nhave done disease predictions based on their symptoms. We studied a range of\nsymptoms and took a survey from people in order to complete the task. Several\nclassification algorithms have been employed to train the model. Furthermore,\nperformance evaluation matrices are used to measure the model's performance.\nFinally, we discovered that the part classifier surpasses the others.\n", "date": "2022-09-22", "categories": []}
{"id": "2209.11172", "title": "EEG-Based Epileptic Seizure Prediction Using Temporal Multi-Channel\n  Transformers", "abstract": "  Epilepsy is one of the most common neurological diseases, characterized by\ntransient and unprovoked events called epileptic seizures. Electroencephalogram\n(EEG) is an auxiliary method used to perform both the diagnosis and the\nmonitoring of epilepsy. Given the unexpected nature of an epileptic seizure,\nits prediction would improve patient care, optimizing the quality of life and\nthe treatment of epilepsy. Predicting an epileptic seizure implies the\nidentification of two distinct states of EEG in a patient with epilepsy: the\npreictal and the interictal. In this paper, we developed two deep learning\nmodels called Temporal Multi-Channel Transformer (TMC-T) and Vision Transformer\n(TMC-ViT), adaptations of Transformer-based architectures for multi-channel\ntemporal signals. Moreover, we accessed the impact of choosing different\npreictal duration, since its length is not a consensus among experts, and also\nevaluated how the sample size benefits each model. Our models are compared with\nfully connected, convolutional, and recurrent networks. The algorithms were\npatient-specific trained and evaluated on raw EEG signals from the CHB-MIT\ndatabase. Experimental results and statistical validation demonstrated that our\nTMC-ViT model surpassed the CNN architecture, state-of-the-art in seizure\nprediction.\n", "date": "2022-09-18", "categories": []}
{"id": "2209.09032", "title": "A cost-based multi-layer network approach for the discovery of patient\n  phenotypes", "abstract": "  Clinical records frequently include assessments of the characteristics of\npatients, which may include the completion of various questionnaires. These\nquestionnaires provide a variety of perspectives on a patient's current state\nof well-being. Not only is it critical to capture the heterogeneity given by\nthese perspectives, but there is also a growing demand for developing\ncost-effective technologies for clinical phenotyping. Filling out many\nquestionnaires may be a strain for the patients and therefore costly. In this\nwork, we propose COBALT -- a cost-based layer selector model for detecting\nphenotypes using a community detection approach. Our goal is to minimize the\nnumber of features used to build these phenotypes while preserving its quality.\nWe test our model using questionnaire data from chronic tinnitus patients and\nrepresent the data in a multi-layer network structure. The model is then\nevaluated by predicting post-treatment data using baseline features (age,\ngender, and pre-treatment data) as well as the identified phenotypes as a\nfeature. For some post-treatment variables, predictors using phenotypes from\nCOBALT as features outperformed those using phenotypes detected by traditional\nclustering methods. Moreover, using phenotype data to predict post-treatment\ndata proved beneficial in comparison with predictors that were solely trained\nwith baseline features.\n", "date": "2022-09-19", "categories": []}
{"id": "2209.09188", "title": "Avoiding Biased Clinical Machine Learning Model Performance Estimates in\n  the Presence of Label Selection", "abstract": "  When evaluating the performance of clinical machine learning models, one must\nconsider the deployment population. When the population of patients with\nobserved labels is only a subset of the deployment population (label\nselection), standard model performance estimates on the observed population may\nbe misleading. In this study we describe three classes of label selection and\nsimulate five causally distinct scenarios to assess how particular selection\nmechanisms bias a suite of commonly reported binary machine learning model\nperformance metrics. Simulations reveal that when selection is affected by\nobserved features, naive estimates of model discrimination may be misleading.\nWhen selection is affected by labels, naive estimates of calibration fail to\nreflect reality. We borrow traditional weighting estimators from causal\ninference literature and find that when selection probabilities are properly\nspecified, they recover full population estimates. We then tackle the\nreal-world task of monitoring the performance of deployed machine learning\nmodels whose interactions with clinicians feed-back and affect the selection\nmechanism of the labels. We train three machine learning models to flag\nlow-yield laboratory diagnostics, and simulate their intended consequence of\nreducing wasteful laboratory utilization. We find that naive estimates of AUROC\non the observed population undershoot actual performance by up to 20%. Such a\ndisparity could be large enough to lead to the wrongful termination of a\nsuccessful clinical decision support tool. We propose an altered deployment\nprocedure, one that combines injected randomization with traditional weighted\nestimates, and find it recovers true model performance.\n", "date": "2022-09-15", "categories": []}
{"id": "2209.09204", "title": "Robustness of an Artificial Intelligence Solution for Diagnosis of\n  Normal Chest X-Rays", "abstract": "  Purpose: Artificial intelligence (AI) solutions for medical diagnosis require\nthorough evaluation to demonstrate that performance is maintained for all\npatient sub-groups and to ensure that proposed improvements in care will be\ndelivered equitably. This study evaluates the robustness of an AI solution for\nthe diagnosis of normal chest X-rays (CXRs) by comparing performance across\nmultiple patient and environmental subgroups, as well as comparing AI errors\nwith those made by human experts.\n  Methods: A total of 4,060 CXRs were sampled to represent a diverse dataset of\nNHS patients and care settings. Ground-truth labels were assigned by a\n3-radiologist panel. AI performance was evaluated against assigned labels and\nsub-groups analysis was conducted against patient age and sex, as well as CXR\nview, modality, device manufacturer and hospital site.\n  Results: The AI solution was able to remove 18.5% of the dataset by\nclassification as High Confidence Normal (HCN). This was associated with a\nnegative predictive value (NPV) of 96.0%, compared to 89.1% for diagnosis of\nnormal scans by radiologists. In all AI false negative (FN) cases, a\nradiologist was found to have also made the same error when compared to final\nground-truth labels. Subgroup analysis showed no statistically significant\nvariations in AI performance, whilst reduced normal classification was observed\nin data from some hospital sites.\n  Conclusion: We show the AI solution could provide meaningful workload savings\nby diagnosis of 18.5% of scans as HCN with a superior NPV to human readers. The\nAI solution is shown to perform well across patient subgroups and error cases\nwere shown to be subjective or subtle in nature.\n", "date": "2022-08-31", "categories": []}
{"id": "2208.08408", "title": "Summarizing Patients Problems from Hospital Progress Notes Using\n  Pre-trained Sequence-to-Sequence Models", "abstract": "  Automatically summarizing patients' main problems from daily progress notes\nusing natural language processing methods helps to battle against information\nand cognitive overload in hospital settings and potentially assists providers\nwith computerized diagnostic decision support. Problem list summarization\nrequires a model to understand, abstract, and generate clinical documentation.\nIn this work, we propose a new NLP task that aims to generate a list of\nproblems in a patient's daily care plan using input from the provider's\nprogress notes during hospitalization. We investigate the performance of T5 and\nBART, two state-of-the-art seq2seq transformer architectures, in solving this\nproblem. We provide a corpus built on top of progress notes from publicly\navailable electronic health record progress notes in the Medical Information\nMart for Intensive Care (MIMIC)-III. T5 and BART are trained on general domain\ntext, and we experiment with a data augmentation method and a domain adaptation\npre-training method to increase exposure to medical vocabulary and knowledge.\nEvaluation methods include ROUGE, BERTScore, cosine similarity on sentence\nembedding, and F-score on medical concepts. Results show that T5 with domain\nadaptive pre-training achieves significant performance gains compared to a\nrule-based system and general domain pre-trained language models, indicating a\npromising direction for tackling the problem summarization task.\n", "date": "2022-08-17", "categories": []}
{"id": "2209.04643", "title": "Examining stability of machine learning methods for predicting dementia\n  at early phases of the disease", "abstract": "  Dementia is a neuropsychiatric brain disorder that usually occurs when one or\nmore brain cells stop working partially or at all. Diagnosis of this disorder\nin the early phases of the disease is a vital task to rescue patients lives\nfrom bad consequences and provide them with better healthcare. Machine learning\nmethods have been proven to be accurate in predicting dementia in the early\nphases of the disease. The prediction of dementia depends heavily on the type\nof collected data which usually are gathered from Normalized Whole Brain Volume\n(nWBV) and Atlas Scaling Factor (ASF) which are normally measured and corrected\nfrom Magnetic Resonance Imaging (MRIs). Other biological features such as age\nand gender can also help in the diagnosis of dementia. Although many studies\nuse machine learning for predicting dementia, we could not reach a conclusion\non the stability of these methods for which one is more accurate under\ndifferent experimental conditions. Therefore, this paper investigates the\nconclusion stability regarding the performance of machine learning algorithms\nfor dementia prediction. To accomplish this, a large number of experiments were\nrun using 7 machine learning algorithms and two feature reduction algorithms\nnamely, Information Gain (IG) and Principal Component Analysis (PCA). To\nexamine the stability of these algorithms, thresholds of feature selection were\nchanged for the IG from 20% to 100% and the PCA dimension from 2 to 8. This has\nresulted in 7x9 + 7x7= 112 experiments. In each experiment, various\nclassification evaluation data were recorded. The obtained results show that\namong seven algorithms the support vector machine and Naive Bayes are the most\nstable algorithms while changing the selection threshold. Also, it was found\nthat using IG would seem more efficient than using PCA for predicting Dementia.\n", "date": "2022-09-10", "categories": []}
{"id": "2209.04830", "title": "Detecting Suicide Risk in Online Counseling Services: A Study in a\n  Low-Resource Language", "abstract": "  With the increased awareness of situations of mental crisis and their\nsocietal impact, online services providing emergency support are becoming\ncommonplace in many countries. Computational models, trained on discussions\nbetween help-seekers and providers, can support suicide prevention by\nidentifying at-risk individuals. However, the lack of domain-specific models,\nespecially in low-resource languages, poses a significant challenge for the\nautomatic detection of suicide risk. We propose a model that combines\npre-trained language models (PLM) with a fixed set of manually crafted (and\nclinically approved) set of suicidal cues, followed by a two-stage fine-tuning\nprocess. Our model achieves 0.91 ROC-AUC and an F2-score of 0.55, significantly\noutperforming an array of strong baselines even early on in the conversation,\nwhich is critical for real-time detection in the field. Moreover, the model\nperforms well across genders and age groups.\n", "date": "2022-09-11", "categories": []}
{"id": "2209.04224", "title": "Modelling Patient Trajectories Using Multimodal Information", "abstract": "  Electronic Health Records (EHRs) aggregate diverse information at the patient\nlevel, holding a trajectory representative of the evolution of the patient\nhealth status throughout time. Although this information provides context and\ncan be leveraged by physicians to monitor patient health and make more accurate\nprognoses/diagnoses, patient records can contain information from very long\ntime spans, which combined with the rapid generation rate of medical data makes\nclinical decision making more complex. Patient trajectory modelling can assist\nby exploring existing information in a scalable manner, and can contribute in\naugmenting health care quality by fostering preventive medicine practices. We\npropose a solution to model patient trajectories that combines different types\nof information and considers the temporal aspect of clinical data. This\nsolution leverages two different architectures: one supporting flexible sets of\ninput features, to convert patient admissions into dense representations; and a\nsecond exploring extracted admission representations in a recurrent-based\narchitecture, where patient trajectories are processed in sub-sequences using a\nsliding window mechanism. The developed solution was evaluated on two different\nclinical outcomes, unexpected patient readmission and disease progression,\nusing the publicly available MIMIC-III clinical database. The results obtained\ndemonstrate the potential of the first architecture to model readmission and\ndiagnoses prediction using single patient admissions. While information from\nclinical text did not show the discriminative power observed in other existing\nworks, this may be explained by the need to fine-tune the clinicalBERT model.\nFinally, we demonstrate the potential of the sequence-based architecture using\na sliding window mechanism to represent the input data, attaining comparable\nperformances to other existing solutions.\n", "date": "2022-09-09", "categories": []}
{"id": "2209.03452", "title": "AILAB-Udine@SMM4H 22: Limits of Transformers and BERT Ensembles", "abstract": "  This paper describes the models developed by the AILAB-Udine team for the\nSMM4H 22 Shared Task. We explored the limits of Transformer based models on\ntext classification, entity extraction and entity normalization, tackling Tasks\n1, 2, 5, 6 and 10. The main take-aways we got from participating in different\ntasks are: the overwhelming positive effects of combining different\narchitectures when using ensemble learning, and the great potential of\ngenerative models for term normalization.\n", "date": "2022-09-07", "categories": []}
{"id": "2209.01469", "title": "Machine learning for dynamically predicting the onset of renal\n  replacement therapy in chronic kidney disease patients using claims data", "abstract": "  Chronic kidney disease (CKD) represents a slowly progressive disorder that\ncan eventually require renal replacement therapy (RRT) including dialysis or\nrenal transplantation. Early identification of patients who will require RRT\n(as much as 1 year in advance) improves patient outcomes, for example by\nallowing higher-quality vascular access for dialysis. Therefore, early\nrecognition of the need for RRT by care teams is key to successfully managing\nthe disease. Unfortunately, there is currently no commonly used predictive tool\nfor RRT initiation. In this work, we present a machine learning model that\ndynamically identifies CKD patients at risk of requiring RRT up to one year in\nadvance using only claims data. To evaluate the model, we studied approximately\n3 million Medicare beneficiaries for which we made over 8 million predictions.\nWe showed that the model can identify at risk patients with over 90%\nsensitivity and specificity. Although additional work is required before this\napproach is ready for clinical use, this study provides a basis for a screening\ntool to identify patients at risk within a time window that enables early\nproactive interventions intended to improve RRT outcomes.\n", "date": "2022-09-03", "categories": []}
{"id": "2209.01517", "title": "Joint Prediction of Meningioma Grade and Brain Invasion via Task-Aware\n  Contrastive Learning", "abstract": "  Preoperative and noninvasive prediction of the meningioma grade is important\nin clinical practice, as it directly influences the clinical decision making.\nWhat's more, brain invasion in meningioma (i.e., the presence of tumor tissue\nwithin the adjacent brain tissue) is an independent criterion for the grading\nof meningioma and influences the treatment strategy. Although efforts have been\nreported to address these two tasks, most of them rely on hand-crafted features\nand there is no attempt to exploit the two prediction tasks simultaneously. In\nthis paper, we propose a novel task-aware contrastive learning algorithm to\njointly predict meningioma grade and brain invasion from multi-modal MRIs.\nBased on the basic multi-task learning framework, our key idea is to adopt\ncontrastive learning strategy to disentangle the image features into\ntask-specific features and task-common features, and explicitly leverage their\ninherent connections to improve feature representation for the two prediction\ntasks. In this retrospective study, an MRI dataset was collected, for which 800\npatients (containing 148 high-grade, 62 invasion) were diagnosed with\nmeningioma by pathological analysis. Experimental results show that the\nproposed algorithm outperforms alternative multi-task learning methods,\nachieving AUCs of 0:8870 and 0:9787 for the prediction of meningioma grade and\nbrain invasion, respectively. The code is available at\nhttps://github.com/IsDling/predictTCL.\n", "date": "2022-09-04", "categories": []}
{"id": "2209.00934", "title": "TB or not TB? Acoustic cough analysis for tuberculosis classification", "abstract": "  In this work, we explore recurrent neural network architectures for\ntuberculosis (TB) cough classification. In contrast to previous unsuccessful\nattempts to implement deep architectures in this domain, we show that a basic\nbidirectional long short-term memory network (BiLSTM) can achieve improved\nperformance. In addition, we show that by performing greedy feature selection\nin conjunction with a newly-proposed attention-based architecture that learns\npatient invariant features, substantially better generalisation can be achieved\ncompared to a baseline and other considered architectures. Furthermore, this\nattention mechanism allows an inspection of the temporal regions of the audio\nsignal considered to be important for classification to be performed. Finally,\nwe develop a neural style transfer technique to infer idealised inputs which\ncan subsequently be analysed. We find distinct differences between the\nidealised power spectra of TB and non-TB coughs, which provide clues about the\norigin of the features in the audio signal.\n", "date": "2022-09-02", "categories": []}
{"id": "2209.00988", "title": "A lightweight hybrid CNN-LSTM model for ECG-based arrhythmia detection", "abstract": "  Electrocardiogram (ECG) is the most frequent and routine diagnostic tool used\nfor monitoring heart electrical signals and evaluating its functionality. The\nhuman heart can suffer from a variety of diseases, including cardiac\narrhythmias. Arrhythmia is an irregular heart rhythm that in severe cases can\nlead to heart stroke and can be diagnosed via ECG recordings. Since early\ndetection of cardiac arrhythmias is of great importance, computerized and\nautomated classification and identification of these abnormal heart signals\nhave received much attention for the past decades. Methods: This paper\nintroduces a light deep learning approach for high accuracy detection of 8\ndifferent cardiac arrhythmias and normal rhythm. To leverage deep learning\nmethod, resampling and baseline wander removal techniques are applied to ECG\nsignals. In this study, 500 sample ECG segments were used as model inputs. The\nrhythm classification was done by an 11-layer network in an end-to-end manner\nwithout the need for hand-crafted manual feature extraction. Results: In order\nto evaluate the proposed technique, ECG signals are chosen from the two\nphysionet databases, the MIT-BIH arrhythmia database and the long-term AF\ndatabase. The proposed deep learning framework based on the combination of\nConvolutional Neural Network(CNN) and Long Short Term Memory (LSTM) showed\npromising results than most of the state-of-the-art methods. The proposed\nmethod reaches the mean diagnostic accuracy of 98.24%. Conclusion: A trained\nmodel for arrhythmia classification using diverse ECG signals were successfully\ndeveloped and tested. Significance: Since the present work uses a light\nclassification technique with high diagnostic accuracy compared to other\nnotable methods, it could successfully be implemented in holter monitor devices\nfor arrhythmia detection.\n", "date": "2022-08-29", "categories": []}
{"id": "2208.08182", "title": "Deep Learning-Based Discrete Calibrated Survival Prediction", "abstract": "  Deep neural networks for survival prediction outper-form classical approaches\nin discrimination, which is the ordering of patients according to their\ntime-of-event. Conversely, classical approaches like the Cox Proportional\nHazards model display much better calibration, the correct temporal prediction\nof events of the underlying distribution. Especially in the medical domain,\nwhere it is critical to predict the survival of a single patient, both\ndiscrimination and calibration are important performance metrics. Here we\npresent Discrete Calibrated Survival (DCS), a novel deep neural network for\ndiscriminated and calibrated survival prediction that outperforms competing\nsurvival models in discrimination on three medical datasets, while achieving\nbest calibration among all discrete time models. The enhanced performance of\nDCS can be attributed to two novel features, the variable temporal output node\nspacing and the novel loss term that optimizes the use of uncensored and\ncensored patient data. We believe that DCS is an important step towards\nclinical application of deep-learning-based survival prediction with\nstate-of-the-art discrimination and good calibration.\n", "date": "2022-08-17", "categories": []}
{"id": "2208.14742", "title": "Enhancing Early Lung Cancer Detection on Chest Radiographs with\n  AI-assistance: A Multi-Reader Study", "abstract": "  Objectives: The present study evaluated the impact of a commercially\navailable explainable AI algorithm in augmenting the ability of clinicians to\nidentify lung cancer on chest X-rays (CXR).\n  Design: This retrospective study evaluated the performance of 11 clinicians\nfor detecting lung cancer from chest radiographs, with and without assistance\nfrom a commercially available AI algorithm (red dot, Behold.ai) that predicts\nsuspected lung cancer from CXRs. Clinician performance was evaluated against\nclinically confirmed diagnoses.\n  Setting: The study analysed anonymised patient data from an NHS hospital; the\ndataset consisted of 400 chest radiographs from adult patients (18 years and\nabove) who had a CXR performed in 2020, with corresponding clinical text\nreports.\n  Participants: A panel of readers consisting of 11 clinicians (consultant\nradiologists, radiologist trainees and reporting radiographers) participated in\nthis study.\n  Main outcome measures: Overall accuracy, sensitivity, specificity and\nprecision for detecting lung cancer on CXRs by clinicians, with and without AI\ninput. Agreement rates between clinicians and performance standard deviation\nwere also evaluated, with and without AI input.\n  Results: The use of the AI algorithm by clinicians led to an improved overall\nperformance for lung tumour detection, achieving an overall increase of 17.4%\nof lung cancers being identified on CXRs which would have otherwise been\nmissed, an overall increase in detection of smaller tumours, a 24% and 13%\nincreased detection of stage 1 and stage 2 lung cancers respectively, and\nstandardisation of clinician performance.\n  Conclusions: This study showed great promise in the clinical utility of AI\nalgorithms in improving early lung cancer diagnosis and promoting health equity\nthrough overall improvement in reader performances, without impacting\ndownstream imaging resources.\n", "date": "2022-08-31", "categories": []}
{"id": "2208.14384", "title": "Expert Opinion Elicitation for Assisting Deep Learning based Lyme\n  Disease Classifier with Patient Data", "abstract": "  Diagnosing erythema migrans (EM) skin lesion, the most common early symptom\nof Lyme disease using deep learning techniques can be effective to prevent\nlong-term complications. Existing works on deep learning based EM recognition\nonly utilizes lesion image due to the lack of a dataset of Lyme disease related\nimages with associated patient data. Physicians rely on patient information\nabout the background of the skin lesion to confirm their diagnosis. In order to\nassist the deep learning model with a probability score calculated from patient\ndata, this study elicited opinion from fifteen doctors. For the elicitation\nprocess, a questionnaire with questions and possible answers related to EM was\nprepared. Doctors provided relative weights to different answers to the\nquestions. We converted doctors evaluations to probability scores using\nGaussian mixture based density estimation. For elicited probability model\nvalidation, we exploited formal concept analysis and decision tree. The\nelicited probability scores can be utilized to make image based deep learning\nLyme disease pre-scanners robust.\n", "date": "2022-08-30", "categories": []}
{"id": "2208.13126", "title": "Learning Clinical Concepts for Predicting Risk of Progression to Severe\n  COVID-19", "abstract": "  With COVID-19 now pervasive, identification of high-risk individuals is\ncrucial. Using data from a major healthcare provider in Southwestern\nPennsylvania, we develop survival models predicting severe COVID-19\nprogression. In this endeavor, we face a tradeoff between more accurate models\nrelying on many features and less accurate models relying on a few features\naligned with clinician intuition. Complicating matters, many EHR features tend\nto be under-coded, degrading the accuracy of smaller models. In this study, we\ndevelop two sets of high-performance risk scores: (i) an unconstrained model\nbuilt from all available features; and (ii) a pipeline that learns a small set\nof clinical concepts before training a risk predictor. Learned concepts boost\nperformance over the corresponding features (C-index 0.858 vs. 0.844) and\ndemonstrate improvements over (i) when evaluated out-of-sample (subsequent time\nperiods). Our models outperform previous works (C-index 0.844-0.872 vs.\n0.598-0.810).\n", "date": "2022-08-28", "categories": []}
{"id": "2208.13319", "title": "Minute ventilation measurement using Plethysmographic Imaging and\n  lighting parameters", "abstract": "  Breathing disorders such as sleep apnea is a critical disorder that affects a\nlarge number of individuals due to the insufficient capacity of the lungs to\ncontain/exchange oxygen and carbon dioxide to ensure that the body is in the\nstable state of homeostasis. Respiratory Measurements such as minute\nventilation can be used in correlation with other physiological measurements\nsuch as heart rate and heart rate variability for remote monitoring of health\nand detecting symptoms of such breathing related disorders. In this work, we\nformulate a deep learning based approach to measure remote ventilation on a\nprivate dataset. The dataset will be made public upon acceptance of this work.\nWe use two versions of a deep neural network to estimate the minute ventilation\nfrom data streams obtained through wearable heart rate and respiratory devices.\nWe demonstrate that the simple design of our pipeline - which includes\nlightweight deep neural networks - can be easily incorporate into real time\nhealth monitoring systems.\n", "date": "2022-08-29", "categories": []}
{"id": "2207.14685", "title": "Replacing the Framingham-based equation for prediction of cardiovascular\n  disease risk and adverse outcome by using artificial intelligence and retinal\n  imaging", "abstract": "  Purpose: To create and evaluate the accuracy of an artificial intelligence\nDeep learning platform (ORAiCLE) capable of using only retinal fundus images to\npredict both an individuals overall 5 year cardiovascular risk (CVD) and the\nrelative contribution of the component risk factors that comprise this risk.\nMethods: We used 165,907 retinal images from a database of 47,236 patient\nvisits. Initially, each image was paired with biometric data age, ethnicity,\nsex, presence and duration of diabetes a HDL/LDL ratios as well as any CVD\nevent wtihin 5 years of the retinal image acquisition. A risk score based on\nFramingham equations was calculated. The real CVD event rate was also\ndetermined for the individuals and overall population. Finally, ORAiCLE was\ntrained using only age, ethnicity, sex plus retinal images. Results: Compared\nto Framingham-based score, ORAiCLE was up to 12% more accurate in prediciting\ncardiovascular event in he next 5-years, especially for the highest risk group\nof people. The reliability and accuracy of each of the restrictive models was\nsuboptimal to ORAiCLE performance ,indicating that it was using data from both\nsets of data to derive its final results. Conclusion: Retinal photography is\ninexpensive and only minimal training is required to acquire them as fully\nautomated, inexpensive camera systems are now widely available. As such,\nAI-based CVD risk algorithms such as ORAiCLE promise to make CV health\nscreening more accurate, more afforadable and more accessible for all.\nFurthermore, ORAiCLE unique ability to assess the relative contribution of the\ncomponents that comprise an individuals overall risk would inform treatment\ndecisions based on the specific needs of an individual, thereby increasing the\nlikelihood of positive health outcomes.\n", "date": "2022-07-17", "categories": []}
{"id": "2208.10245", "title": "When BERT Fails -- The Limits of EHR Classification", "abstract": "  Transformers are powerful text representation learners, useful for all kinds\nof clinical decision support tasks. Although they outperform baselines on\nreadmission prediction, they are not infallible. Here, we look into one such\nfailure case, and report patterns that lead to inferior predictive performance.\n", "date": "2022-07-26", "categories": []}
{"id": "2208.09349", "title": "DCNNV-19: A Deep Convolutional Neural Network for COVID-19 Detection in\n  Chest Computed Tomographies", "abstract": "  This technical report proposes the use of a deep convolutional neural network\nas a preliminary diagnostic method in the analysis of chest computed tomography\nimages from patients with symptoms of Severe Acute Respiratory Syndrome (SARS)\nand suspected COVID-19 disease, especially on occasions when the delay of the\nRT-PCR result and the absence of urgent care could result in serious temporary,\nlong-term, or permanent health damage. The model was trained on 83,391 images,\nvalidated on 15,297, and tested on 22,185 figures, achieving an F1-Score of\n98%, 97.59% in Cohen's Kappa, 98.4% in Accuracy, and 5.09% in Loss. Attesting a\nhighly accurate automated classification and providing results in less time\nthan the current gold-standard exam, Real-Time reverse-transcriptase Polymerase\nChain Reaction (RT-PCR).\n", "date": "2022-08-18", "categories": []}
{"id": "2208.10550", "title": "Atrial Fibrillation Recurrence Risk Prediction from 12-lead ECG Recorded\n  Pre- and Post-Ablation Procedure", "abstract": "  Introduction: 12-lead electrocardiogram (ECG) is recorded during atrial\nfibrillation (AF) catheter ablation procedure (CAP). It is not easy to\ndetermine if CAP was successful without a long follow-up assessing for AF\nrecurrence (AFR). Therefore, an AFR risk prediction algorithm could enable a\nbetter management of CAP patients. In this research, we extracted features from\n12-lead ECG recorded before and after CAP and train an AFR risk prediction\nmachine learning model. Methods: Pre- and post-CAP segments were extracted from\n112 patients. The analysis included a signal quality criterion, heart rate\nvariability and morphological biomarkers engineered from the 12-lead ECG (804\nfeatures overall). 43 out of the 112 patients (n) had AFR clinical endpoint\navailable. These were utilized to assess the feasibility of AFR risk\nprediction, using either pre or post CAP features. A random forest classifier\nwas trained within a nested cross validation framework. Results: 36 features\nwere found statistically significant for distinguishing between the pre and\npost surgery states (n=112). For the classification, an area under the receiver\noperating characteristic (AUROC) curve was reported with AUROC_pre=0.64 and\nAUROC_post=0.74 (n=43). Discussion and conclusions: This preliminary analysis\nshowed the feasibility of AFR risk prediction. Such a model could be used to\nimprove CAP management.\n", "date": "2022-08-22", "categories": []}
{"id": "2208.10463", "title": "Survey of Machine Learning Techniques To Predict Heartbeat Arrhythmias", "abstract": "  Many works in biomedical computer science research use machine learning\ntechniques to give accurate results. However, these techniques may not be\nfeasible for real-time analysis of data pulled from live hospital feeds. In\nthis project, different machine learning techniques are compared from various\nsources to find one that provides not only high accuracy but also low latency\nand memory overhead to be used in real-world health care systems.\n", "date": "2022-08-22", "categories": []}
{"id": "2206.15073", "title": "COVID Detection and Severity Prediction with 3D-ConvNeXt and Custom\n  Pretrainings", "abstract": "  Since COVID strongly affects the respiratory system, lung CT-scans can be\nused for the analysis of a patients health. We introduce a neural network for\nthe prediction of the severity of lung damage and the detection of a\nCOVID-infection using three-dimensional CT-data. Therefore, we adapt the recent\nConvNeXt model to process three-dimensional data. Furthermore, we design and\nanalyze different pretraining methods specifically designed to improve the\nmodels ability to handle three-dimensional CT-data. We rank 2nd in the 1st\nCOVID19 Severity Detection Challenge and 3rd in the 2nd COVID19 Detection\nChallenge.\n", "date": "2022-06-30", "categories": []}
{"id": "2208.07829", "title": "Diagnosis of COVID-19 disease using CT scan images and pre-trained\n  models", "abstract": "  Diagnosis of COVID-19 is necessary to prevent and control the disease. Deep\nlearning methods have been considered a fast and accurate method. In this\npaper, by the parallel combination of three well-known pre-trained networks, we\nattempted to distinguish coronavirus-infected samples from healthy samples. The\nnegative log-likelihood loss function has been used for model training. CT scan\nimages in the SARS-CoV-2 dataset were used for diagnosis. The SARS-CoV-2\ndataset contains 2482 images of lung CT scans, of which 1252 images belong to\nCOVID-19-infected samples. The proposed model was close to 97% accurate.\n", "date": "2022-08-16", "categories": []}
{"id": "2208.06607", "title": "Incoporating Weighted Board Learning System for Accurate Occupational\n  Pneumoconiosis Staging", "abstract": "  Occupational pneumoconiosis (OP) staging is a vital task concerning the lung\nhealthy of a subject. The staging result of a patient is depended on the\nstaging standard and his chest X-ray. It is essentially an image classification\ntask. However, the distribution of OP data is commonly imbalanced, which\nlargely reduces the effect of classification models which are proposed under\nthe assumption that data follow a balanced distribution and causes inaccurate\nstaging results. To achieve accurate OP staging, we proposed an OP staging\nmodel who is able to handle imbalance data in this work. The proposed model\nadopts gray level co-occurrence matrix (GLCM) to extract texture feature of\nchest X-ray and implements classification with a weighted broad learning system\n(WBLS). Empirical studies on six data cases provided by a hospital indicate\nthat proposed model can perform better OP staging than state-of-the-art\nclassifiers with imbalanced data.\n", "date": "2022-08-13", "categories": []}
{"id": "2208.07088", "title": "Enhancing Deep Learning-based 3-lead ECG Classification with Heartbeat\n  Counting and Demographic Data Integration", "abstract": "  Nowadays, an increasing number of people are being diagnosed with\ncardiovascular diseases (CVDs), the leading cause of death globally. The gold\nstandard for identifying these heart problems is via electrocardiogram (ECG).\nThe standard 12-lead ECG is widely used in clinical practice and the majority\nof current research. However, using a lower number of leads can make ECG more\npervasive as it can be integrated with portable or wearable devices. This\narticle introduces two novel techniques to improve the performance of the\ncurrent deep learning system for 3-lead ECG classification, making it\ncomparable with models that are trained using standard 12-lead ECG.\nSpecifically, we propose a multi-task learning scheme in the form of the number\nof heartbeats regression and an effective mechanism to integrate patient\ndemographic data into the system. With these two advancements, we got\nclassification performance in terms of F1 scores of 0.9796 and 0.8140 on two\nlarge-scale ECG datasets, i.e., Chapman and CPSC-2018, respectively, which\nsurpassed current state-of-the-art ECG classification methods, even those\ntrained on 12-lead data. To encourage further development, our source code is\npublicly available at https://github.com/lhkhiem28/LightX3ECG.\n", "date": "2022-08-15", "categories": []}
{"id": "2208.04315", "title": "Patient-Specific Game-Based Transfer Method for Parkinson's Disease\n  Severity Prediction", "abstract": "  Dysphonia is one of the early symptoms of Parkinson's disease (PD). Most\nexisting methods use feature selection methods to find the optimal subset of\nvoice features for all PD patients. Few have considered the heterogeneity\nbetween patients, which implies the need to provide specific prediction models\nfor different patients. However, building the specific model faces the\nchallenge of small sample size, which makes it lack generalization ability.\nInstance transfer is an effective way to solve this problem. Therefore, this\npaper proposes a patient-specific game-based transfer (PSGT) method for PD\nseverity prediction. First, a selection mechanism is used to select PD patients\nwith similar disease trends to the target patient from the source domain, which\ngreatly reduces the risk of negative transfer. Then, the contribution of the\ntransferred subjects and their instances to the disease estimation of the\ntarget subject is fairly evaluated by the Shapley value, which improves the\ninterpretability of the method. Next, the proportion of valid instances in the\ntransferred subjects is determined, and the instances with higher contribution\nare transferred to further reduce the difference between the transferred\ninstance subset and the target subject. Finally, the selected subset of\ninstances is added to the training set of the target subject, and the extended\ndata is fed into the random forest to improve the performance of the method.\nParkinson's telemonitoring dataset is used to evaluate the feasibility and\neffectiveness. Experiment results show that the PSGT has better performance in\nboth prediction error and stability over compared methods.\n", "date": "2022-08-07", "categories": []}
{"id": "2208.06354", "title": "A novel solution of deep learning for enhanced support vector machine\n  for predicting the onset of type 2 diabetes", "abstract": "  Type 2 Diabetes is one of the most major and fatal diseases known to human\nbeings, where thousands of people are subjected to the onset of Type 2 Diabetes\nevery year. However, the diagnosis and prevention of Type 2 Diabetes are\nrelatively costly in today's scenario; hence, the use of machine learning and\ndeep learning techniques is gaining momentum for predicting the onset of Type 2\nDiabetes. This research aims to increase the accuracy and Area Under the Curve\n(AUC) metric while improving the processing time for predicting the onset of\nType 2 Diabetes. The proposed system consists of a deep learning technique that\nuses the Support Vector Machine (SVM) algorithm along with the Radial Base\nFunction (RBF) along with the Long Short-term Memory Layer (LSTM) for\nprediction of onset of Type 2 Diabetes. The proposed solution provides an\naverage accuracy of 86.31 % and an average AUC value of 0.8270 or 82.70 %, with\nan improvement of 3.8 milliseconds in the processing. Radial Base Function\n(RBF) kernel and the LSTM layer enhance the prediction accuracy and AUC metric\nfrom the current industry standard, making it more feasible for practical use\nwithout compromising the processing time.\n", "date": "2022-08-05", "categories": []}
{"id": "2208.01220", "title": "GeoECG: Data Augmentation via Wasserstein Geodesic Perturbation for\n  Robust Electrocardiogram Prediction", "abstract": "  There has been an increased interest in applying deep neural networks to\nautomatically interpret and analyze the 12-lead electrocardiogram (ECG). The\ncurrent paradigms with machine learning methods are often limited by the amount\nof labeled data. This phenomenon is particularly problematic for\nclinically-relevant data, where labeling at scale can be time-consuming and\ncostly in terms of the specialized expertise and human effort required.\nMoreover, deep learning classifiers may be vulnerable to adversarial examples\nand perturbations, which could have catastrophic consequences, for example,\nwhen applied in the context of medical treatment, clinical trials, or insurance\nclaims. In this paper, we propose a physiologically-inspired data augmentation\nmethod to improve performance and increase the robustness of heart disease\ndetection based on ECG signals. We obtain augmented samples by perturbing the\ndata distribution towards other classes along the geodesic in Wasserstein\nspace. To better utilize domain-specific knowledge, we design a ground metric\nthat recognizes the difference between ECG signals based on physiologically\ndetermined features. Learning from 12-lead ECG signals, our model is able to\ndistinguish five categories of cardiac conditions. Our results demonstrate\nimprovements in accuracy and robustness, reflecting the effectiveness of our\ndata augmentation method.\n", "date": "2022-08-02", "categories": []}
{"id": "2208.04732", "title": "Efficient Novelty Detection Methods for Early Warning of Potential Fatal\n  Diseases", "abstract": "  Fatal diseases, as Critical Health Episodes (CHEs), represent real dangers\nfor patients hospitalized in Intensive Care Units. These episodes can lead to\nirreversible organ damage and death. Nevertheless, diagnosing them in time\nwould greatly reduce their inconvenience. This study therefore focused on\nbuilding a highly effective early warning system for CHEs such as Acute\nHypotensive Episodes and Tachycardia Episodes. To facilitate the precocity of\nthe prediction, a gap of one hour was considered between the observation\nperiods (Observation Windows) and the periods during which a critical event can\noccur (Target Windows). The MIMIC II dataset was used to evaluate the\nperformance of the proposed system. This system first includes extracting\nadditional features using three different modes. Then, the feature selection\nprocess allowing the selection of the most relevant features was performed\nusing the Mutual Information Gain feature importance. Finally, the\nhigh-performance predictive model LightGBM was used to perform episode\nclassification. This approach called MIG-LightGBM was evaluated using five\ndifferent metrics: Event Recall (ER), Reduced Precision (RP), average\nAnticipation Time (aveAT), average False Alarms (aveFA), and Event F1-score\n(EF1-score). A method is therefore considered highly efficient for the early\nprediction of CHEs if it exhibits not only a large aveAT but also a large\nEF1-score and a low aveFA. Compared to systems using Extreme Gradient Boosting,\nSupport Vector Classification or Naive Bayes as a predictive model, the\nproposed system was found to be highly dominant. It also confirmed its\nsuperiority over the Layered Learning approach.\n", "date": "2022-08-06", "categories": []}
{"id": "2208.04910", "title": "Deep Learning-Based Objective and Reproducible Osteosarcoma Chemotherapy\n  Response Assessment and Outcome Prediction", "abstract": "  Osteosarcoma is the most common primary bone cancer whose standard treatment\nincludes pre-operative chemotherapy followed by resection. Chemotherapy\nresponse is used for predicting prognosis and further management of patients.\nNecrosis is routinely assessed post-chemotherapy from histology slides on\nresection specimens where necrosis ratio is defined as the ratio of necrotic\ntumor to overall tumor. Patients with necrosis ratio >=90% are known to have\nbetter outcome. Manual microscopic review of necrosis ratio from multiple glass\nslides is semi-quantitative and can have intra- and inter-observer variability.\nWe propose an objective and reproducible deep learning-based approach to\nestimate necrosis ratio with outcome prediction from scanned hematoxylin and\neosin whole slide images. We collected 103 osteosarcoma cases with 3134 WSIs to\ntrain our deep learning model, to validate necrosis ratio assessment, and to\nevaluate outcome prediction. We trained Deep Multi-Magnification Network to\nsegment multiple tissue subtypes including viable tumor and necrotic tumor in\npixel-level and to calculate case-level necrosis ratio from multiple WSIs. We\nshowed necrosis ratio estimated by our segmentation model highly correlates\nwith necrosis ratio from pathology reports manually assessed by experts where\nmean absolute differences for Grades IV (100%), III (>=90%), and II (>=50% and\n<90%) necrosis response are 4.4%, 4.5%, and 17.8%, respectively. We\nsuccessfully stratified patients to predict overall survival with p=10^-6 and\nprogression-free survival with p=0.012. Our reproducible approach without\nvariability enabled us to tune cutoff thresholds, specifically for our model\nand our data set, to 80% for OS and 60% for PFS. Our study indicates deep\nlearning can support pathologists as an objective tool to analyze osteosarcoma\nfrom histology for assessing treatment response and predicting patient outcome.\n", "date": "2022-08-09", "categories": []}
{"id": "2208.00032", "title": "A review of Deep learning Techniques for COVID-19 identification on\n  Chest CT images", "abstract": "  The current COVID-19 pandemic is a serious threat to humanity that directly\naffects the lungs. Automatic identification of COVID-19 is a challenge for\nhealth care officials. The standard gold method for diagnosing COVID-19 is\nReverse Transcription Polymerase Chain Reaction (RT-PCR) to collect swabs from\naffected people. Some limitations encountered while collecting swabs are\nrelated to accuracy and longtime duration. Chest CT (Computed Tomography) is\nanother test method that helps healthcare providers quickly identify the\ninfected lung areas. It was used as a supporting tool for identifying COVID-19\nin an earlier stage. With the help of deep learning, the CT imaging\ncharacteristics of COVID-19. Researchers have proven it to be highly effective\nfor COVID-19 CT image classification. In this study, we review the recent deep\nlearning techniques that can use to detect the COVID-19 disease. Relevant\nstudies were collected by various databases such as Web of Science, Google\nScholar, and PubMed. Finally, we compare the results of different deep learning\nmodels, and CT image analysis is discussed.\n", "date": "2022-07-29", "categories": []}
{"id": "2208.04114", "title": "Automatic lesion analysis for increased efficiency in outcome prediction\n  of traumatic brain injury", "abstract": "  The accurate prognosis for traumatic brain injury (TBI) patients is difficult\nyet essential to inform therapy, patient management, and long-term after-care.\nPatient characteristics such as age, motor and pupil responsiveness, hypoxia\nand hypotension, and radiological findings on computed tomography (CT), have\nbeen identified as important variables for TBI outcome prediction. CT is the\nacute imaging modality of choice in clinical practice because of its\nacquisition speed and widespread availability. However, this modality is mainly\nused for qualitative and semi-quantitative assessment, such as the Marshall\nscoring system, which is prone to subjectivity and human errors. This work\nexplores the predictive power of imaging biomarkers extracted from\nroutinely-acquired hospital admission CT scans using a state-of-the-art, deep\nlearning TBI lesion segmentation method. We use lesion volumes and\ncorresponding lesion statistics as inputs for an extended TBI outcome\nprediction model. We compare the predictive power of our proposed features to\nthe Marshall score, independently and when paired with classic TBI biomarkers.\nWe find that automatically extracted quantitative CT features perform similarly\nor better than the Marshall score in predicting unfavourable TBI outcomes.\nLeveraging automatic atlas alignment, we also identify frontal extra-axial\nlesions as important indicators of poor outcome. Our work may contribute to a\nbetter understanding of TBI, and provides new insights into how automated\nneuroimaging analysis can be used to improve prognostication after TBI.\n", "date": "2022-08-08", "categories": []}
{"id": "2208.02301", "title": "HiCu: Leveraging Hierarchy for Curriculum Learning in Automated ICD\n  Coding", "abstract": "  There are several opportunities for automation in healthcare that can improve\nclinician throughput. One such example is assistive tools to document diagnosis\ncodes when clinicians write notes. We study the automation of medical code\nprediction using curriculum learning, which is a training strategy for machine\nlearning models that gradually increases the hardness of the learning tasks\nfrom easy to difficult. One of the challenges in curriculum learning is the\ndesign of curricula -- i.e., in the sequential design of tasks that gradually\nincrease in difficulty. We propose Hierarchical Curriculum Learning (HiCu), an\nalgorithm that uses graph structure in the space of outputs to design curricula\nfor multi-label classification. We create curricula for multi-label\nclassification models that predict ICD diagnosis and procedure codes from\nnatural language descriptions of patients. By leveraging the hierarchy of ICD\ncodes, which groups diagnosis codes based on various organ systems in the human\nbody, we find that our proposed curricula improve the generalization of neural\nnetwork-based predictive models across recurrent, convolutional, and\ntransformer-based architectures. Our code is available at\nhttps://github.com/wren93/HiCu-ICD.\n", "date": "2022-08-03", "categories": []}
{"id": "2208.01637", "title": "Comparative Analysis of State-of-the-Art Deep Learning Models for\n  Detecting COVID-19 Lung Infection from Chest X-Ray Images", "abstract": "  The ongoing COVID-19 pandemic has already taken millions of lives and damaged\neconomies across the globe. Most COVID-19 deaths and economic losses are\nreported from densely crowded cities. It is comprehensible that the effective\ncontrol and prevention of epidemic/pandemic infectious diseases is vital.\nAccording to WHO, testing and diagnosis is the best strategy to control\npandemics. Scientists worldwide are attempting to develop various innovative\nand cost-efficient methods to speed up the testing process. This paper\ncomprehensively evaluates the applicability of the recent top ten\nstate-of-the-art Deep Convolutional Neural Networks (CNNs) for automatically\ndetecting COVID-19 infection using chest X-ray images. Moreover, it provides a\ncomparative analysis of these models in terms of accuracy. This study\nidentifies the effective methodologies to control and prevent infectious\nrespiratory diseases. Our trained models have demonstrated outstanding results\nin classifying the COVID-19 infected chest x-rays. In particular, our trained\nmodels MobileNet, EfficentNet, and InceptionV3 achieved a classification\naverage accuracy of 95\\%, 95\\%, and 94\\% test set for COVID-19 class\nclassification, respectively. Thus, it can be beneficial for clinical\npractitioners and radiologists to speed up the testing, detection, and\nfollow-up of COVID-19 cases.\n", "date": "2022-07-01", "categories": []}
{"id": "2208.01912", "title": "Cross-Lingual Knowledge Transfer for Clinical Phenotyping", "abstract": "  Clinical phenotyping enables the automatic extraction of clinical conditions\nfrom patient records, which can be beneficial to doctors and clinics worldwide.\nHowever, current state-of-the-art models are mostly applicable to clinical\nnotes written in English. We therefore investigate cross-lingual knowledge\ntransfer strategies to execute this task for clinics that do not use the\nEnglish language and have a small amount of in-domain data available. We\nevaluate these strategies for a Greek and a Spanish clinic leveraging clinical\nnotes from different clinical domains such as cardiology, oncology and the ICU.\nOur results reveal two strategies that outperform the state-of-the-art:\nTranslation-based methods in combination with domain-specific encoders and\ncross-lingual encoders plus adapters. We find that these strategies perform\nespecially well for classifying rare phenotypes and we advise on which method\nto prefer in which situation. Our results show that using multilingual data\noverall improves clinical phenotyping models and can compensate for data\nsparseness.\n", "date": "2022-08-03", "categories": []}
{"id": "2208.02150", "title": "Empirical Study of Overfitting in Deep FNN Prediction Models for Breast\n  Cancer Metastasis", "abstract": "  Overfitting is defined as the fact that the current model fits a specific\ndata set perfectly, resulting in weakened generalization, and ultimately may\naffect the accuracy in predicting future data. In this research we used an EHR\ndataset concerning breast cancer metastasis to study overfitting of deep\nfeedforward Neural Networks (FNNs) prediction models. We included 11\nhyperparameters of the deep FNNs models and took an empirical approach to study\nhow each of these hyperparameters was affecting both the prediction performance\nand overfitting when given a large range of values. We also studied how some of\nthe interesting pairs of hyperparameters were interacting to influence the\nmodel performance and overfitting. The 11 hyperparameters we studied include\nactivate function; weight initializer, number of hidden layers, learning rate,\nmomentum, decay, dropout rate, batch size, epochs, L1, and L2. Our results show\nthat most of the single hyperparameters are either negatively or positively\ncorrected with model prediction performance and overfitting. In particular, we\nfound that overfitting overall tends to negatively correlate with learning\nrate, decay, batch sides, and L2, but tends to positively correlate with\nmomentum, epochs, and L1. According to our results, learning rate, decay, and\nbatch size may have a more significant impact on both overfitting and\nprediction performance than most of the other hyperparameters, including L1,\nL2, and dropout rate, which were designed for minimizing overfitting. We also\nfind some interesting interacting pairs of hyperparameters such as learning\nrate and momentum, learning rate and decay, and batch size and epochs.\nKeywords: Deep learning, overfitting, prediction, grid search, feedforward\nneural networks, breast cancer metastasis.\n", "date": "2022-08-03", "categories": []}
{"id": "2208.02222", "title": "Blockchain associated machine learning and IoT based hypoglycemia\n  detection system with auto-injection feature", "abstract": "  Hypoglycemia is an unpleasant phenomenon caused by low blood glucose. The\ndisease can lead a person to death or a high level of body damage. To avoid\nsignificant damage, patients need sugar. The research aims at implementing an\nautomatic system to detect hypoglycemia and perform automatic sugar injections\nto save a life. Receiving the benefits of the internet of things (IoT), the\nsensor data was transferred using the hypertext transfer protocol (HTTP)\nprotocol. To ensure the safety of health-related data, blockchain technology\nwas utilized. The glucose sensor and smartwatch data were processed via Fog and\nsent to the cloud. A Random Forest algorithm was proposed and utilized to\ndecide hypoglycemic events. When the hypoglycemic event was detected, the\nsystem sent a notification to the mobile application and auto-injection device\nto push the condensed sugar into the victims body. XGBoost, k-nearest neighbors\n(KNN), support vector machine (SVM), and decision tree were implemented to\ncompare the proposed models performance. The random forest performed 0.942\ntesting accuracy, better than other models in detecting hypoglycemic events.\nThe systems performance was measured in several conditions, and satisfactory\nresults were achieved. The system can benefit hypoglycemia patients to survive\nthis disease.\n", "date": "2022-07-27", "categories": []}
{"id": "2206.12088", "title": "Classifying Unstructured Clinical Notes via Automatic Weak Supervision", "abstract": "  Healthcare providers usually record detailed notes of the clinical care\ndelivered to each patient for clinical, research, and billing purposes. Due to\nthe unstructured nature of these narratives, providers employ dedicated staff\nto assign diagnostic codes to patients' diagnoses using the International\nClassification of Diseases (ICD) coding system. This manual process is not only\ntime-consuming but also costly and error-prone. Prior work demonstrated\npotential utility of Machine Learning (ML) methodology in automating this\nprocess, but it has relied on large quantities of manually labeled data to\ntrain the models. Additionally, diagnostic coding systems evolve with time,\nwhich makes traditional supervised learning strategies unable to generalize\nbeyond local applications. In this work, we introduce a general\nweakly-supervised text classification framework that learns from class-label\ndescriptions only, without the need to use any human-labeled documents. It\nleverages the linguistic domain knowledge stored within pre-trained language\nmodels and the data programming framework to assign code labels to individual\ntexts. We demonstrate the efficacy and flexibility of our method by comparing\nit to state-of-the-art weak text classifiers across four real-world text\nclassification datasets, in addition to assigning ICD codes to medical notes in\nthe publicly available MIMIC-III database.\n", "date": "2022-06-24", "categories": []}
{"id": "2208.01127", "title": "Disparate Censorship & Undertesting: A Source of Label Bias in Clinical\n  Machine Learning", "abstract": "  As machine learning (ML) models gain traction in clinical applications,\nunderstanding the impact of clinician and societal biases on ML models is\nincreasingly important. While biases can arise in the labels used for model\ntraining, the many sources from which these biases arise are not yet\nwell-studied. In this paper, we highlight disparate censorship (i.e.,\ndifferences in testing rates across patient groups) as a source of label bias\nthat clinical ML models may amplify, potentially causing harm. Many patient\nrisk-stratification models are trained using the results of clinician-ordered\ndiagnostic and laboratory tests of labels. Patients without test results are\noften assigned a negative label, which assumes that untested patients do not\nexperience the outcome. Since orders are affected by clinical and resource\nconsiderations, testing may not be uniform in patient populations, giving rise\nto disparate censorship. Disparate censorship in patients of equivalent risk\nleads to undertesting in certain groups, and in turn, more biased labels for\nsuch groups. Using such biased labels in standard ML pipelines could contribute\nto gaps in model performance across patient groups. Here, we theoretically and\nempirically characterize conditions in which disparate censorship or\nundertesting affect model performance across subgroups. Our findings call\nattention to disparate censorship as a source of label bias in clinical ML\nmodels.\n", "date": "2022-08-01", "categories": []}
{"id": "2208.01607", "title": "Enabling scalable clinical interpretation of ML-based phenotypes using\n  real world data", "abstract": "  The availability of large and deep electronic healthcare records (EHR)\ndatasets has the potential to enable a better understanding of real-world\npatient journeys, and to identify novel subgroups of patients. ML-based\naggregation of EHR data is mostly tool-driven, i.e., building on available or\nnewly developed methods. However, these methods, their input requirements, and,\nimportantly, resulting output are frequently difficult to interpret, especially\nwithout in-depth data science or statistical training. This endangers the final\nstep of analysis where an actionable and clinically meaningful interpretation\nis needed.This study investigates approaches to perform patient stratification\nanalysis at scale using large EHR datasets and multiple clustering methods for\nclinical research. We have developed several tools to facilitate the clinical\nevaluation and interpretation of unsupervised patient stratification results,\nnamely pattern screening, meta clustering, surrogate modeling, and curation.\nThese tools can be used at different stages within the analysis. As compared to\na standard analysis approach, we demonstrate the ability to condense results\nand optimize analysis time. In the case of meta clustering, we demonstrate that\nthe number of patient clusters can be reduced from 72 to 3 in one example. In\nanother stratification result, by using surrogate models, we could quickly\nidentify that heart failure patients were stratified if blood sodium\nmeasurements were available. As this is a routine measurement performed for all\npatients with heart failure, this indicated a data bias. By using further\ncohort and feature curation, these patients and other irrelevant features could\nbe removed to increase the clinical meaningfulness. These examples show the\neffectiveness of the proposed methods and we hope to encourage further research\nin this field.\n", "date": "2022-08-02", "categories": []}
{"id": "2206.06279", "title": "A Machine Learning Model for Predicting, Diagnosing, and Mitigating\n  Health Disparities in Hospital Readmission", "abstract": "  The management of hyperglycemia in hospitalized patients has a significant\nimpact on both morbidity and mortality. Therefore, it is important to predict\nthe need for diabetic patients to be hospitalized. However, using standard\nmachine learning approaches to make these predictions may result in health\ndisparities caused by biases in the data related to social determinants (such\nas race, age, and gender). These biases must be removed early in the data\ncollection process, before they enter the system and are reinforced by model\npredictions, resulting in biases in the model's decisions. In this paper, we\npropose a machine learning pipeline capable of making predictions as well as\ndetecting and mitigating biases in the data and model predictions. This\npipeline analyses the clinical data and determines whether biases exist in the\ndata, if so, it removes those biases before making predictions. We evaluate the\nperformance of the proposed method on a clinical dataset using accuracy and\nfairness measures. The findings of the results show that when we mitigate\nbiases early during the data ingestion, we get fairer predictions.\n", "date": "2022-06-13", "categories": []}
{"id": "2207.01873", "title": "ICE-NODE: Integration of Clinical Embeddings with Neural Ordinary\n  Differential Equations", "abstract": "  Early diagnosis of disease can lead to improved health outcomes, including\nhigher survival rates and lower treatment costs. With the massive amount of\ninformation available in electronic health records (EHRs), there is great\npotential to use machine learning (ML) methods to model disease progression\naimed at early prediction of disease onset and other outcomes. In this work, we\nemploy recent innovations in neural ODEs combined with rich semantic embeddings\nof clinical codes to harness the full temporal information of EHRs. We propose\nICE-NODE (Integration of Clinical Embeddings with Neural Ordinary Differential\nEquations), an architecture that temporally integrates embeddings of clinical\ncodes and neural ODEs to learn and predict patient trajectories in EHRs. We\napply our method to the publicly available MIMIC-III and MIMIC-IV datasets, and\nwe find improved prediction results compared to state-of-the-art methods,\nspecifically for clinical codes that are not frequently observed in EHRs. We\nalso show that ICE-NODE is more competent at predicting certain medical\nconditions, like acute renal failure, pulmonary heart disease and birth-related\nproblems, where the full temporal information could provide important\ninformation. Furthermore, ICE-NODE is also able to produce patient risk\ntrajectories over time that can be exploited for further detailed predictions\nof disease evolution.\n", "date": "2022-07-05", "categories": []}
{"id": "2207.11382", "title": "Density-Aware Personalized Training for Risk Prediction in Imbalanced\n  Medical Data", "abstract": "  Medical events of interest, such as mortality, often happen at a low rate in\nelectronic medical records, as most admitted patients survive. Training models\nwith this imbalance rate (class density discrepancy) may lead to suboptimal\nprediction. Traditionally this problem is addressed through ad-hoc methods such\nas resampling or reweighting but performance in many cases is still limited. We\npropose a framework for training models for this imbalance issue: 1) we first\ndecouple the feature extraction and classification process, adjusting training\nbatches separately for each component to mitigate bias caused by class density\ndiscrepancy; 2) we train the network with both a density-aware loss and a\nlearnable cost matrix for misclassifications. We demonstrate our model's\nimproved performance in real-world medical datasets (TOPCAT and MIMIC-III) to\nshow improved AUC-ROC, AUC-PRC, Brier Skill Score compared with the baselines\nin the domain.\n", "date": "2022-07-23", "categories": []}
{"id": "2208.00033", "title": "Personalised recommendations of sleep behaviour with neural networks\n  using sleep diaries captured in Sleepio", "abstract": "  SleepioTM is a digital mobile phone and web platform that uses techniques\nfrom cognitive behavioural therapy (CBT) to improve sleep in people with sleep\ndifficulty. As part of this process, Sleepio captures data about the sleep\nbehaviour of the users that have consented to such data being processed. For\nneural networks, the scale of the data is an opportunity to train meaningful\nmodels translatable to actual clinical practice. In collaboration with Big\nHealth, the therapeutics company that created and utilizes Sleepio, we have\nanalysed data from a random sample of 401,174 sleep diaries and built a neural\nnetwork to model sleep behaviour and sleep quality of each individual in a\npersonalised manner. We demonstrate that this neural network is more accurate\nthan standard statistical methods in predicting the sleep quality of an\nindividual based on his/her behaviour from the last 10 days. We compare model\nperformance in a wide range of hyperparameter settings representing various\nscenarios. We further show that the neural network can be used to produce\npersonalised recommendations of what sleep habits users should follow to\nmaximise sleep quality, and show that these recommendations are substantially\nbetter than the ones generated by standard methods. We finally show that the\nneural network can explain the recommendation given to each participant and\ncalculate confidence intervals for each prediction, all of which are essential\nfor clinicians to be able to adopt such a tool in clinical practice.\n", "date": "2022-07-29", "categories": []}
{"id": "2208.00296", "title": "ANOVA-based Automatic Attribute Selection and a Predictive Model for\n  Heart Disease Prognosis", "abstract": "  Studies show that Studies that cardiovascular diseases (CVDs) are malignant\nfor human health. Thus, it is important to have an efficient way of CVD\nprognosis. In response to this, the healthcare industry has adopted machine\nlearning-based smart solutions to alleviate the manual process of CVD\nprognosis. Thus, this work proposes an information fusion technique that\ncombines key attributes of a person through analysis of variance (ANOVA) and\ndomain experts' knowledge. It also introduces a new collection of CVD data\nsamples for emerging research. There are thirty-eight experiments conducted\nexhaustively to verify the performance of the proposed framework on four\npublicly available benchmark datasets and the newly created dataset in this\nwork. The ablation study shows that the proposed approach can achieve a\ncompetitive mean average accuracy (mAA) of 99.2% and a mean average AUC of\n97.9%.\n", "date": "2022-07-30", "categories": []}
{"id": "2208.00784", "title": "Deep COVID-19 Recognition using Chest X-ray Images: A Comparative\n  Analysis", "abstract": "  The novel coronavirus variant, which is also widely known as COVID-19, is\ncurrently a common threat to all humans across the world. Effective recognition\nof COVID-19 using advanced machine learning methods is a timely need. Although\nmany sophisticated approaches have been proposed in the recent past, they still\nstruggle to achieve expected performances in recognizing COVID-19 using chest\nX-ray images. In addition, the majority of them are involved with the complex\npre-processing task, which is often challenging and time-consuming. Meanwhile,\ndeep networks are end-to-end and have shown promising results in image-based\nrecognition tasks during the last decade. Hence, in this work, some widely used\nstate-of-the-art deep networks are evaluated for COVID-19 recognition with\nchest X-ray images. All the deep networks are evaluated on a publicly available\nchest X-ray image dataset. The evaluation results show that the deep networks\ncan effectively recognize COVID-19 from chest X-ray images. Further, the\ncomparison results reveal that the EfficientNetB7 network outperformed other\nexisting state-of-the-art techniques.\n", "date": "2022-07-26", "categories": []}
{"id": "2207.14238", "title": "Re-thinking and Re-labeling LIDC-IDRI for Robust Pulmonary Cancer\n  Prediction", "abstract": "  The LIDC-IDRI database is the most popular benchmark for lung cancer\nprediction. However, with subjective assessment from radiologists, nodules in\nLIDC may have entirely different malignancy annotations from the pathological\nground truth, introducing label assignment errors and subsequent supervision\nbias during training. The LIDC database thus requires more objective labels for\nlearning-based cancer prediction. Based on an extra small dataset containing\n180 nodules diagnosed by pathological examination, we propose to re-label LIDC\ndata to mitigate the effect of original annotation bias verified on this robust\nbenchmark. We demonstrate in this paper that providing new labels by similar\nnodule retrieval based on metric learning would be an effective re-labeling\nstrategy. Training on these re-labeled LIDC nodules leads to improved model\nperformance, which is enhanced when new labels of uncertain nodules are added.\nWe further infer that re-labeling LIDC is current an expedient way for robust\nlung cancer prediction while building a large pathological-proven nodule\ndatabase provides the long-term solution.\n", "date": "2022-07-28", "categories": []}
{"id": "2207.12283", "title": "MedML: Fusing Medical Knowledge and Machine Learning Models for Early\n  Pediatric COVID-19 Hospitalization and Severity Prediction", "abstract": "  The COVID-19 pandemic has caused devastating economic and social disruption,\nstraining the resources of healthcare institutions worldwide. This has led to a\nnationwide call for models to predict hospitalization and severe illness in\npatients with COVID-19 to inform distribution of limited healthcare resources.\nWe respond to one of these calls specific to the pediatric population. To\naddress this challenge, we study two prediction tasks for the pediatric\npopulation using electronic health records: 1) predicting which children are\nmore likely to be hospitalized, and 2) among hospitalized children, which\nindividuals are more likely to develop severe symptoms.\n  We respond to the national Pediatric COVID-19 data challenge with a novel\nmachine learning model, MedML. MedML extracts the most predictive features\nbased on medical knowledge and propensity scores from over 6 million medical\nconcepts and incorporates the inter-feature relationships between heterogeneous\nmedical features via graph neural networks (GNN). We evaluate MedML across\n143,605 patients for the hospitalization prediction task and 11,465 patients\nfor the severity prediction task using data from the National Cohort\nCollaborative (N3C) dataset. We also report detailed group-level and\nindividual-level feature importance analyses to evaluate the model\ninterpretability.\n  MedML achieves up to a 7% higher AUROC score and up to a 14% higher AUPRC\nscore compared to the best baseline machine learning models and performs well\nacross all nine national geographic regions and over all three-month spans\nsince the start of the pandemic. Our cross-disciplinary research team has\ndeveloped a method of incorporating clinical domain knowledge as the framework\nfor a new type of machine learning model that is more predictive and\nexplainable than current state-of-the-art data-driven feature selection\nmethods.\n", "date": "2022-07-25", "categories": []}
{"id": "2207.13223", "title": "XADLiME: eXplainable Alzheimer's Disease Likelihood Map Estimation via\n  Clinically-guided Prototype Learning", "abstract": "  Diagnosing Alzheimer's disease (AD) involves a deliberate diagnostic process\nowing to its innate traits of irreversibility with subtle and gradual\nprogression. These characteristics make AD biomarker identification from\nstructural brain imaging (e.g., structural MRI) scans quite challenging.\nFurthermore, there is a high possibility of getting entangled with normal\naging. We propose a novel deep-learning approach through eXplainable AD\nLikelihood Map Estimation (XADLiME) for AD progression modeling over 3D sMRIs\nusing clinically-guided prototype learning. Specifically, we establish a set of\ntopologically-aware prototypes onto the clusters of latent clinical features,\nuncovering an AD spectrum manifold. We then measure the similarities between\nlatent clinical features and well-established prototypes, estimating a \"pseudo\"\nlikelihood map. By considering this pseudo map as an enriched reference, we\nemploy an estimating network to estimate the AD likelihood map over a 3D sMRI\nscan. Additionally, we promote the explainability of such a likelihood map by\nrevealing a comprehensible overview from two perspectives: clinical and\nmorphological. During the inference, this estimated likelihood map served as a\nsubstitute over unseen sMRI scans for effectively conducting the downstream\ntask while providing thorough explainable states.\n", "date": "2022-07-27", "categories": []}
{"id": "2207.11846", "title": "Mixture of Input-Output Hidden Markov Models for Heterogeneous Disease\n  Progression Modeling", "abstract": "  A particular challenge for disease progression modeling is the heterogeneity\nof a disease and its manifestations in the patients. Existing approaches often\nassume the presence of a single disease progression characteristics which is\nunlikely for neurodegenerative disorders such as Parkinson's disease. In this\npaper, we propose a hierarchical time-series model that can discover multiple\ndisease progression dynamics. The proposed model is an extension of an\ninput-output hidden Markov model that takes into account the clinical\nassessments of patients' health status and prescribed medications. We\nillustrate the benefits of our model using a synthetically generated dataset\nand a real-world longitudinal dataset for Parkinson's disease.\n", "date": "2022-07-24", "categories": []}
{"id": "2207.12218", "title": "Cov3d: Detection of the presence and severity of COVID-19 from CT scans\n  using 3D ResNets", "abstract": "  Deep learning has been used to assist in the analysis of medical imaging. One\nsuch use is the classification of Computed Tomography (CT) scans when detecting\nfor COVID-19 in subjects. This paper presents Cov3d, a three dimensional\nconvolutional neural network for detecting the presence and severity of COVID19\nfrom chest CT scans. Trained on the COV19-CT-DB dataset with human expert\nannotations, it achieves a macro f1 score of 0.9476 on the validation set for\nthe task of detecting the presence of COVID19. For the task of classifying the\nseverity of COVID19, it achieves a macro f1 score of 0.7552. Both results\nimprove on the baseline results of the `AI-enabled Medical Image Analysis\nWorkshop and Covid-19 Diagnosis Competition' (MIA-COV19D) in 2022.\n", "date": "2022-07-05", "categories": []}
{"id": "2207.10872", "title": "Assessing mortality prediction through different representation models\n  based on concepts extracted from clinical notes", "abstract": "  Recent years have seen particular interest in using electronic medical\nrecords (EMRs) for secondary purposes to enhance the quality and safety of\nhealthcare delivery. EMRs tend to contain large amounts of valuable clinical\nnotes. Learning of embedding is a method for converting notes into a format\nthat makes them comparable. Transformer-based representation models have\nrecently made a great leap forward. These models are pre-trained on large\nonline datasets to understand natural language texts effectively. The quality\nof a learning embedding is influenced by how clinical notes are used as input\nto representation models. A clinical note has several sections with different\nlevels of information value. It is also common for healthcare providers to use\ndifferent expressions for the same concept. Existing methods use clinical notes\ndirectly or with an initial preprocessing as input to representation models.\nHowever, to learn a good embedding, we identified the most essential clinical\nnotes section. We then mapped the extracted concepts from selected sections to\nthe standard names in the Unified Medical Language System (UMLS). We used the\nstandard phrases corresponding to the unique concepts as input for clinical\nmodels. We performed experiments to measure the usefulness of the learned\nembedding vectors in the task of hospital mortality prediction on a subset of\nthe publicly available Medical Information Mart for Intensive Care (MIMIC-III)\ndataset. According to the experiments, clinical transformer-based\nrepresentation models produced better results with getting input generated by\nstandard names of extracted unique concepts compared to other input formats.\nThe best-performing models were BioBERT, PubMedBERT, and UmlsBERT,\nrespectively.\n", "date": "2022-07-22", "categories": []}
{"id": "2207.10169", "title": "Pediatric Bone Age Assessment using Deep Learning Models", "abstract": "  Bone age assessment (BAA) is a standard method for determining the age\ndifference between skeletal and chronological age. Manual processes are\ncomplicated and necessitate the expertise of experts. This is where deep\nlearning comes into play. In this study, pre-trained models like VGG-16,\nInceptionV3, XceptionNet, and MobileNet are used to assess the bone age of the\ninput data, and their mean average errors are compared and evaluated to see\nwhich model predicts the best.\n", "date": "2022-07-20", "categories": []}
{"id": "2206.06182", "title": "AI-based Data Preparation and Data Analytics in Healthcare: The Case of\n  Diabetes", "abstract": "  The Associazione Medici Diabetologi (AMD) collects and manages one of the\nlargest worldwide-available collections of diabetic patient records, also known\nas the AMD database. This paper presents the initial results of an ongoing\nproject whose focus is the application of Artificial Intelligence and Machine\nLearning techniques for conceptualizing, cleaning, and analyzing such an\nimportant and valuable dataset, with the goal of providing predictive insights\nto better support diabetologists in their diagnostic and therapeutic choices.\n", "date": "2022-06-13", "categories": []}
{"id": "2207.08040", "title": "Reinforcement Learning For Survival, A Clinically Motivated Method For\n  Critically Ill Patients", "abstract": "  There has been considerable interest in leveraging RL and stochastic control\nmethods to learn optimal treatment strategies for critically ill patients,\ndirectly from observational data. However, there is significant ambiguity on\nthe control objective and on the best reward choice for the standard RL\nobjective. In this work, we propose a clinically motivated control objective\nfor critically ill patients, for which the value functions have a simple\nmedical interpretation. Further, we present theoretical results and adapt our\nmethod to a practical Deep RL algorithm, which can be used alongside any value\nbased Deep RL method. We experiment on a large sepsis cohort and show that our\nmethod produces results consistent with clinical knowledge.\n", "date": "2022-07-17", "categories": []}
{"id": "2207.09667", "title": "Generalizable and Robust Deep Learning Algorithm for Atrial Fibrillation\n  Diagnosis Across Ethnicities, Ages and Sexes", "abstract": "  To drive health innovation that meets the needs of all and democratize\nhealthcare, there is a need to assess the generalization performance of deep\nlearning (DL) algorithms across various distribution shifts to ensure that\nthese algorithms are robust. This retrospective study is, to the best of our\nknowledge, the first to develop and assess the generalization performance of a\ndeep learning (DL) model for AF events detection from long term beat-to-beat\nintervals across ethnicities, ages and sexes. The new recurrent DL model,\ndenoted ArNet2, was developed on a large retrospective dataset of 2,147\npatients totaling 51,386 hours of continuous electrocardiogram (ECG). The\nmodels generalization was evaluated on manually annotated test sets from four\ncenters (USA, Israel, Japan and China) totaling 402 patients. The model was\nfurther validated on a retrospective dataset of 1,730 consecutives Holter\nrecordings from the Rambam Hospital Holter clinic, Haifa, Israel. The model\noutperformed benchmark state-of-the-art models and generalized well across\nethnicities, ages and sexes. Performance was higher for female than male and\nyoung adults (less than 60 years old) and showed some differences across\nethnicities. The main finding explaining these variations was an impairment in\nperformance in groups with a higher prevalence of atrial flutter (AFL). Our\nfindings on the relative performance of ArNet2 across groups may have clinical\nimplications on the choice of the preferred AF examination method to use\nrelative to the group of interest.\n", "date": "2022-07-20", "categories": []}
{"id": "2207.08998", "title": "Discovering novel systemic biomarkers in photos of the external eye", "abstract": "  External eye photos were recently shown to reveal signs of diabetic retinal\ndisease and elevated HbA1c. In this paper, we evaluate if external eye photos\ncontain information about additional systemic medical conditions. We developed\na deep learning system (DLS) that takes external eye photos as input and\npredicts multiple systemic parameters, such as those related to the liver\n(albumin, AST); kidney (eGFR estimated using the race-free 2021 CKD-EPI\ncreatinine equation, the urine ACR); bone & mineral (calcium); thyroid (TSH);\nand blood count (Hgb, WBC, platelets). Development leveraged 151,237 images\nfrom 49,015 patients with diabetes undergoing diabetic eye screening in 11\nsites across Los Angeles county, CA. Evaluation focused on 9 pre-specified\nsystemic parameters and leveraged 3 validation sets (A, B, C) spanning 28,869\npatients with and without diabetes undergoing eye screening in 3 independent\nsites in Los Angeles County, CA, and the greater Atlanta area, GA. We compared\nagainst baseline models incorporating available clinicodemographic variables\n(e.g. age, sex, race/ethnicity, years with diabetes). Relative to the baseline,\nthe DLS achieved statistically significant superior performance at detecting\nAST>36, calcium<8.6, eGFR<60, Hgb<11, platelets<150, ACR>=300, and WBC<4 on\nvalidation set A (a patient population similar to the development sets), where\nthe AUC of DLS exceeded that of the baseline by 5.2-19.4%. On validation sets B\nand C, with substantial patient population differences compared to the\ndevelopment sets, the DLS outperformed the baseline for ACR>=300 and Hgb<11 by\n7.3-13.2%. Our findings provide further evidence that external eye photos\ncontain important biomarkers of systemic health spanning multiple organ\nsystems. Further work is needed to investigate whether and how these biomarkers\ncan be translated into clinical impact.\n", "date": "2022-07-19", "categories": []}
{"id": "2207.06414", "title": "Modeling Long-term Dependencies and Short-term Correlations in Patient\n  Journey Data with Temporal Attention Networks for Health Prediction", "abstract": "  Building models for health prediction based on Electronic Health Records\n(EHR) has become an active research area. EHR patient journey data consists of\npatient time-ordered clinical events/visits from patients. Most existing\nstudies focus on modeling long-term dependencies between visits, without\nexplicitly taking short-term correlations between consecutive visits into\naccount, where irregular time intervals, incorporated as auxiliary information,\nare fed into health prediction models to capture latent progressive patterns of\npatient journeys. We present a novel deep neural network with four modules to\ntake into account the contributions of various variables for health prediction:\ni) the Stacked Attention module strengthens the deep semantics in clinical\nevents within each patient journey and generates visit embeddings, ii) the\nShort-Term Temporal Attention module models short-term correlations between\nconsecutive visit embeddings while capturing the impact of time intervals\nwithin those visit embeddings, iii) the Long-Term Temporal Attention module\nmodels long-term dependencies between visit embeddings while capturing the\nimpact of time intervals within those visit embeddings, iv) and finally, the\nCoupled Attention module adaptively aggregates the outputs of Short-Term\nTemporal Attention and Long-Term Temporal Attention modules to make health\npredictions. Experimental results on MIMIC-III demonstrate superior predictive\naccuracy of our model compared to existing state-of-the-art methods, as well as\nthe interpretability and robustness of this approach. Furthermore, we found\nthat modeling short-term correlations contributes to local priors generation,\nleading to improved predictive modeling of patient journeys.\n", "date": "2022-07-13", "categories": []}
{"id": "2207.07458", "title": "Joint Application of the Target Trial Causal Framework and Machine\n  Learning Modeling to Optimize Antibiotic Therapy: Use Case on Acute Bacterial\n  Skin and Skin Structure Infections due to Methicillin-resistant\n  Staphylococcus aureus", "abstract": "  Bacterial infections are responsible for high mortality worldwide.\nAntimicrobial resistance underlying the infection, and multifaceted patient's\nclinical status can hamper the correct choice of antibiotic treatment.\nRandomized clinical trials provide average treatment effect estimates but are\nnot ideal for risk stratification and optimization of therapeutic choice, i.e.,\nindividualized treatment effects (ITE). Here, we leverage large-scale\nelectronic health record data, collected from Southern US academic clinics, to\nemulate a clinical trial, i.e., 'target trial', and develop a machine learning\nmodel of mortality prediction and ITE estimation for patients diagnosed with\nacute bacterial skin and skin structure infection (ABSSSI) due to\nmethicillin-resistant Staphylococcus aureus (MRSA). ABSSSI-MRSA is a\nchallenging condition with reduced treatment options - vancomycin is the\npreferred choice, but it has non-negligible side effects. First, we use\npropensity score matching to emulate the trial and create a treatment\nrandomized (vancomycin vs. other antibiotics) dataset. Next, we use this data\nto train various machine learning methods (including boosted/LASSO logistic\nregression, support vector machines, and random forest) and choose the best\nmodel in terms of area under the receiver characteristic (AUC) through\nbootstrap validation. Lastly, we use the models to calculate ITE and identify\npossible averted deaths by therapy change. The out-of-bag tests indicate that\nSVM and RF are the most accurate, with AUC of 81% and 78%, respectively, but\nBLR/LASSO is not far behind (76%). By calculating the counterfactuals using the\nBLR/LASSO, vancomycin increases the risk of death, but it shows a large\nvariation (odds ratio 1.2, 95% range 0.4-3.8) and the contribution to outcome\nprobability is modest. Instead, the RF exhibits stronger changes in ITE,\nsuggesting more complex treatment heterogeneity.\n", "date": "2022-07-15", "categories": []}
{"id": "2207.06408", "title": "ECG beat classification using machine learning and pre-trained\n  convolutional neural networks", "abstract": "  The electrocardiogram (ECG) is routinely used in hospitals to analyze\ncardiovascular status and health of an individual. Abnormal heart rhythms can\nbe a precursor to more serious conditions including sudden cardiac death.\nClassifying abnormal rhythms is a laborious process prone to error. Therefore,\ntools that perform automated classification with high accuracy are highly\ndesirable. The work presented classifies five different types of ECG arrhythmia\nbased on AAMI EC57 standard and using the MIT-BIH data set. These include\nnon-ectopic (normal), supraventricular, ventricular, fusion, and unknown beat.\nBy appropriately transforming pre-processed ECG waveforms into a rich feature\nspace along with appropriate post-processing and utilizing deep convolutional\nneural networks post fine-tuning and hyperparameter selection, it is shown that\nhighly accurate classification for the five waveform types can be obtained.\nPerformance on the test set indicated higher overall accuracy (98.62%), as well\nas better performance in classifying each of the five waveforms than hitherto\nreported in literature.\n", "date": "2022-06-14", "categories": []}
{"id": "2207.06911", "title": "Improving self-supervised pretraining models for epileptic seizure\n  detection from EEG data", "abstract": "  There is abundant medical data on the internet, most of which are unlabeled.\nTraditional supervised learning algorithms are often limited by the amount of\nlabeled data, especially in the medical domain, where labeling is costly in\nterms of human processing and specialized experts needed to label them. They\nare also prone to human error and biased as a select few expert annotators\nlabel them. These issues are mitigated by Self-supervision, where we generate\npseudo-labels from unlabelled data by seeing the data itself. This paper\npresents various self-supervision strategies to enhance the performance of a\ntime-series based Diffusion convolution recurrent neural network (DCRNN) model.\nThe learned weights in the self-supervision pretraining phase can be\ntransferred to the supervised training phase to boost the model's prediction\ncapability. Our techniques are tested on an extension of a Diffusion\nConvolutional Recurrent Neural network (DCRNN) model, an RNN with graph\ndiffusion convolutions, which models the spatiotemporal dependencies present in\nEEG signals. When the learned weights from the pretraining stage are\ntransferred to a DCRNN model to determine whether an EEG time window has a\ncharacteristic seizure signal associated with it, our method yields an AUROC\nscore $1.56\\%$ than the current state-of-the-art models on the TUH EEG seizure\ncorpus.\n", "date": "2022-06-28", "categories": []}
{"id": "2207.05138", "title": "Towards Personalized Healthcare in Cardiac Population: The Development\n  of a Wearable ECG Monitoring System, an ECG Lossy Compression Schema, and a\n  ResNet-Based AF Detector", "abstract": "  Cardiovascular diseases (CVDs) are the number one cause of death worldwide.\nWhile there is growing evidence that the atrial fibrillation (AF) has strong\nassociations with various CVDs, this heart arrhythmia is usually diagnosed\nusing electrocardiography (ECG) which is a risk-free, non-intrusive, and\ncost-efficient tool. Continuously and remotely monitoring the subjects' ECG\ninformation unlocks the potentials of prompt pre-diagnosis and timely\npre-treatment of AF before the development of any life-threatening\nconditions/diseases. Ultimately, the CVDs associated mortality could be\nreduced. In this manuscript, the design and implementation of a personalized\nhealthcare system embodying a wearable ECG device, a mobile application, and a\nback-end server are presented. This system continuously monitors the users' ECG\ninformation to provide personalized health warnings/feedbacks. The users are\nable to communicate with their paired health advisors through this system for\nremote diagnoses, interventions, etc. The implemented wearable ECG devices have\nbeen evaluated and showed excellent intra-consistency (CVRMS=5.5%), acceptable\ninter-consistency (CVRMS=12.1%), and negligible RR-interval errors (ARE<1.4%).\nTo boost the battery life of the wearable devices, a lossy compression schema\nutilizing the quasi-periodic feature of ECG signals to achieve compression was\nproposed. Compared to the recognized schemata, it outperformed the others in\nterms of compression efficiency and distortion, and achieved at least 2x of CR\nat a certain PRD or RMSE for ECG signals from the MIT-BIH database. To enable\nautomated AF diagnosis/screening in the proposed system, a ResNet-based AF\ndetector was developed. For the ECG records from the 2017 PhysioNet CinC\nchallenge, this AF detector obtained an average testing F1=85.10% and a best\ntesting F1=87.31%, outperforming the state-of-the-art.\n", "date": "2022-07-11", "categories": []}
{"id": "2207.05247", "title": "FedPseudo: Pseudo value-based Deep Learning Models for Federated\n  Survival Analysis", "abstract": "  Survival analysis, time-to-event analysis, is an important problem in\nhealthcare since it has a wide-ranging impact on patients and palliative care.\nMany survival analysis methods have assumed that the survival data is centrally\navailable either from one medical center or by data sharing from multi-centers.\nHowever, the sensitivity of the patient attributes and the strict privacy laws\nhave increasingly forbidden sharing of healthcare data. To address this\nchallenge, the research community has looked at the solution of decentralized\ntraining and sharing of model parameters using the Federated Learning (FL)\nparadigm. In this paper, we study the utilization of FL for performing survival\nanalysis on distributed healthcare datasets. Recently, the popular Cox\nproportional hazard (CPH) models have been adapted for FL settings; however,\ndue to its linearity and proportional hazards assumptions, CPH models result in\nsuboptimal performance, especially for non-linear, non-iid, and heavily\ncensored survival datasets. To overcome the challenges of existing federated\nsurvival analysis methods, we leverage the predictive accuracy of the deep\nlearning models and the power of pseudo values to propose a first-of-its-kind,\npseudo value-based deep learning model for federated survival analysis (FSA)\ncalled FedPseudo. Furthermore, we introduce a novel approach of deriving pseudo\nvalues for survival probability in the FL settings that speeds up the\ncomputation of pseudo values. Extensive experiments on synthetic and real-world\ndatasets show that our pseudo valued-based FL framework achieves similar\nperformance as the best centrally trained deep survival analysis model.\nMoreover, our proposed FL approach obtains the best results for various\ncensoring settings.\n", "date": "2022-07-12", "categories": []}
{"id": "2207.05289", "title": "PLM-ICD: Automatic ICD Coding with Pretrained Language Models", "abstract": "  Automatically classifying electronic health records (EHRs) into diagnostic\ncodes has been challenging to the NLP community. State-of-the-art methods\ntreated this problem as a multilabel classification problem and proposed\nvarious architectures to model this problem. However, these systems did not\nleverage the superb performance of pretrained language models, which achieved\nsuperb performance on natural language understanding tasks. Prior work has\nshown that pretrained language models underperformed on this task with the\nregular finetuning scheme. Therefore, this paper aims at analyzing the causes\nof the underperformance and developing a framework for automatic ICD coding\nwith pretrained language models. We spotted three main issues through the\nexperiments: 1) large label space, 2) long input sequences, and 3) domain\nmismatch between pretraining and fine-tuning. We propose PLMICD, a framework\nthat tackles the challenges with various strategies. The experimental results\nshow that our proposed framework can overcome the challenges and achieves\nstate-of-the-art performance in terms of multiple metrics on the benchmark\nMIMIC data. The source code is available at https://github.com/MiuLab/PLM-ICD\n", "date": "2022-07-12", "categories": []}
{"id": "2207.05322", "title": "Using Interpretable Machine Learning to Predict Maternal and Fetal\n  Outcomes", "abstract": "  Most pregnancies and births result in a good outcome, but complications are\nnot uncommon and when they do occur, they can be associated with serious\nimplications for mothers and babies. Predictive modeling has the potential to\nimprove outcomes through better understanding of risk factors, heightened\nsurveillance, and more timely and appropriate interventions, thereby helping\nobstetricians deliver better care. For three types of complications we identify\nand study the most important risk factors using Explainable Boosting Machine\n(EBM), a glass box model, in order to gain intelligibility: (i) Severe Maternal\nMorbidity (SMM), (ii) shoulder dystocia, and (iii) preterm preeclampsia. While\nusing the interpretability of EBM's to reveal surprising insights into the\nfeatures contributing to risk, our experiments show EBMs match the accuracy of\nother black-box ML methods such as deep neural nets and random forests.\n", "date": "2022-07-12", "categories": []}
{"id": "2207.04212", "title": "COVID-19 Disease Identification on Chest-CT images using CNN and VGG16", "abstract": "  A newly identified coronavirus disease called COVID-19 mainly affects the\nhuman respiratory system. COVID-19 is an infectious disease caused by a virus\noriginating in Wuhan, China, in December 2019. Early diagnosis is the primary\nchallenge of health care providers. In the earlier stage, medical organizations\nwere dazzled because there were no proper health aids or medicine to detect a\nCOVID-19. A new diagnostic tool RT-PCR (Reverse Transcription Polymerase Chain\nReaction), was introduced. It collects swab specimens from the patient's nose\nor throat, where the COVID-19 virus gathers. This method has some limitations\nrelated to accuracy and testing time. Medical experts suggest an alternative\napproach called CT (Computed Tomography) that can quickly diagnose the infected\nlung areas and identify the COVID-19 in an earlier stage. Using chest CT\nimages, computer researchers developed several deep learning models identifying\nthe COVID-19 disease. This study presents a Convolutional Neural Network (CNN)\nand VGG16-based model for automated COVID-19 identification on chest CT images.\nThe experimental results using a public dataset of 14320 CT images showed a\nclassification accuracy of 96.34% and 96.99% for CNN and VGG16, respectively.\n", "date": "2022-07-09", "categories": []}
{"id": "2207.02922", "title": "Exploring Runtime Decision Support for Trauma Resuscitation", "abstract": "  AI-based recommender systems have been successfully applied in many domains\n(e.g., e-commerce, feeds ranking). Medical experts believe that incorporating\nsuch methods into a clinical decision support system may help reduce medical\nteam errors and improve patient outcomes during treatment processes (e.g.,\ntrauma resuscitation, surgical processes). Limited research, however, has been\ndone to develop automatic data-driven treatment decision support. We explored\nthe feasibility of building a treatment recommender system to provide runtime\nnext-minute activity predictions. The system uses patient context (e.g.,\ndemographics and vital signs) and process context (e.g., activities) to\ncontinuously predict activities that will be performed in the next minute. We\nevaluated our system on a pre-recorded dataset of trauma resuscitation and\nconducted an ablation study on different model variants. The best model\nachieved an average F1-score of 0.67 for 61 activity types. We include medical\nteam feedback and discuss the future work.\n", "date": "2022-07-06", "categories": []}
{"id": "2207.02941", "title": "Boosting the interpretability of clinical risk scores with intervention\n  predictions", "abstract": "  Machine learning systems show significant promise for forecasting patient\nadverse events via risk scores. However, these risk scores implicitly encode\nassumptions about future interventions that the patient is likely to receive,\nbased on the intervention policy present in the training data. Without this\nimportant context, predictions from such systems are less interpretable for\nclinicians. We propose a joint model of intervention policy and adverse event\nrisk as a means to explicitly communicate the model's assumptions about future\ninterventions. We develop such an intervention policy model on MIMIC-III, a\nreal world de-identified ICU dataset, and discuss some use cases that highlight\nthe utility of this approach. We show how combining typical risk scores, such\nas the likelihood of mortality, with future intervention probability scores\nleads to more interpretable clinical predictions.\n", "date": "2022-07-06", "categories": []}
{"id": "2207.03342", "title": "Monkeypox Skin Lesion Detection Using Deep Learning Models: A\n  Feasibility Study", "abstract": "  The recent monkeypox outbreak has become a public health concern due to its\nrapid spread in more than 40 countries outside Africa. Clinical diagnosis of\nmonkeypox in an early stage is challenging due to its similarity with\nchickenpox and measles. In cases where the confirmatory Polymerase Chain\nReaction (PCR) tests are not readily available, computer-assisted detection of\nmonkeypox lesions could be beneficial for surveillance and rapid identification\nof suspected cases. Deep learning methods have been found effective in the\nautomated detection of skin lesions, provided that sufficient training examples\nare available. However, as of now, such datasets are not available for the\nmonkeypox disease. In the current study, we first develop the ``Monkeypox Skin\nLesion Dataset (MSLD)\" consisting skin lesion images of monkeypox, chickenpox,\nand measles. The images are mainly collected from websites, news portals, and\npublicly accessible case reports. Data augmentation is used to increase the\nsample size, and a 3-fold cross-validation experiment is set up. In the next\nstep, several pre-trained deep learning models, namely, VGG-16, ResNet50, and\nInceptionV3 are employed to classify monkeypox and other diseases. An ensemble\nof the three models is also developed. ResNet50 achieves the best overall\naccuracy of $82.96(\\pm4.57\\%)$, while VGG16 and the ensemble system achieved\naccuracies of $81.48(\\pm6.87\\%)$ and $79.26(\\pm1.05\\%)$, respectively. A\nprototype web-application is also developed as an online monkeypox screening\ntool. While the initial results on this limited dataset are promising, a larger\ndemographically diverse dataset is required to further enhance the\ngeneralizability of these models.\n", "date": "2022-07-06", "categories": []}
{"id": "2207.02445", "title": "Distillation to Enhance the Portability of Risk Models Across\n  Institutions with Large Patient Claims Database", "abstract": "  Artificial intelligence, and particularly machine learning (ML), is\nincreasingly developed and deployed to support healthcare in a variety of\nsettings. However, clinical decision support (CDS) technologies based on ML\nneed to be portable if they are to be adopted on a broad scale. In this\nrespect, models developed at one institution should be reusable at another. Yet\nthere are numerous examples of portability failure, particularly due to naive\napplication of ML models. Portability failure can lead to suboptimal care and\nmedical errors, which ultimately could prevent the adoption of ML-based CDS in\npractice. One specific healthcare challenge that could benefit from enhanced\nportability is the prediction of 30-day readmission risk. Research to date has\nshown that deep learning models can be effective at modeling such risk. In this\nwork, we investigate the practicality of model portability through a cross-site\nevaluation of readmission prediction models. To do so, we apply a recurrent\nneural network, augmented with self-attention and blended with expert features,\nto build readmission prediction models for two independent large scale claims\ndatasets. We further present a novel transfer learning technique that adapts\nthe well-known method of born-again network (BAN) training. Our experiments\nshow that direct application of ML models trained at one institution and tested\nat another institution perform worse than models trained and tested at the same\ninstitution. We further show that the transfer learning approach based on the\nBAN produces models that are better than those trained on just a single\ninstitution's data. Notably, this improvement is consistent across both sites\nand occurs after a single retraining, which illustrates the potential for a\ncheap and general model transfer mechanism of readmission risk prediction.\n", "date": "2022-07-06", "categories": []}
{"id": "2207.01742", "title": "Anomaly-aware multiple instance learning for rare anemia disorder\n  classification", "abstract": "  Deep learning-based classification of rare anemia disorders is challenged by\nthe lack of training data and instance-level annotations. Multiple Instance\nLearning (MIL) has shown to be an effective solution, yet it suffers from low\naccuracy and limited explainability. Although the inclusion of attention\nmechanisms has addressed these issues, their effectiveness highly depends on\nthe amount and diversity of cells in the training samples. Consequently, the\npoor machine learning performance on rare anemia disorder classification from\nblood samples remains unresolved. In this paper, we propose an interpretable\npooling method for MIL to address these limitations. By benefiting from\ninstance-level information of negative bags (i.e., homogeneous benign cells\nfrom healthy individuals), our approach increases the contribution of anomalous\ninstances. We show that our strategy outperforms standard MIL classification\nalgorithms and provides a meaningful explanation behind its decisions.\nMoreover, it can denote anomalous instances of rare blood diseases that are not\nseen during the training phase.\n", "date": "2022-07-04", "categories": []}
{"id": "2207.00804", "title": "An AIoT-enabled Autonomous Dementia Monitoring System", "abstract": "  An autonomous Artificial Internet of Things (AIoT) system for elderly\ndementia patients monitoring in a smart home is presented. The system mainly\nimplements two functions based on the activity inference of the sensor data,\nwhich are real time abnormal activity monitoring and trend prediction of\ndisease related activities. Specifically, CASAS dataset is employed to train a\nRandom Forest (RF) model for activity inference. Then, another RF model trained\nby the output data of activity inference is used for abnormal activity\nmonitoring. Particularly, RF is chosen for these tasks because of its balanced\ntrade offs between accuracy, time efficiency, flexibility, and\ninterpretability. Moreover, Long Short Term Memory (LSTM) is utilised to\nforecast the disease related activity trend of a patient. Consequently, the\naccuracy of two RF classifiers designed for activity inference and abnormal\nactivity detection is greater than 99 percent and 94 percent, respectively.\nFurthermore, using the duration of sleep as an example, the LSTM model achieves\naccurate and evident future trends prediction.\n", "date": "2022-07-02", "categories": []}
{"id": "2207.00066", "title": "Advances in Prediction of Readmission Rates Using Long Term Short Term\n  Memory Networks on Healthcare Insurance Data", "abstract": "  30-day hospital readmission is a long standing medical problem that affects\npatients' morbidity and mortality and costs billions of dollars annually.\nRecently, machine learning models have been created to predict risk of\ninpatient readmission for patients with specific diseases, however no model\nexists to predict this risk across all patients. We developed a bi-directional\nLong Short Term Memory (LSTM) Network that is able to use readily available\ninsurance data (inpatient visits, outpatient visits, and drug prescriptions) to\npredict 30 day re-admission for any admitted patient, regardless of reason. The\ntop-performing model achieved an ROC AUC of 0.763 (0.011) when using\nhistorical, inpatient, and post-discharge data. The LSTM model significantly\noutperformed a baseline random forest classifier, indicating that understanding\nthe sequence of events is important for model prediction. Incorporation of\n30-days of historical data also significantly improved model performance\ncompared to inpatient data alone, indicating that a patients clinical history\nprior to admission, including outpatient visits and pharmacy data is a strong\ncontributor to readmission. Our results demonstrate that a machine learning\nmodel is able to predict risk of inpatient readmission with reasonable accuracy\nfor all patients using structured insurance billing data. Because billing data\nor equivalent surrogates can be extracted from sites, such a model could be\ndeployed to identify patients at risk for readmission before they are\ndischarged, or to assign more robust follow up (closer follow up, home health,\nmailed medications) to at-risk patients after discharge.\n", "date": "2022-06-30", "categories": []}
{"id": "2207.00106", "title": "GaitForeMer: Self-Supervised Pre-Training of Transformers via Human\n  Motion Forecasting for Few-Shot Gait Impairment Severity Estimation", "abstract": "  Parkinson's disease (PD) is a neurological disorder that has a variety of\nobservable motor-related symptoms such as slow movement, tremor, muscular\nrigidity, and impaired posture. PD is typically diagnosed by evaluating the\nseverity of motor impairments according to scoring systems such as the Movement\nDisorder Society Unified Parkinson's Disease Rating Scale (MDS-UPDRS).\nAutomated severity prediction using video recordings of individuals provides a\npromising route for non-intrusive monitoring of motor impairments. However, the\nlimited size of PD gait data hinders model ability and clinical potential.\nBecause of this clinical data scarcity and inspired by the recent advances in\nself-supervised large-scale language models like GPT-3, we use human motion\nforecasting as an effective self-supervised pre-training task for the\nestimation of motor impairment severity. We introduce GaitForeMer, Gait\nForecasting and impairment estimation transforMer, which is first pre-trained\non public datasets to forecast gait movements and then applied to clinical data\nto predict MDS-UPDRS gait impairment severity. Our method outperforms previous\napproaches that rely solely on clinical data by a large margin, achieving an F1\nscore of 0.76, precision of 0.79, and recall of 0.75. Using GaitForeMer, we\nshow how public human movement data repositories can assist clinical use cases\nthrough learning universal motion representations. The code is available at\nhttps://github.com/markendo/GaitForeMer .\n", "date": "2022-06-30", "categories": []}
{"id": "2206.14903", "title": "CIRDataset: A large-scale Dataset for Clinically-Interpretable lung\n  nodule Radiomics and malignancy prediction", "abstract": "  Spiculations/lobulations, sharp/curved spikes on the surface of lung nodules,\nare good predictors of lung cancer malignancy and hence, are routinely assessed\nand reported by radiologists as part of the standardized Lung-RADS clinical\nscoring criteria. Given the 3D geometry of the nodule and 2D slice-by-slice\nassessment by radiologists, manual spiculation/lobulation annotation is a\ntedious task and thus no public datasets exist to date for probing the\nimportance of these clinically-reported features in the SOTA malignancy\nprediction algorithms. As part of this paper, we release a large-scale\nClinically-Interpretable Radiomics Dataset, CIRDataset, containing 956\nradiologist QA/QC'ed spiculation/lobulation annotations on segmented lung\nnodules from two public datasets, LIDC-IDRI (N=883) and LUNGx (N=73). We also\npresent an end-to-end deep learning model based on multi-class Voxel2Mesh\nextension to segment nodules (while preserving spikes), classify spikes\n(sharp/spiculation and curved/lobulation), and perform malignancy prediction.\nPrevious methods have performed malignancy prediction for LIDC and LUNGx\ndatasets but without robust attribution to any clinically reported/actionable\nfeatures (due to known hyperparameter sensitivity issues with general\nattribution schemes). With the release of this comprehensively-annotated\nCIRDataset and end-to-end deep learning baseline, we hope that malignancy\nprediction methods can validate their explanations, benchmark against our\nbaseline, and provide clinically-actionable insights. Dataset, code, pretrained\nmodels, and docker containers are available at\nhttps://github.com/nadeemlab/CIR.\n", "date": "2022-06-29", "categories": []}
{"id": "2206.14972", "title": "Machine Learning Approaches to Predict Breast Cancer: Bangladesh\n  Perspective", "abstract": "  Nowadays, Breast cancer has risen to become one of the most prominent causes\nof death in recent years. Among all malignancies, this is the most frequent and\nthe major cause of death for women globally. Manually diagnosing this disease\nrequires a good amount of time and expertise. Breast cancer detection is\ntime-consuming, and the spread of the disease can be reduced by developing\nmachine-based breast cancer predictions. In Machine learning, the system can\nlearn from prior instances and find hard-to-detect patterns from noisy or\ncomplicated data sets using various statistical, probabilistic, and\noptimization approaches. This work compares several machine learning\nalgorithm's classification accuracy, precision, sensitivity, and specificity on\na newly collected dataset. In this work Decision tree, Random Forest, Logistic\nRegression, Naive Bayes, and XGBoost, these five machine learning approaches\nhave been implemented to get the best performance on our dataset. This study\nfocuses on finding the best algorithm that can forecast breast cancer with\nmaximum accuracy in terms of its classes. This work evaluated the quality of\neach algorithm's data classification in terms of efficiency and effectiveness.\nAnd also compared with other published work on this domain. After implementing\nthe model, this study achieved the best model accuracy, 94% on Random Forest\nand XGBoost.\n", "date": "2022-06-30", "categories": []}
{"id": "2206.15069", "title": "PVT-COV19D: Pyramid Vision Transformer for COVID-19 Diagnosis", "abstract": "  With the outbreak of COVID-19, a large number of relevant studies have\nemerged in recent years. We propose an automatic COVID-19 diagnosis framework\nbased on lung CT scan images, the PVT-COV19D. In order to accommodate the\ndifferent dimensions of the image input, we first classified the images using\nTransformer models, then sampled the images in the dataset according to normal\ndistribution, and fed the sampling results into the modified PVTv2 model for\ntraining. A large number of experiments on the COV19-CT-DB dataset demonstrate\nthe effectiveness of the proposed method.\n", "date": "2022-06-30", "categories": []}
{"id": "2206.15431", "title": "Ensemble CNN models for Covid-19 Recognition and Severity Perdition From\n  3D CT-scan", "abstract": "  Since the appearance of Covid-19 in late 2019, Covid-19 has become an active\nresearch topic for the artificial intelligence (AI) community. One of the most\ninteresting AI topics is Covid-19 analysis of medical imaging. CT-scan imaging\nis the most informative tool about this disease. This work is part of the 2nd\nCOV19D competition, where two challenges are set: Covid-19 Detection and\nCovid-19 Severity Detection from the CT-scans. For Covid-19 detection from\nCT-scans, we proposed an ensemble of 2D Convolution blocks with Densenet-161\nmodels. Here, each 2D convolutional block with Densenet-161 architecture is\ntrained separately and in testing phase, the ensemble model is based on the\naverage of their probabilities. On the other hand, we proposed an ensemble of\nConvolutional Layers with Inception models for Covid-19 severity detection. In\naddition to the Convolutional Layers, three Inception variants were used,\nnamely Inception-v3, Inception-v4 and Inception-Resnet. Our proposed approaches\noutperformed the baseline approach in the validation data of the 2nd COV19D\ncompetition by 11% and 16% for Covid-19 detection and Covid-19 severity\ndetection, respectively.\n", "date": "2022-06-29", "categories": []}
{"id": "2206.14198", "title": "Predicting the Need for Blood Transfusion in Intensive Care Units with\n  Reinforcement Learning", "abstract": "  As critically ill patients frequently develop anemia or coagulopathy,\ntransfusion of blood products is a frequent intervention in the Intensive Care\nUnits (ICU). However, inappropriate transfusion decisions made by physicians\nare often associated with increased risk of complications and higher hospital\ncosts. In this work, we aim to develop a decision support tool that uses\navailable patient information for transfusion decision-making on three common\nblood products (red blood cells, platelets, and fresh frozen plasma). To this\nend, we adopt an off-policy batch reinforcement learning (RL) algorithm,\nnamely, discretized Batch Constrained Q-learning, to determine the best action\n(transfusion or not) given observed patient trajectories. Simultaneously, we\nconsider different state representation approaches and reward design mechanisms\nto evaluate their impacts on policy learning. Experiments are conducted on two\nreal-world critical care datasets: the MIMIC-III and the UCSF. Results\ndemonstrate that policy recommendations on transfusion achieved comparable\nmatching against true hospital policies via accuracy and weighted importance\nsampling evaluations on the MIMIC-III dataset. Furthermore, a combination of\ntransfer learning (TL) and RL on the data-scarce UCSF dataset can provide up to\n$17.02% improvement in terms of accuracy, and up to 18.94% and 21.63%\nimprovement in jump-start and asymptotic performance in terms of weighted\nimportance sampling averaged over three transfusion tasks. Finally, simulations\non transfusion decisions suggest that the transferred RL policy could reduce\npatients' estimated 28-day mortality rate by 2.74% and decreased acuity rate by\n1.18% on the UCSF dataset.\n", "date": "2022-06-26", "categories": []}
{"id": "2206.14520", "title": "Imaging the time series of one single referenced EEG electrode for\n  Epileptic Seizures Risk Analysis", "abstract": "  The time series captured by a single scalp electrode (plus the reference\nelectrode) of refractory epileptic patients is used to forecast seizures\nsusceptibility. The time series is preprocessed, segmented, and each segment\ntransformed into an image, using three different known methods: Recurrence\nPlot, Gramian Angular Field, Markov Transition Field. The likelihood of the\noccurrence of a seizure in a future predefined time window is computed by\naveraging the output of the softmax layer of a CNN, differently from the usual\nconsideration of the output of the classification layer. By thresholding this\nlikelihood, seizure forecasting has better performance. Interestingly, for\nalmost every patient, the best threshold was different from 50%. The results\nshow that this technique can predict with good results for some seizures and\npatients. However, more tests, namely more patients and more seizures, are\nneeded to better understand the real potential of this technique.\n", "date": "2022-06-29", "categories": []}
{"id": "2206.13516", "title": "Improving Clinical Efficiency and Reducing Medical Errors through\n  NLP-enabled diagnosis of Health Conditions from Transcription Reports", "abstract": "  Misdiagnosis rates are one of the leading causes of medical errors in\nhospitals, affecting over 12 million adults across the US. To address the high\nrate of misdiagnosis, this study utilizes 4 NLP-based algorithms to determine\nthe appropriate health condition based on an unstructured transcription report.\nFrom the Logistic Regression, Random Forest, LSTM, and CNNLSTM models, the\nCNN-LSTM model performed the best with an accuracy of 97.89%. We packaged this\nmodel into a authenticated web platform for accessible assistance to\nclinicians. Overall, by standardizing health care diagnosis and structuring\ntranscription reports, our NLP platform drastically improves the clinical\nefficiency and accuracy of hospitals worldwide.\n", "date": "2022-06-27", "categories": []}
{"id": "2206.11878", "title": "Predicting the meal macronutrient composition from continuous glucose\n  monitors", "abstract": "  Sustained high levels of blood glucose in type 2 diabetes (T2DM) can have\ndisastrous long-term health consequences. An essential component of clinical\ninterventions for T2DM is monitoring dietary intake to keep plasma glucose\nlevels within an acceptable range. Yet, current techniques to monitor food\nintake are time intensive and error prone. To address this issue, we are\ndeveloping techniques to automatically monitor food intake and the composition\nof those foods using continuous glucose monitors (CGMs). This article presents\nthe results of a clinical study in which participants consumed nine\nstandardized meals with known macronutrients amounts (carbohydrate, protein,\nand fat) while wearing a CGM. We built a multitask neural network to estimate\nthe macronutrient composition from the CGM signal, and compared it against a\nbaseline linear regression. The best prediction result comes from our proposed\nneural network, trained with subject-dependent data, as measured by root mean\nsquared relative error and correlation coefficient. These findings suggest that\nit is possible to estimate macronutrient composition from CGM signals, opening\nthe possibility to develop automatic techniques to track food intake.\n", "date": "2022-06-23", "categories": []}
{"id": "2206.10592", "title": "Identifying Electrocardiogram Abnormalities Using a\n  Handcrafted-Rule-Enhanced Neural Network", "abstract": "  A large number of people suffer from life-threatening cardiac abnormalities,\nand electrocardiogram (ECG) analysis is beneficial to determining whether an\nindividual is at risk of such abnormalities. Automatic ECG classification\nmethods, especially the deep learning based ones, have been proposed to detect\ncardiac abnormalities using ECG records, showing good potential to improve\nclinical diagnosis and help early prevention of cardiovascular diseases.\nHowever, the predictions of the known neural networks still do not\nsatisfactorily meet the needs of clinicians, and this phenomenon suggests that\nsome information used in clinical diagnosis may not be well captured and\nutilized by these methods. In this paper, we introduce some rules into\nconvolutional neural networks, which help present clinical knowledge to deep\nlearning based ECG analysis, in order to improve automated ECG diagnosis\nperformance. Specifically, we propose a Handcrafted-Rule-enhanced Neural\nNetwork (called HRNN) for ECG classification with standard 12-lead ECG input,\nwhich consists of a rule inference module and a deep learning module.\nExperiments on two large-scale public ECG datasets show that our new approach\nconsiderably outperforms existing state-of-the-art methods. Further, our\nproposed approach not only can improve the diagnosis performance, but also can\nassist in detecting mislabelled ECG samples. Our codes are available at\nhttps://github.com/alwaysbyx/ecg_processing.\n", "date": "2022-06-16", "categories": []}
{"id": "2206.10612", "title": "Metareview-informed Explainable Cytokine Storm Detection during CAR-T\n  cell Therapy", "abstract": "  Cytokine release syndrome (CRS), also known as cytokine storm, is one of the\nmost consequential adverse effects of chimeric antigen receptor therapies that\nhave shown promising results in cancer treatment. When emerging, CRS could be\nidentified by the analysis of specific cytokine and chemokine profiles that\ntend to exhibit similarities across patients. In this paper, we exploit these\nsimilarities using machine learning algorithms and set out to pioneer a\nmeta--review informed method for the identification of CRS based on specific\ncytokine peak concentrations and evidence from previous clinical studies. We\nargue that such methods could support clinicians in analyzing suspect cytokine\nprofiles by matching them against CRS knowledge from past clinical studies,\nwith the ultimate aim of swift CRS diagnosis. During evaluation with\nreal--world CRS clinical data, we emphasize the potential of our proposed\nmethod of producing interpretable results, in addition to being effective in\nidentifying the onset of cytokine storm.\n", "date": "2022-06-20", "categories": []}
{"id": "2206.10912", "title": "AI-based software for lung nodule detection in chest X-rays -- Time for\n  a second reader approach?", "abstract": "  Objectives: To compare artificial intelligence (AI) as a second reader in\ndetecting lung nodules on chest X-rays (CXR) versus radiologists of two\nbinational institutions, and to evaluate AI performance when using two\ndifferent modes: automated versus assisted (additional remote radiologist\nreview).\n  Methods: The CXR public database (n = 247) of the Japanese Society of\nRadiological Technology with various types and sizes of lung nodules was\nanalyzed. Eight radiologists evaluated the CXR images with regard to the\npresence of lung nodules and nodule conspicuity. After radiologist review, the\nAI software processed and flagged the CXR with the highest probability of\nmissed nodules. The calculated accuracy metrics were the area under the curve\n(AUC), sensitivity, specificity, F1 score, false negative case number (FN), and\nthe effect of different AI modes (automated/assisted) on the accuracy of nodule\ndetection.\n  Results: For radiologists, the average AUC value was 0.77 $\\pm$ 0.07, while\nthe average FN was 52.63 $\\pm$ 17.53 (all studies) and 32 $\\pm$ 11.59 (studies\ncontaining a nodule of malignant etiology = 32% rate of missed malignant\nnodules). Both AI modes -- automated and assisted -- produced an average\nincrease in sensitivity (by 14% and 12%) and of F1-score (5% and 6%) and a\ndecrease in specificity (by 10% and 3%, respectively).\n  Conclusions: Both AI modes flagged the pulmonary nodules missed by\nradiologists in a significant number of cases. AI as a second reader has a high\npotential to improve diagnostic accuracy and radiology workflow. AI might\ndetect certain pulmonary nodules earlier than radiologists, with a potentially\nsignificant impact on patient outcomes.\n", "date": "2022-06-22", "categories": []}
{"id": "2206.06701", "title": "CNN-based Classification Framework for Lung Tissues with Auxiliary\n  Information", "abstract": "  Interstitial lung diseases are a large group of heterogeneous diseases\ncharacterized by different degrees of alveolitis and pulmonary fibrosis.\nAccurately diagnosing these diseases has significant guiding value for\nformulating treatment plans. Although previous work has produced impressive\nresults in classifying interstitial lung diseases, there is still room for\nimproving the accuracy of these techniques, mainly to enhance automated\ndecision-making. In order to improve the classification precision, our study\nproposes a convolutional neural networks-based framework with auxiliary\ninformation. Firstly, ILD images are added with their medical information by\nre-scaling the original image in Hounsfield Units. Secondly, a modified CNN\nmodel is used to produce a vector of classification probability for each\ntissue. Thirdly, location information of the input image, consisting of the\noccurrence frequencies of different diseases in the CT scans on certain\nlocations, is used to calculate a location weight vector. Finally, the Hadamard\nproduct between two vectors is used to produce a decision vector for the\nprediction. Compared to the state-of-the-art methods, the results using a\npublicly available ILD database show the potential of predicting these using\ndifferent auxiliary information.\n", "date": "2022-06-14", "categories": []}
{"id": "2206.09074", "title": "Weakly Supervised Classification of Vital Sign Alerts as Real or\n  Artifact", "abstract": "  A significant proportion of clinical physiologic monitoring alarms are false.\nThis often leads to alarm fatigue in clinical personnel, inevitably\ncompromising patient safety. To combat this issue, researchers have attempted\nto build Machine Learning (ML) models capable of accurately adjudicating Vital\nSign (VS) alerts raised at the bedside of hemodynamically monitored patients as\nreal or artifact. Previous studies have utilized supervised ML techniques that\nrequire substantial amounts of hand-labeled data. However, manually harvesting\nsuch data can be costly, time-consuming, and mundane, and is a key factor\nlimiting the widespread adoption of ML in healthcare (HC). Instead, we explore\nthe use of multiple, individually imperfect heuristics to automatically assign\nprobabilistic labels to unlabeled training data using weak supervision. Our\nweakly supervised models perform competitively with traditional supervised\ntechniques and require less involvement from domain experts, demonstrating\ntheir use as efficient and practical alternatives to supervised learning in HC\napplications of ML.\n", "date": "2022-06-18", "categories": []}
{"id": "2206.09128", "title": "A Combined PCA-MLP Network for Early Breast Cancer Detection", "abstract": "  Breast cancer is the second most responsible for all cancer types and has\nbeen the cause of numerous deaths over the years, especially among women. Any\nimprovisation of the existing diagnosis system for the detection of cancer can\ncontribute to minimizing the death ratio. Moreover, cancer detection at an\nearly stage has recently been a prime research area in the scientific community\nto enhance the survival rate. Proper choice of machine learning tools can\nensure early-stage prognosis with high accuracy. In this paper, we have studied\ndifferent machine learning algorithms to detect whether a patient is likely to\nface breast cancer or not. Due to the implicit behavior of early-stage\nfeatures, we have implemented a multilayer perception model with the\nintegration of PCA and suggested it to be more viable than other detection\nalgorithms. Our 4 layers MLP-PCA network has obtained the best accuracy of 100%\nwith a mean of 90.48% accuracy on the BCCD dataset.\n", "date": "2022-06-18", "categories": []}
{"id": "2206.09752", "title": "A Comparative Study on Application of Class-Imbalance Learning for\n  Severity Prediction of Adverse Events Following Immunization", "abstract": "  In collaboration with the Liaoning CDC, China, we propose a prediction system\nto predict the subsequent hospitalization of children with adverse reactions\nbased on data on adverse events following immunization. We extracted multiple\nfeatures from the data, and selected \"hospitalization or not\" as the target for\nclassification. Since the data are imbalanced, we used various class-imbalance\nlearning methods for training and improved the RUSBoost algorithm. Experimental\nresults show that the improved RUSBoost has the highest Area Under the ROC\nCurve on the target among these algorithms. Additionally, we compared these\nclass-imbalance learning methods with some common machine learning algorithms.\nWe combined the improved RUSBoost with dynamic web resource development\ntechniques to build an evaluation system with information entry and vaccination\nresponse prediction capabilities for relevant medical practitioners.\n", "date": "2022-06-20", "categories": []}
{"id": "2206.10573", "title": "H&E-based Computational Biomarker Enables Universal EGFR Screening for\n  Lung Adenocarcinoma", "abstract": "  Lung cancer is the leading cause of cancer death worldwide, with lung\nadenocarcinoma being the most prevalent form of lung cancer. EGFR positive lung\nadenocarcinomas have been shown to have high response rates to TKI therapy,\nunderlying the essential nature of molecular testing for lung cancers. Despite\ncurrent guidelines consider testing necessary, a large portion of patients are\nnot routinely profiled, resulting in millions of people not receiving the\noptimal treatment for their lung cancer. Sequencing is the gold standard for\nmolecular testing of EGFR mutations, but it can take several weeks for results\nto come back, which is not ideal in a time constrained scenario. The\ndevelopment of alternative screening tools capable of detecting EGFR mutations\nquickly and cheaply while preserving tissue for sequencing could help reduce\nthe amount of sub-optimally treated patients. We propose a multi-modal approach\nwhich integrates pathology images and clinical variables to predict EGFR\nmutational status achieving an AUC of 84% on the largest clinical cohort to\ndate. Such a computational model could be deployed at large at little\nadditional cost. Its clinical application could reduce the number of patients\nwho receive sub-optimal treatments by 53.1% in China, and up to 96.6% in the\nUS.\n", "date": "2022-06-21", "categories": []}
{"id": "2206.08557", "title": "COVID-19 Detection using Transfer Learning with Convolutional Neural\n  Network", "abstract": "  The Novel Coronavirus disease 2019 (COVID-19) is a fatal infectious disease,\nfirst recognized in December 2019 in Wuhan, Hubei, China, and has gone on an\nepidemic situation. Under these circumstances, it became more important to\ndetect COVID-19 in infected people. Nowadays, the testing kits are gradually\nlessening in number compared to the number of infected population. Under recent\nprevailing conditions, the diagnosis of lung disease by analyzing chest CT\n(Computed Tomography) images has become an important tool for both diagnosis\nand prophecy of COVID-19 patients. In this study, a Transfer learning strategy\n(CNN) for detecting COVID-19 infection from CT images has been proposed. In the\nproposed model, a multilayer Convolutional neural network (CNN) with Transfer\nlearning model Inception V3 has been designed. Similar to CNN, it uses\nconvolution and pooling to extract features, but this transfer learning model\ncontains weights of dataset Imagenet. Thus it can detect features very\neffectively which gives it an upper hand for achieving better accuracy.\n", "date": "2022-06-17", "categories": []}
{"id": "2206.08787", "title": "Leveraging Uncertainty in Deep Learning for Pancreatic Adenocarcinoma\n  Grading", "abstract": "  Pancreatic cancers have one of the worst prognoses compared to other cancers,\nas they are diagnosed when cancer has progressed towards its latter stages. The\ncurrent manual histological grading for diagnosing pancreatic adenocarcinomas\nis time-consuming and often results in misdiagnosis. In digital pathology,\nAI-based cancer grading must be extremely accurate in prediction and\nuncertainty quantification to improve reliability and explainability and are\nessential for gaining clinicians trust in the technology. We present Bayesian\nConvolutional Neural Networks for automated pancreatic cancer grading from MGG\nand HE stained images to estimate uncertainty in model prediction. We show that\nthe estimated uncertainty correlates with prediction error. Specifically, it is\nuseful in setting the acceptance threshold using a metric that weighs\nclassification accuracy-reject trade-off and misclassification cost controlled\nby hyperparameters and can be employed in clinical settings.\n", "date": "2022-06-15", "categories": []}
{"id": "2206.07365", "title": "Modern Machine-Learning Predictive Models for Diagnosing Infectious\n  Diseases", "abstract": "  Controlling infectious diseases is a major health priority because they can\nspread and infect humans, thus evolving into epidemics or pandemics. Therefore,\nearly detection of infectious diseases is a significant need, and many\nresearchers have developed models to diagnose them in the early stages. This\npaper reviewed research articles for recent machine-learning (ML) algorithms\napplied to infectious disease diagnosis. We searched the Web of Science,\nScienceDirect, PubMed, Springer, and IEEE databases from 2015 to 2022,\nidentified the pros and cons of the reviewed ML models, and discussed the\npossible recommendations to advance the studies in this field. We found that\nmost of the articles used small datasets, and few of them used real-time data.\nOur results demonstrated that a suitable ML technique depends on the nature of\nthe dataset and the desired goal.\n", "date": "2022-06-15", "categories": []}
{"id": "2206.07573", "title": "AI and Pathology: Steering Treatment and Predicting Outcomes", "abstract": "  The combination of data analysis methods, increasing computing capacity, and\nimproved sensors enable quantitative granular, multi-scale, cell-based\nanalyses. We describe the rich set of application challenges related to tissue\ninterpretation and survey AI methods currently used to address these\nchallenges. We focus on a particular class of targeted human tissue analysis -\nhistopathology - aimed at quantitative characterization of disease state,\npatient outcome prediction and treatment steering.\n", "date": "2022-06-15", "categories": []}
{"id": "2206.04732", "title": "AI-MIA: COVID-19 Detection & Severity Analysis through Medical Imaging", "abstract": "  This paper presents the baseline approach for the organized 2nd Covid-19\nCompetition, occurring in the framework of the AIMIA Workshop in the European\nConference on Computer Vision (ECCV 2022). It presents the COV19-CT-DB database\nwhich is annotated for COVID-19 detction, consisting of about 7,700 3-D CT\nscans. Part of the database consisting of Covid-19 cases is further annotated\nin terms of four Covid-19 severity conditions. We have split the database and\nthe latter part of it in training, validation and test datasets. The former two\ndatasets are used for training and validation of machine learning models, while\nthe latter will be used for evaluation of the developed models. The baseline\napproach consists of a deep learning approach, based on a CNN-RNN network and\nreport its performance on the COVID19-CT-DB database.\n", "date": "2022-06-09", "categories": []}
{"id": "2206.05615", "title": "Machine learning approaches for COVID-19 detection from chest X-ray\n  imaging: A Systematic Review", "abstract": "  There is a necessity to develop affordable, and reliable diagnostic tools,\nwhich allow containing the COVID-19 spreading. Machine Learning (ML) algorithms\nhave been proposed to design support decision-making systems to assess chest\nX-ray images, which have proven to be useful to detect and evaluate disease\nprogression. Many research articles are published around this subject, which\nmakes it difficult to identify the best approaches for future work. This paper\npresents a systematic review of ML applied to COVID-19 detection using chest\nX-ray images, aiming to offer a baseline for researchers in terms of methods,\narchitectures, databases, and current limitations.\n", "date": "2022-06-11", "categories": []}
{"id": "2206.03222", "title": "Improved Cardiac Arrhythmia Prediction Based on Heart Rate Variability\n  Analysis", "abstract": "  Many types of ventricular and atrial cardiac arrhythmias have been discovered\nin clinical practice in the past 100 years, and these arrhythmias are a major\ncontributor to sudden cardiac death. Ventricular tachycardia, ventricular\nfibrillation, and paroxysmal atrial fibrillation are the most\ncommonly-occurring and dangerous arrhythmias, therefore early detection is\ncrucial to prevent any further complications and reduce fatalities. Implantable\ndevices such as pacemakers are commonly used in patients at high risk of sudden\ncardiac death. While great advances have been made in medical technology, there\nremain significant challenges in effective management of common arrhythmias.\nThis thesis proposes novel arrhythmia detection and prediction methods to\ndifferentiate cardiac arrhythmias from non-life-threatening cardiac events, to\nincrease the likelihood of detecting events that may lead to mortality, as well\nas reduce the incidence of unnecessary therapeutic intervention. The methods\nare based on detailed analysis of Heart Rate Variability (HRV) information. The\nresults of the work show good performance of the proposed methods and support\nthe potential for their deployment in resource-constrained devices for\nventricular and atrial arrhythmia prediction, such as implantable pacemakers\nand defibrillators.\n", "date": "2022-06-07", "categories": []}
{"id": "2206.04934", "title": "Response to: Significance and stability of deep learning-based\n  identification of subtypes within major psychiatric disorders. Molecular\n  Psychiatry (2022)", "abstract": "  Recently, Winter and Hahn [1] commented on our work on identifying subtypes\nof major psychiatry disorders (MPDs) based on neurobiological features using\nmachine learning [2]. They questioned the generalizability of our methods and\nthe statistical significance, stability, and overfitting of the results, and\nproposed a pipeline for disease subtyping. We appreciate their earnest\nconsideration of our work, however, we need to point out their misconceptions\nof basic machine-learning concepts and delineate some key issues involved.\n", "date": "2022-06-10", "categories": []}
{"id": "2206.04479", "title": "BSM loss: A superior way in modeling aleatory uncertainty of\n  fine_grained classification", "abstract": "  Artificial intelligence(AI)-assisted method had received much attention in\nthe risk field such as disease diagnosis. Different from the classification of\ndisease types, it is a fine-grained task to classify the medical images as\nbenign or malignant. However, most research only focuses on improving the\ndiagnostic accuracy and ignores the evaluation of model reliability, which\nlimits its clinical application. For clinical practice, calibration presents\nmajor challenges in the low-data regime extremely for over-parametrized models\nand inherent noises. In particular, we discovered that modeling data-dependent\nuncertainty is more conducive to confidence calibrations. Compared with\ntest-time augmentation(TTA), we proposed a modified Bootstrapping loss(BS loss)\nfunction with Mixup data augmentation strategy that can better calibrate\npredictive uncertainty and capture data distribution transformation without\nadditional inference time. Our experiments indicated that BS loss with\nMixup(BSM) model can halve the Expected Calibration Error(ECE) compared to\nstandard data augmentation, deep ensemble and MC dropout. The correlation\nbetween uncertainty and similarity of in-domain data is up to -0.4428 under the\nBSM model. Additionally, the BSM model is able to perceive the semantic\ndistance of out-of-domain data, demonstrating high potential in real-world\nclinical practice.\n", "date": "2022-06-09", "categories": []}
{"id": "2206.03716", "title": "Performance, Transparency and Time. Feature selection to speed up the\n  diagnosis of Parkinson's disease", "abstract": "  Accurate and early prediction of a disease allows to plan and improve a\npatient's quality of future life. During pandemic situations, the medical\ndecision becomes a speed challenge in which physicians have to act fast to\ndiagnose and predict the risk of the severity of the disease, moreover this is\nalso of high priority for neurodegenerative diseases like Parkinson's disease.\nMachine Learning (ML) models with Features Selection (FS) techniques can be\napplied to help physicians to quickly diagnose a disease. FS optimally subset\nfeatures that improve a model performance and help reduce the number of needed\ntests for a patient and hence speeding up the diagnosis. This study shows the\nresult of three Feature Selection (FS) techniques pre-applied to a classifier\nalgorithm, Logistic Regression, on non-invasive test results data. The three FS\nare Analysis of Variance (ANOVA) as filter based method, Least Absolute\nShrinkage and Selection Operator (LASSO) as embedded method and Sequential\nFeature Selection (SFS) as wrapper method. The outcome shows that FS technique\ncan help to build an efficient and effective classifier, hence improving the\nperformance of the classifier while reducing the computation time.\n", "date": "2022-06-08", "categories": []}
{"id": "2206.01696", "title": "Deep Learning Prediction of Severe Health Risks for Pediatric COVID-19\n  Patients with a Large Feature Set in 2021 BARDA Data Challenge", "abstract": "  Most children infected with COVID-19 have no or mild symptoms and can recover\nautomatically by themselves, but some pediatric COVID-19 patients need to be\nhospitalized or even to receive intensive medical care (e.g., invasive\nmechanical ventilation or cardiovascular support) to recover from the\nillnesses. Therefore, it is critical to predict the severe health risk that\nCOVID-19 infection poses to children to provide precise and timely medical care\nfor vulnerable pediatric COVID-19 patients. However, predicting the severe\nhealth risk for COVID-19 patients including children remains a significant\nchallenge because many underlying medical factors affecting the risk are still\nlargely unknown. In this work, instead of searching for a small number of most\nuseful features to make prediction, we design a novel large-scale bag-of-words\nlike method to represent various medical conditions and measurements of\nCOVID-19 patients. After some simple feature filtering based on logistical\nregression, the large set of features is used with a deep learning method to\npredict both the hospitalization risk for COVID-19 infected children and the\nsevere complication risk for the hospitalized pediatric COVID-19 patients. The\nmethod was trained and tested the datasets of the Biomedical Advanced Research\nand Development Authority (BARDA) Pediatric COVID-19 Data Challenge held from\nSept. 15 to Dec. 17, 2021. The results show that the approach can rather\naccurately predict the risk of hospitalization and severe complication for\npediatric COVID-19 patients and deep learning is more accurate than other\nmachine learning methods.\n", "date": "2022-06-03", "categories": []}
{"id": "2206.03009", "title": "Self-Knowledge Distillation based Self-Supervised Learning for Covid-19\n  Detection from Chest X-Ray Images", "abstract": "  The global outbreak of the Coronavirus 2019 (COVID-19) has overloaded\nworldwide healthcare systems. Computer-aided diagnosis for COVID-19 fast\ndetection and patient triage is becoming critical. This paper proposes a novel\nself-knowledge distillation based self-supervised learning method for COVID-19\ndetection from chest X-ray images. Our method can use self-knowledge of images\nbased on similarities of their visual features for self-supervised learning.\nExperimental results show that our method achieved an HM score of 0.988, an AUC\nof 0.999, and an accuracy of 0.957 on the largest open COVID-19 chest X-ray\ndataset.\n", "date": "2022-06-07", "categories": []}
{"id": "2206.03239", "title": "Analyzing the impact of feature selection on the accuracy of heart\n  disease prediction", "abstract": "  Heart Disease has become one of the most serious diseases that has a\nsignificant impact on human life. It has emerged as one of the leading causes\nof mortality among the people across the globe during the last decade. In order\nto prevent patients from further damage, an accurate diagnosis of heart disease\non time is an essential factor. Recently we have seen the usage of non-invasive\nmedical procedures, such as artificial intelligence-based techniques in the\nfield of medical. Specially machine learning employs several algorithms and\ntechniques that are widely used and are highly useful in accurately diagnosing\nthe heart disease with less amount of time. However, the prediction of heart\ndisease is not an easy task. The increasing size of medical datasets has made\nit a complicated task for practitioners to understand the complex feature\nrelations and make disease predictions. Accordingly, the aim of this research\nis to identify the most important risk-factors from a highly dimensional\ndataset which helps in the accurate classification of heart disease with less\ncomplications. For a broader analysis, we have used two heart disease datasets\nwith various medical features. The classification results of the benchmarked\nmodels proved that there is a high impact of relevant features on the\nclassification accuracy. Even with a reduced number of features, the\nperformance of the classification models improved significantly with a reduced\ntraining time as compared with models trained on full feature set.\n", "date": "2022-06-07", "categories": []}
{"id": "2206.01897", "title": "Modeling of Textures to Predict Immune Cell Status and Survival of Brain\n  Tumour Patients", "abstract": "  Radiomics has shown a capability for different types of cancers such as\nglioma to predict the clinical outcome. It can have a non-invasive means of\nevaluating the immunotherapy response prior to treatment. However, the use of\ndeep convolutional neural networks (CNNs)-based radiomics requires large\ntraining image sets. To avoid this problem, we investigate a new imaging\nfeatures that model distribution with a Gaussian mixture model (GMM) of learned\n3D CNN features. Using these deep radiomic features (DRFs), we aim to predict\nthe immune marker status (low versus high) and overall survival for glioma\npatients. We extract the DRFs by aggregating the activation maps of a\npre-trained 3D-CNN within labeled tumor regions of MRI scans that corresponded\nimmune markers of 151 patients. Our experiments are performed to assess the\nrelationship between the proposed DRFs, three immune cell markers (Macrophage\nM1, Neutrophils and T Cells Follicular Helper), and measure their association\nwith overall survival. Using the random forest (RF) model, DRFs was able to\npredict the immune marker status with area under the ROC curve (AUC) of 78.67,\n83.93 and 75.67\\% for Macrophage M1, Neutrophils and T Cells Follicular Helper,\nrespectively. Combined the immune markers with DRFs and clinical variables,\nKaplan-Meier estimator and Log-rank test achieved the most significant\ndifference between predicted groups of patients (short-term versus long-term\nsurvival) with p\\,=\\,4.31$\\times$10$^{-7}$ compared to p\\,=\\,0.03 for Immune\ncell markers, p\\,=\\,0.07 for clinical variables , and\np\\,=\\,1.45$\\times$10$^{-5}$ for DRFs. Our findings indicate that the proposed\nfeatures (DRFs) used in RF models may significantly consider prognosticating\npatients with brain tumour prior to surgery through regularly acquired imaging\ndata.\n", "date": "2022-06-04", "categories": []}
{"id": "2206.01344", "title": "Detecting Pulmonary Embolism from Computed Tomography Using\n  Convolutional Neural Network", "abstract": "  The clinical symptoms of pulmonary embolism (PE) are very diverse and\nnon-specific, which makes it difficult to diagnose. In addition, pulmonary\nembolism has multiple triggers and is one of the major causes of vascular\ndeath. Therefore, if it can be detected and treated quickly, it can\nsignificantly reduce the risk of death in hospitalized patients. In the\ndetection process, the cost of computed tomography pulmonary angiography (CTPA)\nis high, and angiography requires the injection of contrast agents, which\nincrease the risk of damage to the patient. Therefore, this study will use a\ndeep learning approach to detect pulmonary embolism in all patients who take a\nCT image of the chest using a convolutional neural network. With the proposed\npulmonary embolism detection system, we can detect the possibility of pulmonary\nembolism at the same time as the patient's first CT image, and schedule the\nCTPA test immediately, saving more than a week of CT image screening time and\nproviding timely diagnosis and treatment to the patient.\n", "date": "2022-06-03", "categories": []}
{"id": "2206.01436", "title": "Modeling electronic health record data using a knowledge-graph-embedded\n  topic model", "abstract": "  The rapid growth of electronic health record (EHR) datasets opens up\npromising opportunities to understand human diseases in a systematic way.\nHowever, effective extraction of clinical knowledge from the EHR data has been\nhindered by its sparsity and noisy information. We present KG-ETM, an\nend-to-end knowledge graph-based multimodal embedded topic model. KG-ETM\ndistills latent disease topics from EHR data by learning the embedding from the\nmedical knowledge graphs. We applied KG-ETM to a large-scale EHR dataset\nconsisting of over 1 million patients. We evaluated its performance based on\nEHR reconstruction and drug imputation. KG-ETM demonstrated superior\nperformance over the alternative methods on both tasks. Moreover, our model\nlearned clinically meaningful graph-informed embedding of the EHR codes. In\nadditional, our model is also able to discover interpretable and accurate\npatient representations for patient stratification and drug recommendations.\n", "date": "2022-06-03", "categories": []}
{"id": "2606.21329", "title": "MedTS-TTT: Test-Time Training for Medical Time Series Classification", "abstract": "Medical time series (MedTS) signals such as electroencephalography (EEG) and electrocardiography (ECG) support many clinical applications. However, substantial subject-level heterogeneity often induces subject-level distribution shift, causing a fixed parameter set to generalize poorly to unseen individuals. Compared with domain adaptation methods that often depend on extra adaptation components or target-batch statistics, Test-Time Training (TTT) provides a more practical solution for sequential clinical data by enabling online adaptation from unlabeled test samples. However, many representative TTT methods require iterative inner-loop optimization, increasing test-time overhead. In this paper, we propose MedTS-TTT, a test-time training framework for medical time series modeling. MedTS-TTT is built upon Closed-Loop Self-Alignment Test-Time Training (CLSA-TTT) and a Gated Convolutional Backbone (GCB). CLSA-TTT constructs a token-level self-supervised target and performs a single-step fast-weight update for intra-layer closed-loop alignment, enabling rapid sample-wise adaptation without iterative inner-loop optimization. GCB combines CLSA-TTT-based fast adaptation and token-level fusion with a gated convolutional branch to balance local dynamic modeling and information-flow control. On 4 public datasets (2 EEG and 2 ECG) with subject-independent splits, MedTS-TTT achieves 11 top-1 rankings out of 12 evaluations across 9 baselines and 3 metrics. The code is publicly available at https://github.com/mingzhi-c/MedTS-TTT.", "date": "2026-06-19", "categories": []}
{"id": "2606.21474", "title": "Towards Transparent Mental Health Insights: An Explainable AI Model for Career-Related Depression and Anxiety Among University Students Using Structured Data", "abstract": "Career anxiety and depression among university students present a growing challenge to mental health and academic achievement. This study proposes an Explainable AI (XAI) framework using multimodal data and Federated Learning (FL) to identify early indicators of career-related mental health problems in a privacy-preserving and culturally responsive manner. The framework combines structured behavioral data and facial emotion features from interview videos via an intermediate fusion neural network with attention mechanisms. Label smoothing was applied to improve model generalizability. FL was used across institutions to enable collaborative training without raw data sharing. Evaluation was conducted using the Student Mental Health Survey dataset from university students across Pakistan. Our model attained an F1-score of 89.12%, recall of 86.54%, accuracy of 92.08%, and precision of 91.88%. Using Integrated Gradients and SHAP, the model identified key behavioral markers of depression including avoidance of direct gaze, lower facial expressiveness, and social withdrawal, consistent with psychological theory. This research presents an interpretable, scalable, and context-sensitive AI system for mental health pre-diagnosis with potential integration into student support services globally.", "date": "2026-06-19", "categories": []}
{"id": "2606.21492", "title": "Predicting High-Risk Colorectal Polyps in African Americans Using Pre-Colonoscopy Clinical Features: Machine Learning Model Development and Temporal Validation", "abstract": "Risk stratification for advanced colorectal polyps typically relies on colonoscopy and/or pathology findings. However, there is growing interest in whether non-invasive features available prior to colonoscopy can help identify patients at higher risk. Such approaches may enhance clinical decision-making by prioritizing surveillance for individuals most likely to harbor high-risk polyps, when colonoscopy resources are limited while potentially reducing unnecessary procedures in lower-risk patients. Importantly, the use of non-invasive, pre-procedural information may also help promote more equitable access to risk stratification, particularly in settings where colonoscopy resources are limited or unevenly distributed. We aimed to develop and externally validate machine learning models to predict high-risk colorectal polyps using only non-invasive, pre-colonoscopy demographic, clinical, and behavioral features in a diverse, predominantly African American, urban cohort.\n  We conducted a retrospective cohort study using demographic, lifestyle, and comorbidity data from patients who underwent colonoscopy at Howard University Hospital to develop and validate several machine learning models, including neural networks, random forest, support vector machines (SVM), Naive Bayes, logistic regression, decision trees, k-nearest neighbors (KNN), and XGBoost, for predicting high-risk colorectal polyps. High-risk polyps (HRP) were defined as villous or tubullovillous adenomas, high-grade dysplasia, polyps >= 10 mm in size, and/or the presence of >= 3 polyps per procedure; all other cases were classified as low-risk polyps (LRP). The dataset included 4,681 patients from 2015-2022 used for internal validation and 1,562 patients from 2023-2024 used for external validation.", "date": "2026-06-19", "categories": []}
{"id": "2606.21689", "title": "Clinical Term Extraction using Open-Source Small Language Models", "abstract": "Clinical information for amyotrophic lateral sclerosis (ALS) care documented in unstructured clinical notes limits downstream analysis without extraction into structured formats. Open-source small language models with few-shot prompting for detecting the presence of ALS-relevant clinical terms in patient documentation were evaluated without task-specific training data. The detection task targeted 17 categories spanning functional scores, respiratory measures, medications, and related clinical and non-clinical attributes. Clinical note content was normalized from JSON-encoded discharge summaries and processed with a prompt template having structured JSON outputs. We compared 26 open-source models using aggregate, label-level, and manual-validation multilabel classification metrics. Manual validation showed that a regex rule baseline had higher overall micro-F1 and lower Hamming loss than any single SLM or TF-IDF baseline, while Qwen3-4B-Instruct-2507 was the highest-performing SLM by micro-F1. Model rankings varied by metric and label category, with the TF-IDF baseline showing high recall but low precision, some SLMs showing higher precision but lower recall, and Hammer2.1-7b showing strong performance for ALSFRS-R subscore detection. These findings support targeted hybrid extraction workflows rather than replacement of existing rule-based methods.", "date": "2026-06-19", "categories": []}
{"id": "2606.21885", "title": "Cohort-Anchored Foundation Models for Electronic Health Records: From Risk Scores to Auditable Peer Cohorts", "abstract": "Foundation models have achieved remarkable performance across medical question answering, imaging, and electronic health record (EHR) tasks, yet reliable clinical deployment remains challenging due to limited interpretability, vulnerability to distribution shift, and weak alignment with clinician reasoning. We argue that these limitations arise because existing approaches prioritize representation learning while treating patient comparison as an emergent property rather than a primary source of clinical evidence. To address this gap, we propose CAFM, a Cohort-Anchored Foundation Model framework that elevates patient cohorts to a first-class object throughout the learning pipeline. The framework consists of four stages: deviation-aware data curation, cohort-conditioned pretraining, multimodal cohort alignment, and clinician-in-the-loop refinement. Together, these stages improve data quality, organize representations around clinically meaningful cohort structure, preserve modality-specific relationships, and support auditable clinical decision-making. The framework is compositional and can augment existing EHR foundation models without modifying their underlying encoders. We illustrate CAFM through four clinical case studies spanning acute kidney injury prediction, cardiovascular risk stratification from electrocardiograms, optic neuropathy triage from orbital imaging, and electroretinogram-grounded report generation. We further present five empirically testable hypotheses and identify open challenges in data quality, irregular temporality, multimodal learning, distribution shift, and evaluation beyond predictive accuracy. We argue that explicitly anchoring foundation models to patient cohorts provides a principled path toward trustworthy clinical AI.", "date": "2026-06-20", "categories": []}
{"id": "2606.22101", "title": "OphthaDT: Generative Digital Twins for Forecasting Visual Acuity Trajectories in Ophthalmology", "abstract": "Precision medicine in ophthalmology requires accurate longitudinal predictions, but the fragmented nature of multimodal clinical data remains a barrier to forecasting. We introduce OphthaDT, an LLM-based digital twin for ophthalmology that serializes longitudinal patient histories from 3,220 patients across four Phase III clinical trials into structured narratives to forecast best corrected visual acuity (BCVA). In benchmarks spanning up to 100 weeks, OphthaDT demonstrated the lowest prediction error in neovascular age-related macular degeneration (nAMD), achieving an average mean absolute error (MAE) reduction of 6.0% compared to all baselines. In diabetic macular edema (DME), OphthaDT demonstrated competitive performance against all baselines while outperforming Random Forest and XGBoost by an average MAE reduction of 2.6% and 6.9%, respectively. Results reveal that OphthaDT's predictive advantage scales with trajectory complexity: whereas linear models remain effective for the more stable treatment responses of DME, OphthaDT's capacity is better suited for capturing the high longitudinal variability of nAMD. Finally, OphthaDT handles irregular sampling without imputation, positioning LLM-based clinical trajectory modeling as a methodology that could reduce patient burden and accelerate drug development.", "date": "2026-06-20", "categories": []}
{"id": "2606.22442", "title": "Efficient Multimodal Clinical Question Answering for Pulmonary Embolism Risk Assessment", "abstract": "Pulmonary embolism (PE) is a high risk cardiopulmonary condition whose management requires both timely diagnosis and reliable assessment of future clinical risk. Because PE care routinely combines computed tomography pulmonary angiography (CTPA), radiology interpretation, and longitudinal electronic health record (EHR) evidence, it provides a clinically meaningful setting for evaluating compact multimodal language models. In this work, we build a benchmark using efficient multimodal large language models (MLLMs) on INSPECT, a multimodal PE dataset containing 23,248 CTPA studies from 19,402 patients. We formulate eight diagnostic and prognostic tasks as structured clinical question answering problems and evaluate on typical efficient MLLMs under CTPA-Only, EHR-Only, and CTPA+EHR settings with zero-shot and few-shot prompting. Results show that Gemma4 E4B and Gemma4 E2B perform more strongly when EHR evidence is available, especially under CTPA+EHR input. Task level analysis further shows that PE diagnosis achieves higher performance than prognostic tasks, particularly readmission prediction. These observations suggest that compact multimodal models have the great potential in early stage PE risk detection and explanation.", "date": "2026-06-21", "categories": []}
{"id": "2606.22780", "title": "Explainable AI for Mental Health Prediction in Drug-Affected Populations with Dragonfly Algorithm and GAN Oversampling", "abstract": "Mental illnesses among drug users are an increasing international issue, particularly in regions where early detection cannot be easily undertaken. The current literature tends to ignore the use of AI-based mental health analysis in drug users, and low quality of the class imbalance treatment, low interpretability, and optimal hyperparameter optimization can lower predictive quality and clinical utility. This study present a detailed, explainable machine learning (ML) model of multiclass mental health prediction, using a multidimensional data set of drug-affected persons. We combine hybrid PCA-Information Gain (PCA-IG) feature selection, Generative Adversarial Network (GAN)-based oversampling, and Dragonfly Algorithm (DA)-optimized XGBoost to address some of the limitations of existing methods. The suggested framework is effective to work with high-dimensional categorical data, address the issue of class imbalance, and improve predictive performance due to intelligent hyperparameter tuning. The experimental findings show that the XGBoost model optimized using the DA, in combination with GAN-based oversampling, has an accuracy of 94.17% and a weighted F1-score of 93.80%, which is better than the traditional and baseline models. The behavioral, lifestyle, and health factors, particularly sleep quality, physical health, and emotional regulation, are strongly predictive of mental health, with demographic factors having little impact, as seen through feature analysis. SHAP-based explainable AI provides easy-to-understand, instance-level information, enhancing interpretability and trust in models to be used in clinical settings. The results indicate that this framework has the potential to generate valid mental health forecasting tools, which would facilitate early intervention and enhance the treatment of drug-influenced people.", "date": "2026-06-22", "categories": []}
{"id": "2606.23005", "title": "From Point Estimates to Distributions: GMM Pooling for MIL in Preterm Birth Prediction", "abstract": "Preterm birth (PTB) prediction can enable targeted surveillance and timely intervention, yet most ultrasound-based models use a single selected transvaginal ultrasound (TVUS) frame per patient despite routine exams acquiring multiple cervical images. We formulate PTB prediction as a multiple instance learning (MIL) problem, representing each patient as a variable-sized bag of TVUS images with a single outcome label. To move beyond standard MIL aggregators that collapse a bag into a point estimate, we propose a Gaussian Mixture Model (GMM) pooling, which summarizes all images in a bag into a fixed-length representation by modeling their feature distribution. This design captures intra-patient variability. We evaluate the method on a private clinical cohort and on a public lymph node metastasis benchmark. For PTB prediction, GMM pooling improves over the instance-based model PR-AUC from 0.44 to 0.56. On the lymph node benchmark, it achieves state-of-the-art performance with 0.91 F1-score and 0.89 ROC-AUC for classification and 0.18 MAE for regression. The code is publicly available at https://github.com/HussainAlasmawi/GMM_Pooling.", "date": "2026-06-22", "categories": []}
{"id": "2606.23213", "title": "Deep learning-based detection of cessation of breathing in pre-term infants", "abstract": "Apnoea of prematurity is characterised by recurrent episodes of cessation of breathing and remains difficult to detect reliably using routinely monitored physiological signals in the Neonatal Intensive Care Unit (NICU). Existing bedside monitors rely primarily on respiratory rate and oxygen saturation thresholds, often generating high false-positive alarm rates and missing short or irregular events. Improving automated detection using routinely acquired clinical signals could enhance identification of clinically meaningful events without additional sensing hardware.\n  We evaluated deep learning-based detection of apnoea-related Cessation Of BrEathing (COBE) events using impedance pneumography (IP), electrocardiography (ECG), and photoplethysmography (PPG) signals from approximately 430 hours of NICU recordings collected from 24 pre-term infants. Three independent reviewers annotated COBE events, producing a dataset of 346 COBE and 608 non-COBE events. We compared a shallow convolutional neural network (CNN), residual networks (ResNets), and a ConvNeXt architecture using an independent held-out test set.\n  Across all architectures, detection performance was influenced more strongly by signal modality than by architectural complexity. Unimodal IP-based models achieved balanced accuracies of 86.8-88.0%, outperforming ECG-derived (62.6-69.7%) and PPG-derived (65.1-66.4%) respiratory surrogates. Multimodal fusion yielded modest improvements over IP alone. The best-performing model, a ConvNeXt architecture combining IP and PPG inputs, achieved 88.7% balanced accuracy and an F1 score of 0.75 on the independent test set.\n  These findings demonstrate that deep learning models applied to routinely monitored NICU signals can reliably detect COBE events and highlight the importance of signal modality in data-constrained neonatal monitoring settings.", "date": "2026-06-22", "categories": []}
{"id": "2606.23631", "title": "AI-driven Optimisation of Quality of Recovery (QoR) in Remote Patient Monitoring", "abstract": "Remote patient monitoring depends on patient-reported data to capture the subjective dimension of recovery that devices cannot measure. The Quality of Recovery (QoR-15) survey is the gold-standard instrument for this purpose. It was designed and validated for occasional in-hospital assessment, yet remote monitoring now administers it to patients daily. In our own post-surgical deployment, only 55% of patients submitted the survey more than 14 days of 30 monitoring days. We developed QoR-compact, a five-item daily input for the RPM prediction pathway. Setting a deployment-driven target of one-third of the daily items, we exhaustively evaluated all 3,003 five-question subsets of the QoR-15 and tested whether the best of them matches the full instrument in predicting near-term postoperative recovery severity. QoR-compact achieves a mean AUC-ROC of 0.968 (95% CI 0.915-0.988), statistically comparable to the 0.964 baseline obtained with one-third of the items. Patient-level backtesting indicates that it tracks readmission events as faithfully as the full form. Its five items span the physical and psychological axes of recovery: Q3 (feeling rested), Q9 (feeling comfortable and in control), Q10 (general well-being), Q12 (severe pain), and Q14 (feeling worried or anxious). The QoR-15 remains the gold-standard measure of recovery; QoR-compact complements it as a shorter daily input designed for prediction. This parity provides the basis for a prospective study of whether a lighter daily input is, in turn, completed more consistently. External validation on larger cohorts is required before clinical use.", "date": "2026-06-22", "categories": []}
{"id": "2606.23673", "title": "PsyBridge: A Hybrid Intelligent Framework for Multi-Dimensional Mental Health Assessment and Decision Support", "abstract": "Mental health assessment commonly relies on isolated screening instruments or data-driven models that often lack interpretability and multi-dimensional integration. Existing approaches frequently focus on individual indicators such as depression or anxiety while providing limited support for comprehensive and explainable decision-making. To address this limitation, this study proposes PsyBridge, a hybrid intelligent decision-support framework designed for multi-dimensional mental health assessment through the integration of clinically validated screening tools, cognitive evaluation, and personality profiling within a unified architecture. The proposed framework incorporates PHQ-9 and GAD-7 assessments alongside cognitive and behavioural indicators using a modular design and a weighted aggregation mechanism to generate interpretable mental health risk classifications and recommendations. To evaluate the framework, a semi-synthetic dataset consisting of 500 patient profiles representing varying severity levels was constructed based on clinically grounded score distributions. Experimental results demonstrate that PsyBridge achieves an overall accuracy of 0.84, outperforming standalone PHQ-9 and GAD-7 assessments while improving precision, recall, and F1-score. Sensitivity analysis and ablation studies further indicate that integrating cognitive and personality components contributes to more stable classification performance and reduces inconsistencies in moderate-risk prediction. The findings suggest that PsyBridge provides a scalable and interpretable approach for AI-assisted mental health decision support, particularly within digital healthcare and telehealth environments.", "date": "2026-06-22", "categories": []}
{"id": "2606.23871", "title": "Federated Survival Analysis in Healthcare: A Multi-Model Evaluation on Cross-Institutional Heterogeneous Breast Cancer Data", "abstract": "Survival analysis is central to clinical decision-making, yet reliable time-to-event models require large, diverse cohorts that are rarely available at a single institution, while privacy regulations restrict the centralization of patient data. Federated learning (FL) offers a privacy-preserving alternative by training shared models without exchanging raw data, but its effectiveness for survival modeling under realistic, heterogeneous conditions remains insufficiently understood. This paper presents a systematic, multi-model evaluation of federated survival analysis on a cross-institutional breast cancer cohort with naturally heterogeneous distributed clients. Three representative survival models, the Cox Proportional Hazards model, DeepSurv, and Random Survival Forest (RSF), are compared across centralized, local, and federated training, and three federated optimization strategies (FedAvg, FedProx, and FedAdam) are assessed for the gradient-based models. Results show that FL consistently outperforms local training and approaches, and occasionally exceeds, centralized performance, while RSF offers the best overall balance of discrimination, calibration, and robustness across heterogeneous clients. We further find that performance depends on the diversity of client distributions, and that FedAvg and FedProx are stronger and more stable than FedAdam. Based on these findings, we derive practical, decision-oriented guidelines mapping data, privacy, interpretability, and resource constraints to recommended model and training-paradigm choices for federated survival modeling in healthcare.", "date": "2026-06-22", "categories": []}
{"id": "2606.24047", "title": "Ensemble Feature Selection and Harris Hawks Optimization for Explainable Mental Health Risk Prediction in Female Sex Workers", "abstract": "One of the significant mental health issues affecting female sex workers (FSWs) is mental disorders, especially depression. Exposure to violence, stigma, and economic hardship further increases their psychological risk. Current machine learning (ML) models are typically ineffective at capturing the high-dimensional and complex risk patterns that exist in this marginalized group. This paper suggests a hybrid predictive model that merges an ensemble feature selection strategy using ANOVA and mutual information and Harris Hawks optimization-tuned logistic regression and represents a new application of swarm intelligence to predict mental health in vulnerable groups. The explainable AI (XAI) methods can be used to understand the factors of trauma associated with model predictions. When applied to a group of 3,005 FSWs, it can be seen that the proposed model is more effective than traditional classifiers, with an accuracy of 95.78%, an F1 score of 95.77%, and an AUC of 0.96, and identifying post-traumatic stress, client-related violence, and occupational factors as major contributors to depression. This work bridges the gaps between conventional and ML approaches to develop an XAI tool that enables vulnerable groups to receive early assistance, evidence-based targeted psychosocial care, and health planning.", "date": "2026-06-23", "categories": []}
{"id": "2606.24102", "title": "PORTER: Language-Grounded Event Representations for Portable Structured EHR Foundation Models", "abstract": "Most electronic health record (EHR) foundation models encode clinical events as discrete event tokens from a fixed vocabulary and therefore cannot directly represent events containing unseen concepts or new combinations of concepts and attributes such as numeric values. This limits transfer across institutions and even across deployment pipelines within the same institution. We introduce PORTER, a language-grounded structured EHR foundation model that decouples event representation from this fixed vocabulary. PORTER represents events through their descriptions using a frozen text encoder, integrates numeric values through a dedicated pathway, and learns clinical dynamics over patient timelines with an autoregressively pretrained temporal backbone. Across 74 clinical prediction tasks at a pediatric hospital, PORTER matched the mean AUROC of a fixed-vocabulary model with the same temporal backbone and pretraining objective. When the same patient timelines were rendered using event descriptions not seen during pretraining, PORTER transferred without retraining or vocabulary mapping, recovering 97.1% of the mean AUROC of a model trained directly on the target vocabulary. When transferred to MIMIC, PORTER outperformed the fixed-vocabulary model, which dropped 69% of events because their tokens were unseen. Mechanistic analyses showed cross-vocabulary transfer tracked preservation of patient-level representation geometry rather than the scale of the text encoder, and the numeric pathway improved sensitivity to magnitude without disrupting clinical concept identity. PORTER also achieved higher AUROC than a task-specific text serialization comparator, at 329-fold lower amortized compute. PORTER is a step toward vocabulary-independent EHR foundation models that reduce the need for vocabulary harmonization while preserving in-domain performance and enabling efficient cross-task reuse.", "date": "2026-06-23", "categories": []}
{"id": "2606.24604", "title": "Uncertainty-Aware Longitudinal Forecasting of Alzheimer's Disease Progression Using Deep Learning", "abstract": "Longitudinal modelling of Alzheimer's disease progression is clinically useful only if it can describe not just the most likely next diagnosis, but how a patient may evolve over time and how reliable that forecast is. Most deep learning approaches reduce this problem to single-step classification, treating cognitively normal, mild cognitive impairment, and dementia as flat categories while providing limited insight into how uncertainty accumulates across future visits. We propose a probabilistic framework that combines ordinal diagnosis prediction, multi-horizon trajectory generation, and decomposed uncertainty estimation. A Temporal Fusion Transformer encoder is adapted with a CORAL ordinal output layer, asymmetric loss weighting, and converter oversampling to respect disease-stage ordering and improve sensitivity to MCI-to-dementia transitions. Conditioned on the learned patient-context representation, an autoregressive Mixture Density Network generates five-year probabilistic trajectories for diagnosis state, CDR Sum of Boxes, MMSE orientation, and hippocampal volume. On ADNI, the model outperforms linear, recurrent, and transformer baselines for next-visit diagnosis prediction, with the strongest gains on MCI-versus-dementia discrimination. Generated trajectories achieve near-nominal 90% credible interval coverage, widening uncertainty across the forecast horizon, and biomarker dynamics consistent with expected Alzheimer's disease progression. We further separate aleatoric from epistemic uncertainty using analytic mixture variance and a five-member bootstrap ensemble, which provides the strongest encoder diversity and output-level epistemic signal. Epistemic uncertainty is higher for rare progression archetypes, MCI and dementia patients, and under external evaluation on OASIS-3, where it increases alongside prediction error.", "date": "2026-06-23", "categories": []}
{"id": "2606.24960", "title": "Enhancing Clinician Decision-Making via Uncertainty-Aware Multi-Expert Fusion for Stroke Rehabilitation", "abstract": "Tailoring stroke rehabilitation requires assessing how movements are organized, not merely if they succeed. Currently, this assessment is a rate-limiting bottleneck. Instruments like the Action Research Arm Test (ARAT) compress rich behavioral observations into single ordinal endpoints, discarding the movement-quality details that distinguish recovery from compensation. Automated alternatives typically chase accuracy on noisy, single-observer labels to output opaque scores - a technology-centric approach that rarely reaches clinical practice. To address this, we present xAARA: an engine designed to augment rather than replace clinical judgment. From multi-view video, xAARA returns ARAT assessments with calibrated uncertainty and explanations across task, movement-phase, and movement-quality levels. Treating clinical scoring as an ill-posed inference problem, xAARA composes 692 calibrated multimodal models via a Dynamic Bayesian Network with entropy-based gating. It qualifies results against clinical validity rules and defers low-confidence cases. In 105 stroke survivors (788 exercises), xAARA achieved 94.2% task accuracy (Cohen's kappa=0.934) and 81.3% movement-phase accuracy (kappa=0.727), reducing predictive uncertainty by 96.1% compared to single-clinician scoring. For subjective cases, it matched at least one rater 100% of the time and never returned out-of-range scores. Four independent clinicians validated the assessments and indicated willingness to adopt the system. We argue that principled uncertainty quantification and clinician-aligned explainability are the critical bridges moving automated assessment from technical demonstration to a deployable clinical tool.", "date": "2026-06-23", "categories": []}
{"id": "2606.25185", "title": "Neural operator-based digital twins for modeling amyloid-$\\beta$ and tau propagation and treatment optimization in Alzheimer's disease", "abstract": "Accurately predicting the spatiotemporal evolution of amyloid-$\\beta$ and tau proteins at the individual level is critical for improving the diagnosis and treatment of Alzheimer's disease. We consider the problem of constructing patient-specific digital twins that model the propagation of these biomarkers on the cortical surface using reaction--diffusion dynamics. A major challenge is that the underlying nonlinear aggregation mechanisms are unknown and must be inferred from sparse, noisy, and heterogeneous longitudinal PET imaging data. To address this, we develop a data-driven framework that learns biomarker dynamics directly from clinical observations. The approach combines operator learning with reduced-order representations to infer governing equations of disease progression from data. Using this framework, we achieve predictive accuracies of 87\\% for amyloid-$\\beta$ and 81\\% for tau. Building on the learned dynamics, we further formulate a PDE-constrained optimal control problem to design personalized therapeutic strategies that regulate pathological protein propagation. By integrating data-driven dynamical modeling with treatment optimization, the proposed digital twin framework provides an interpretable and predictive platform for understanding disease progression and enabling precision interventions in neurodegenerative disorders.", "date": "2026-06-23", "categories": []}
{"id": "2606.25270", "title": "Inverse Reinforcement Learning for Interpretable Keystroke Biomarkers in Parkinson's Disease", "abstract": "Keystroke dynamics have been explored extensively as a passive digital biomarker for Parkinson's disease (PD), typically by extracting summary statistics from typing timing and training a classifier to discriminate PD from healthy controls. We instead apply inverse reinforcement learning (IRL) to keystroke data, modeling each keystroke as a discrete choice over typing speed and recovering, per subject, an interpretable reward function that explains their observed timing behavior. To our knowledge this is the first application of IRL to keystroke dynamics. On the public neuroQWERTY MIT-CSXPD dataset (85 subjects, 42 with PD), an initial four-parameter reward decomposition (speed, effort, smoothness, hand-alternation cost) was found to suffer severe feature collinearity between two terms ($r=1.000$ in typical contexts); we diagnose and correct this, yielding an identifiable three-parameter model. The recovered speed-preference weight correlates with UPDRS-III severity at $r=-0.607$ ($p<0.001$, $n=42$), replicates independently across two sub-cohorts, is stable across nine sensitivity configurations, and retains a statistically significant contribution beyond raw typing speed alone (incremental $R^2$ from 0.194 to 0.338, $p=0.006$). Two other recovered weights (consistency, hand-alternation) did not survive confound checks and are reported as negative results. We document two implementation bugs found during adversarial code review (session-boundary contamination, a rolling-window data leakage) and show the headline result is materially unchanged after fixing both. We discuss this result in the context of a literature where reported accuracies vary widely between studies (pooled AUC 0.85, I^2=94% in a 2022 meta-analysis), and argue that the validation process itself, not only the correlation coefficient, is part of the contribution.", "date": "2026-06-24", "categories": []}
{"id": "2606.25434", "title": "Interpretable Concept-Guided Polynomial Tabular Kolmogorov-Arnold Network for EEG-Based Mild Cognitive Impairment Detection", "abstract": "Early and scalable detection of mild cognitive impairment (MCI) remains an unresolved clinical challenge. Existing EEG-based screening approaches are constrained by handcrafted feature pipelines that discard neurophysiologically meaningful domain structure and deep learning classifiers that sacrifice interpretability for performance. No existing work unifies physiologically organized concept encoders, cross-concept interaction modeling, and nonlinear tabular classification in a sleep EEG-based MCI detection framework. This study proposes Concept-guided Polynomial-transformed Tabular learning using Kolmogorov-Arnold Network (CPTabKAN), which maps heterogeneous EEG-derived features into domain-informed concept representations, expands them via degree-2 polynomial transformation to expose first- and second-order interactions, and applies a Fourier-parameterized TabKAN classifier to learn nonlinear decision boundaries. CPTabKAN was evaluated on the Study of Osteoporotic Fractures cohort (372 subjects, overnight polysomnography), using 1,379 features organized into ten physiologically motivated concept groups. Under 10-fold cross-validation, CPTabKAN-Second Order achieved a weighted F1-score of 0.9038 (SD 0.034), outperforming GradientBoosting by 5.65 percentage points (t(9)=1.934,p=0.043, one-sided paired test), with advantages persisting under SMOTE-based balancing. Ablation analysis confirmed independent contributions from each component. Concept importance analysis revealed that power spectral density, multi-scale entropy, and Hjorth parameters dominated first-order weights, while cross-concept interactions involving Lempel-Ziv-Welch complexity, statistics, demographics, and slow oscillations exceeded all first-order scores. These results demonstrate that concept-structured, interaction-aware tabular learning surfaces physiologically coherent reasoning, supporting clinical trust.", "date": "2026-06-24", "categories": []}
{"id": "2606.25579", "title": "Cross-Attention Multimodal Learning for Predicting Response to Neoadjuvant Imatinib in Gastrointestinal Stromal Tumors: A Multicenter Retrospective Study", "abstract": "Background: Response to neoadjuvant imatinib in gastrointestinal stromal tumors (GISTs) is highly variable and cannot be reliably predicted using current clinical or molecular markers. This study developed and evaluated an explainable multimodal deep learning framework integrating computed tomography (CT) imaging and clinical variables to predict treatment response. Methods: Patients from four tertiary centers were retrospectively included between 2000-2023 in independent pretraining (n=935) and prediction (n=213) cohorts. A cross-attention framework integrating clinical variables and tumor-centered CT imaging was developed to predict response to neoadjuvant imatinib. Two training strategies were evaluated: (1) self-supervised pretraining with low-rank adaptation and (2) training from scratch. Hyperparameters were optimized using SMAC3. Performance was assessed through internal cross-validation and external testing. Ablation analyses and attention-based explanations were used to quantify modality contributions. Results: Among 213 patients (54.5% responders), responders had larger tumors (112 vs. 89 mm, P=0.026), higher mitotic index (3 vs. 0, P<0.001), and more frequent KIT mutations (69.0% vs. 56.7%, P=0.019). Cross-attention models achieved the highest internal performance (AUC up to 0.99) but lower external performance (AUC 0.60-0.63). Clinical-only performance was moderate (AUC 0.66), whereas imaging-only models showed limited generalizability (AUC 0.56-0.66). Explainability analyses identified significant differences in feature importance between responders and non-responders, including CD117, BRAF, PDGFRA, age, sex, disease status, and comorbidities (FDR-adjusted P<=0.036). Conclusion: The cross-attention framework shows potential for improving imatinib response prediction in GIST while providing interpretable insights into multimodal determinants of treatment response.", "date": "2026-06-24", "categories": []}
{"id": "2606.25956", "title": "Pulmonary Embolism Risk Stratification from CTPA and Medical Records: Vascular Graphs Are Not All You Need", "abstract": "Risk stratification for pulmonary embolism (PE) is critical for clinical decision-making. Stratification guidelines are based on patient medical records, parameters measured from computed tomography pulmonary angiography (CTPA), and blood tests. However, blood tests are often missing in routine practice. This work studies whether state-of-the-art models can accurately classify risk stratification from only medical records and biomarkers extracted from CTPA images. We benchmark different approaches to combine medical records and cardiac biomarkers with rich pulmonary vascular information; we add vascular biomarkers to tabular models and apply graph neural networks (GNNs) on the vascular tree's intrinsic graph representation. We use a private dataset (n=353) with uniquely complete data for PE risk stratification. Our results show that, among global features, medical records and cardiac biomarkers are the most significant predictors, while vascular biomarkers do not further improve stratification. Even more surprising, even GNNs on vascular graphs fail to outperform strong tabular baseline on global features. We consider hypotheses, on both models and data, that could explain this suboptimal performance. Our investigation suggests that, counter-intuitively, vascular graphs might hold no discriminative information for PE risk stratification. Code is available from https://github.com/creatis-myriad/GENESIS.", "date": "2026-06-24", "categories": []}
{"id": "2606.26561", "title": "Explainable Ensemble-Based Machine Learning Models for Detecting the Presence of Cirrhosis in Hepatitis C Patients", "abstract": "Hepatitis C is a liver infection caused by a virus, which results in mild to severe inflammation of the liver. Over many years, hepatitis C gradually damages the liver, often leading to permanent scarring, known as cirrhosis. Patients sometimes have moderate or no symptoms of liver illness for decades before developing cirrhosis. Cirrhosis typically worsens to the point of liver failure. Patients with cirrhosis may also experience brain and nerve system damage, as well as gastrointestinal hemorrhage. Treatment for cirrhosis focuses on preventing further progression of the disease. Detecting cirrhosis earlier is therefore crucial for avoiding complications. Machine learning (ML) has been shown to be effective at providing precise and accurate information for use in diagnosing several diseases. Despite this, no studies have so far used ML to detect cirrhosis in patients with hepatitis C. This study obtained a dataset consisting of 28 attributes of 2038 Egyptian patients from the ML Repository of the University of California at Irvine. Four ML algorithms were trained on the dataset to diagnose cirrhosis in hepatitis C patients: a Random Forest, a Gradient Boosting Machine, an Extreme Gradient Boosting, and an Extra Trees model. The Extra Trees model outperformed the other models achieving an accuracy of 96.92%, a recall of 94.00%, a precision of 99.81%, and an area under the receiver operating characteristic curve of 96% using only 16 of the 28 features.", "date": "2026-06-25", "categories": []}
{"id": "2606.28623", "title": "Improving Patient Subtyping on Longitudinal Data using Representations from Mamba-based Architecture", "abstract": "Effective sub-typing (also known as grouping or clustering) of patients using their electronic health record (EHR) data can greatly inform precision medicine efforts. However, subtyping temporal EHR datasets is known to be challenging due to inherent EHR issues, including complexity and irregularity. In this study, we propose a self-supervised Mamba-based model that learns effective EHR representations and enables enhanced patient subtyping. We evaluate the proposed model on public and private real-world EHR datasets to classify the data based on the available labels and subtype patients based on the representations learned from the model. Through an extensive set of experiments, we demonstrate that our model's design choices lead to better performance compared to competitive baseline models for prediction. Moreover, we evaluate several clustering techniques to demonstrate that our findings offer valuable insights into subtyping patients based on temporal records from EHR models\\footnote{Our implementations are available at https://github.com/healthylaife/triplet_mamba.", "date": "2026-06-26", "categories": []}
{"id": "2606.28798", "title": "Primary ICD Category Prediction using LLM-based Probing", "abstract": "Objective: ICD codes are central to reimbursement, research, and population health surveillance, yet automated coding systems often struggle to integrate diagnostic signals from both clinical narratives and structured electronic health record (EHR) variables. We evaluated whether frozen medical large language model (LLM) representations can serve as a shared embedding space for multimodal primary diagnosis category prediction.\n  Materials and Methods: We constructed a MIMIC-IV cohort of 13,645 admissions from the 10 most frequent primary ICD-10 codes, consolidated into seven categories. Structured variables were serialized into clinical narratives and combined with leakage-pruned discharge notes. Using a frozen MedFound-Llama3-8B-finetuned backbone, we extracted hidden states from five transformer layers and trained linear probes for structured-only, unstructured-only, and combined inputs, comparing against XGBoost and information-matched PLM-ICD baselines and evaluating MIMIC-III adaptation with a compact bottleneck adapter.\n  Results: The combined probe performed best on MIMIC-IV (87.69% strict; 91.45% medical accuracy), exceeding both single-modality probes and baselines. The structured-only probe outperformed its standard baseline by 6.19 points in medical accuracy. Diagnostic information became increasingly linearly separable in deeper layers, and a 2M-parameter adapter restored cross-dataset transfer to MIMIC-III using only 5% of target labels.\n  Discussion: LLM embeddings can unify structured and narrative EHR information for multimodal diagnosis prediction, supporting efficient reuse of clinical representations across modalities and datasets through a small representation-level module.\n  Conclusion: Multimodal probing of frozen medical LLM representations provides a practical approach for studying EHR modalities and adapting clinical representations across datasets.", "date": "2026-06-27", "categories": []}
{"id": "2606.29386", "title": "Interventional Flow Matching: Prospective Dose-Response Forecasting with Velocity-Field Jacobian Regularization", "abstract": "Predicting a patient's physiological trajectory under a planned treatment sequence is a prospective interventional problem, not standard time-series extrapolation. We study this problem in glucose management, where insulin and carbohydrate records are policy-dependent: future drivers are coupled to patient state, behavior, and clinical decision rules, so observational forecasting accuracy alone does not guarantee correct responses to planned interventions.\n  We introduce Interventional Flow Matching (IFM), a continuous-time generative framework for physiologically constrained prospective forecasting. IFM conditions a flow-matching velocity field on patient history and planned future drivers in a bounded latent glucose space. Rather than embedding strict mechanistic glucose--insulin ODE equations or enforcing causality through rollout-based simulations, IFM uses a solver-free regularization: it penalizes the Jacobian of the instantaneous velocity field with respect to smoothed treatment drivers. This imposes signed, dose-bounded local sensitivities directly on the learned dynamics: insulin lowers glucose, carbohydrates raise it, and both responses remain within plausible ranges.\n  On a simulated UVA/Padova type 1 diabetes cohort, IFM achieves the strongest balance between observed-driver RMSE and interventional response metrics. Across experiments, it consistently produces physiologically correct responses to both insulin and carbohydrate drivers while maintaining high directional, and ranking consistency.", "date": "2026-06-28", "categories": []}
{"id": "2606.29907", "title": "CW-B: Class Weighted Boosting Framework for Imbalance Resilient Multi Class Cardiac Phenotyping", "abstract": "Cardiac discharge phenotyping informs post-discharge treatment and follow-up, but real-world records are often incomplete and class-imbalanced, increasing the risk of missed high-risk phenotypes. We propose CW-B, a clinical risk-aligned class-weighted XGBoost pipeline for five-class cardiac discharge phenotyping under real-world class imbalance and missingness. CW-B combines fold-specific class-balanced instance weighting, missingness-indicator augmentation, and classwise error auditing to improve recognition of clinically prioritized phenotypes while preserving interpretable and auditable decision logic. In five-fold stratified cross-validation, CW-B achieves the best Accuracy, Macro-F1, Balanced Accuracy, and Prioritized F1 among tree-based, ensemble, and neural baselines. Overall, CW-B provides a practical and deployment-oriented approach for more reliable cardiac discharge phenotyping in real-world clinical settings.", "date": "2026-06-29", "categories": []}
{"id": "2606.29928", "title": "Latent-CURE for Breast Cancer Diagnosis", "abstract": "Multimodal Large Models have significantly advanced automated breast ultrasound diagnosis. However, most existing frameworks utilize opaque, end-to-end paradigms prioritizing global statistical correlations over structured clinical reasoning. Consequently, these models remain susceptible to shortcut learning amid extreme real-world epidemiological imbalances, often bypassing rare but decisive malignant indicators for dominant benign patterns. To address this disconnect, we propose Latent-CURE, a novel diagnostic framework driven by asymmetric weighted chain-of-thought methodology grounded in latent space reasoning. Unlike traditional approaches, our framework constructs an implicit reasoning trajectory forcing the model to sequentially infer standardized BI-RADS morphological descriptors before converging on a final diagnosis. Furthermore, to combat the extreme scarcity of critical malignant features, we couple this architecture with a dual-asymmetric optimization strategy. By dynamically adjusting margins and weights, this strategy safeguards high-specificity malignant descriptors from being overshadowed by common benign priors. Comprehensive evaluations demonstrate that our knowledge-injected approach provides transparent clinical evidence while achieving robust, accurate diagnostic performance in imbalanced medical cohorts.", "date": "2026-06-29", "categories": []}
{"id": "2606.30236", "title": "CaresAI at CT-DEB26: Detecting Dosing Errors In Clinical Trials Using Domain-Specific Transformer Embeddings and Classification Models", "abstract": "Medication errors, particularly dosing errors in clinical trials (CT), can lead to patient harm, adverse drug events and worse patient outcomes. Dosing errors are preventable, and early identification can improve trial integrity and mitigate subsequent clinical and financial burden. This study aims to detect dosing errors within CT protocols by evaluating text representations of trial information using transformer-based language models trained on biomedical corpora. CT textual data was encoded using several models, including ClinicalBERT, PubMedBERT, BioBERT, and MedCPT, and integrated with categorical features. These text embeddings were used as input to classical machine learning models and neural network architectures within an experimental framework. Performance was primarily assessed using ROC-AUC with respect to predicting dosage error. Under a logistic regression baseline, BioBERT consistently outperformed alternative encoders, achieving an ROC-AUC of 0.794, a 3.95% improvement over the ClinicalBERT baseline. Combining multiple embeddings did not yield improvements, indicating that domain alignment outweighs representational stacking. Gradient boosting models, support vector classifiers, logistic regression, and residual neural networks achieved the strongest performance for predicting dosage error, achieving ROC-AUCs: 0.821 to 0.853. Overall, the integration of domain-specific transformer embeddings with structured metadata enables discrimination of trials meeting a predefined elevated dosing error risk criterion, advancing safety monitoring and supporting informed regulatory decision-making.", "date": "2026-06-29", "categories": []}
{"id": "2606.30398", "title": "ENC-ODE: Event-level Neurodegenerative Modeling in Continuous Time with Neural ODEs", "abstract": "Accurately predicting the temporal evolution of clinical biomarkers is crucial for the early diagnosis and management of neurodegenerative diseases such as Alzheimer's disease. However, this relies on longitudinal data to capture biomarker changes over time, which is often sparse and irregular due to the high cost, labor-intensive nature, and patient burden. To address these challenges, we propose ENC-ODE, an Event-level Neurodegenerative modeling in Continuous time with neural Ordinary Differential Equations. ENC-ODE predicts future biomarker evolution by modeling clinical events through diagnosis-conditioned continuous dynamics. A target-conditioned attention mechanism weights and aggregates event-level predictions for the target time and modality without history compression. Extensive experiments on Alzheimer's Disease Neuroimaging Initiative (ADNI) dataset demonstrate that ENC-ODE outperforms representative sequence models while offering a scalable and neuroscientifically grounded solution for clinical support. The code is available at https://github.com/JardinDelSol/enc-ode.", "date": "2026-06-29", "categories": []}
{"id": "2606.30702", "title": "Accelerometry-Derived Digital Biomarkers for Cardiometabolic Risk: A Population-Representative Tabular Benchmark with Uncertainty Quantification", "abstract": "Structured tabular data dominates clinical medicine, yet existing benchmarks fail to reflect real-world properties like complex survey sampling, demographic oversampling, and subgroup fairness. We introduce the NHANES Accelerometry Cardiometabolic Benchmark, derived from NHANES 2003-2006, comprising 1,381 adults with hip-worn accelerometry, fasting laboratory biomarkers, dietary intake, and anthropometrics. We evaluate three tabular learning methods -- ridge regression, XGBoost, and the foundation model TabPFN v2 -- to predict glycated haemoglobin (HbA1c), fasting triglycerides, and C-reactive protein (CRP) from activity phenotypes and lifestyle covariates. TabPFN v2 achieves the best overall performance (HbA1c R^2=0.156, CRP R^2=0.383), while triglycerides remain largely unpredictable (R^2 < 0.05), consistent with known genetic dominance. We apply split conformal prediction to generate distribution-free 90% prediction intervals and evaluate demographic coverage equity across sex and race/ethnicity subgroups. Marginal coverage aligns with the 90% target for CRP and HbA1c but falls below for triglycerides. At the subgroup level, we observe localized undercoverage (e.g., HbA1c for Mexican American participants), illustrating the gap between marginal guarantees and the conditional coverage required for clinical fairness. Code and data are at https://github.com/felizzi/nhanes-accel-cardiometabolic-benchmark.", "date": "2026-06-29", "categories": []}
{"id": "2606.31036", "title": "Teaching LLMs to Recommend and Defer in Underrepresented Epilepsy Care", "abstract": "Specialist epilepsy expertise is scarce in resource-constrained settings, making LLM-based decision support attractive for frontline clinicians managing longitudinal treatment. Such systems must adapt to local prescribing practice and know when to defer. We study this problem in Ugandan pediatric epilepsy care, predicting anti-seizure medication regimens from longitudinal unstructured clinic notes. Standard prompting achieves non-trivial agreement with physician prescriptions, but neurologist review shows that many errors reflect distribution-miscalibrated prescribing defaults rather than failures to parse the local record. We introduce MANANA, a non-parametric prompt-learning framework that learns local prescribing guidance from a small patient-level training set. MANANA converts observed prescription errors into auditable prompt memories, instantiated in single-agent and multi-agent variants, and improves over classical ML models, direct LLM prompting, and prompt-optimization baselines across two independently collected Ugandan cohorts. We further propose Bayesian prompt averaging, which converts the learned prompt trajectory into prescription likelihoods and an uncertainty-based deferral signal. On the independently collected held-out cohort, this improves visit-level top-3 prescription accuracy by 4-8 percentage points over prompt-optimization baselines and enables selective prediction: the system can auto-handle the most confident half of cases at 95% precision, or the most confident quarter at 99% precision, while deferring lower-confidence cases for specialist review.", "date": "2026-06-30", "categories": []}
{"id": "2606.31348", "title": "Patient-Level Elbow Abnormality Detection: Leakage-Aware Evaluation of Learned Preprocessing, Calibration, and Triage-Oriented Operating Points", "abstract": "In this study, we examine learned preprocessing pipelines in the context of triage-oriented orthopedic abnormality detection task using elbow radiographs from MURA dataset. The evaluation focuses on patient-level detection of musculoskeletal abnormalities under a leakage-aware protocol. We compare multiple preprocessing pipelines, with and without a lightweight DnCNN module as a learned preprocessing component, to assess their impact on discrimination and calibration. Performance is assessed using discrimination metrics (AUROC, PR-AUC), calibration measures (ECE, Brier score), and validation-selected operating point analysis targeting high specificity. Results show that differences across preprocessing strategies are modest and configuration-dependent, with no consistent discrimination advantage over the raw-input DenseNet121 baseline. The raw and diverse inputs combined with the DnCNN front-end showed reduced ECE and Brier score, while CLAHE combined with DnCNN did not improve calibration. Overall, the results suggest that under patient-level evaluation, preprocessing gains are modest and configuration-dependent; the raw-input DenseNet121 baseline remains competitive throughout, and no tested preprocessing strategy produced a consistent discrimination advantage across all metrics.", "date": "2026-06-30", "categories": []}